U.S. patent application number 11/836095 was filed with the patent office on 2007-11-29 for methods for classification of somatic embryos.
This patent application is currently assigned to WEYERHAEUSER COMPANY. Invention is credited to William C. Carlson, Timnit Ghermay, James A. Grob, Roger Timmis, Mitchell R. Toland.
Application Number | 20070276640 11/836095 |
Document ID | / |
Family ID | 32072685 |
Filed Date | 2007-11-29 |
United States Patent
Application |
20070276640 |
Kind Code |
A1 |
Timmis; Roger ; et
al. |
November 29, 2007 |
METHODS FOR CLASSIFICATION OF SOMATIC EMBRYOS
Abstract
The present invention is directed towards methods for the
classification of plant embryos by the application of one or more
classification algorithms to analyze digitized images and
absorption, transmittance, or reflectance spectra. The methods are
generally applicable and emphasize the importance of acquiring and
using as much image and absorption, transmittance, or reflectance
spectral information as possible, based on objective criteria. The
present invention allows automated selection of embryos most
suitable for further culture and rejection of those seen as less
suitable.
Inventors: |
Timmis; Roger; (Olympia,
WA) ; Toland; Mitchell R.; (Puyallup, WA) ;
Ghermay; Timnit; (Ann Arbor, MI) ; Carlson; William
C.; (Olympia, WA) ; Grob; James A.; (Auburn,
WA) |
Correspondence
Address: |
WEYERHAEUSER COMPANY;INTELLECTUAL PROPERTY DEPT., CH 1J27
P.O. BOX 9777
FEDERAL WAY
WA
98063
US
|
Assignee: |
WEYERHAEUSER COMPANY
33663 Weyerhaeuser Way South
Federal Way
WA
98003
|
Family ID: |
32072685 |
Appl. No.: |
11/836095 |
Filed: |
August 8, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10680676 |
Oct 7, 2003 |
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11836095 |
Aug 8, 2007 |
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09700037 |
Jul 2, 2001 |
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PCT/US99/12128 |
Jun 1, 1999 |
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10680676 |
Oct 7, 2003 |
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60087524 |
Jun 1, 1998 |
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Current U.S.
Class: |
703/11 ;
800/319 |
Current CPC
Class: |
C12N 15/8207 20130101;
A01H 4/00 20130101; A01H 1/04 20130101; A01C 1/00 20130101; B07C
2501/009 20130101; G06K 9/00147 20130101 |
Class at
Publication: |
703/011 ;
800/319 |
International
Class: |
A01H 7/00 20060101
A01H007/00; G06G 7/58 20060101 G06G007/58 |
Claims
1. A method for classifying plant embryos according to their
germination potential comprising: (a) developing a single metric
classification model by (i) acquiring raw digital image data of
reference samples of whole plant embryos or any portion thereof of
known germination potential; (ii) calculating a metric value from
the acquired raw digital image data of each embryo of known
germination potential; (iii) dividing the metric values obtained in
step (a)(ii) into two sets of metric values according to their
known germination potential; (iv) calculating a Lorenz curve from
the two sets of metric values; (v) using any point on the Lorenz
curve calculated in step (a)(iv) as a threshold value to arrive at
a single metric classification model for classifying plant embryos
by their germination potential; (b) acquiring raw digital image
data of a whole plant embryo or any portion thereof of unknown
germination potential; and (c) applying the developed single metric
classification model to the raw digital image data of step (b) in
order to classify the plant embryo of unknown germination potential
according to its presumed germination potential.
2. A method according to claim 1, wherein two or more single metric
classification models derived from different metrics are combined
using one or more classification algorithms to develop a
classification model for classifying plant embryos.
3. A method according to claim 1, wherein the raw digital image
data acquired in step (a)(i) is preprocessed using one or more
preprocessing algorithms before step (a)(ii); the raw digital image
data acquired in step (b) is preprocessed using one or more
preprocessing algorithms; and step (c) is carried out using the
preprocessed raw digital image data.
4. A method according to claim 3, wherein the preprocessing
algorithm removes raw image data that is not from the plant embryo
or plant embryo organ.
5. A method according to claim 3, wherein the preprocessing
algorithm reduces the amount of raw image data.
6. A method according to claim 1, wherein the raw digital image
data is acquired from more than one view of the plant embryo or
plant embryo organ.
7. A method according to claim 1, wherein the plant embryo is a
plant somatic embryo.
8. A method according to claim 7, wherein the plant is a tree.
9. A method according to claim 8, wherein the tree is a member of
the order Coniferales.
10. A method according to claim 8, wherein the tree is a member of
the family Pinaceae.
11. A method according to claim 8, wherein the tree is selected
from the group consisting of genera Pseudotsuga and Pinus.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This is a continuation of U.S. patent application Ser. No.
10/680,676, filed Oct. 7, 2003, which is a divisional of U.S.
patent application Ser. No. 09/700,037, filed Jul. 2, 2001, which
is the National Stage of International Application No.
PCT/US99/12128, filed Jun. 1, 1999, which in turn claims the
benefit of U.S. Application No. 60/087,524, filed Jun. 1, 1998.
FIELD OF THE INVENTION
[0002] The present invention relates to classification of plant
embryos for determination of suitability for germination or other
treatments. In particular, it is concerned with selection of
conifer somatic embryos most likely to be successfully germinated
and to produce normal plants.
BACKGROUND
[0003] Reproduction of selected plant varieties by tissue culture
has been a commercial success for many years. The technique has
enabled mass production of genetically identical selected
ornamental plants, agricultural plants and forest species. The
woody plants in this last group have perhaps posed the greatest
challenges. Some success with conifers was achieved in the 1970s
using organogenesis techniques wherein a bud, or other organ, was
placed on a culture medium where it was ultimately replicated many
times. The newly generated buds were placed on a different medium
that induced root development. From there, the buds having roots
were planted in soil.
[0004] While conifer organogenesis was a breakthrough, costs were
high due to the large amount of handling needed. There was also
some concern about possible genetic modification. It was a decade
later before somatic embryogenesis achieved a sufficient success
rate so as to become the predominant approach to conifer tissue
culture. With somatic embryogenesis, an explant, usually a seed or
seed embryo, is placed on an initiation medium where it multiplies
into a multitude of genetically identical immature embryos. These
can be held in culture for long periods and multiplied to bulk up a
particularly desirable clone. Ultimately, the immature embryos are
placed on a development or maturation medium where they grow into
somatic analogs of mature seed embryos. These embryos are then
individually selected and placed on a germination medium for
further development. Alternatively, the embryos may be used in
manufactured seeds.
[0005] There is now a large body of general technical literature
and a growing body of patent literature on embryogenesis of plants.
Examples of procedures for conifer tissue culture are found in U.S.
Pat. Nos. 5,036,007 and 5,236,841 to Gupta et al.; U.S. Pat. No.
5,183,757 to Roberts; U.S. Pat. No. 5,464,769 to Attree et al.; and
U.S. Pat. No. 5,563,061 to Gupta.
[0006] One of the more labor intensive and subjective steps in the
embryogenesis procedure is the selection from the maturation medium
of individual embryos suitable for germination. The embryos may be
present in a number of stages of maturity and development. Those
that are most likely to successfully germinate into normal plants
are preferentially selected using a number of visually evaluated
screening criteria. Morphological features such as axial symmetry,
cotyledon development, surface texture, color, and others are
examined and applied as a pass/fail test before the embryos are
passed on for germination. This is a skilled yet tedious job that
is time consuming and expensive. Further, it poses a major
production bottleneck when the ultimate desired output will be in
the millions of plants.
[0007] It has been proposed to use some form of instrumental image
analysis for embryo selection to replace the visual evaluation
described above. For examples, refer to Cheng, Z. and P. P. Ling,
"Machine Vision Techniques for Somatic Coffee Embryo Morphological
Feature Extraction," Trans. Amer. Soc. Agri. Eng. 37:1663-1669
(1994), or Chi, C. M., C. Zhang, E. J. Staba, T. J. Cooke, and W-S.
Hu, "An Advanced Inage Analysis System for Evaluation of Somatic
Embryo Development," Biotech. and Bioeng. 50:65-72 (1996). All of
these methods require considerable pre-judgment of which
morphological features are important and the development of
mathematical methods to extract this information from the images.
Relatively little of the information from the image has actually
been used.
[0008] The problem of how to best use image analysis to automate
the selection of somatic embryos after they had been separated from
residual tissue, singulated, and imaged in color from multiple
positions has not been successfully addressed. Various methods are
known for extracting size and shape information from scanned
images. As one example, Moghaddam et al., U.S. Pat. No. 5,710,833,
describes a method useful for recognition of any multifeatured
entity such as a human face. Sclaroff et al., U.S. Pat. No.
5,590,261, describes a method that can be used for object
recognition purposes.
[0009] Where embryos are concerned, a further problem using
scanning technology is that morphology differs between clones
within a given species. The differences between acceptable and
rejected embryos can be very subtle, varying by clone. Hence, the
choice of selection criteria for machine use tends to be
subjective, difficult to specify mathematically, and may be clone
specific.
[0010] The development of high speed computers and new
spectroscopic hardware has led to the development of new
instruments which have the capability to rapidly acquire spectra on
large numbers of samples. However, the acquisition of vast amounts
of spectral data from a sample necessitates the development of
similarly powerful data analysis tools to uncover subtle
relationships between the collected spectra and the chemical
properties of the sample. One such data analysis methodology,
commonly known as chemometrics, applies multivariate statistical
techniques to complex chemical systems in order to facilitate the
discovery of the relationship between the absorption, transmittance
or reflectance spectral data acquired from a sample and some
specified property of the sample that is subject to independent
measurement. The end result of multivariate analysis is the
development of a predictive classification model that allows new
samples of unknown properties to be rapidly and accurately
classified according to a specified property based upon the
acquired spectral data. For example, multivariate analysis
techniques such as: principal component analysis (PCA) and a
principal component-based method, projection to latent structures
(PLS), have been used to explore the multivariate information in
previous applications of near-infrared (NIR) spectroscopy to the
pulp and paper industry to develop classification models for paper
quality. See, for example, U.S. Pat. Nos. 5,638,284, 5,680,320,
5,680,321, and 5,842,150.
SUMMARY
[0011] The present invention is based on classification of plant
embryos by the application of classification algorithms to
digitized images and absorption, transmittance, or reflectance
spectra of the embryos. The methods are generally applicable and
emphasize the importance of acquiring and using as much image and
absorption, transmittance, or reflectance spectral information as
possible, based on objective criteria. One goal has been automated
classification and selection of embryos most suitable for further
culture and rejection of those seen as less suitable. The technique
is capable of utilizing more complex imaging technology; e.g.,
multi-viewpoint images and images in color or from non-visible
portions of the electromagnetic spectrum.
[0012] In one aspect of the present invention, a method for
classifying plant embryos according to embryo quality is provided.
