ArticleFoods (Basel, Switzerland)2022
Determination of Pork Meat Storage Time Using Near-Infrared Spectroscopy Combined with Fuzzy Clustering Algorithms.
Article in Foods (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Intelligent Responsive Nanostructures Toward Food Preservation: Synergizing Antibacterial Activity and Environmental Regulation.Foods (Basel, Switzerland) · 2026Review
- Near-Infrared Spectroscopy in Food Analysis: Applications, Chemometric Strategies, and Technological Advances.Foods (Basel, Switzerland) · 2026Review
- Recent Advances in Spectroscopy and Imaging Techniques for Nondestructive Detection of Meat Quality and Safety.Food science & nutrition · 2025Review
- Nondestructive Detection of Soluble Solids Content in Apples Based on Multi-Attention Convolutional Neural Network and Hyperspectral Imaging Technology.Foods (Basel, Switzerland) · 2025Article
- Innovative Technologies Reshaping Meat Industrialization: Challenges and Opportunities in the Intelligent Era.Foods (Basel, Switzerland) · 2025Review
- Advances in Non-Thermal Processing of Meat and Monitoring Meat Protein Gels Through Vibrational Spectroscopy.Foods (Basel, Switzerland) · 2025Review
- The Effect of the Level of Goat Liver Addition to Goat Minced Meat on the Near-Infrared Spectra, Colour, and Shelf Life of Samples.Foods (Basel, Switzerland) · 2025Article
- Recent Advances in Light Penetration Depth for Postharvest Quality Evaluation of Fruits and Vegetables.Foods (Basel, Switzerland) · 2024Review
- High-accuracy classification and origin traceability of peanut kernels based on near-infrared (NIR) spectroscopy using Adaboost - Maximum uncertainty linear discriminant analysis.Current research in food science · 2024Article
- Non-Destructive Detection of the Freshness of Air-Modified Mutton Based on Near-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2023Article
- Discrimination of Minced Mutton Adulteration Based on Sized-Adaptive Online NIRS Information and 2D Conventional Neural Network.Foods (Basel, Switzerland) · 2022Article
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Authors and funding
7 authors.
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Abstract
The identification of pork meat quality is a significant issue in food safety. In this paper, a novel strategy was proposed for identifying pork meat samples at different storage times via Fourier transform near-infrared (FT-NIR) spectroscopy and fuzzy clustering algorithms. Firstly, the FT-NIR spectra of pork meat samples were collected by an Antaris II spectrometer. Secondly, after spectra preprocessing with multiplicative scatter correction (MSC), the orthogonal linear discriminant analysis (OLDA) method was applied to reduce the dimensionality of the FT-NIR spectra to obtain the discriminant information. Finally, fuzzy C-means (FCM) clustering, K-harmonic means (KHM) clustering, and Gustafson-Kessel (GK) clustering were performed to establish the recognition model and classify the feature information. The highest clustering accuracies of FCM and KHM were both 93.18%, and GK achieved a clustering accuracy of 65.90%. KHM performed the best in the FT-NIR data of pork meat considering the clustering accuracy and computation. The overall experiment results demonstrated that the combination of FT-NIR spectroscopy and fuzzy clustering algorithms is an effective method for distinguishing pork meat storage times and has great application potential in quality evaluation of other kinds of meat.
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