1.江南大学智能制造学院，江苏 无锡 214122;2.山东碧海机械科技有限公司，山东 临沂 276600
宋春芳（1974—），女，博士，教授，博士研究生导师，主要从事农产品无损检测与控制研究。E-mail：songcf@jiangnan.edu.cn
紫金县市级农业科技园区（茶叶）科技计划项目。
1.School of Intelligent Manufacturing, Jiangnan University, Wuxi 214122, China;2.Shandong Bihai Machinery Technology Co., Ltd., Linyi 276600, China
【目的】鉴定烘焙过程中铁观音乌龙茶的品质。【方法】使用近红外光谱技术对烘焙过程中的铁观音乌龙茶进行无损检测，并使用化学测量法对铁观音乌龙茶内在品质的变化进行分析。基于近红外光谱数据，对光谱进行预处理和降维分析，使用支持向量机（support vector machine，SVM）和反向传播神经网络（back propagation neural network，BPNN）构建铁观音乌龙茶烘焙程度判别模型。基于近红外光谱数据结合内在品质，使用偏最小二乘回归（partial least squares regression，PLSR）构建品质预测模型。【结果】通过茶叶内在品质的变化和感官评审将铁观音乌龙茶的烘焙程度划分为烘焙不足、烘焙适度和烘焙过度。在全波段光谱数据模型下，用多元散射校正（multiple scatter correction，MSC）进行预处理，用BPNN构建判别模型时，判别效果最优，测试集准确率为100.00%。对4个内在品质进行预测时，连续投影变换（successive projections algorithm，SPA）算法结合PLSR的预测模型的精度均为最优。游离氨基酸、茶多酚、儿茶素和咖啡碱的最佳预测模型的测试集决定系数（determination coefficient of prediction，R
[Objective] This study aims to assess the quality of Tieguanyin oolong tea during the baking process. [Method] Near-infrared spectroscopy was used for non-destructive detection of Tieguanyin oolong tea during the baking process and chemometrics was employed to analyze the changes in the intrinsic quality of the Tieguanyin oolong tea. Spectral preprocessing and dimensionality reduction were conducted for the near-infrared spectral data. Support vector machine (SVM) and back propagation neural network (BPNN) were adopted to construct discrimination models for the baking degree of Tieguanyin oolong tea. Combining the near-infrared spectral data with intrinsic quality data, partial least squares regression (PLSR) was employed to build quality prediction models. [Result] According to the changes in intrinsic quality and sensory evaluation results, the baking degree of Tieguanyin oolong tea can be graded into under baking, moderate baking, and over baking. Under the full-spectrum spectral data model, with preprocessing as multiple scatter correction (MSC) and the discriminant model as BPNN, the discriminant effect was the best, and the accuracy rate of the test set was 100.00%. When predicting the four intrinsic quality properties, the prediction model combining the successive projections algorithm (SPA) with PLSR had the best accuracy. The determination coefficients of prediction (R
黄丽竹,卞芸,李臻峰,李先峰,宋春芳.基于近红外光谱的铁观音乌龙茶烘焙程度判别及品质预测[J].食品与生物技术学报,2025,(9):153-162.
HUANG Lizhu, BIAN Yun, Li Zhenfeng, Li Xianfeng, SONG Chunfang. Baking Degree Discrimination and Quality Prediction of Tieguanyin Oolong Tea Based on Near-infrared Spectroscopy[J]. Journal of Food Science and Biotechnology,2025,(9):153-162.
版权所有：《食品与生物技术学报》编辑部
地址：江苏省无锡市蠡湖大道1800号 邮政编码：214122
电话：0510-85913526 电子邮件：xbbjb@jiangnan.edu.cn
