Rapid discrimination of quality grade of black tea based on near-infrared spectroscopy (NIRS), electronic nose (E-nose) and data fusion

Authors：Xia, Hongling; Chen, Wei; Hu, Die; Miao, Aiqing; Qiao, Xiaoyan; Qiu, Guangjun; Liang, Jianhua; Guo, Weiqing^*; Ma, Chengying^*

Source：Food Chemistry, 2024, 440: 138242.

DOI：10.1016/j.foodchem.2023.138242

• Tea
• Quality evaluation
• Near -infrared spectroscopy
• Electronic nose
• Data fusion

For the manufacturing and sale of tea, rapid discrimination of overall quality grade is of great importance. However, present evaluation methods are time-consuming and labor-intensive. This study investigated the feasibility of combining advantages of near-infrared spectroscopy (NIRS) and electronic nose (E-nose) to assess the tea quality. We found that NIRS and E-nose models effectively identify taste and aroma quality grades, with the highest accuracies of 99.63% and 97.00%, respectively, by comparing different principal component numbers and classification algorithms. Additionally, the quantitative models based on NIRS predicted the con-tents of key substances. Based on this, NIRS and E-nose data were fused in the feature-level to build the overall quality evaluation model, achieving accuracies of 98.13%, 96.63% and 97.75% by support vector machine, K -nearest neighbors, and artificial neural network, respectively. This study reveals that the integration of NIRS and E-nose presents a novel and effective approach for rapidly identifying tea quality.

• Institution
  广东省农业科学院