猪肉理化指标的近红外光谱无损检测

doi:10.3964/j.issn.1000-0593(2024)05-1346-08

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Abstract China is the world's largest pork producer and consumer. The quality of pork affects the quality of protein intake for people. There needs to be more effective rapid non-destructive testing methods to cope with the huge testing needs. In order to rapidly determine volatile salt nitrogen (TVB-N), pH and moisture content of frozen pork and to propose a new method for pork quality testing, this paper uses near-infrared spectroscopy combined with chemometric methods to establish mathematical models for TVB-N, pH and moisture content of frozen pork. The NIR spectral data were collected and combined with chemometric tests to obtain the measured values of TVB-N, pH and moisture content. ~5 000 cm^-1 region, while the absorption peaks around 8 600～8 450 cm^-1 were significantly smaller than the other absorption peaks. The SPXY (sample set partitioning based on joint X-Y distances) algorithm was used to partition the data set into a training set and a test set in the ratio of 3∶1. The abnormal data were removed using Monte Carlo cross-validation (MCCV) and a partial least squares regression (PLSR) was used to establish The regression relationships of TVB-N, pH, water content and full-band spectral information were established by partial least squares regression (PLSR), and the raw spectra were pre-processed using data centering, Savitzky-Golay(S-G) first-order derivatization, S-G second-order derivatization, direct difference second-order derivatization and multiple scattering corrections to explore the appropriate pre-processing methods. The results show that the data centering, direct differential second-order derivation and second-order derivation achieve good experimental results, so the combination of competitive adaptive reweighted sampling (CARS), uninformative variables elimination (UVE), and multiplicative scatter correction (MSC) has been applied. The PLSR feature band model was developed and analysed by combining the competitive adaptive reweighted sampling (CARS), uninformative variables elimination (UVE) and successive projections algorithm (SPA). The results showed that the prediction models for TVB-N, pH and water content had excellent performance when the structures were data centered-CARS-PLSR, direct difference second-order derivative-CARS-PLSR and second-order derivative-CARS-PLSR, respectively, where the training set correlation coefficients R_C were 0.947 1, 0.998 8 and 0.997 1, respectively, and the root mean square errors (RMSE) were 1.208 8, 0.008 7 and 0.001 5, respectively; the test set correlation coefficients R_P were 0.927 5, 0.963 0 and 0.945 9, respectively, and the RMSE were 1.683 6, 0.051 7 and 0.005 6, respectively. In summary, it can be seen that the NIR band region is significantly correlated with pork TVB-N, pH and moisture content, and the use of NIR spectra can The NIR spectra can be used to predict TVB-N, pH and moisture content in pork accurately.

Key words：Pork; Near-infrared spectroscopy; Volatile salt-based nitrogen(TVB-N); pH; Water content

Received: 2023-02-24 Accepted: 2023-05-05

ZTFLH:

O657.33

Corresponding Authors: WANG Qiao-hua E-mail: wqh@mail.hzau.edu.cn

Cite this article:

LIU Yu-ming,WANG Qiao-hua,CHEN Yuan-zhe, et al. Non-Destructive Near-Infrared Spectroscopy of Physical and Chemical Indicator of Pork Meat[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1346-1353.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2024)05-1346-08 OR https://www.gpxygpfx.com/EN/Y2024/V44/I05/1346

