Non-targeted HPLC-FLD fingerprints and chemometrics for Chinese aged vinegar classification and adulteration detection

The classification and authentication of Chinese aged vinegar (CAV) are of primary importance for consumers and the vinegar industry. In this study, HPLC-FLD fingerprints and chemometrics were used for the classification of CAVs from different brands/origins and the detection of adulteration in Shanxi extra aged vinegar (SAV). HPLC fingerprints were acquired by recording emission at 338 nm and excitation from 200 to 300 nm under a rapid isocratic elution mode. Principal component analysis (PCA) was used to explore the natural distribution and clustering of the samples. Subsequently, classification models were developed using principal component analysis-discriminant analysis (PCA-DA), partial least squares-discriminant analysis (PLS-DA), and N-way partial least squares-discriminant analysis (NPLS-DA). PLS-DA and NPLS-DA provided the best classification results, with 100% classification accuracy for both the training and internal prediction sets. Furthermore, PLS and N -PLS were applied for the quantitative analysis of SAV adulteration. Better quantitation performance was achieved by PLS regression models, with low errors for both calibration (below 2.01%) and internal prediction sets (below 8.89%). The generalization ability of the developed models was further evaluated using external prediction samples. These results demonstrated the potential of the proposed method for quality control and adulteration detection of CAVs in the vinegar industry. • Non-targeted HPLC-FLD fingerprints for CAV classification and adulteration detection. • Satisfactory classification results through PLS-DA and NPLS-DA (100% classification accuracy). • Better quantitative performance in CAV adulteration detection by PLS in comparison to N-PLS. • Both classification and regression models showed excellent generalization ability.