• Spectroscopy and Spectral Analysis
  • Vol. 41, Issue 8, 2591 (2021)
SUN Zong-bao*, LI Jun-kui, LIANG Li-ming, ZOU Xiao-bo*;, LIU Xiao-yu, NIU Zeng, and GAO Yun-long
Author Affiliations
  • School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
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    DOI: 10.3964/j.issn.1000-0593(2021)08-2591-07 Cite this Article
    Zong-bao SUN, Jun-kui LI, Li-ming LIANG, Xiao-bo ZOU, Xiao-yu LIU, Zeng NIU, Yun-long GAO. Prediction and Distribution Visualization of Salmon Quality Based on Hyperspectral Imaging Technology[J]. Spectroscopy and Spectral Analysis, 2021, 41(8): 2591 Copy Citation Text show less

    Abstract

    In this study, color, shear force and K value was used to evaluate the quality of salmon with different freeze-thaw times, and then predicted by hyperspectral imaging technology combined with chemometric methods. Besides, the prediction performance of the PLS model developed with characteristic variables was compared and discussed to select the optimal variable selection method for color, shear force and K value. The prepared salmon samples with different freeze-thaw times were scanned and analyzed to obtain hyperspectral data and the true values of quality indicators (color, shear force, K value). Afterwards, six different pretreatment methods were used to reduce dark current and noise interference in the spectral data. The competitive adaptive reweighting algorithm (CARS), interval variable iterative space shrinkage approach (iVISSA), and iVISSA-CARS algorithms were applied to screen out variables related to the indicators to improve the prediction performance of the model. The optimal variable selection method was determined according to the prediction performance of the PLS model built by the characteristic variables screened by the three-wavelength selection algorithms. The result exhibited that the 1st Der-CARS-PLS model developed by 51 characteristic variables related to a* possessed the best prediction with Rc of 0.931 6, Rp of 0.929 7, RMSECV and RMSEP of 0.72 and 0.74, respectively. Similarly, in shear force prediction, 2nd Der proved to be the best pretreatment method and 2nd Der -CARS -PLS model developed by 61 characteristic variables displayed the best prediction with Rc of 0.885 3, Rp of 0.860 9, RMSECV and RMSEP of 0.69 N and 0.90 N respectively. Besides, the N-CARS-PLS model built by 51 characteristic variables achieved the best predictive effect on K value and obtained Rc of 0.951 3, Rp of 0.946 0, RMSECV and RMSEP of 1.33 and 1.53, respectively. It indicates that CARS can effectively extract variables related to feature indicators and improve the prediction performance of the PLS model. Besides, the combined algorithm iVISSA-CARS-PLS also achieved a significant results in the prediction of the three indicators. The Rp of the test set was 97.48%, 97.02% and 98.98% of the CARS-PLS prediction model. In comparison, the number of variables used was only 60.78%, 62.29% and 60.78% of CARS-PLS, indicating that the variable selection combined algorithm greatly reduces the amount of data. The CARS-PLS and iVISSA-CARS-PLS models of the three indicators show higher prediction performance than iVISSA, which indicates that the feature variable selection strategy of CARS is more advantages than iVISSA in predicting of the above three quality indicators of salmon. Using the optimized PLS model, the visual distribution map of salmon quality indexes with different freezing and thawing time was constructed in the form of a pseudo color images, which provided more detailed and intuitive information for understanding the quality of salmon. In general, the combination of hyperspectral imaging combined with chemometrics, can accurately and non-destructively determine the quality indicators in salmon. This study can provide the same theoretical reference for the simultaneous rapid detection of multiple quality indicators of salmon.