【摘要】 针对固态复合调味料鲜美度感官评价主观性强、数据可靠性低的问题，本研究提出一种基于近红外光谱(NIRS)与深度学习融合的鲜美度预测模型。通过筛选1963个市售样品，结合消费者鲜美度感官评价能力评估，优化数据质量，并分别构建一维卷积神经网络(1D-CNN)和二维卷积神经网络(2D-CNN)定量预测模型。结果表明：未筛选消费者评价数据时，模型平均相对误差(MRE)为12.79%～15.86%,相关系数(R)为0.70～0.74;经筛选剔除6名评价能力较差的消费者评价数据后，2D-CNN模型性能显著提升(建模集MRE=4.94%,R=0.90;验证集MRE=5.25%,R=0.87)。研究表明，消费者鲜美度感官评价能力筛选与二维卷积特征提取可有效提高模型预测精度，为固态复合调味料品质评价及新产品开发提供高效、客观的技术支持。更多还原

【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.更多还原