Classification of Soy Seeds by Quality Categories Using Machine Learning Methods for Diffuse Refraction Spectra

A methodological approach has been developed using diffuse reflectance spectroscopy in the UV-Vis-NIR range in combination with machine learning methods to construct classification models for grading soybean seeds into groups based on the type of defect and by food quality category. Classification models for soybean seeds by defect groups and quality categories were developed. Over a wide spectral range (350–2500 nm),  a set of five informative spectral bands was identified, attributed to functional groups of soybean seed components and making the greatest contribution to seed grading into quality categories. Classification functions were obtained that can be used to assign new samples to specific categories. The soybean seed classification model has sufficient accuracy (90%). Reducing the number of spectrum wavelengths allows for a significant reduction in the time required for data processing and decision making.

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