ArticleFoods (Basel, Switzerland)2022
Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm.
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 9 papers.
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Who cites it
9 citing papers in PubMed.
- Precise Recognition of Adulterated Sliced Mutton Using Machine Vision on Mobile Phone Images.Foods (Basel, Switzerland) · 2026Article
- Establishing a Detection Method Based on Multiplex PCR for Identification of Sheep Meat, Goat Meat and Common Adulterant Meats.Foods (Basel, Switzerland) · 2025Article
- Non-Destructive Detection of Chilled Mutton Freshness Using a Dual-Branch Hierarchical Spectral Feature-Aware Network.Foods (Basel, Switzerland) · 2025Article
- Nutrient Content Prediction and Geographical Origin Identification of Bananas by Combining Hyperspectral Imaging with Chemometrics.Foods (Basel, Switzerland) · 2024Article
- Early diagnosis of Cladosporium fulvum in greenhouse tomato plants based on visible/near-infrared (VIS/NIR) and near-infrared (NIR) data fusion.Scientific reports · 2024Article
- Article
- Adulteration Detection of Pork in Mutton Using Smart Phone with the CBAM-Invert-ResNet and Multiple Parts Feature Fusion.Foods (Basel, Switzerland) · 2023Article
- Research on Automatic Classification and Detection of Mutton Multi-Parts Based on Swin-Transformer.Foods (Basel, Switzerland) · 2023Article
- Online Machine Vision-Based Modeling during Cantaloupe Microwave Drying Utilizing Extreme Learning Machine and Artificial Neural Network.Foods (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000-2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an R
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