The method first develops a classification model by acquiring raw
digital image data of reference samples of plant embryos of known
embryo quality. Optionally, the raw digital image data is
preprocessed using one or more preprocessing algorithms to reduce
the amount of raw image data yet retain substantially all of the
image data that contains geometric and color information regarding
the embryo or embryo organ. An example of such an optional
preprocessing technique involves removing image data that is not
derived from the plant embryo or plant embryo organ. Another
optional preprocessing step results in the calculation of metrics
which emphasize image features that are particularly important in
embryo quality classification. Data analysis is performed on the
raw digital image data, or on the preprocessed image data depending
upon which method is followed, using one or more classification
algorithms to develop a classification model for classifying plant
embryos by embryo quality. During this data analysis one or more of
the classification algorithms utilizes raw digital image data
representative of more than just the embryo perimeter, or the
preprocessed image data to develop the classification model. The
embryo quality of the reference samples is determined by reference
to such qualities as morphological comparison to normal zygotic
plant embryos, determination of the reference embryo's conversion
potential, resistance to pathogens, drought resistance and the
like. Raw digital image data of plant embryos of unknown embryo
quality is then acquired using the same methods as performed on the
reference samples. The acquired raw digital image data is then
analyzed using classification algorithms used to develop the
classification model in order to classify the quality of the plant
embryo of unknown quality. A more robust method is obtained by
acquiring raw digital image data of multiple views of the embryo,
such as end-on views of the embryo and/or longitudinal views.
[0013] In another aspect of the present invention plant quality is
classified by developing a single metric classification model by
acquiring raw digital image data of reference samples of whole
plant embryos or any portion thereof from plant embryos of known
embryo quality. A metric value is calculated from the acquired raw
digital image data of each embryo of known quality. The metric
values are divided into two sets of metric values based upon the
known embryo quality. A Lorenz curve is calculated from each set of
metric values. A threshold value is determined from a point on the
Lorenz curve which serves as a single metric classification model
to classify plant embryos by embryo quality. Raw image data is
acquired from a whole plant embryo or any portion thereof from a
plant embryo of unknown quality. The single metric classification
model developed from embryos of know quality is applied to the raw
image data acquired from plant embryos of unknown quality in order
to classify the quality of the unknown plant embryo. Single metric
classification models can optionally be combined using one or more
classification algorithms to develop more robust classification
models for classifying plant embryos by embryo quality.
[0014] In another embodiment of the present invention, plant embryo
quality is classified by collecting absorption, transmittance or
reflectance spectral raw data from plant embryos or portions
thereof and processing the data using classification algorithms.
The inventive method first requires that a classification model be
developed by acquiring absorption, transmittance or reflectance
spectral raw data of reference samples of plant embryos or portions
thereof whose embryo quality is known. In one alternative
embodiment, prior to making the classification model, the spectral
raw data in whole or in specific parts is preprocessed to among
other things, reduce noise and adjust for drift and diffuse light
scatter. The classification model is then made by performing a data
analysis using classification algorithms on the preprocessed
spectral raw data. Absorption, transmittance or reflectance
spectral raw data is then acquired from a plant embryo of unknown
embryo quality. The spectral raw data collected from the embryo of
unknown quality is either applied directly to the embryo quality
classification model or preprocessed to reduce noise and adjust for
drift and diffuse light scatter and then the preprocessed spectral
data is applied to the classification model depending upon which
method was used to make the classification model in use. In either
case, the application of the unknown spectral data to the
classification model allows classification of the quality of the
plant embryo of unknown plant embryo quality.
DESCRIPTION OF THE DRAWINGS
[0015] The foregoing aspects and many of the attendant advantages
of this invention will become more readily appreciated as the same
becomes better understood by reference to the following detailed
description, when taken in conjunction with the accompanying
drawings, wherein:
[0016] FIG. 1 shows a diagrammatic representation of a tree embryo
8. The circled areas represent the embryo regions representative of
the three embryo organs known as cotyledon 10, hypocotyl 12, and
radicle 14.
[0017] FIG. 2A displays a scoreplot obtained from principal
component analysis of spectral data collected from Douglas-fir
zygotic embryos of three different developmental stages and a set
of Douglas-fir somatic embryos (genotype 1). The units on the
principal component (PC) axes are universal standard deviations for
the set.
[0018] FIG. 2B shows the loadings spectra for each PC depicted in
FIG. 2A. Each curve shows the relative contribution that each
wavelength makes in accounting for the variance depicted along the
scoreplot axes in FIG. 2A.
[0019] FIG. 3A displays a scoreplot obtained from principal
component analysis of spectral data collected from loblolly pine
zygotic embryos of two different developmental stages and two sets
of somatic embryos (genotypes 5 and 7). The units on the PC axes
are universal standard deviations for the set, and the crossover of
zero axes is the average behavior of all the embryos.
[0020] FIG. 3B shows the loadings spectra for each PC depicted in
FIG. 3A. Each curve shows the relative contribution that each
wavelength makes in accounting for the variance depicted along the
scoreplot axes in FIG. 3A.
[0021] FIG. 4A displays a scoreplot obtained from principal
component analysis of spectral data collected from Douglas-fir
somatic embryos at the cotyledonary stage (genotype 2) that have
"good" and "poor" embryo morphology. The units on the PC axes are
universal standard deviations for the set.
[0022] FIG. 4B shows the loadings spectra for each PC depicted in
FIG. 4A. Each curve shows the relative contribution that each
wavelength makes in accounting for the variance depicted along the
scoreplot axes in FIGURE B.
[0023] FIG. 5A displays a scoreplot obtained from principal
component analysis of spectral data collected from loblolly pine
somatic embryos (genotype 5) at the cotyledonary stage that have
"good" and "poor" embryo morphology. The units on the PC axes are
universal standard deviations for the set.
[0024] FIG. 5B shows the loadings spectra for each PC depicted in
FIG. 5A. Each curve shows the relative contribution that each
wavelength makes in accounting for the variance depicted along the
scoreplot axes in FIG. 5A.
[0025] FIG. 6A displays a scoreplot obtained from principal
component analysis of spectral data collected from Douglas-fir
somatic embryos (genotype 3). The scanned somatic embryos were of
two different developmental stages, the cotyledon stage and "dome"
or "just cotyledon" stage. The units on the PC axes are universal
standard deviations for the set.
[0026] FIG. 6B shows the loadings spectra for each PC depicted in
FIG. 6A. Each curve shows the relative contribution that each
wavelength makes in accounting for the variance depicted along the
scoreplot axes in FIG. 6A.
[0027] FIG. 7A displays a scoreplot obtained from principal
component analysis of spectral data collected from Douglas-fir
somatic embryos (genotypes 3 and 4). A set of somatic embryos from
each genotype were either subjected to a cold treatment (which
improves germination) or received no cold treatment (Control). The
units on the PC axes are universal standard deviations for the
set.
[0028] FIG. 7B shows the loadings spectra for each PC depicted in
FIG. 7A. Each curve shows the relative contribution that each
wavelength makes in accounting for the variance depicted along the
scoreplot axes in FIG. 7A.
[0029] FIG. 8A displays a scoreplot obtained from principal
component analysis of spectral data collected from loblolly pine
somatic embryos (genotypes 5 and 7) at the cotyledonary stage. A
set of somatic embryos from each genotype were either subjected to
a cold treatment (which improves germination) or received no cold
treatment (Control). The units on the PC axes are universal
standard deviations for the set.
[0030] FIG. 8B shows the loadings spectra for each PC depicted in
FIG. 8A. Each curve shows the relative contribution that each
wavelength makes in accounting for the variance depicted along the
scoreplot axes in FIG. 8A.
DETAILED DESCRIPTION
[0031] The inventive methods are used to classify any type of plant
embryos, such as, for example, zygotic and somatic embryos, by any
embryo quality that is amenable to characterization. For example,
embryo quality can be defined using morphological criteria such as
axial symmetry, cotyledon development, surface texture and color.
As used herein "zygotic morphology" refers to morphological
criteria, such as axial symmetry, cotyledon development, surface
texture and color that are characteristic of a normal zygotic plant
embryo. Alternatively, embryos can be classified using
developmental or functional criteria, such as embryo germination
and subsequent plant growth and development, often collectively
referred to in the literature as "conversion."As used herein
"conversion potential" refers to the capacity of a somatic embryo
to germinate and/or survive and grow in soil, preceded or not by
desiccation or cold treatment of the embryo. In addition, "plant
embryo quality" refers to other plant characteristics such as
resistance to pathogens, drought resistance, heat and cold
resistance, salt tolerance, preference for light quality,
suitability for long term storage of somatic embryos or any other
plant quality susceptible to quantification.
[0032] Embryos from all plant species can be adapted to the
inventive methods. The methods have particular application to
agricultural plant species where large numbers of somatic embryos
are used to propagate desirable genotypes such as with forest tree
species. In particular, the methods can be used to classify somatic
embryos from conifer tree family Pinaceae, particularly from the
genera: Pseudotsuga and Pinus. A diagrammatic drawing of a
Pseudotsuga tree embryo 8 is presented in FIG. 1 in which the
general locations of the three embryo organs, cotyledon 10,
hypocotyl 12, and radicle 14 are indicated.
[0033] In one embodiment of the present invention images of plant
embryos or plant embryo organs are acquired in a digital form by
scanning one or more views of the embryos or organs from multiple
positions using known technology, such as electronic camera
containing a charge couple devise (CCD) linked to a digital storage
devise. A classification model for plant embryo quality is then
developed by performing a data analysis on the digital image data
using one or more classification algorithms. Examples of such
classification algorithms include, but are not limited to,
principal components analysis (see, for example, Jackson, J. E., A
User's Guide to Principal Components, John Wiley and Sons, New York
(1991); Jolliffe, I. T., Principal Components Analysis,
Springer-Verlag, New York (1986); Wold, S., "Pattern Recognition by
Means of Disjoint Principal Components Models," Pattern Recognition
8:127-139 (1976); and Watanapongse, P. and H. H. Szu, "Application
of Principal Wavelet Component in Pattern Classification,"
Proceedings of SPIE, Wavelet Applications V, H. H. Szu, Editor,
vol. 3391, pp. 194-205 (1998)), Artificial Neural Networks
(Mitchell, Tom M., Machine Learning, WCB/McGraw-Hill pp. 112-115,
(1997)), Bayesian Classifiers (Mitchell at 174-176), Probably
Approximately Correct (PAC) Learning (Mitchell at 203-220), Radial
Basis Functions which includes the statistical technique of fitting
mixture distribution models to data (Mitchell, pp. 238-240), and
Nearest-Neighbor Methods (Mitchell at 231-236). In addition to the
aforementioned classification algorithms, a new classification
algorithm is provided in the present invention to classify plant
embryos based upon the Lorenz curve. For a brief introduction to
Lorenz curves, see Johnson, S. and N. L. Kotz, Eds. Encyclopedia of
Statistical Sciences, John Wiley, vol. 5, pp. 156-161 (1985).
[0034] It is also well known in the art of data analysis that
several different algorithms besides Principal Component Analysis
(PCA) can be used to develop and use classification models. More
specifically, the following statistical techniques can also be
adapted to the present invention: Partial Least Squares Regression,
Principal Components Regression (PCR), Multiple Linear Regression
Analysis (MLR), Discriminant Analysis, Canonical Correlation
Analysis, Multivariate Multiple Regression, Classification
Analysis, Regression Tree Analysis which includes Classification
Analysis by Regression Trees (CART.TM., Salford Systems, San Diego,
Calif.), and Logistic and Probit Regression. See U.S. Pat. No.
