TITLE:
Discriminant Analysis of Liquor Brands Based on Moving-Window Waveband Screening Using Near-Infrared Spectroscopy
AUTHORS:
Jie Zhong, Jiemei Chen, Lijun Yao, Tao Pan
KEYWORDS:
Liquor Brands, Near-Infrared Spectroscopy, Partial Least Squares Discriminant Analysis, Moving-Window Waveband Screening, Simplified Optimal Model Set
JOURNAL NAME:
American Journal of Analytical Chemistry,
Vol.9 No.3,
March
12,
2018
ABSTRACT: Partial least squares discriminant analysis (PLS-DA) with integrated moving-window
(MW) waveband screening was applied to the discriminant analysis of liquor
brands with near-infrared (NIR) spectroscopy. Luzhou Laojiao, a popular liquor with strong fragrant flavor, was
used as the identified liquor brand (160 samples, negative, 52 vol
alcoholicity). Liquors of 10 other brands with strong fragrant flavor were used
as the interferential brands (200 samples, positive, 52 vol alcoholicity). The
Kennard-Stone algorithm was used for the division of modeling samples to
achieve uniformity and representativeness. Based on the MW-PLS-DA, a simplified
optimal model set with 157 wavebands was further proposed. This set contained
five types of wavebands corresponding to the NIR absorption bands of water,
ethanol, and other micronutrients (i.e.,
acids, aldehydes, phenols, and aromatic compounds) in liquor for practical
choice. Using five selected simple models with 4775 - 4239, 7804 - 6569, 6264 -
5844, 9435 - 7896, and 12066 - 10373 cm-1, the validation
recognition rates were obtained as 99.3% or higher. Results show good
prediction performance and low model complexity, and also provided a valuable
reference for designing small dedicated instruments. The proposed method is a
promising tool for large-scale inspection of liquor food safety.