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Multi-sensor integration approach based on hyperspectral imaging and electronic nose for quantitation of fat and peroxide value of pork meat

Abstract

The study assessed the feasibility of merging data acquired from hyperspectral imaging (HSI) and electronic nose (e-nose) to develop a robust method for the rapid prediction of intramuscular fat (IMF) and peroxide value (PV) of pork meat affected by temperature and NaCl treatments. Multivariate calibration models for prediction of IMF and PV using median spectra features (MSF) and image texture features (ITF) from HSI data and mean signal values (MSV) from e-nose signals were established based on support vector machine regression (SVMR). Optimum wavelengths highly related to IMF and PV were selected from the MSF and ITF. Next, recurring optimum wavelengths from the two feature groups were manually obtained and merged to constitute “combined attribute features” (CAF) which yielded acceptable results with (Rc2 = 0.877, 0.891; RMSEC = 2.410, 1.109; Rp2 = 0.790, 0.858; RMSEP = 3.611, 2.013) respectively for IMF and PV. MSV yielded relatively low results with (Rc2 = 0.783, 0.877; RMSEC = 4.591, 0.653; Rp2 = 0.704, 0.797; RMSEP = 3.991, 0.760) respectively for IMF and PV. Finally, data fusion of CAF and MSV was performed which yielded relatively improved prediction results with (Rc2 = 0.936, 0.955; RMSEC = 1.209, 0.997; Rp2 = 0.895, 0.901; RMSEP = 2.099, 1.008) respectively for IMF and PV. The results obtained demonstrate that it is feasible to mutually integrate spectral and image features with volatile information to quantitatively monitor IMF and PV in processed pork meat.

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Acknowledgments

This work was sponsored by the National Key Research and Development Program of China (Grant/Award number: 2017YFD0400100)

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Correspondence to Xingyi Huang or Xiaoyu Tian.

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Aheto, J.H., Huang, X., Tian, X. et al. Multi-sensor integration approach based on hyperspectral imaging and electronic nose for quantitation of fat and peroxide value of pork meat. Anal Bioanal Chem 412, 1169–1179 (2020). https://doi.org/10.1007/s00216-019-02345-5

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Keywords

  • Hyperspectral imaging
  • Electronic nose
  • Intramuscular fat, peroxide value
  • Data fusion