State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China.
State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China.
Food Chem. 2023 Feb 1;401:134090. doi: 10.1016/j.foodchem.2022.134090. Epub 2022 Oct 14.
Fermentation is a key black tea processing step and makes an important contribution to quality formation. Current approaches to fermentation monitoring are costly or laboratory-based. Here, we first evaluated the potential of at-line computer vision for detecting fermentation quality in a tea factory. A self-built industrial camera was used to collect tea samples at various fermentation durations. The correlations of color variables that were extracted from the images with key quality indicators in the tea samples were verified. Subsequently, partial least-squares regression models based on the color variables showed high prediction accuracy with residual prediction deviation values of 4.13, 3.53, and 3.39 for catechins, theaflavins and chlorophylls, respectively. Finally, the spatial and temporal distributions of indicators during fermentation were mapped to visualize the fermentation quality. This study realized low-cost, at-line and real-time detection for black tea fermentation, which provides technical support for the industrial and intelligent production of black tea.
发酵是红茶加工的关键步骤，对品质形成有重要贡献。目前的发酵监测方法要么成本高，要么基于实验室。在这里，我们首次评估了在线计算机视觉在茶叶厂检测发酵质量的潜力。使用自建的工业相机在不同的发酵时间采集茶叶样本。验证了从图像中提取的颜色变量与茶叶样本中关键质量指标的相关性。随后，基于颜色变量的偏最小二乘回归模型显示出较高的预测准确性，儿茶素、茶黄素和叶绿素的残差预测偏差值分别为 4.13、3.53 和 3.39。最后，将发酵过程中指标的时空分布映射出来，以可视化发酵质量。本研究实现了红茶发酵的低成本、在线和实时检测，为红茶的工业化和智能化生产提供了技术支持。