5,842,150 and (Mitchell, Tom M., Machine Learning, WCB/McGraw-Hill
pp. 112-115, 238-240 (1997)).
[0035] The classification model is deduced from a "training" data
set of multiple images of plant embryos or plant embryo organs
acquired from embryos having known embryo quality. Embryos
providing the training set images are classified as acceptable or
unacceptable based on biological fact data such as morphological
similarity to normal zygotic embryos or proven ability to germinate
or convert to plants. The inventive methods are generally adaptable
to any plant quality that is susceptible to quantification.
Unclassified embryos are classified as acceptable or not based on
how close images of the unclassified embryos fit to the
classification model developed from the training set groups.
[0036] As used herein the term "classification algorithm" refers to
any sequence of mathematical or statistical calculations, formulae,
functions, models or transforms of image or spectral data from
embryos used for the purpose of classifying embryos according to
embryo quality. A classification algorithm can have just one step
or many. In addition, classification algorithms of the present
invention can be constructed by combining intermediate
classification models or single metric classification models
through the use of mathematical algorithms such as the Bayes
optimal classifier, neural networks or the Lorenz curve. Except for
the single metric classification models, the image classification
models of the present invention are derived from a data analysis of
more than just embryo perimeter image data acquired from plant
embryos or embryo organs during the training sessions that lead to
the identification of an embryo quality classification model. That
is, the classification models of the present invention, except for
the single metric classification models, are developed using at
least one classification algorithm which considers more of the
acquired raw digital image data than required to define the
perimeter of the embryo. Thus, the classification algorithms
perform a data analyses that results in the development of a
classification model from the image or spectral data without any
subjective assumptions being made regarding which data features are
important for embryo quality classification.
[0037] As used herein "embryo perimeter" means the pixels in raw
digital image data or preprocessed digital image data which define
the outer perimeter of an imaged embryo.
[0038] Optionally, the raw digital image data can be preprocessed
using preprocessing algorithms. As used hereafter the term
"preprocessing algorithm" refers to any sequence of mathematical or
statistical calculations, formulae, functions, models or transforms
of image or spectral data from embryos used for the purpose of
manipulating image or spectral data in order to: 1) remove image or
spectral data that is derived from non-embryo sources, i.e.,
background light scatter or other noise sources; 2) reduce the size
of the digital data file that is used to represent the acquired
image or spectra of the embryo while retaining substantially all of
the data that represents informational features such as geometric
embryo shape and surface texture, color, and light absorption,
transmittance or reflectance, of the acquired image or spectra; and
3) calculate metrics from the acquired raw image or spectral data
and from values obtained during other preprocessing steps, in order
to identify and emphasize embryo data that is useful in development
of an embryo quality classification model.
[0039] For example, U.S. Pat. No. 5,842,150 discloses that NIR
spectral data can be preprocessed prior to multivariate analysis
using the Kubelka-Munk transformation, the Multiplicative Scatter
Correction (MSC), e.g., up to the fourth order derivatives, the
Fourier transformation or by using the Standard Normal Variate
transformation, all of which can be used to reduce noise and adjust
for drift and diffuse light scatter.
[0040] Alternatively, the amount of digital data required to
represent an acquired image or spectrum of an embryo can be reduced
using preprocessing algorithms such as wavelet decomposition. See,
for example, Chui, C. K., An Introduction to Wavelets, Academic
Press, San Diego (1992); Kaiser, Gerald, A Friendly Guide to
Wavelets, Birkhauser, Boston; and Strang, G. and T. Nguyen,
Wavelets and Filter Banks Wellesley-Cambridge Press, Wellesley,
Mass. Wavelet decomposition has been used extensively for reducing
the amount of data in an image and for extracting and describing
features from biological data. For example, wavelet techniques have
been used to reduce the size of fingerprint image files to minimize
computer storage requirements. A biological example is the
development of a method for diagnosing obstructive sleep apnea from
the wavelet decomposition of heart beat data. Wavelets enable
rearrangement of the information in a picture of an embryo into
size and feature categories. For example, size and shape data may
be separated from texture. The results of a wavelet decomposition
or functions thereof are then used as inputs to the classification
algorithms described above. A variety of other interpolation
methods can be used to similarly reduce the amount of data in an
image or spectral data file, such as, calculation of adjacent
averages, Spline methods (see, for example, C. de Boor, A Practical
Guide to Splines, Springer-Verlag, (1978)), Kriging methods (see,
for example, Noel A. C. Cressie, Statistics for Spatial Data, John
Wiley, 1993)) and other interpolation methods which are commonly
available in software packages that handle images and matrices.
[0041] Other preprocessing algorithms can be used to process data
collected from an embryo in order to obtain the most robust
correlation of the acquired data to embryo quality. For example, in
Example 1 several statistical values were calculated to recapture
some of the data information that was lost when a wavelet
decomposition was used to reduce the size of the image. The
recaptured information represented in the metrics allowed the
development of a classification model that was better at predicting
embryo quality than a model developed from principal component
analysis of image data that was preprocessed using wavelet methods.
As used hereinafter "metrics" refers to any scalar statistical
value that captures geometric, color, or spectral features which
contains information about the embryos, such as central and
non-central moments, function of the spectral energy at specific
wavelengths or any function of one or more of these statistics. In
image processing language sets of metrics are also known as feature
vectors. In addition, metrics can be derived from external
considerations, such as embryo processing costs, embryo processing
time, and the complexity of an assembly line sorting embryos by
quality.
[0042] In another embodiment of the present invention embryo
regions are scanned and spectral data is acquired regarding
absorption, transmittance or reflectance of electromagnetic
radiation (hereinafter referred to as light) at multiple discrete
wavelengths ranging from 180 nm to 4000 nm. Differences in spectral
data collected from embryos of high quality (for example, high
conversion potential or high morphological similarity to normal
zygotic embryos) versus those of low quality are presumed to
reflect differences in chemical composition that are related to
embryo quality. Numerous studies assert that embryo quality is
related to gross chemical composition of the embryo or its parts,
especially the amounts of water and storage compounds (proteins,
lipids, and carbohydrates). Some examples include: Chanprame, S.,
T. M. Kuo, and J. M. Widholm, "Soluble Carbohydrate Content of
Soybean [Glycine max (L.) Merr.] Somatic and Zygotic Embryos During
Development," In Vitro Cell Dev. Biol-Plant. 34:64-68 (1998);
Dodeman, V. L., M. Le Guilloux, G. Ducreux, and D. de Vienne,
"Somatic and Zygotic Embryos of Daucus carota L. Display Different
Protein Patterns Until Conversion to Plants," Plant Cell Physiol.
39:1104-1110 (1998); Morcillo, F., F. Aberlenc-Bertossi, S. Hamon,
and Y. Duval, "Accumulation of Storage Protein and 7S Globulins
During Zygotic and Somatic Embryo Development in Elaeis
Guineensis," Plant Physiol. Biochem. 36:509-514 (1998); and
Obendorf, R. L., A. M. Dickerman, T. M. Pflum, M. A. Kacalanos, and
M. E. Smith, "Drying Rate Alters Soluble Carbohydrates, Desiccation
Tolerance, and Subsequent Seedling Growth of Soybean (Glycine mac
L. Merrill) Zygotic Embryos During In Vitro Maturation," Plant Sci.
132:1-12 (1998).
[0043] Spectrometric analysis of embryos can be performed using a
data collection setup that includes a light source, a microscope, a
light sensor, and a data processor. Preferably, each embryo region
undergoes multiple light scans in order to obtain a representative
average spectrum. In addition, it is useful that the data processor
include a built-in calibration program which is run periodically
throughout the data collection phase to recalibrate the internal
baseline to correct for dark current, and to recalibrate against
the standard white background material upon which the embryo
sits.
[0044] Preferably, the light sensor has a measuring interval of at
the most 10 nm, preferably 2 nm, and most preferably 1 nm or less.
The detection of light is performed in the ultraviolet, visible,
and near infrared (including Raman spectroscopy) wavelength range
of 180 nm to 4000 nm. This can be accomplished by the use of a
scanning instrument, a diode array instrument, a Fourier transform
instrument or any other similar equipment, known to the person of
skill in the art.
[0045] The classification of embryos according to quality (as
defined above) by the spectrometric measurements comprises two main
steps. The first is the development of a classification model,
involving the substeps of development of training and cross
validating sets. Spectral data is acquired from embryos or embryo
regions of known embryo quality, optionally a preprocessing of the
acquired spectral data is performed, and then a data analysis is
performed using one or more classification algorithms to develop a
classification model for embryo quality. The second main step is
the acquisition of spectrometric data from an embryo whose quality
is unknown, optionally performing preprocessing of the acquired
spectral data, followed by data analysis of the acquired spectral
data using the classification model developed in the first main
step.
[0046] Model training sets consist of a large number of absorption,
transmittance or reflectance spectra acquired from embryos that
have a known high or low quality. The training sets are used in the
classification algorithms to develop a classification model. As
previously noted, a variety of preprocessing algorithms are
available that can be used to first reduce noise and adjust for
base line drift. However, for some data sets it may not be
necessary to preprocess the data to reduce background noise.
[0047] There are many data analysis methods that can be applied to
develop and use classification models that allow plant embryos to
be classified by quality. The above described mathematical methods
are a sampling of some of the major techniques. However, it should
be emphasized that data analysis techniques can be put together in
an almost infinite number of combinations to achieve the desired
results. For example, a soft independent modeling of class analogy
(SIMCA) method can be used on images of embryos which have their
color information collapsed into a single array using principal
components and then the result can be shrunk using wavelets. SIMCA
can then be used to build principal component regression models for
each classification category. The Bayes optimal classifier can then
be used to combine the classification decisions from six SIMCA
model pairs. Partial least squares regression can be used in place
of principal component regression in the SIMCA step. Similarly,
neural networks can be used in place of Bayes optimal classifier to
combine classification decisions into a final classification
model.
[0048] In addition, the methods described for classifying plant
embryos using embryo image data or absorption, transmittance or
reflectance spectral data can be combined together in a number of
different ways. For example, data analysis of the acquired raw
visual and spectral data can be performed in parallel to develop a
unitary classification model or the analysis can be conducted in
series whereby two independent classification models are developed
using the image and spectral data separately. Many permutations of
the methods described herein are possible to accomplish the
classification of plant embryos by embryo quality.
[0049] The following nonlimiting examples illustrate the inventive
methods and the use of them to classify plant embryos that are most
likely to be successfully germinated and produce normal plants.
EXAMPLE 1
Mathematical Methods
[0050] There are three main steps in using light images to separate
somatic embryos. They are: 1) cleaning the images to remove raw
image data that is not from the plant embryo or embryo organ; 2)
reducing the amount of raw image data acquired from the embryo or
embryo organ while retaining as much embryo information as
possible; and 3) applying one or more classification algorithms to
develop and use a classification model for plant embryo
quality.
