Quantitative Detection of Mutton Hardness Based on Twice Iterative Monte Carlo Method
BAI Xue-bing1, LI Xin-xing1, ZHANG Xiao-shuan2, LUO Hai-ling3, FU Ze-tian2*
1. Beijing Laboratory of Food Quality and Safety, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. College of Engineering, China Agricultural University, Beijing 100083, China
3. State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100083, China
Abstract：Mutton, as a kind of meat with high protein content and low fat and cholesterol content, is becoming more and more popular with consumers. The demand for mutton is on the rise. According to the National Bureau of Statistics, China’s mutton production rose from 6.27% to 9.02% from 2012 to 2019. This study proposed a quantitative detection PLSR model of mutton hardness based on the twice iterative Monte Carlo (MC) method. In this study, the Image-λ-V10E-H camera of the GaiaSorter hyperspectral sorter was used to collect the hyperspectral data of mutton samples at 400~950 nm, and the Image-λ-N17E camera was used to collect the hyperspectral data of mutton samples at 900~1 650 nm. Firstly, the study compared and analyzed four spectral pretreatment methods (S-G smoothing, 2 derivations, MSC and SNV) in eliminating interference factors, such as noise and baseline drift. Then, in the first MC sampling, the samples were divided into normal samples, suspicious samples and abnormal samples according to the 2.5 and 3 times the means of the prediction error means and standard deviations of each sample. The second MC sampling was performed based on rejecting abnormal samples, retaining and labeling suspicious samples. The new abnormal samples were eliminated by 3 times of the means of the prediction error means and standard deviations of each sample. Finally, the PLSR model based on the full wavelengths and the characteristic wavelengths extracted by the regression coefficient method (RC) were established and analyzed. The experiment results show that the twice iterative Monte Carlo method proposed in the study could abnormal samples, optimize the sample set, and provide a good foundation for modeling. With MSC as the spectral preprocessing algorithm, the PLSR model based on 400~950 and 900~1 650 nm hyperspectral data was superior to the other three spectral preprocessing algorithms R2P=0.947 2 and 0.978 3，RMSEP=47.789 9 g and 30.590 1 g. And, the accuracy and stability of the PLSR model based on 900~1 650 nm were significantly better than that based on 400~950 nm. 14 characteristic wavelengths (410, 438, 450, 464, 539, 558, 612, 684, 701, 734, 778, 866, 884, 935 nm) and 10 characteristic wavelengths (915, 949, 1 085, 1 156, 1 206, 1 262, 1 318, 1 384, 1 542 and 1 580 nm) of mutton hardness were selected by RC algorithm from 900~1 650 and 400~950 nm. The PLSR model based on 900~1 650 nm was the optimal model for predicting the hardness of mutton with R2P=0.985 0 and RMSEP=24.397 0 g. In conclusion, the PLSR model based on the twice iterative MC algorithm can effectively predict the changing trend of mutton hardness during cold storage and provide a reference for related research on non-destructive detection of mutton quality.
白雪冰, 李鑫星, 张小栓，罗海玲，傅泽田. 基于二次迭代Monte Carlo的羊肉硬度定量检测研究[J]. 光谱学与光谱分析, 2021, 41(07): 2057-2063.
BAI Xue-bing, LI Xin-xing, ZHANG Xiao-shuan, LUO Hai-ling, FU Ze-tian. Quantitative Detection of Mutton Hardness Based on Twice Iterative Monte Carlo Method. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2057-2063.
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