Abstract：Hyperspectral imaging is a new non-destructive testing technology which combines imaging and spectrum. It is an indirect analysis method. The establishment of the analytical model is critical, which needs to comprehensively consider the interaction among various modeling factors. This paper aimed to investigate the optimization of visible/near-infrared hyperspectral quantitative detection model for protein content in chilled Tan mutton based on the Box-Behnken design. The hyperspectral images of meat samples were collected by the visible/near-infrared hyperspectral imaging system. The reflectance spectral characteristics of chilled Tan mutton were analyzed. The protein contents were regarded as an external disturbance. The dynamic change of spectral signal was studied by two-dimensional correlation spectra under disturbance conditions. The synchronization spectra and autocorrelation spectra were analyzed to find the sensitive variables related to protein contents. Multiplicative scatter correction (MSC) and standard normalized variate (SNV) were used to extract useful signal and optimize the spectral quality of selected characteristic bands. In order to achieve data dimensionality reduction and reduce the burden of processing a large number of spectral data, competitive adaptive reweighted sampling (CARS) and variable combination population analysis (VCPA) were used to perform secondary extracted feature wavelengths. Extraction method, spectral pretreatment and multivariate calibration methods were factors, and each factor had 3 different levels. The response surface experimental design was used to build an optimal detection system for protein content analysis of chilled Tan mutton. The results indicated that there were strong autocorrelation peaks at 473, 679, 734 and 814 nm. The feature bands in the range of 473~814 nm were a sensitive area of protein detection in mutton. MSC and SNV could effectively eliminate the interference of scattering. Sixteen and nine characteristic wavelengths were selected by CARS and VCPA from 2DCOS, respectively. The factors in descending order affecting the predictive performance of the model were detection band, preprocessing method and modeling method. The 2DCOS-SNV-LSSVM model was selected with a high operating rate and prediction capability (Rc=0.858 8, RMSEC=0.005 8; Rp=0.860 4, RMSEP=0.005 7). The results showed that the application of the box-behnken method in the optimization of visible/near-infrared hyperspectral (400~1 000 nm) modeling parameters could effectively realize the intelligent monitoring and fast non-destructive analysis of Tan mutton quality. It could also provide a theoretical reference for the optimization of the model and improving prediction accuracy.
Key words：Visible-near infrared hyperspectral; Box-Behnken design; Two-dimensional correlation spectra; Tan mutton; Protein
樊奈昀，刘贵珊，张晶晶，张 翀，袁瑞瑞，班晶晶. Box-Behnken法冷鲜滩羊肉蛋白质的高光谱模型优化[J]. 光谱学与光谱分析, 2021, 41(03): 918-923.
FAN Nai-yun, LIU Gui-shan, ZHANG Jing-jing, ZHANG Chong, YUAN Rui-rui, BAN Jing-jing. Hyperspectral Model Optimization for Protein of Tan Mutton Based on Box-Behnken. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 918-923.
[1] Balage J M, Amigo J M, Antonelo D S, et al. Meat Science, 2018, 143: 30.
[2] Ma J, Cheng J, Sun D, et al. LWT, 2019, 110: 338.
[3] Cheng W, Sun D, Pu H, et al. Journal of Food Engineering, 2019, 246: 200.
[4] Zheng X, Li Y, Wei W, et al. Meat Science, 2019, 149: 55.
[5] HUANG Liu-rong, CHEN Tian, ZHAO Yun-shu, et al(黄六容, 陈 甜, 赵匀淑, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(12): 294.
[6] Guo T, Huang M, Zhu Q, et al. Journal of Food Engineering, 2018, 218: 61.
[7] Kamruzzaman M, Makino Y, Oshita S. Meat Science, 2016, 116: 110.
[8] Siedliska A, Baranowski P, Zubik M, et al. Postharvest Biology & Technology, 2018, 139: 115.
[9] WANG Wen-xiu, PENG Yan-kun, FANG Xiao-qian, et al(王文秀, 彭彦昆, 房晓倩, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(7): 2094.
[10] Cheng W, Sun D, Pu H, et al. Food Chemistry, 2018, 248: 119.
[11] Feng C, Makino Y, Yoshimura M, et al. Food Chemistry, 2018, 264: 419.
[12] Li H D, Liang Y Z, Xu Q S, et al. Analytica Chimica Acta, 2009, 648(1)：77.
[13] ZHAO Huan, HUAN Ke-wei, ZHENG Feng, et al(赵 环, 宦克为, 郑 峰, 等). Journal of Changchun University of Science and Technology(长春理工大学学报), 2016, 39(5): 51.
