Machine Learning Algorithms for Protein Physicochemical Component Prediction Using Near Infrared Spectroscopy in Chickpea Germplasm

Abstract

Prediction of physicochemical components of chickpea fl our using near infrared spectroscopy requires discovering
exact wavelength regions that provide the most useful data before preprocessing. This study used six essential
machine learning techniques to develop models for predicting proteinphysicochemical component in chickpea:
Linear Regression (LR), Artifi cial Neural Network (ANN), Partial Least Squares Regression (PLSR), Random
Forest (RF), Support Vector Regression (SVR) and Decision Tree Regression (DTR). Performance measurements
such as Root Mean Square Error and Karl Pearson’s Correlation Coeffi cient and Coeffi cient of Determination
were used to validate the models. RF and ANN models showed signifi cant improvement over all other models
in terms of accuracy.