Cleaning the Images
[0051] Image cleaning requires replacing the background in an image
with zeros or pure black. The reason for this is to reduce
variation between images. It is desired that the only differences
between images be due to the embryos so that comparisons are not
confounded with changes in the background. Since the images are
magnified, slight variations in position, reflections, glints off
leftover material from previous embryos are magnified and
contribute to the differences between the images. Cleaning refers
to the image processing steps used to eliminate all the variations
in the background.
[0052] There is no set recipe for cleaning the embryo images since
it is anticipated that as new imaging hardware and software are
developed more suitable image cleaning technique will evolve.
However, several techniques are generally useful. The examples
described below are merely illustrative and are not meant to limit
the present invention.
[0053] In the Examples that follow, the image of an embryo, its
reflection on its stage and the remaining background were separated
from each other using only the red component from the color image.
The histogram of the red pixel values was positively skewed. A
mixture distribution composed of three normal distributions was fit
to the histogram by means of the EM algorithm. For a brief
description of the EM algorithm see Mitchell, Tom M., Machine
Learning, WCB/McGraw-Hill, pp. 191-196 (1997). The first normal
picked up the background, the second normal picked up the
reflection and the third component picked up the embryo. The mean
of the second normal plus two times its standard deviation was used
as the boundary between the reflection and the embryo. The red
image was thresholded at this value. The resulting binary image
still had some pixels that belonged to the reflection included in
it. These were removed by using morphological operations on the
binary image. Usually, one to three erosions followed by the same
number of dilations are successful in cleaning up the image.
Sometimes an extra couple of dilations were needed to restore the
embryo part of the binary image to its proper size. Any holes in
the embryo part of the binary image were then filled. The resulting
binary image was then used to crop the color image and zero all
non-embryo parts of the image. Each of the three color matrices in
the original image were multiplied by the binary image and then
cropped to within two pixels of the embryo. This method worked for
all three views of the embryo.
[0054] Alternatively, a different method for cleaning each of the
three embryo views can be used. In this alternative method the
longitudinal top view of the embryo was preprocessed by first
converting the red-green-blue values to hue. Saturation and
intensity were not needed for this view. Taking the cotangent of
1/255.sup.th of the hue flattened the range of the hue values
making it easier to pick up more of the dark tail of the embryo.
Only the positive hue values were used since most of the background
ends up with negative or zero values for hue. Sometimes the
positive hue values alone were enough. A binary image was created
by thresholding the cotangent values at 100. Values above 100 were
set to 1. One erosion followed by two dilations eliminated the
spurious pixels from the background. The largest contiguous group
of ones were kept as the embryo. Erosions and dilations were not
done as many times as in the previous method, in order to keep the
radical or tail portion of the embryo image attached to the main
embryo body. Hole filling was done before the erosion and dilations
in order to maintain the radical portion of the embryo image.
[0055] The longitudinal side view of the embryo (camera angle was
rotated 90 degrees relative to the top view) was preprocessed by
creating a matrix of maximum color values. The maximum color values
at a pixel was the largest of the red, green, and blue color
values. The maximum color values were used to ensure maximum
retention of the embryo radical image. The embryo had a horizontal
position in this image. Therefore, the row average was calculated
from the maximum color values. The lowest average value between
rows 200 and 260 corresponded to the gap between the embryo and the
edge of the stage on which it sits. Everything below the row
corresponding to the gap was set to zero. The rest of the image was
thresholded so that values above ten were set to one. Again the
binary image was eroded once and dilated twice to remove spurious
pixels. A blob labeling routine labeled the remaining groups of
pixels with values of ones and the largest one was kept as the
embryo. If a second blob of ones had at least 25% of the number of
pixels in it as the largest blob then the radicle was assumed to
have been separated by the morphological operations and was
included. Hole filling was done and then the binary image was used
to zero the background parts of the original image and crop it as
in the case of the top view.
[0056] The apical or end view of the embryo was preprocessed by one
of two ways. The first method was to use the same method as
described for the side view with three changes. After the stage
part of the image was set to zero the remaining maximum values were
thresholded at 20 instead of 10. The resulting binary image was
eroded 3 times and dilated 5 times. Finally, no second largest blob
was kept. The second method was to create a binary image from the
product of two other binary images. The first binary image was
created from the matrix of maximum values by setting all values
greater than 20 to one and zero otherwise. The second binary image
was made by creating a matrix of hue values as for the top view and
then setting the positive values to one and all others to zero. The
product of these two binary images eliminates almost all background
features. The resulting binary image was eroded and dilated as in
the first method. Finally, the binary image was used to zero the
background and crop the original image as in the top view.
[0057] The reason the images were cropped was to concentrate later
analytical effort on the embryo portion of the images as much as
possible and to reduce the demands on computer memory. The three
views of an embryo represented three correlated measurements of a
single experimental unit. It took hundreds of thousands of numbers
to describe the measurements. The embryo only covers about 5% of
the total area of an image, so most of an image was background.
Carrying along the background information needlessly uses up memory
and can hamper later methods used to classify the embryos.
Image Reduction
[0058] Since embryo image data sets are often large, further image
size reduction was performed in order to get the all of the data
into computer memory. Also, the embryo classification algorithms
that were used to sort the embryos required that all of the images
of a particular embryo view be the same size. The sizes of the
largest top view, side view and end-on view were found after all
the images had been preprocessed and cropped as described in the
preceding section. All top views were zero padded out to the size
of the largest top view with the cotyledon embryo head placed as
close to one of the corners of the image as possible. In other
words, the extra zeros were added to the radicle end of the image
and to one of the sides. Zero padding for the side and end views
was similar. The zero padding scheme was performed in an effort to
get all the embryo heads in the same place in the images, while the
radical tail portion of the embryo, which is highly variable in
size and shape, were left to occupy what ever image space they
needed.
[0059] With the images of each embryo view reset to the smallest
common size, the images were then shrunk using wavelet
computational methods. The first step in reducing the images was to
calculate the principal components of the red, green, and blue
color matrices pixelwise. Each color matrix was strung out into a
single long vector by appending the columns to each other. The
first column was at the top of the vector and the last column was
at the bottom. The red, green, and blue vectors were formed into a
matrix with three columns and the singular value decomposition of
this matrix was calculated. The left eigenvectors from the
decomposition were principal components with unit length. The first
eigenvector corresponded to the principal component that accounted
for the most variation in the color values. On average the first
principal component (PC) accounted for 95% of the variation. The
first PC represents the optimal weighted average of the red, green,
and blue values for explaining variation and is similar to a
calculated grayscale value. The first eigenvector was then reshaped
into a matrix and was used in place of the color array. This step
reduced the computer memory requirements by 1/3 by replacing three
matrices with a single matrix whose values were similar to a gray
scale image. The single matrix carries all of the geometric
information of the original. The second step was to do a two level
two dimensional wavelet decomposition on the first PC image in
order to reduce its size. The approximation
coeffiecence[coefficient??] from the second level of the wavelet
decomposition are used as the reduced image. The reduced image
retains at least 75% of the variability in the original PC
image.
Metrics
[0060] Reducing the image data using the aforementioned methods
means that some of the information in the original color data is
lost. In an attempt to keep some of this information, several
statistics were calculated as the data reduction process was
performed. First, the mean standard deviation, coefficient of
skewness and coefficient of kurtosis were calculated for each color
as well as hue, saturation and intensity. Next, the coefficients of
the wavelet decomposition at each scale were summarized by their
first five raw moments about zero. In a two level decomposition
there are six matrices of detail coefficients and one of smooth
coefficients. The detail coefficients contain information on
texture. The first five raw moments about zero were estimated for
each of these matrices as well as the smooth coefficients. The five
moments about zero were the mean, mean squared value, mean cubed
value, mean quartic value and mean quintic value. To obtain central
moments like the variance, skewness, etc., one subtracts the mean
from the individual values first. However, central moments were
more similar for classification groups than for raw moments. A
third set of statistics were calculated from the perimeter of the
embryo and its wavelet decomposition and are intended to quantify
shape information.
[0061] The perimeter of the embryo was traced in a clockwise
direction and the row and column coordinates of the edge pixels
were obtained. The pixel coordinates were interpolated to generate
row and column vectors with 1024 elements in each. Because many of
the embryo perimeters were concave curves, equiangular
interpolation could not be used. Instead, linear interpolation was
used to create 1024 equally spaced coordinates. The coordinates
were mean centered and then radii were calculated from them. When
plotted in sequence the radii formed a lumpy sinusoid. When plotted
in polar coordinates they traced the embryo. A ten level wavelet
decomposition was performed on the radii and the first seven raw
moments about zero were calculated for each level. A similar method
has been used by L. M. Bruce ("Centroid Sensitivity of
Wavelet-based Shape Features," Proceedings of SPIE, Wavelet
Applications V, Harold H. Szu, Editor, 3391:358-366 (1998)) to
classify breast tumors as cancerous or benign.
[0062] In addition to the moments of the wavelet coefficients from
the radii, the area enclosed by the perimeter and it's length were
calculated from the original coordinates. Also, the area and length
of the convex hull of the perimeter were calculated. Lastly, the
ratio of the perimeter area to the convex hull area and the ratio
of the perimeter length to the convex hull length were calculated.
If the embryo perimeter was a convex curve, then the last two
ratios will be unity. Otherwise, the area ratio will decrease
toward zero and the perimeter ratio will increase.
[0063] In all, 142 metrics were described for the above embryo
images. These metrics were intended to capture some of the
information on color, shape and texture that is lost when the
somatic embryo images are reduced in size. Some of the information
such as the perimeter shape information was still in the reduced
images. Adding the metrics the classification model emphasizes the
metrics information. In some analyses, (see Example 4, Tables 2 and
3) the logarithm of the metric is taken to reduce variability.
Embryo Classification Models
Principal Component Analysis/SIMCA
[0064] The primary classification method used in the Examples of
the present invention was soft independent modeling of class
analogy SIMCA. See Jolliffe, I. T., Principal Component Analysis,
Springer-Verlag p. 161 (1986). SIMCA was used on each set of
reduced images and metrics. This resulted in six intermediate
classification of each embryo. These six intermediate
classifications were combined using the Bayes optimal classifier.
See Mitchell, Tom M., Machine Learning, WCB/McGraw-Hill pp.
174-176, 197, 222 (1997). SIMCA works by calculating a separate set
of principal components for each category based on training data.
The principal components which account for the majority of the
variation are kept. Then data from a new sample is regressed on the
principal components from each group. The residual mean square
errors are calculated for each category. The category with the
smallest residual mean square error is the category to which the
new sample is assigned. Six SIMCAs are done for each embryo.
