Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample

Rapid and non-invasive discrimination of two types of similar baijiu with the same alcohol content was investigated using chemometrics-assisted Raman spectroscopy. Baijiu samples manufactured across six months were subjected to Raman spectrum acquisition to form chronologically independent training and testing data sets for the optimization and evaluation of chemometric approaches. Discrimination capacities of machine learning (ML) algorithms were enhanced by the supervised spectral dimension reduction with linear discrimination analysis (LDA). The LDA-LightGBM model optimized with the binary training set led to an overall testing discrimination accuracy of 79%. Supplementing the Raman spectra of an auxiliary type of baijiu into the binary training data set considerably enhances the feature extraction capacity of LDA. Performances of the LDA-ML models were further elevated by the spectral preprocessing of “constant background subtraction” that shifts each spectrum downwards by its spectral intensity minimum. The chemometric approach that involved auxiliary training data, constant background subtraction, LDA dimension reduction, and ensemble learning classifier contributed to an overall testing discrimination accuracy of 97%. The proposed chemometric-assisted Raman spectroscopic approach facilitated robust and sensitive discrimination of highly similar baijiu products, which highlights the potential applications of the technique in manufacturing quality control and product authentication.