基于拉曼光谱技术的不同贮藏条件下明虾品质变化预测模型的研究

doi:10.3964/j.issn.1000-0593(2020)05-1607-07

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Abstract About the prawn’s freshness characteristics of quality deterioration, the research takes color (L^*, a^*, b^*), volatile base nitrogen(TVB-N), ph, and other quality indexes as the object of the study, and uses Raman nondestructive testing technology to select the spectral information of fresh prawn on the temperature of 4 ℃ and under -20 ℃, also makes the quick quantitative test by combining with ridge regression, partial least squares method and forward stepwise regression, establishes the quantitative mode of the quality index. And the spectral data preprocessing includes SG smoothing, background deduction, second order differential and standard normal variable transform, combines 4 types of preprocessing in a certain way and deals with the data by PCA dimension reduction technology, in order to select the best mode. The result shows that, when using ridge regression to establish the quantitative mode of color (a^*, b^*), under the combined pretreatment mode, the modeling centralization R are 0.983 and 0.973 respectively, RMSE are 0.114 and 0.179 respectively; the forecast concentration R are 0.513 and 0.564 respectively, RMSE are 0.615 and 0.918 respectively, the accuracy of the modeling set is much higher than that of the prediction set, which indicates that there exists over-fitting, and the over-fitting decreases after dimension reduction by PCA, but the prediction effect of prediction sets is not satisfactory; partial least squares method and the ridge regression are about the same on the accuracy of indicator modeling sets, the accuracy of partial least squares method is lower on the prediction sets. After PCA dimension reduction, the related coefficient of partial index modeling sets decrease, the root mean square error increases, and the prediction accuracy decreases. The final result shows that, after 4 types of preprocessing, the mode of forward stepwise regression is the best, the modeling centralization L^*, a^*, b^*, pH, TVB-N index R are 0.904, 0.885, 0.864, 0.934, 0.940 respectively, RMSE are 1.141, 0.280, 0.535, 0.131, 2.345 respectively; the forecast concentration R are 0.863, 0.850, 0.859, 0.900, 0.916 respectively, RMSE are 1.394, 0.406, 0.605, 0.194, 2.734 respectively, the modeling effect is good. Therefore, it is practicable to use the Raman spectroscopy technology, combining with forwarding stepwise regression to quick test the prawn’s L^*, a^*, b^*, pH and volatile base nitrogen content, which provide meaningful guidance for the application of Raman technology in prawn quality detection.

Key words：Raman spectroscopy technology; Prawn; Storage condition; Quality index; Prediction model

Received: 2019-04-28 Accepted: 2019-08-16

ZTFLH:

O657.3

Corresponding Authors: ZHU Cheng E-mail: pzhch@cjlu.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)05-1607-07 OR https://www.gpxygpfx.com/EN/Y2020/V40/I05/1607