[0065] Combining the Intermediate Classifications Using the Bayes
Optimal Classifier
[0066] Two to six or so intermediate classifications can be
combined into a single classification rule by first converting the
resulting strings of zeros and ones into a binary code. For two
intermediate classifications there are four binary combinations,
for three intermediate classifications there are eight binary
combinations, and so on. For `k` intermediate classifications there
are 2.sup.k binary combinations. Each binary combination is
assigned a label or code. For each embryo quality class the
probability of observing each code is estimated. Then the
embryo-quality-class-by-binary-code probabilities are divided by
the probability of the corresponding code occurring in all the data
from both embryo quality classes. The resulting probabilities are
the conditional probability of an embryo quality class given a
code. An embryo's binary code is calculated and the embryo is
assigned to the embryo quality class for which the conditional
probability is highest for the observed binary code. Ties can be
assigned randomly or assigned to one of the embryo quality classes
based on other considerations such economics.
Using the Lorenz Curve for Classifying Embryos
[0067] Originally, the Lorenz curve was developed to compare income
distribution among different groups of people. A Lorenz curve is
created by plotting the fraction of income versus the fraction of
the population that owns that fraction of the income. In the
present invention, the Lorenz curve is viewed as a comparison of
two paired cumulative distribution functions where the fractional
values of one cumulative distribution function are plotted verses
the fractional values of the second cumulative distribution
function. If the two distributions are the same the Lorenz curve
will plot the straight line y=x. The point farthest from the line
y=x corresponds to the balance point between accumulating more of
one distribution than the other. The balance or extreme point is an
objective point at which to separate the two distributions.
[0068] The Lorenz curve classification method of the present
invention has four steps. First, Lorenz curves are calculated for
each metric in a set of metrics. The points on these Lorenz curves
the furthest from the line, y=x, are found. Second, the metric
values corresponding to the extreme points on the Lorenz curves are
used as the threshold values to make single metric classifications
of the embryos: values of a metric less than its threshold are
assigned to one embryo quality class and values greater than the
threshold are assigned to the other embryo quality class. Third,
the set of metrics is subsetted to reduce the number of
combinations that must be searched in the final stage. Fourth,
pairs, triples, quadruples, etc., of the single metric
classifications are combined into binary codes and used in the
Bayes optimal classifier to create classification models for
assigning embryos to one of two quality classes. Classification
models are made for all possible pairs, triples, quadruples, etc.,
and the best model is retained in each case.
[0069] Calculating the Lorenz Curve for a Single Metric
[0070] The metric values for the two embryo quality classifications
are combined and all the distinct metric values identified.
Alternatively, the minimum and maximum value of all the metric
values for both embryo quality classifications combined are found
and a user specified number of equally spaced steps between the
minimum and maximum are used. When there are too many distinct
values, this second option is useful. In either case, for each
distinct metric value, the fraction of metric values less than or
equal to the distinct value is recorded for each embryo quality
class. Thus, two paired cumulative distribution curves are
obtained. Plotting these two sets of fractions against each other
constitutes the Lorenz curve. If the two distributions are the
same, the Lorenz curve is the line, y=x.
[0071] Finding the Extreme Points on the Lorenz Curves
[0072] The distance of a point, (x.sub.0,y.sub.0) from the line,
y=x, is the absolute value of the difference between y.sub.0 and
x.sub.0 divided by the square-root of two: |y.sub.0-x.sub.0|/
{square root over (2)}. The absolute value of the difference
between the cumulative distribution functions of the two classes of
embryo quality for a metric is searched for its highest point. The
corresponding metric value is used as the threshold. This extreme
point is the balance point between one distribution accumulating
more probability than the other distribution. The extreme point was
used as the threshold in the metric classification models developed
in Example 4. Other points on the Lorenz curve may be used as
thresholds based on other considerations such processing costs. If
a point other than the extreme point is used as the threshold, the
Lorenz curve can be used to determine the tradeoff in
miss-classification error rates.
[0073] Single Metric Classifications
[0074] Metric values less than the threshold are assigned to one of
the embryo quality classes and values greater than the threshold
are assigned to the other quality class. These single metric
classifications result in an embryo metric value being assigned a
zero or one. This is done for each metric used, one embryo quality
class is set to one and the other is set to zero. Several single
metric classifications can then be combined to yield a final
classification that has a lower misclassification error rate than
any of the individual single metric classifications.
[0075] Combining the Lorenz Curve Single Metric Classifications
Using the Bayes Optimal Classifier
[0076] Two or more single metric classification models can be
combined into a single classification rule using the same Bayes
optimal classifier method previously described to combine
intermediate SIMCA classification models. Alternatively, single
metric classification models or intermediate SIMCA classification
models can serve as the input data to neural network algorithm to
arrive at a final classification model for plant embryo quality.
However, as described below, when single metric classification
models are combined to arrive at a final classification rule
special problems arise.
[0077] Subsetting the Metrics to be Combined into a Single
Classification Model
[0078] The Lorenz curve can be used to find an optimal threshold
value for a single metric. Optimal is here defined in the sense of
balancing probability accumulation. However, the Lorenz curve
cannot handle the case when several metrics are considered together
because the Lorenz curve can only compare two distributions at a
time. One solution is to feed sets of metrics into an artificial
neural network to find an optimal classification rule. However,
with hundreds of metrics, it would be necessary to either fit very
large networks or fit a very large number of small networks. For
the purpose of this application, the simpler the classification
rule the better. It is recognized that the thresholds found for
individual metrics may not be the best ones to use when combining
several metrics through their single metric classifications.
Nevertheless, it is possible to search large numbers of
combinations of single metric classifications by calculating the
results of the Bayes optimal classifier approach outlined above and
comparing them for various combinations of the single metric
classifications. Yet there are still limitations on the number of
combinations that can be searched. When there are 682 metrics being
considered, then there are 8.935 billion distinct four-metric
combinations alone. As computers get faster such a number will not
pose much of a problem. However, for limited computing hardware,
subsetting the metrics will greatly reduce the amount of work.
[0079] Two subsetting criterion present themselves. First, the
metrics whose single metric classifications are above some limit
can be kept. Second, many of the metrics are correlated with each
other. The metrics highly correlated with the better metrics can be
dropped from consideration since they are informational twins to
the better metrics: a metric perfectly correlated with another
contains no information not already in the other metric. Metrics
with very low correlations among them are more likely to create
useful binary codes. These subsetting criterion can be used
together to reduce the number of metrics.
[0080] Several different examples of classification techniques are
specifically demonstrated in the Examples 2-4.
EXAMPLE 2
Somatic Embryo Sorting Based Upon Visual Embryo Quality
[0081] Douglas-fir somatic embryos were cultured to the cotyledon
stage by the methods outlined in Gupta et al., U.S. Pat. No.
5,036,007, and Gupta, U.S. Pat. No. 5,563,061, which patents are
herein incorporated in their entirety by reference. Embryos were
individually removed from the development stage medium. From this
point they would normally be manually screened and selected for
germination.
[0082] In the present case, two hundred embryos from the same clone
of Douglas-fir genotype 5 were preselected by morphology using the
usual zygotic embryo criteria of color, axial symmetry, freedom
from obvious flaws, and cotyledon development. Half of the sample
was considered to be "good" embryos; i.e., embryos that met visual
criteria for further processing in germination medium. The other
half were "bad" embryos that did not meet the criteria, The "truth
criterion" for the following analysis was the presence or absence
of normal zygotic-like morphology.
[0083] After selection, the embryos were placed against a dark
background and illuminated by cool fiber optic light. Each embryo
was individually color-imaged in rapid sequence by three cameras
mounted perpendicular to each other. Two longitudinal views
90.degree. to each other and an apical end-on view of the cotyledon
region were acquired. Images were acquired as digitized data
suitable for computer analysis. Prior to analysis the images were
preprocessed to isolate the embryo and thus eliminate interfering
background data.
[0084] In this example, a subset of the embryo top view images were
used to calculate the principal components. The first 80 components
were kept as they account for about 98% of the variation in the
images. Principal components were calculated for the "good"
embryos, i.e., those embryos that possess good visual criteria that
are associated with a high germination rate, as well as for embryos
that lack the good visual features. The principal components were
calculated using the singular value decomposition algorithm. The
singular value decomposition algorithm is available with any
software capable of handling matrices. The principal components
used were the left eigenvectors from the singular value
decomposition which were the principal components normalized to
have unit length. This normalization process does not have an
adverse effect because the principal components were being used in
this method as a set orthogonal basis vectors in a multiple
regression. The embryos that were not included in the training data
set were then regressed on the two sets of principal components
exactly as done in multiple regression. For each regression the
residual mean square error was calculated. A test embryo was
classified as having either good or bad embryo visual quality
depending on which category has the smaller residual mean square
error. Using this method test embryos were classified based on the
longitudinal top view of an embryo.
[0085] Similar to the longitudinal top view images, the
longitudinal side view and end view images were divided into a
training set and test set of embryos. The training set of embryos
were used for calculating the principal components and the test set
of embryos were regressed on them and classified. Likewise, the
metrics were used to calculate principal components and classify
the embryos in the test set. In the case of the metrics, 40
principal components were kept and they were based on the natural
logarithm of the absolute value of the metrics multiplied by the
sign of the metric or the Box-Cox transformation (Myers, R. H. and
D. C. Montgomery, Response Surface Methodology: Process and Product
Optimization Using Designed Experiments, Wiley, pp. 260-264 (1995))
of the metrics using an odd root such as a 1/101 which approximates
the natural logarithm, preserves the sign, and still works on zero.
The transformation helps reduce the variability of the higher order
moments. As a result each embryo in the test set ends up with six
classifications from each of the SIMCAs: three classifications from
the three images and three classifications from the three sets of
metrics.
[0086] The six classifications were combined into a single
classification using Bayes optimal classifier as follows. See
Mitchell, T. M. Machine Learning, WCB/McGraw-Hill, pp. 174-176,
197, 222 (1997). Each classification was either zero or one: one
meaning that the embryo had a good visual quality and zero meaning
that the embryo did not have good visual characteristics. These six
binary classification scores were converted to a multi-valued code
by multiplying the side view image score by 32 and adding it to 16
times the end view image score plus 8 times the top view image
score plus 4 times the side view metric score plus 2 times the end
view metric score plus the top view metric score. This composite
score takes on integer values ranging from 0 to 31. For each
composite score, the number of good visual quality embryos were
counted as well as the number of bad visual quality embryos.
Dividing by the total number of embryos in the test set yields the
probabilities of observing each score and one of the embryo
categories. The probability of each composite score occurring was
calculated by counting how many times each score occurred and
dividing by the total number of embryos in the test set. Next, each
probability of observing a composite score and one of the
categories was divided by the probability of the composite score
occurring. This calculation gave the probability of a category
given a composite score. Composite scores where the probability of
observing a visually correct embryo was greater than or equal to
50% were assigned as having a good embryo quality. All other scores
were assigned to the bad embryo quality category. In this way the
information from the six SIMCA classifications were combined into a
single classification.
[0087] Basically, the Bayes optimal classifier assigns a composite
score to the category which generates the most of that particular
score. If an embryo has a value that is in the middle it was put
into the good embryo quality category. The whole process was
repeated many times and the average performance reported.
