芳香
近红外光谱
过程(计算)
融合
质量(理念)
传感器融合
校准
工艺工程
模式识别(心理学)
计算机科学
人工智能
数学
化学
食品科学
工程类
统计
语言学
物理
哲学
认识论
操作系统
量子力学
作者
Yanna Rong,Tahreem Riaz,Hao Lin,Zhen Wang,Quansheng Chen,Qin Ouyang
标识
DOI:10.1016/j.saa.2023.123385
摘要
The drying process is a critical stage in developing the aroma quality of tencha. In our research, visible near infrared (Vis-NIR) and colorimetric sensor array (Vis-NIR-CSA) were used for evaluating the aroma quality of tencha drying process. Vis-NIR recorded the spectral signal of CSA after the reaction in samples. Subsequently, the aroma quality was predicted by a combination of different data fusion strategies and classification and regression tree (CART) in tencha drying process. The high-level fusion strategy showed the best performance, with calibration and prediction set accuracy of 94.68% and 93.48%, respectively. The results indicated that Vis-NIR-CSA combined with high-level data fusion could be applied satisfactorily in the aroma quality evaluation of tencha. Moreover, pentanal was identified to be highly correlated with aroma quality during tencha drying process, which verified the sensor identification results. This study contributed to controlling good manufacturing practices and designing optimal tencha processing systems.
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