[1] TANG Lu-yun, QI Chun-jie(汤路昀, 祁春节). Chinese Journal of Agricultural Resources and Regional Planning(中国农业资源与区划), 2021, 42(7): 37.
[2] WU Yi-ming(吴艺鸣). Modern Food Science and Technology(现代食品科技), 2020, 36(3): 127.
[3] LI Hua-jian, CHEN Tao, YANG Bo-ruo, et al(李华健, 陈韬, 杨波若, 等). Food Science(食品科学), 2021, 42(3): 14.
[4] Jang J H, Kang H J, Adede J I, et al. Food Chemistry, 2023, 403: 134366.
[5] Cheng J, Sun J, Yao K, et al. LWT—Food Science and Technology, 2022, 165: 113778.
[6] Barlocco N, Vadell A, Ballesteros F, et al. Animal Science an International Journal of Fundamental & Applied Research, 2006, 82(1): 111.
[7] WANG Wen-xiu, PENG Yan-kun, WANG Fan, et al(王文秀, 彭彦昆, 王凡, 等). Food Science(食品科学), 2019, 40(14): 339.
[8] Sanchez P D C, Arogancia H B T, Boyles K M, et al. Applied Food Research, 2022: 100147.
[9] Perez-Palacios T, Caballero D, González-Mohíno A, et al. Journal of Food Engineering, 2019, 263: 417.
[10] Cáceres-Nevado J M, Garrido-Varo A, De Pedro-Sanz E, et al. Meat Science, 2021, 175: 108440.
[11] ZHAO Mao-cheng, WU Ze-ben, WANG Xi-wei, et al(赵茂程, 吴泽本, 汪希伟, 等). Transcations of the Chinese Society for Agricultural Machinery(农业机械学报), 2022, 53(3): 412.
[12] PENG Yan-kun, YANG Qing-hua, WANG Wen-xiu, et al(彭彦昆, 杨清华, 王文秀, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2018, 49(3): 347.
[13] Zhang Fan, Kang Tonghai, Sun Jianfeng, et al. Meat Science, 2022, 188: 108801.
[14] WANG Wen-xiu, PENG Yan-kun, XU Tian-feng, et al(王文秀, 彭彦昆, 徐田锋, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(12): 4001.
[15] Wang Wenxiu, Peng Yankun, Sun Hongwei, et al. Journal of Food Engineering, 2018, 237: 103.
[16] Adebayo S E, Hashim N, Abdan K, et al. Journal of Food Engineering, 2016, 169: 155.
[17] Mishra P, Verkleij T, Klont R. Infrared Physics & Technology, 2021, 113: 103643.
[18] GAO Sheng, WANG Qiao-hua, SHI Hang, et al(高升, 王巧华, 施行, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2021, 52(2): 308.
[19] Li Pao, Li Shangke, Du Guorong, et al. Food Science & Nutrition, 2020，8(5): 2543.
[20] Johnson J B, Thani P R, Naiker M. Chemical Papers, 2023, 77(4): 2009.
[21] Xie Anguo, Sun Dawen, Xu Zhongyue, et al. Talanta, 2015, 139(1): 208.
[22] Barbin D, Elmasry G, Sun D W, et al. Journal of Food Engineering, 2012, 110(1): 127.
[23] LIU Cui-ling, HU Yu-jun, WU Sheng-nan, et al(刘翠玲, 胡玉君, 吴胜男, 等). Journal of Food Science and Technology(食品科学技术学报), 2014, 32(5): 74.
[24] Mishra P, Biancolillo A, Roger J M, et al. TrAC Trends in Analytical Chemistry, 2020, 132: 116045.
[25] Monroy M, Prasher S, Ngadi M O, et al. Biosystems Engineering, 2010, 107(3): 271.
[26] Del Moral F G, Guillén A, Del Moral L G, et al. Journal of Food Engineering, 2009, 90(4): 540.
[27] Issued by: National Health and Family Planning Commission of the People's Republic of China, The State Food and Drug Administration(中华人民共和国国家卫生和计划生育委员会，国家食品药品监督管理总局发布). National Standard of the People's Republic of China. GB 2707—2016 Food Safety National Standard, Fresh (Frozen) Livestock and Poultry Products(中华人民共和国国家标准GB 2707—2016. 食品安全国家标准鲜（冻）畜、禽产品), 2016.
[28] Simone G, Dyson M J, Janssen R A J, et al. Advanced Functional Materials, 2020, 30(20): 1904205.
[29] Lee C C, Estrada R, Li Y Z, et al. Advanced Optical Materials, 2020, 8(17): 2000519.