[0088] Using the above methods two additional sets of somatic
embryos of two different genotypes (genotypes 6 and 7) were
classified as having good or bad morphological qualities as
compared to normal zygotic embryos. The results of the three sets
are given in Table 1. TABLE-US-00001 TABLE 1 Visual Quality
Classification Results From the Bayes Optimal Classifier for Three
Genotypes of Douglas-Fir Somatic Embryos Percent of Embryos Percent
of Embryos Classified Correctly as Correctly Classified as Having
"Good" Visual Having "Bad" Visual Douglas-fir Genotype Embryo
Quality Embryo Quality 5 80.0 75.0 (Three views of 200 embryos) 6
88.7 70.5 (Three views of 1000 embryos) 7 87.0 78.5 (End & Top
views of 1000 embryos)
EXAMPLE 3
Somatic Embryo Sorting Based Upon Visual Embryo Quality and Actual
Germination
[0089] A sample of 400 embryos judged to be of high morphological
quality, as previously defined, from the Douglas-fir genotype 5 was
evaluated in two ways. After evaluation the embryos were germinated
to determine whether germination success correlated with predicted
success based on eight additional morphological features. The base
case was visual selection based on morphology. The first procedure
was a nonparametric statistical treatment based on four observed
features (symmetry, surface roughness, presence of fused
cotyledons, and presence of gaps between cotyledons) and four
measured embryo dimensions (hypocotyle length, radical length,
cotyledon length, and cotyledon number) the measurements being made
on digital color images acquired under sterile conditions from a
single viewpoint perpendicular to the long axis of the embryo. This
statistical procedure is known as binary recursive classification
and was carried out using software named CART.TM. (for
Classification and Regression Tree) (Salford Systems, San Diego,
Calif.). Reliability of this classification method was assessed and
probabilities for future similar data sets were derived by
validating the classification on a specified number; e.g., 20,
random subsets of the data. CART.TM. classification is binary and
all possible splits were tested on all variables. The second
evaluation method was principal components analysis of the
images.
[0090] Results showed principal components analysis was superior to
the CART.TM. statistical procedure and was a major improvement over
technician selection. A 66.3% germination rate was found for the
base populations (selected for good similarity to normal zygotic
embryos). This improved to 75.0% for embryos classified by the
CART.TM. procedure as most likely to germinate. A germination
success of 79.7% was achieved in embryos chosen by the principal
components/SIMCA analysis method.
EXAMPLE 4
Somatic Embryo Sorting Based Embryo Germination
Comparison of Classification Methods
[0091] The methods in Examples 1-3 were used to develop
classification models and classify 1000 somatic embryos of
Douglas-fir genotype 6 by their capability to germinate. Table 2
contains the results of presenting different inputs to the Bayes
optimal classifier when classifying the germination versus
nongermination capabilities of the Douglas-fir genotype 6 embryos.
When the data input was somatic image data that was first
preprocessed using the method of Example 1 the training set model
for the classification of embryos by germination was accurate 59%
of the time at correctly classifying embryos as embryos that would
germinate and about 64% accurate at classifying embryos that would
not germinate. This is an average accuracy of 61.7%. In contrast,
when metrics image data was captured and added to the preprocessed
image data following the methods in Example 1, the accuracy of
embryo classification into germinating and non-germinating embryos
was increased to about 71% (column 4 of Table 2). Thus, as in
Example 2, an increased accuracy in classifying potential
germinants was achieved using the present invention. TABLE-US-00002
TABLE 2 Germination Classification of Douglas-Fir Genotype 6
Somatic Embryos Using Different Inputs to Bayes Optimal Classifier
Compared With Germination Results of Manual Selection Based on
Morphology Percent of Percent of Germinating Non-Germinating
Combinations of Embryos Embryos Average SIMCA Results Used in
Correctly Correctly Success in Bayes Optimal Classified as
Classified as Classifying Classifier Germinating Non-Germinating
Correctly Images Only 59.3 64.1 61.7 Images + Metrics 67.6 74.6
71.1 Images + Log (Metrics) 68.5 74.1 71.3 Manual Selection Based
71.7 66.2 68.9 on Morphology
[0092] Table 3 presents the germination classification results for
Douglas-fir genotype 6 of the individual SIMCA runs from each set
of images and metrics of the somatic embryos. Comparing the results
presented in Table 3 with those shown in Table 2 demonstrates the
statistical advantage of combining the individual SIMCA
classifications using the Bayes optimal classifier of each of three
different somatic embryo views. Also, the utility of adding the
metrics is illustrated. TABLE-US-00003 TABLE 3 Germination
Classification of Douglas-Fir Genotype 6 Somatic Embryos: Results
From the Individual SIMCA Runs Percent of Percent of Non-
Germinating Embryos Germinating Embryos Correctly Classified
Correctly Classified Data Used as Germinating as Non-Germinating
Top View Images 66 54 Top View Log(Metrics) 46 63 End View Images
70 45 End View Log(Metrics) 52 52 Side View Images 48 59 Side View
Log(Metrics) 52 53
Additional Classification Methods
[0093] Two additional classification methods were performed with
data collected from somatic embryos: neural networks (Douglas-fir
genotype 6) and a classification method based on the Lorenz curve
(Douglas-fir genotypes 6 and 7). The method based on SIMCA uses
hyperplanes as boundaries between categories. A two dimensional
hyperplane is a line and a three dimension hyperplane is a regular
plane or flat surface. In short, hyperplanes are just higher
dimensional cousins to lines and regular planes. As a result they
are best for separating categories that are linearly separable,
i.e., they have straight boundaries and can be separated by a
"line". Often nature does not have linear boundaries but very
curved boundaries. Simple back-propagation neural networks using
nonlinear transfer functions for the hidden nodes and output nodes
can handle very nonlinear boundaries between categories. See Hagan,
M. T., H. B. Demuth, and M. Beale, Neural Network Design, PWS
Publishing Company, Chapters 11 and 12 (1996). These have been used
to discriminate between images of people looking in different
directions. Id. pp. 112-115.
Neural Network
[0094] Back-propagation neural networks were used to classify
embryos of genotype 6 as germinating or non-germinating. The end
view and top view somatic embryo images were reduced in size by
wavelets in order to reduce the number of network input nodes as
was suggested by T. M. Mitchell (Machine Learning, WCB/McGraw-Hill,
pp. 112-115 (1997)). Mitchell used adjacent averages to reduce his
images. Here the smooth coefficients from the 3.sup.rd level of the
two-dimensional wavelet decomposition were used since they preserve
much more detail than averages. The embryo side view was not
included to reduce the amount of computation and because as shown
in Table 3 this view carries the least amount of information about
germination of three views. The input layer of the network just fed
in the pixel values from the reduced images from both views. The
hidden layer had either 18 or 80 hidden nodes using the logistic
transfer function, 1/(1+exp(-x)). The output layer had two nodes
again using logistic functions. The output target values were (0.9,
0.1) for germinating somatic embryos and (0.1, 0.9) for
non-germinating embryos. The sum of the squared differences between
the target vectors and their predicted vectors were minimized. Half
the data was used for training and half was used for validation.
Any training set and even all of the embryos could be perfectly
classified with the 18 hidden node model. The best either of the
neural network models could do on a validation or test set was 61%
correct classification of embryos into both the germinating and
non-germinating classes.
Use of the Lorenz Curve Classification Method to Classify
Embryos
[0095] As previously noted the Lorenz curve classification method
has four steps. In this example, 625 and 457 different metrics were
calculated for Douglas-fir genotypes 6 and 7, respectively. Metric
values corresponding to the extreme points on the Lorenz curves for
each metric were set as threshold values for classifying embryo
quality. In addition, the set of single metric classifications
which were searched for robust combination classification models
was reduced using the subsetting routine described in Example 1.
Lastly, double, triple, quadruple, etc., combinations of the single
metric classification models were combined into binary codes and
used in the Bayes optimal classifier to create classification rules
for assigning embryos to one of the two embryo quality classes.
Classification models were made for all possible pairs, triples,
and quadruples and the best model was retained in each case.
[0096] Table 4 contains the results of classifying embryos
according to their morphological similarity to normal zygotic
embryos by using the Lorenz Curve classification method combining
1, 2, 3, and 4 single metric classifications via the Bayes optimal
classifier. TABLE-US-00004 TABLE 4 Morphology Classification
Results From the Best Bayes Optimal Classifier Combining 1, 2, 3
& 4 Lorenz Curve Single Metric Classifications for Douglas-Fir
Genotypes 6 and 7 Percent of Good Percent of Bad Morphology
Morphology Number of Metrics Embryos Correctly Embryos Correctly
Used to Create Classified as Having Classified as Having
Douglas-fir Genotype Classification Model Good Morphology Bad
Morphology 6 1 82.30 70.44 (end, side & top views) (Skewness
coefficient, .beta..sub.1, of all the intensity pixel values from
the embryo end view) 6 2 72.63 83.27 (end, side & top views)
(Skewness coefficient, .beta..sub.1, of all the intensity pixel
values from the embryo end view, and Range of the perimeter radii
from the embryo end view) 6 3 79.69 78.96 (end, side & top
views) (Skewness coefficient, .beta..sub.1, of all the intensity
pixel values from the embryo end view, range of the perimeter radii
from the end view, and standard deviation of the area of the
cotyledons from the embryo end view) 6 4 84.72 75.75 (end, side
& top views) (Skewness coefficient, .beta..sub.1, of all the
intensity pixel values from the embryo end view, range of the
perimeter radii from the end view, standard deviation of the area
of the cotyledons from the embryo end view, and mean area of the
cotyledons touching the bounding convex hull of the embryo end
view) 7 1 88.59 71.61 (end & top views only) (Lower quartile of
the perimeter radii from the embryo top view) 7 2 71.33 89.74 (end
& top views only) (Lower quartile of the perimeter radii from
the embryo top view and skewness coefficient, .beta..sub.1, of the
blue pixel values from the embryo end view) 7 3 85.71 84.97 (end
& top views only) (Skewness coefficient, .beta..sub.1, of all
the blue pixel values from the end view, standard deviation of all
the green pixel values from the end view, and 4.sup.th moment about
zero of the detail coefficients of the 8.sup.th level of a 10 level
wavelet decomposition of the embryo end view perimeter) 7 4 85.10
87.05 (end & top views only) (Skewness coefficient,
.beta..sub.1, of all the blue pixel values from the end view,
standard deviation of all the green pixel values from the end view,
4.sup.th moment about zero of the detail coefficients of the
8.sup.th level of the wavelet decomposition of the end view
perimeter, and lower quartile of the perimeter radii from the
embryo top view)
[0097] Comparing the results in Table 4 with the corresponding
results in Table 1 from combining 6 SIMCA intermediate
classifications by the Bayes optimal classifier suggests that the
Lorenz curve based method performs as well as or better than the
SIMCA based method for classifying embryos according to morphology.
Similarly, Table 5 contains the results from classifying embryos
according to germination classes by the Lorenz curve method.
