Quantitative Detection of Myoglobin Content in Tan Mutton During Cold Storage by Near-infrared Hyperspectral Imaging

Sun, Yourui; Zhang, Haonan; Liu, Guishan; He, Junfeng; Cheng, Lijuan; Li, Yue; Pu, Fangning; Wang, Hao

doi:10.1007/s12161-022-02275-6

Cited by 6 publications

(1 citation statement)

References 59 publications

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“…The data analysis and modelling mainly depend on machine learning (ML) methods. Support vector machine (SVM), back propagation neural network (BP) and extreme learning machine (ELM) have been widely used in the detection of meat and adulteration [ 16 , 24 , 25 , 26 , 27 ]. The uses of new algorithms to optimise parameters and their combination with practical problems have become an important research direction for machine learning in recent years [ 28 , 29 , 30 ].…”

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confidence: 99%

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

Fan

Zhu

et al. 2022

Foods

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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 RP2 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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