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 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 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.