Nondestructive phenolic compounds measurement and origin discrimination of peated barley malt using near-infrared hyperspectral imagery and machine learning.
Yan, Yijun; Ren, Jinchang; Tschannerl, Julius; Zhao, Huimin; Harrison, Barry; Jack, Frances
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 paper 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 modelling. A classification accuracy up to 99.5% and a squared-correlation-coefficient up to 98.57% are achieved in our experiments, 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.
YAN, Y., REN, J., TSCHANNERL, J., ZHAO, H., HARRISON, B. and JACK, F. . Nondestructive phenolic compounds measurement and origin discrimination of peated barley malt using near-infrared hyperspectral imagery and machine learning. IEEE transactions on instrumentation and measurement [online], Early Access. Available from: https://doi.org/10.1109/TIM.2021.3082274
|Journal Article Type||Article|
|Acceptance Date||Oct 26, 2020|
|Online Publication Date||May 21, 2021|
|Deposit Date||Jun 1, 2021|
|Publicly Available Date||Jun 1, 2021|
|Journal||IEEE Transactions on Instrumentation and Measurement|
|Publisher||Institute of Electrical and Electronics Engineers|
|Peer Reviewed||Peer Reviewed|
|Keywords||Phenolic compound measurement; Origin discrimination; Near infrared (NIR); Hyperspectral imagery; Peated barley malt; Machine learning|
YAN 2021 Nondestructive phenolic
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