The spatial differences of the Chinese sauce aroma liquor Daqu and identification by FT-MIR spectroscopy

• Published in: Food control Vol. 154; p. 109990
• Main Authors: Wang, Lingchang, Shen, Yi, Wang, Xi, Gan, Langfei, Zhong, Kai, He, Qiang, Luo, Aimin, Gao, Hong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023
• Subjects: Chemical analysis; Colorimetric analysis; colorimetry; discriminant analysis; empirical research; fermentation; flavor; food safety; Fourier transform infrared spectroscopy; FT-MIR; Industrial standardization; odors; raw materials; Sauce aroma Daqu; Spatial difference; Industrial standardization; Spatial difference; Chemical analysis; Sauce aroma Daqu; Colorimetric analysis; FT-MIR
• ISSN: 0956-7135
• DOI: 10.1016/j.foodcont.2023.109990

Chinese sauce aroma liquor (CSL) is one of main types of Baijiu. As the fermentation starter and raw material, CSL Daqu (CSDQ) influences the flavor formation of CSL. Owing to the spatial difference during processing, fermented CSDQ has three types and the quality of a CSDQ is inconsistent. Traditional empirical method has limitations in classification precision and standardization. In this study, the subtype was defined by the layers and types of CSDQ. Differences of CSDQs were systematically evaluated by colorimetric, chemical, and Fourier transform mid-infrared spectroscopy (FT-MIR) analysis. Linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), and k-nearest neighbor method (KNN) algorithms were used to classify the subtypes of CSDQ based on the FT-MIR spectra. Furthermore, the models were optimized, and the wavenumbers with high discrimination were marked by chemical bonds. FT-MIR LDA model achieved 95.61% accuracy after proper preprocessing. An even sampling method was proposed to represent the overall properties of a CSDQ. These findings provided a better understanding of CSDQ and a theoretical basis for promoting industrial standardization. •The colors of sauce Daqu change with the depth is confirmed by colorimetry.•Physical and chemical indicators support and explain the color difference.•An efficient nondestructive discriminant method is established based on FT-MIR.•The optimized LDA is the superior model with 95.61% accuracy to classify CSDQs.•The wavenumbers with high discrimination were marked by chemical bonds.