Comparing Table 5 with Table 2 shows that the Lorenz curve method
does not perform as well as the SIMCA based method. Also, Table 4
and Table 5 show that combining the information in multiple metrics
reduces the misclassification error rate. TABLE-US-00005 TABLE 5
Germination Classification Results From the Best Bayes Optimal
Classifier Combining 1, 2, 3 & 4 Lorenz Curve Single Metric
Classifications for Douglas-Fir Genotype 6 Percent of Douglas-fir
Germinating Percent of Genotype Embryos NonGerminating using (end,
Number of Metrics Correctly Embryos Correctly side & top Used
to Create Classified as Classified as views) Classification Model
Germinating NonGerminating 6 1 70.51 60.12 (Skewness coefficient,
.beta..sub.1, of all the blue pixel values from the embryo end
view) 6 2 66.51 65.45 (Skewness coefficient, .beta..sub.1, of all
the blue pixel values from the embryo end view, and 10.sup.th level
detail coefficient from a 10 level wavelet decomposition of the
embryo side view perimeter) 6 3 71.56 62.40 (Skewness coefficient,
.beta..sub.1, of all the blue pixel values from the embryo end
view, kurtosis coefficient, .beta..sub.2, the perimeter radii from
the embryo top view, and mean of the level 9 detail coefficients
from a 10 level wavelet decomposition from the embryo side view
perimeter) 6 4 65.33 70.70 (Skewness coefficient, .beta..sub.1, of
all the blue pixel values from the embryo end view, kurtosis
coefficient, .beta..sub.2, the perimeter radii from the embryo top
view, mean of the level 9 detail coefficients from a 10 level
wavelet decomposition from the embryo side view perimeter, and
kurtosis coefficient, .beta..sub.2, of all the green pixel values
from the embryo side view)
Classification Trees Based on the Lorenz Curve
[0098] An alternative method for classifying embryos uses Lorenz
curve as the method for splitting nodes in classification trees.
Usually to construct a classification tree the metrics are searched
to find a variable that separates the quality classes the most
based on a measure of distance or spread. Multivariate statistics
can also be used to examine sets of metrics, however, the
computation required increases rapidly with the number of metrics
in a set. The Lorenz curve method outlined above can also be used
as a node splitting criterion. The Lorenz curve method outlined
above was used to search for a single best metric to split the
embryo quality classes. The two subsets thus created were each
submitted to the Lorenz method to find a metric that best split
them. This process can be repeated as long as the number of metric
values from each embryo quality class are large enough to provide a
good estimate of the distribution functions. The entire set of
metrics is searched each time because the act of splitting the
distributions, alters the distributions, and metrics that at first
provided poor separation may provide good separation at later
stages. This method of method of creating a classification tree is
very computationally intensive. As a result the metrics can be
subsetted in order to get the computations done in a reduced time.
A two level classification tree based on the Lorenz curve was
created for Douglas-fir genotype 7. The results are in Table 6.
TABLE-US-00006 TABLE 6 Morphology Classification Results From a Two
Level Classification and Regression Tree Using Lorenz Curves to
Split Nodes for Douglas-Fir Genotype 7 Percent of Good Percent of
Bad Morphology Morphology Douglas-fir Embryos Embryos Genotype 7
Correctly Correctly using Number of Metrics Classified Classified
(end & top Used to Create as Having Good as Having Bad views
only) Classification Model Morphology Morphology 2 81.22 82.25
(Standard deviation of all the red pixel values from the embryo end
view, and 2.sup.nd moment about zero of all the pixel values in the
1.sup.st principal component image (the view created by collapsing
the red, green, and blue color matrices into a single matrix using
principal components) of the end view)
[0099] The techniques described in Examples 1-4 can be readily
adapted to continuous examination of somatic embryos as might be
required in a large scale production facility. In addition, these
methods can be combined in series with themselves or with the
spectroscopy methods described in Example 5 to create an efficient
and cost effective screening methodology for classifying somatic
embryos by their germination potential.
EXAMPLE 5
Spectrophotometric and Multivariate Methods for Classifying Somatic
Embryos
[0100] Spectral data was collected and analyzed from zygotic and
somatic embryos populations that from experience are known to
differ considerably in germination vigor.
Zygotic Embryos
[0101] Fresh zygotic embryos were collected at two intervals about
three weeks apart from one orchard grown Douglas-fir tree
(Pseudotsuga menziesii). The degree of embryo development
corresponded to Stages 7 and 8a in the classification published by
Pullman et al. (Pullman, G. S. and D. T. Webb, "An Embryo Staging
System for Comparison of Zygotic and Somatic Embryo Development,"
Proc. TAPPI [Technical Association of the Pulp and Paper Industry]
Biological Sciences Symposium, Minneapolis, Minn., Oct. 3-6, 1994,
pp. 31-33. TAPPI Press, Atlanta, Ga. (1994)) for the July 23 and
August 13 collections, respectively. These stages may be described
as "just cotyledonary" and "cotyledonary, immature." In addition,
fully mature zygotic embryos were obtained from mature seed
obtained from a seed store collected from a mix of different trees
grown in the same orchard. Immature loblolly pine (Pinus taeda)
zygotic embryos were collected from one tree on August 10, at which
date they were at Stage 7 in Pullman et al.'s classification system
cited above. Mature loblolly pine seed embryos were obtained from
freezer storage, and the decoated seed allowed to imbibe water for
14 hours before extraction of the embryos for analysis. Cones and
seed were stored at 4-6.degree. C. after collection until spectral
analysis was performed.
Somatic Embryos
[0102] Douglas-fir somatic embryos of four different genotypes,
designated 1, 2, 3, and 4, were analyzed in this study. The
Douglas-fir somatic embryos were cultured as described in Example
2. Where a cold treatment is noted, the Douglas-fir somatic embryos
received cold treatment at 4-6.degree. C. for four weeks prior to
spectral analysis. Two genotypes of loblolly pine somatic embryos
were used in the study, designated genotypes 5 and 7. After
completing their development to the cotyledonary embryo stage on
petri plates, half of the somatic loblolly pine embryos from each
genotype received a partial drying treatment for 10 days at about
97% relative humidity while still on the culture medium, followed
by cold treatment at 4-6.degree. C. for four weeks. The other half
of the loblolly somatic embryos did not receive this treatment. The
loblolly somatic embryos were produced using standard somatic
embryo plating methods described in Gupta et al., U.S. Pat. No.
5,036,007 and Gupta, U.S. Pat. No. 5,563,061.
[0103] For each population, spectral analysis was performed on
about 10 embryos except for some somatic embryos where spectral
data was collected from about 15-40 embryos. Spectra were taken
usually from the cotyledon region of an embryo (FIG. 1). However,
it should be understood that the inventive method can be practiced
by collecting spectral data from the entire embryo or from the
hypocotyl (12) or radical (14) portions of the embryo as diagrammed
in FIG. 1. In some instances the classification was improved by
using both cotyledon (10) and radical (14) data in sequence.
Collection of Spectral Data
[0104] The experimental setup consisted of a light source, a
binocular microscope, a NIR sensor, and a portable NIR processor
with computer. A FieldSpec FR (350-2500 nm) Spectrometer
(Analytical Spectral Devices, Inc., Boulder, Colo.) equipped with a
fiber optic probe which gathers light reflected from any surface
was used to collect embryo spectral data. The fiber optic probe of
the spectrometer was fitted with a 5 degree fore-optic and inserted
into the auxiliary observation (camera) port of a binocular
microscope.
[0105] Spectra were acquired sequentially from groups of ten
somatic embryos immediately after hand-transferring from a culture
plate, and from zygotic embryos on a one-by-one basis immediately
after excision from decoated seeds using the apparatus and
procedures described below. The halogen lamp was set at 40 degree
angle from the vertical at a distance of 17 cm from the embryos.
Samples were placed on a white Teflon surface to minimize
background absorption while being viewed with the 6.5.times.,
10.times., or 40.times. microscope objective. A "white balance"
program that is part of the spectrometer, was run periodically
throughout the measurements to recalibrate the instrument against
the white background when no embryos were present.
[0106] Spectra were measured in the region from visible to very
near IR range (350 to 2500 nm). Spectral intensities were measured
at 1 nm increments. The spectrometer was programmed to complete 30
spectral scans of each embryo in order to obtain a representative
average spectrum--a process which took a total of 30 seconds per
embryo for separate cotyledon and radical sampling, including the
time to reposition for the next embryo.
Data Processing and Information Extraction
[0107] Analysis of spectral data was performed using a Principal
Component Analysis software package ("The Unscrambler" by Camo ASA,
Oslo, Norway). The scores and loadings matrices were converted to
the "scoreplots" and "loadings spectra" shown in the figures. The
principal component analysis algorithm extracted the best set of
axes that described the data set. The scoreplots show the
relationships among the embryos, and embryo classes, while the
loadings spectra show which spectral features were responsible for
the class distinctions.
Principal Component Analysis of Spectra From Zygotic and Somatic
Embryos
[0108] A comparison of Douglas-fir zygotic embryos of three
different developmental stages and somatic embryos from Genotype 1
was performed. The three zygotic stages consisted of two immature
cotyledonary stages, identifiable as stages 7 and 8 in Pullman et
al. (Pullman, G. S. and D. T. Webb, "An Embryo Staging System for
Comparison of Zygotic and Somatic Embryo Development," Proc. TAPPI
[Technical Association of the Pulp and Paper Industry] Biological
Sciences Symposium, Minneapolis, Minn., Oct. 3-6, 1994, pp. 31-33.
TAPPI Press, Atlanta, Ga. (1994)) collected from the field in
Rochester, Wash., on July 23 and August 14, respectively, and
mature dry seed from a seedstore. Previous data showed that whereas
90-95% of the mature-seed embryos would germinate normally in
vitro, only about 75% and 43% of the stage 8 and stage 7 embryos
respectively would so germinate. The rates of shoot and root
elongation--measures of germination vigor--had even greater
sensitivity to developmental stage, these rates being reduced to
80% and 20% for the two immature stages. Germination was reduced to
about 15% and zero, respectively, for the two immature stages after
desiccation of the embryos to 10% moisture content. These data
exemplify, for Douglas-fir, the large contrast in embryo quality
between embryos at these stages of development, which is well-known
to those skilled in plant embryo development. In further contrast,
quality of the somatic embryos, which were closest, but not truly
equivalent to, zygotic developmental stage 8, was characterized by
significantly lower germination normalcy and vigor than the stage 7
zygotic embryos. The genotype tested was representative of many
somatic embryo genotypes.
[0109] Inspection of the scoreplot in FIG. 2A shows that these four
populations of contrasting embryo quality separate into four
clearly distinct groups when plotted with respect to the first
three principal components. The embryo groups are: mature dry
zygotics (black circles), August 14 zygotics (inverted white
triangles), July 23 zygotics (black squares) and genotype 1
somatics ("+" symbol). The centroid of the somatic embryo group was
shifted 8-10 standard deviations to the right along the PC1 axis
compared with all stages of zygotic embryos, which were separated
primarily along the axes for PCs 2 and 3. Variability within the
somatic embryos was much greater than within any of the zygotic
embryo groups.
