ArticleFoods (Basel, Switzerland)2022
Discrimination of Minced Mutton Adulteration Based on Sized-Adaptive Online NIRS Information and 2D Conventional Neural Network.
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 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Quantitative Detection of Key Parameters and Authenticity Verification for Beer Using Near-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2025Article
- Establishing a Detection Method Based on Multiplex PCR for Identification of Sheep Meat, Goat Meat and Common Adulterant Meats.Foods (Basel, Switzerland) · 2025Article
- Transcriptomic and flavor metabolomic exploration of the genetic basis of meat quality and flavor in Tibetan sheep.BMC genomics · 2025Article
- Nutritional profiling of horse gram through NIRS-based multi-trait prediction modelling.Scientific reports · 2025Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Single-probe near-infrared spectroscopy (NIRS) usually uses different spectral information for modelling, but there are few reports about its influence on model performance. Based on sized-adaptive online NIRS information and the 2D conventional neural network (CNN), minced samples of pure mutton, pork, duck, and adulterated mutton with pork/duck were classified in this study. The influence of spectral information, convolution kernel sizes, and classifiers on model performance was separately explored. The results showed that spectral information had a great influence on model accuracy, of which the maximum difference could reach up to 12.06% for the same validation set. The convolution kernel sizes and classifiers had little effect on model accuracy but had significant influence on classification speed. For all datasets, the accuracy of the CNN model with mean spectral information per direction, extreme learning machine (ELM) classifier, and 7 × 7 convolution kernel was higher than 99.56%. Considering the rapidity and practicality, this study provides a fast and accurate method for online classification of adulterated mutton.
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Registered trials
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