School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
Food Chem. 2024 Jan 30;432:137190. doi: 10.1016/j.foodchem.2023.137190. Epub 2023 Aug 20.
The aroma produced during drying is an important indicator of tencha and needs to be monitored. This study constructed an olfactory visualization system for assessing tencha aroma using colorimetric sensor array (CSA) combined with chemometric methods. The 16 chemically responsive dyes were selected to obtain aroma information of tencha samples and extracted image data of aroma information by machine vision algorithms. Subsequently, k-nearest neighbor, support vector machine, classification and regression tree, and random forest (RF), four qualitative models were applied to build the mathematical models. The RF model with nine principal components was preferred, with recognition rate of 100.00% and 91.07% for the training and prediction sets, respectively. The experimental results showed that CSA combined with the RF model can be effectively applied to assess tencha aroma. This study provided a scientific and novel method to maintain the stability of tencha quality in the production process.
干燥过程中产生的香气是抹茶的重要指标，需要进行监测。本研究构建了一种利用比色传感器阵列（CSA）结合化学计量学方法评估抹茶香气的嗅觉可视化系统。选择了 16 种对化学物质有响应的染料，以获取抹茶样品的香气信息，并通过机器视觉算法提取香气信息的图像数据。随后，应用了 k-最近邻、支持向量机、分类和回归树以及随机森林（RF）四种定性模型来构建数学模型。具有九个主成分的 RF 模型是首选模型，其对训练集和预测集的识别率分别为 100.00%和 91.07%。实验结果表明，CSA 结合 RF 模型可有效应用于评估抹茶香气。本研究为保持抹茶在生产过程中的质量稳定性提供了一种科学新颖的方法。
