Discrimination of Chinese Huzhu Qingke Liquor by using Ultra-Violet Spectroscopy Combined with Chemometrics

Abstract

Qingke liquor, a renowned Tibetan alcoholic beverage derived from hull-less highland barley exclusively cultivated in the Qinghai-Tibetan Plateau, has witnessed a surge in sales. However, the issue of adulteration has emerged as a pressing concern demanding immediate attention. The research focuses on the rapid identification methods of 'Huzhu' brand Qingke liquor, a geographical indications protection product, using UV spectroscopy. Two approaches are proposed: principal component analysis-support vector machine (PCA-SVM) and multi-model partial least squares-discriminant analysis (MPLS-DA). Three categories of liquors are considered: Chinese 'Huzhu' Qingke Liquors (CHQL), Other Brand Qingke Liquors (OBQL), and Non-Qingke-Based Liquors (NQBL). SVM is performed using two principal components to solve the binary classification problem, while PLS1 algorithm is used for each column of the dummy variable Y in MPLS-DA to integrate prediction results from submodels. Both PCA-SVM and MPLS-DA successfully build discrimination models for CHQL. PCA-SVM distinguishes CHQL from OBQL and NQBL but cannot differentiate between OBQL and NQBL. In contrast, MPLS-DA correctly identifies all three classes of samples. These results demonstrate that the proposed method can serve as a simple and rapid identification approach for CHQL, with MPLS-DA exhibiting superior sample recognition capabilities.