Construction of a NIR Solid-State Composite Seasoning Freshness AI Model Based on Consumer Sensory Evaluation Ability Assessment
SHU Qin-da1, ZHANG Jia-hui2*, WANG Qi3, YUE Bao-hua3, LI Qian-qian1*
1. Department of Business Administration, School of Management, Shanghai University, Shanghai 200444, China
2. Shanghai TotoleFood Co., Ltd., Shanghai 201802, China
3. College of Sciences Shanghai University, Shanghai 200444, China
Abstract：To address the issues of intense subjectivity and low reliability in sensory evaluation of umami intensity in solid composite seasonings, this study proposes a prediction model integrating near-infrared spectroscopy (NIRS) and deep learning. By screening 1963 commercial samples and optimizing data quality through consumer sensory evaluation capability assessment, one-dimensional convolutional neural network (1D-CNN) and two-dimensional convolutional neural network (2D-CNN) models were constructed for quantitative prediction. The results showed that without consumer screening, the model achieved a mean relative error (MRE) of 12.79%~15.86% and a correlation coefficient (R) of 0.70~0.74. After excluding data from 6 consumers with poor evaluation capability, the performance of the 2D-CNN model significantly improved (training set: MRE=4.94%, R=0.90; validation set: MRE=5.25%, R=0.87). This study demonstrates that consumer evaluation capability screening and 2D-CNN-based feature extraction effectively enhance prediction accuracy, providing a robust and objective technical solution for quality assessment and product development of solid composite seasonings.
束沁炟，张佳汇，王 琪，岳宝华，李倩倩. 基于消费者感官评价能力评估的近红外固态复合调味料鲜美度AI模型构建[J]. 光谱学与光谱分析, 2025, 45(08): 2228-2233.
SHU Qin-da, ZHANG Jia-hui, WANG Qi, YUE Bao-hua, LI Qian-qian. Construction of a NIR Solid-State Composite Seasoning Freshness AI Model Based on Consumer Sensory Evaluation Ability Assessment. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(08): 2228-2233.
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