Journal
FOODS
Volume 11, Issue 15, Pages -Publisher
MDPI
DOI: 10.3390/foods11152278
Keywords
food additive; mutton adulteration; near-infrared hyperspectral imaging; sparrow search algorithm; machine learning
Categories
Funding
- National Natural Science Foundation of China [31860465]
- Bingtuan Innovation Leadership Technologies for Young and Middle-aged Scientists [2020CB016]
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This study proposes a method based on near-infrared hyperspectral imaging, machine learning, and sparrow search algorithm for classification and quantification of adulterated mutton under the effect of mutton flavor essence. The performance of the models is improved through optimization, achieving high accuracy in the classification and quantification of mutton adulteration.
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-P(2) of 0.9304 and an RMSEP of 0.0458 g.g(-1). Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.
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