He Qinghai, Guo Yihang, Li Xiaoli, He Yong, Lin Zhi, Zeng Hui
School of Mechanical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.
Shandong Academy of Agricultural Machinery Science, Jinan 250100, China.
Foods. 2024 Nov 29;13(23):3862. doi: 10.3390/foods13233862.
The quality and flavor of tea leaves are significantly influenced by chemical composition, with the content of free amino acids serving as a key indicator for assessing the quality of Tencha. Accurately and quickly measuring free amino acids during tea processing is crucial for monitoring and optimizing production processes. However, traditional chemical analysis methods are often time-consuming and costly, limiting their application in real-time quality control. Hyperspectral imaging (HSI) has shown significant effectiveness as a component detection tool in various agricultural applications. This study employs VNIR-HSI combined with machine learning algorithms to develop a model for visualizing the total free amino acid content in Tencha samples that have undergone different processing steps on the production line. Four pretreating methods were employed to preprocess the spectra, and partial least squares regression (PLSR) and least squares support vector machine regression (LS-SVR) models were established from the perspectives of individual processes and the entire process. Combining competitive adaptive reweighted sampling (CARS) and variable iterative space shrinkage approach (VISSA) methods for characteristic band selection, specific bands were chosen to predict the amino acid content. By comparing modeling evaluation indicators for each model, the optimal model was identified: the overall model CT+CARS+PLSR, with predictive indicators Rc = 0.9885, Rp = 0.9566, RMSEC = 0.0956, RMSEP = 0.1749, RPD = 4.8021, enabling the visualization of total free amino acid content in processed Tencha leaves. Here, we establish a benchmark for machine learning-based HSI, integrating this technology into the tea processing workflow to provide a real-time decision support tool for quality control, offering a novel method for the rapid and accurate prediction of free amino acids during tea processing. This achievement not only provides a scientific basis for the tea processing sector but also opens new avenues for the application of hyperspectral imaging technology in food science.
茶叶的品质和风味受化学成分的显著影响，其中游离氨基酸的含量是评估碾茶品质的关键指标。在茶叶加工过程中准确快速地测定游离氨基酸对于监控和优化生产过程至关重要。然而，传统的化学分析方法往往耗时且成本高，限制了它们在实时质量控制中的应用。高光谱成像（HSI）在各种农业应用中作为成分检测工具已显示出显著成效。本研究采用可见近红外高光谱成像（VNIR-HSI）结合机器学习算法，开发了一个模型，用于可视化生产线上经过不同加工步骤的碾茶样品中的总游离氨基酸含量。采用四种预处理方法对光谱进行预处理，并从单个过程和整个过程的角度建立了偏最小二乘回归（PLSR）和最小二乘支持向量机回归（LS-SVR）模型。结合竞争性自适应重加权采样（CARS）和可变迭代空间收缩方法（VISSA）进行特征波段选择，选择特定波段来预测氨基酸含量。通过比较每个模型的建模评估指标，确定了最优模型：整体模型CT+CARS+PLSR，其预测指标Rc = 0.9885，Rp = 0.9566，RMSEC = 0.0956，RMSEP = 0.1749，RPD = 4.8021，能够可视化加工后碾茶叶片中的总游离氨基酸含量。在此，我们建立了基于机器学习的高光谱成像的基准，将该技术集成到茶叶加工工作流程中，为质量控制提供实时决策支持工具，为茶叶加工过程中游离氨基酸的快速准确预测提供了一种新方法。这一成果不仅为茶叶加工行业提供了科学依据，也为高光谱成像技术在食品科学中的应用开辟了新途径。
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