Nondestructive Phenolic Compounds Measurement and Origin Discrimination of Peated Barley Malt Using Near-Infrared Hyperspectral Imagery and Machine Learning

Abstract

Quantifying phenolic compound in peated barley malt and discriminating its origin are essential to maintain the aroma of high-quality Scottish whisky during the manufacturing process. The content of the total phenol varies in peated barley malts, which is critical in measuring the associated peatiness level. Existing methods for measuring such phenols are destructive and/or time-consuming. To tackle these issues, we propose in this article a novel nondestructive system for fast and effective estimating the phenolic concentrations and discriminating their origins with the near-infrared hyperspectral imagery and machine learning. First, novel ways of data acquisition and normalization are developed for robustness. Then, the principal component analysis (PCA) and folded PCA are fused for extracting the global and local spectral features, followed by the support vector machine (SVM)-based origin discrimination and deep neural network-based phenolic measurement. In total, 27 categories of peated barley malts from eight suppliers are utilized to form thousands of spectral samples for modeling. A classification accuracy up to 99.5% and a squared correlation coefficient up to 98.57% are achieved, outperforming a few state of the art. These have fully demonstrated the efficacy of our system in automated phenolic measurement and origin discrimination to benefit the quality monitoring in the whisky industry.