[0110] The loadings spectrum for PC1 (FIG. 2B, curve 20) contained
mainly two peaks, at 1450 and 1920 nm, attributable to water,
indicating that the large separation and variability was due to a
greater amount and variability of somatic embryo water. In
contrast, separation among the zygotic groups was mainly along PCs
2 (curve 22) and 3 (curve 24), whose loadings spectra suggest a
basis in greater lipid content (the double peak at 1720-1750 nm,
and the peak at 2300 nm) for more mature embryos. Also, there are
negative peaks around 1400 and 1900 nm that may have to do with
hydrogen-bonded water. The somatic embryos were also separated from
the two more mature zygotic groups along the PC2 axis, due in part
to their putative lower lipid concentration, as well as absorption
differences in the visible region. The percent of total spectral
variation accounted for by each PC was 84% for PC1, 8% for PC2, and
4% for PC3. Table 7 summarizes the quality of separation obtained
among the four embryo groups after principal component analyses of
the spectral data. The summary data tables for the various somatic
embryo classifications list the chemical features that are inferred
to be associated with specific wavelengths based upon the known
spectrophotometric behavior of that chemical class. TABLE-US-00007
TABLE 7 Douglas-Fir Zygotic Embryos at Three Developmental Stages
Compared With One Another, and With Somatic Embryos Immature
Zygotic Embryos Principal Wavelength/Inferred Stage 7 Stage 8
Mature Seed Somatic Components Chemical embryos embryos Embryos
Embryos Needed Features Involved 15/15* 14/14* 8/9* 9/10* 1st Water
(1450 nm + 1920 nm) (100%) (100%) (89%) (90%) 2nd Lipid (1700-1750
nm) 3rd Lipid + feature at 1890 nm Lipid (2300 nm) + feature at
1870 nm *Number correctly classified/number tested
[0111] The results with loblolly pine somatic and zygotic embryos
are shown in FIG. 3A and Table 8. In this case, stage 8 zygotic
embryos (black squares) and water-imbibed mature zygotic embryos
(black triangles) are compared with two genotypes of somatic
embryos (genotype 5 denoted as "+" and genotype 7 denoted as "o")
pretreated by partial drying then cold. Somatic embryos were
separated from zygotic embryos mainly by PC1, which, as in case of
Douglas-fir embryos, was probably due to the somatic embryos'
higher water content relative to lipids (curve 26). Also, many
loblolly pine somatic embryos were separated from zygotic embryos
along PC2, which featured a dominant broad peak around 1800 nm of
unknown source (curve 28). PC3 further distinguished the mature
imbibed zygotic embryo group from the somatic embryo group, based
on a combination of features, including a lipid (-ve) peak,
pigmentation in the visible region, and a small -ve peak around
1210 nm (which is about where the second overtone of C-H stretches
in protein lie) shown in curve 30. Together, these three PCs
accounted for 97% of variation in the spectra (FIG. 3B). The
percent of total spectral variation accounted for by each PC was
92% for PC1 (curve 26), 4% for PC2 (curve 28), and 1% for PC3
(curve 30). TABLE-US-00008 TABLE 8 Loblolly Pine Zygotic Embryos at
Two Developmental Stages and Loblolly Pine Somatic Embryos Immature
(stage 8) Mature Principal Zygotic Zygotic Com- Wavelength/Inferred
Embryos Embryos Somatic ponents Chemical (Aug. 10) (October)
Embryos Needed Features Involved 10/10* 13/13* 28/29* 1st Water
(1450 + 1920 nm) (100%) (100%) (97%) 2nd Lipid (1700-1750 nm) 3rd
1800 nm broad peak Lipid (-ve 2300 nm) Protein (1210 nm) Lipid
(1700-1750 nm) Pigments (400-500 nm) *Number correctly
classified/number tested
[0112] Taken together, these data demonstrate that embryos can be
accurately separated by their NIR spectral characteristics into
groups of differing germination potential.
Principal Component Analysis of Spectra From Somatic Embryos of
High- and Low-Quality Appearance
[0113] Ten cotyledonary-stage somatic embryos of high- and
low-quality appearance were selected from a single plate each of
Douglas-fir (genotype 2) and loblolly pine (genotype 5) embryos,
based upon traditional morphological indications of embryo quality,
i.e., morphologies that are most likely to result in a high or low
frequency of germination.
[0114] A summary of the separation obtained is presented in Table
9. For Douglas-fir, it was possible to draw a straight line on the
scoreplot of PC3 versus PC1 that completely separated the high
quality ("+") and low quality (black circles) groups (FIG. 4A).
Most of this separation occurred along the third PC (FIG. 4B, curve
32), which represented about 2% of the overall variation. PC3 was
distinguished in part by absorption bands from pigments in the
visible region, including chlorophyll. PC1 (curve 34) represented
about 96% of the total spectral variation. TABLE-US-00009 TABLE 9
Cotyledon Stage Somatic Embryos With "High" vs. "Low" Quality
Morphology High Quality Low Quality Wavelength/Inferred Morphology
Morphology PC's Needed Chemical Features Douglas-Fir 10/10* 9/9* 1
Water (1450, 1920 nm) (100%) (100%) 3 Pigments in visible region
shoulder feature (1850-1920 nm) Loblolly Pine 9/10* 9/10* 1 Water
(1450, 1920 nm) (90%) (90%) 3 Unknown (1400-1500 nm) Lipid (1710,
2300 nm) Bound water (1870 nm) *Number correctly classified/number
tested
[0115] FIG. 5A shows the scoreplot obtained from loblolly pine
somatic embryos having high quality morphology ("+") as compared to
embryos having low quality morphology (black circles). Almost
complete (90%) separation was achieved, with the first and third
PCs combined. In the PC3 loadings spectrum (FIG. 5B, curve 40),
there was a strong, slightly bimodal negative peak around 1450 nm
(not water), plus putative lipid (1700 and 2300 nm) and bound water
(1870 nm) features, as well as absorption peaks in the visible
region (380-600 nm). PC3 accounted for about 1% of the total
spectral variation. PC1 (curve 36) represented about 95% of the
total spectral variation and was mostly water. PCs 1 and 2 combined
also provided good separation, the PC2 loadings spectrum (curve 39)
being dominated by the shoulder feature between 1760 and 1900 nm.
PC2 accounted for about 3% of the total spectral variation. These
results demonstrate that principal component analysis of spectral
data from somatic embryos having high- and low-quality
morphological appearance provides a basis for developing a
classification model that will allow somatic embryos to be rapidly
categorized with regards to their germination potential.
Principal Component Analysis of Spectra from Somatic Embryos in the
Cotyledon (stage 8) and "Dome" (Stage 5) or "Just Cotyledon" (JC)
(Stage 6) Stages
[0116] Douglas-fir somatic embryos in two distinct developmental
stages were selected from plates of genotype 3. Somatic embryos in
the cotyledon stage are known to have a much higher frequency of
germination than somatic embryos that are in the less mature "dome"
or "just cotyledonary" (JC) developmental stages.
[0117] Dome/JC embryos (black circles in FIG. 6A) and cotyledonary
(stage 8) embryos ("+") that were plucked from the same plate
formed two distinct groups on a 3D scoreplot formed from PCs 1-3,
such that only one embryo of the 19 just fell within the wrong
group (FIG. 6A). The strongest contributors to separation were PCs
1 (curve 42) and 2 (curve 44), which are associated with (1) water
and (2) lipid, possibly protein N-H, regions, plus the 1800 nm
`shoulder` feature, respectively (FIG. 6B). PCs 1 and 2 account for
82% and 9% of the total spectral variation, respectively, whereas
PC 3 (curve 46) accounted for 4% of the total spectral variation.
Table 10 presents a summary of the accuracy of the spectral
separations obtained using the cotyledon stage and "dome" or "just
cotyledonary" stage somatic embryos. TABLE-US-00010 TABLE 10
Cotyledon vs. Earlier Developmental Stages of Douglas-Fir Somatic
Embryos From Genotype 3 "Dome" or "Just PC's Wavelength/Inferred
Cotyledon Stage Cotyledon" Stage Needed Chemical Features 10/10*
8/9* 1 Water (100%) (89%) 2 Lipid (1700-1800 nm) Unknown (1420 nm)
*Number correctly classified/number tested
[0118] These results demonstrate that NIR spectral data can
accurately distinguish between early developmental stages of
somatic embryos, which are germination-incompetent, and the final
stage of development on petri plates (approximately equivalent to
zygotic stage 8 embryos), many of which are capable of germinating
and producing seedlings.
Principal Component Analysis of Spectra From Cold-treated and
Control Somatic Embryos
[0119] Subjecting embryos to a 4-7.degree. C. cold treatment on
low-osmolality media in the dark for 1-5 weeks may increase the
frequency of subsequent embryo germination by 20 to 200%.
[0120] Principal component analysis of spectral data collected from
cold-treated and control Douglas-fir somatic embryos of two
genotypes (3 and 4) are presented in FIGS. 7A and 7B. In FIG. 7A
solid black circles or triangles identify cold-treated embryos for
genotypes 3 and 4, respectively, and the corresponding open symbols
identify non-cold-treated embryos of the same two genotypes. For
each genotype, a straight line can be drawn that will largely
separate the two populations with the degree of success (from
79-100%) shown in Table 11. The separation was determined mainly by
the PC2 axis, whose loadings spectrum (FIG. 7B, curve 50) has both
lipid and pigment components and accounts for about 4% of the total
spectral variation. PC1 (curve 48) accounts for about 91% of the
spectral variation. TABLE-US-00011 TABLE 11 Somatic Embryos That
Have or Have Not Received Cold Treatment Species Specific and PC's
Wavelength/Inferred Genotype Control Cold-treated Needed Chemical
Features Douglas-Fir Genotype 3 9/10* 10/10* 2 Lipids (1700-1750
nm) (90%) (100%) Shoulder region (1800-1900 nm) Genotype 4 26/33*
9/10* 1 Water (79%) (90%) Loblolly Pine Genotype 5 19/20* 10/10* 1
Water (95%) (100%) Genotype 7 28/40* 17/20* 3 Lipid (1700-1750 nm)
(70%) (85%) 2 Shoulder region (1800-1900 nm) *Number correctly
classified/number tested
[0121] The results of principal component analysis for the
equivalent contrast using loblolly pine somatic embryos appears in
FIGS. 8A and 8B. Loblolly pine somatic embryos from genotype 5
(circles) exhibit a clear separation of cold-treated (solid
circles) and control groups (open circles) in (FIG. 8A). Loblolly
pine genotype 7 (triangles) exhibits a similar tendency in regard
to these two treatment groups. In general, embryos that were
partially dried then cold-treated show higher, and greater
variation in, water contents than those that were not. The
separations, for each genotype, were by PCs 1 and 2 combined, which
incorporate the water, lipid and 1800-1900 nm shoulder features
noted for Douglas-fir. PC1 (curve 52) and PC2 (curve 54) account
for 92% and 4% of the total spectral variation, respectively.
[0122] These results demonstrate that NIR spectral data can
distinguish between developmentally similar (approx. stage 8)
somatic embryos having higher germination potential (on account of
prior cold or cold and partial drying treatment) from those embryos
of lower germination potential (having not received such
treatments).
[0123] While the preferred embodiment of the invention has been
illustrated and described, it will be appreciated that various
changes can be made therein without departing from the spirit and
scope of the invention.
* * * * *