ArticleFoods (Basel, Switzerland)2020
Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging.
Article in Foods (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Comparative Characterization of Pork Jowl and Three Commercial Cuts from DLY Pigs: Instrumental Quality, Nutritional Composition, Water Distribution, and Volatile Profiles.Foods (Basel, Switzerland) · 2026Article
- Application of Portable Near-Infrared Spectroscopy for Quantitative Prediction of Protein Content inFoods (Basel, Switzerland) · 2025Article
- Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning.Food chemistry: X · 2025Article
- Flower Species Ingredient Verification Using Orthogonal Molecular Methods.Foods (Basel, Switzerland) · 2024Article
- Rapid discrimination ofFood science and biotechnology · 2024Article
- Rapid analysis technologies with chemometrics for food authenticity field: A review.Current research in food science · 2024Review
- Raw Beef Patty Analysis Using Near-Infrared Hyperspectral Imaging: Identification of Four Patty Categories.Sensors (Basel, Switzerland) · 2023Article
- Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review.Foods (Basel, Switzerland) · 2022Review
- Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm.Foods (Basel, Switzerland) · 2022Article
- Quality Assessment of Fruits and Vegetables Based on Spatially Resolved Spectroscopy: A Review.Foods (Basel, Switzerland) · 2022Review
- Nondestructive Testing of Pear Based on Fourier Near-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2022Article
- A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose.Foods (Basel, Switzerland) · 2022Article
- Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain.Food engineering reviews · 2022Review
- Hyperspectral Imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends.Current research in food science · 2022Review
- Chemometrics in Tandem with Hyperspectral Imaging for Detecting Authentication of Raw and Cooked Mutton Rolls.Foods (Basel, Switzerland) · 2021Article
- Non-Destructive Spectroscopic and Imaging Techniques for the Detection of Processed Meat Fraud.Foods (Basel, Switzerland) · 2021Review
- Standardized Extraction Techniques for Meat Analysis with the Electronic Tongue: A Case Study of Poultry and Red Meat Adulteration.Sensors (Basel, Switzerland) · 2021Article
- Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins.Frontiers in nutrition · 2021Review
- Development of a Non-Destructive Method for Detection of the Juiciness of Pear via VIS/NIR Spectroscopy Combined with Chemometric Methods.Foods (Basel, Switzerland) · 2020Article
- Advances of Spectrometric Techniques in Food Analysis and Food Authentication Implemented with Chemometrics.Foods (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%-100% (w/w) at 10% increments using a visible and near-infrared (400-1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with R
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Registered trials
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