Near-infrared spectroscopy of Chinese soy sauce for quality evaluation
Xiaoqian Chen, Chuanwei Li, Xiaofang Liu, Yu Su, Ziang Sun, Lei Yuan, Shuo Wang
Abstract
The feasibility of near-infrared (NIR) spectroscopy and partial least-squares regression (PLSR) was investigated for rapid prediction of the quality of Chinese soy sauce. Twenty-four soy sauce samples from eight common brands available in China were analyzed for the contents of various components that may affect the quality of soy sauce. Sensory evaluation was also conducted to determine the relationship between components and the sensory quality of soy sauce. Subsequently, NIR spectra (400–2500 nm) of the samples were obtained, and the raw spectra were subjected to different pretreatment methods. PLSR was performed on the raw and treated spectra to construct models using a calibration set. The performance of models was evaluated by comparing the determination coefficient of prediction (R2P) and root-mean-square error of prediction (RMSEP). The results showed that the models constructed using the moisture content (R2P of 0.825 and RMSEP of 1.73), amino acid nitrogen content (R2P of 0.785 and RMSEP of 0.071), and taste scores (R2P of 0.733 and RMSEP of 11.93) performed well, and the interactions between amino acid nitrogen content and taste of soy sauce were clarified. This study demonstrates that NIR spectroscopy can be used as a valid alternative method for rapid prediction of the sensory quality of soy sauce during processing.