中国酱油近红外光谱质量评价

Xiaoqian Chen, Chuanwei Li, Xiaofang Liu, Yu-lan Su, Ziang Sun, L. Yuan, Shuo Wang
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引用次数: 0

摘要

研究了近红外光谱(NIR)和偏最小二乘回归(PLSR)快速预测酱油质量的可行性。本文分析了国内8个常见品牌的24份酱油样品中可能影响酱油质量的各种成分的含量。通过感官评价来确定各成分与酱油感官品质之间的关系。随后,对样品进行了400 ~ 2500 nm的近红外光谱分析,并进行了不同的预处理方法。使用校准集对原始和处理过的光谱进行PLSR以构建模型。通过比较预测决定系数(R2P)和预测均方根误差(RMSEP)来评价模型的性能。结果表明:水分含量(R2P为0.825,RMSEP为1.73)、氨基酸氮含量(R2P为0.785,RMSEP为0.071)和口感分数(R2P为0.733,RMSEP为11.93)构建的模型性能良好,阐明了氨基酸氮含量与酱油口感之间的相互作用。该研究表明,近红外光谱技术可作为酱油加工过程中感官质量快速预测的有效替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Near-infrared spectroscopy of Chinese soy sauce for quality evaluation
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.
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