近红外光谱的河蟹新鲜度快速检测研究

doi:10.3964/j.issn.1000-0593(2020)01-0189-06

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Abstract The freshness of river crab is the most important factor that most consumers consider when buying. Total volatile base nitrogen (TVB-N) is a commonly used international index for evaluating meat freshness, However, its detection process is cumbersome and time-consuming, which can not meet the urgent needs of the current market for rapid and objective evaluation of river crab freshness. Therefore, it is an urgent problem to establish a rapid method for detecting freshness of river crabs. A total of 126 crabs purchased from aquatic market were rapidly transported to the laboratory by polyethylene oxygenation bag. After treatment on a clean bench, the crabs were divided into 42 experimental samples, with 3 fresh crabs in each sample; 42 experimental samples were stored in a constant temperature biochemical incubator at low temperature of 4 ℃. 6 crab samples were taken from the incubator on time every day for spectral data collection and freshness index determination for 7 days. In this paper, near infrared spectroscopy (NIRS) was used to evaluate the freshness of river crabs stored at different time, and total volatile base nitrogen (TVB-N) was used as an index to evaluate the freshness of crabs. Firstly, by comparing influence on the model prediction effect of 5-fold Cross Validation, Kennard-stone algorithm and sample set partitioning based on joint X-Y distance algorithm, finally, the 5-fold CrossValidation algorithm was used to divide the samples. 32 samples were used as training sets for model building, and the remaining 10 samples were used as test sets for model testing. Then, on the basis of dividing the samples by five fold cross validation algorithm, wavelet transform (WT), Savitzky-Golay smoothing, first derivative (Db1), second derivative (Db2) and wavelet transform (WT) combined with Savitzky-Golay smoothing were used to pretreat. Wave transform (WT) pretreatment was the best spectral pretreatment method, which eliminated the useless information in the spectrum and improved the signal-to-noise ratio. Once more, on the basis of the WT pretreatment, principal component analysis (PCA) and successive projection algorithm (SPA) were used to extract spectral feature bands, and the principal component analysis (PCA) was used as the optimal wavelength selection method by comparing the model prediction effect. With the selected 16 feature bands as the input of the model, which not only improved the running speed of the model, but also improve the stability of the model. Finally, after PCA feature extraction, by using partial least squares regression (PLSR) and multiple linear regression (MLR) built the TVB-N quantitative prediction model, by comparing the two kinds of model prediction effect to determined the partial least squares regression (PLSR) model for the optimal modeling method, this paper finally determine the optimal model based on WT-PCA-PLSR model, model prediction determination coefficient R² was 0.89, and the root mean square error of prediction RMSEP was 3.00. In conclusion, the prediction model established in this study has a high accuracy, and this method can realize the rapid detection of the freshness of river crabs, and has a good market application prospect.

Key words：Near infrared spectroscopy; Total volatile base nitrogen; Freshness; Wavelet transform; Principal component analysis; Partial least squares regression

Received: 2018-11-09 Accepted: 2019-03-24

ZTFLH:

O433.4

Corresponding Authors: ZHANG Xiao-shuan E-mail: zhxshuan@cau.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)01-0189-06 OR https://www.gpxygpfx.com/EN/Y2020/V40/I01/189