ArticleFoods (Basel, Switzerland)2024
Spectral Fingerprinting of Tencha Processing: Optimising the Detection of Total Free Amino Acid Content in Processing Lines by Hyperspectral Analysis.
Article in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Beyond Composition: Processing-Driven Transformations and Functional Trade-Offs of Matcha Tea in Foods.Foods (Basel, Switzerland) · 2026Review
- Rapid and Quantitative Prediction of Tea Pigments Content During the Rolling of Black Tea by Multi-Source Information Fusion and System Analysis Methods.Foods (Basel, Switzerland) · 2025Article
- Detection of microplastics stress on rice seedling by visible/near-infrared hyperspectral imaging and synchrotron radiation Fourier transform infrared microspectroscopy.Frontiers in plant science · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
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
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Registered trials
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