ArticleFrontiers in nutrition2021
Non-destructive Detection of Insect Foreign Bodies in Finishing Tea Product Based on Terahertz Spectrum and Image.
Article in Frontiers in nutrition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 36 citations in OpenAlex.
- Non-destructive detection of micro-impurities in tea using the YOLOv11-PFT model.NPJ science of food · 2026Article
- The value of terahertz three-dimensional tomography for the differential diagnosis of tumor tissue and normal tissue in lung cancer nude mice.Frontiers in bioengineering and biotechnology · 2026Article
- Nondestructive determination of ash content in wheat flour via terahertz time-domain spectroscopy.Frontiers in plant science · 2026Article
- The Detection of Pest Contaminants in Chocolate Using Visible-Near-Infrared Single-Pixel Imaging Technology.Foods (Basel, Switzerland) · 2025Article
- Detection of Low-Density Foreign Objects in Infant Snacks Using a Continuous-Wave Sub-Terahertz Imaging System for Industrial Applications.Sensors (Basel, Switzerland) · 2024Article
- Review
- Study on qualitative identification of aflatoxin solution based on terahertz metamaterial enhancement.RSC advances · 2023Article
- CMOS Detector Staggered Array Module for Sub-Terahertz Imaging on Conveyor Belt System.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-destructive testing of low-density and organic foreign bodies is the main challenge for food safety control. Terahertz time-domain spectroscopy (THz-TDS) and imaging technologies were applied to explore the feasibility of detection for insect foreign bodies in the finishing tea products. THz-TDS of tea leaves and foreign bodies of insects demonstrated significant differences in terms of time domain and frequency signals in the range of 0.3-1.0 THz. These signals were corrected by the use of adaptive iteratively reweighted penalized least squares (AirPLS), asymmetric least squares (AsLS), and baseline estimation and de-noising using sparsity (BEADS) for reducing baseline drift and enhancing effective spectral information. The K-nearest neighbor (KNN) and partial least squares discrimination analysis (PLS-DA) models showed the best performance after AirPLS correction with the prediction accuracy of 98 and 100%, respectively. In addition, the locations and outlines of insect bodies could be clearly presented
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Registered trials
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