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| Rapid Determination of TBARS Contents in Tan Mutton Using Hyperspectral Imaging |
| FAN Nai-yun, LIU Gui-shan*, ZHANG Jing-jing, YUAN Rui-rui, SUN You-rui, LI Yue |
School of Food & Wine, Ningxia University, Yinchuan 750021, China
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Abstract Thiobarbituric acid reactive substances (TBARS) is the main chemical information that can effectively characterize the lipid oxidation degree in meat. A combination of hyperspectral technology with 2DCOS was investigated to develop the quantitative analysis model for assessing TBARS contents. The hyperspectral images of Tan mutton were collected in the spectral range of 400~1 000 nm. The regions of interest were manually set on the hyperspectral image of the, sample and the raw spectra data were extracted by ENVI 4.8 software. Partial least squares regression (PLSR) was established to correlate the spectra data with measured TBARS values. The kennard-Stone (KS) method was used to divide the whole data set to validate the calibration model. Savitzky-Golay (SG), de-trending and SG+de-trending were used to correct raw data for eliminating the interference information. The TBARS contents were regarded as a perturbation. Two-dimensional correlation spectra and their slice spectra were analyzed to determine the key variables associated with perturbation. Variable combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) was used to select variables from full spectra and 2DCOS analysis. PLSR algorithm was used to establish the hyperspectral quantitative analysis model of TBARS contents based on key variables. The result showed the PLSR model established by de-trending pretreatment presented good performance (R2C=0.874, RMSEC=0.106 mg·kg-1, R2P=0.853, RMSEP=0.139 mg·kg-1). The auto-correlation peaks related to TBARS values were observed at 579, 699, 756 and 867 nm. The wavelengths in the spectra range of 579～867 nm were the research area for detecting of TBARS contents. VCPA, CARS and SPA extracted 7, 16, 20, 8, 24 and 14 key variables from full spectra and 2DCOS analysis, respectively. Based on the accuracy and reliability of the obtained model, the model developed based on effective wavelengths selected by CARS from 2DCOS analysis could assess TBARS contents in an accurate and non-destructive manner. The quantitative model was: Y(TBARS)=-0.15+2.99λ588-7.01λ593+7.45λ598-6.14λ603+7.06λ612-8.25λ622+2.64λ631-4.18λ636+13.91λ646-11.3λ655+12.64λ675-8.51λ684-7.81λ689+1.08λ703-2.54λ713+5.47λ727+6.62λ742+5.69λ751+2.48λ775-1.93λ780-6.95λ790+7.09λ799-3.56λ809+1.82λ819 (R2C=0.857，RMSEC=0.113 mg·kg-1). The research demonstrates that 2DCOS provide new insights into variable selection in spectra analysis. The combination of hyperspectral technology and 2DCOS is feasible for non-destructive monitoring of TBARS contents in mutton.
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Received: 2021-02-22
Accepted: 2021-06-06
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Corresponding Authors:
LIU Gui-shan
E-mail: liugs2018@163.com
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[1] Cifuni G F, Conto M, Failla S. Journal of Food Engineering, 2016, 169: 85.
[2] Cheng W, Sørensend K M, Engelsen S B, et al. Journal of Food Engineering, 2019, 263: 311.
[3] Cheng L J, Liu G S, He J G, et al. Meat Science, 2020, 167: 107988.
[4] Wang C X, Wang S L, He X G, et al. Meat Science, 2020, 169: 108194.
[5] Yuan R, Liu G S, He J G, et al. Journal of Food Science, 2020, 85: 1403.
[6] Cheng W, Sun D W, Pu H B, et al. Journal of Food Engineering, 2019, 246: 200.
[7] Cheng W, Sun D W, Pu H B, et al. Food Chemistry, 2018, 248: 119.
[8] CHENG Yi-yun, ZHAO Rui-ying, QI Tian-ci, et al(陈奕云, 赵瑞瑛, 齐天赐, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(7): 2133.
[9] WANG Wen-xiu, PENG Yan-kun, SUN Hong-wei, et al(王文秀, 彭彦昆, 孙宏伟, 等). Food Science(食品科学), 2018, 39(18): 273.
[10] Cheng L J, Liu G S, He J G, et al. Food Chemistry, 2020, 314: 128351.
[11] 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: 51.
[12] Xiong Z J, Sun D W, Pu H B, et al. Food Chemistry, 2015, 179: 175.
[13] Li H D, Liang Y Z, Xu Q S, et al. Analytica Chimica Acta, 2009, 648: 77.
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