Rapid and comprehensive quality assessment of Zhenjiang aromatic vinegar via near-infrared spectroscopy coupled with optimized chemometric models

The quality evaluation of Zhenjiang aromatic vinegar (ZAV) is of paramount significance for standardizing the market and safeguarding consumers’ interests. Near-infrared (NIR) spectroscopy combined with chemometrics was investigated to achieve the simultaneous discrimination of ZAV quality grades and quantification of key physicochemical parameters (total acid (TA), reducing sugar (RS), amino acid nitrogen (AAN)), thereby developing a rapid and comprehensive strategy of quality evaluation of ZAV. The denoising performance of five preprocessing methods was systematically evaluated prior to implementing competitive adaptive reweighted sampling (CARS) and variable combination population analysis (VCPA) algorithms for characteristic wavelength selection. The VCPA-based linear discriminant analysis model achieved a 97.14% recognition rate of prediction set in ZAV grade discrimination. While partial least squares regression and support vector machine regression models based on the wavelengths selected by CARS yielded superior predictive ability for the three key parameters, with relative prediction deviation values of 4.03 (TA), 3.91 (RS) and 4.30 (AAN), respectively. This study provides a robust, cost-effective, and non-destructive alternative to traditional methods, establishing NIR spectroscopy as a highly promising tool for ensuring quality control and compliance in the vinegar industry. • VCPA-LDA model attained optimal recognition rates in grade discrimination of ZAV. • Quantitative models for TA, RS and AAN contents were established and compared. • CARS-PLSR/SVR models exhibited superior prediction of key ZAV quality parameters. • The potential of NIR for quality control in vinegar production was demonstrated.