ArticleScientific reports2022
Near-infrared spectroscopy and machine learning-based technique to predict quality-related parameters in instant tea.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Near-Infrared Spectroscopy Coupled with Chemometrics for Rapid Determination of pH, Moisture and Lycopene Content in Lycopene Liquid Beverages.Foods (Basel, Switzerland) · 2026Article
- Enhancing strawberry maturity assessment using mid-infrared spectral analysis with advanced variable selection and supervised classification.Scientific reports · 2026Article
- Leveraging GWAS-Identified Markers in Combination with Bayesian and Machine Learning Models to Improve Genomic Selection in Soybean.International journal of molecular sciences · 2025Article
- Synchronous detection method for senescence quality of damaged Korla fragrant pears during storage.Frontiers in plant science · 2025Article
- YOLOv7-DWS: tea bud recognition and detection network in multi-density environment via improved YOLOv7.Frontiers in plant science · 2024Article
- A Review of Machine Learning for Near-Infrared Spectroscopy.Sensors (Basel, Switzerland) · 2022Review
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9 authors.
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Abstract
The traditional method for analyzing the content of instant tea has disadvantages such as complicated operation and being time-consuming. In this study, a method for the rapid determination of instant tea components by near-infrared (NIR) spectroscopy was established and optimized. The NIR spectra of 118 instant tea samples were used to evaluate the modeling and prediction performance of a combination of binary particle swarm optimization (BPSO) with support vector regression (SVR), BPSO with partial least squares (PLS), and SVR and PLS without BPSO. Under optimal conditions, Rp for moisture, caffeine, tea polyphenols, and tea polysaccharides were 0.9678, 0.9757, 0.7569, and 0.8185, respectively. The values of SEP were less than 0.9302, and absolute values of Bias were less than 0.3667. These findings indicate that machine learning can be used to optimize the detection model of instant tea components based on NIR methods to improve prediction accuracy.
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