Malt quality profile of barley predicted by near-infrared spectroscopy using partial least squares, Bayesian regression, and artificial neural network models

Abstract

Due to the significant cost and time involved in identifying barley lines with superior malting quality, the malting industry is searching for accurate and rapid methods to expedite the selection of superior barley lines that meet breeder's goals. The aim of this study is to compare partial least squares regression (PLSR) with advanced statistical models (Bayesian and machine learning) and reliably assess their performance in predicting malt quality traits from near infra-red (NIR) spectral data using barley grains. Using spectral data as predictors and the malt quality traits as references, PLSR outperformed Bayesian and PCA-ANN models for diastatic power (DP), alpha amylase (AA), malt extract (ME), wort protein (WP), soluble to total protein (S/T) ratio, and free amino nitrogen (FAN). WP had the best prediction performance for all models, with the best-performing model, PLSR, having $R^2$ (RPD) values of 0.55 (1.5). The influential wavelength regions identified based on the variable importance in projection (VIP) scores and coefficient estimates for PLSR and Bayesian models, respectively, were comparatively similar for all malt quality traits. Based on these findings, PLSR analysis and wavelength selection techniques would enhance the future design and optimization of NIR prediction models in malt quality improvement programs.