ArticleFood science & nutrition2020
Qualitative discrimination of Chinese dianhong black tea grades based on a handheld spectroscopy system coupled with chemometrics.
Article in Food science & nutrition, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Digital Sensing for Comprehensive Tea Quality Evaluation: From Dry Tea to Tea Infusion and Infused Leaves.Comprehensive reviews in food science and food safety · 2026Review
- Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach.Sensors (Basel, Switzerland) · 2024Article
- Article
- Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM.Foods (Basel, Switzerland) · 2022Article
- Differentiation between wild and artificial cultivatedFood science & nutrition · 2020Article
- Chemometric analysis combined with FTIR spectroscopy of milk and Halloumi cheese samples according to species' origin.Food science & nutrition · 2020Article
- Qualitative discrimination of Chinese dianhong black tea grades based on a handheld spectroscopy system coupled with chemometrics.Food science & nutrition · 2020Article
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Authors and funding
8 authors.
Funding
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Abstract
The evaluation of Chinese dianhong black tea (CDBT) grades was an important indicator to ensure its quality. A handheld spectroscopy system combined with chemometrics was utilized to assess CDBT from eight grades. Both variables selection methods, namely genetic algorithm (GA) and successive projections algorithm (SPA), were employed to acquire the feature variables of each sample spectrum. A partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) algorithms were applied for the establishment of the grading discrimination models based on near-infrared spectroscopy (NIRS). Comparisons of the portable and benchtop NIRS systems were implemented to obtain the optimal discriminant models. Experimental results showed that GA-SVM models by the handheld sensors yielded the best predictive performance with the correct discriminant rate (CDR) of 98.75% and 100% in the training set and prediction set, respectively. This study demonstrated that the handheld system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of CDBT rankings. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades.
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