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| Identification and Restoration of Pseudo-Hydrolyzed Animal Protein of Lacteus Camelus Based on iPLS Model of Near-Infrared Measurement Spectrum of 6 mm Detection Plate |
| YUAN Ke-yan 1, WANG Rong2, WANG Xiang-xiang2, XUE Li-ping2, YU Li2* |
1. Huhhot City Inspection and Testing Center，Huhhot 010018，China
2. School of Environment and Energy Engineering, Anhui Jianzhu University，Hefei 230601，China
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Abstract Camel milk has gradually become a health care dairy product trusted by consumers because of its high nutrition and unique health care effects. However, due to the small output of camel milk and its high market value, this provides a profitable operating space for the hybridization of camel milk. With the further strengthening of the state’s crackdown on the illegal addition of melamine in dairy products, inferior hydrolyzed animal protein has gradually become a new favorite for counterfeiting in dairy products due to its high protein content, and low price and strong concealment of illegal addition. Preventing and cracking down on fake and inferior hydrolyzed animal protein in camel milk has become a huge challenge faced by consumers and practitioners in the camel milk industry. How to detect fake and low-cost animal hydrolyzed protein in camel milk has become an urgent problem to be developed. With the rapid development of near-infrared spectral analysis technology in the past ten years, near-infrared spectral analysis technology has gradually become widely used in many fields such as petrochemical, food, agriculture, medicine, etc. widely used. In this paper, the near-infrared spectrometer with a 6 mm sample dish was used to measure the animal hydrolyzed protein of camel milk ginseng with different contents to obtain the original spectral matrix. The original spectra were preprocessed by order derivative+SNV, SG+SNV and other methods, and the 10 principal component regression models of the global spectrum were used for evaluation. By adjusting the calculation scale of principal components, the optimal calculation scale of principal components is determined to be 10. By adjusting the number of interval divisions and using the R2 and RMSECV values of the corresponding model as evaluation criteria, the optimal number of interval divisions is finally determined to be 30. Through experiments and calculations, the principal component score of 6 was obtained in the range of 7 887.87~7 590.87 cm-1, the correlation coefficient was 0.945 1, and the RMSECV value was 0.200 1, was the best prediction model for camel milk adulterated hydrolyzed animal protein. After internal interactive verification, the modified model can well predict the situation of adulterated and hydrolyzed animal protein in recovered camel milk in this system, which can provide technical reference for research in related fields.
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Received: 2021-08-03
Accepted: 2022-03-22
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Corresponding Authors:
YU Li
E-mail: ronger@ahjzu.edu.cn
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[1] Bouhaddaou I S, Chabi R R, Errachid I F，et al. The Scientific World Journal, 2019, 2019: 2517293.
[2] RONG Han, GAN Lu-jing, WANG Lei(荣 菡，甘露菁，王 磊). China Condiment(中国调味品), 2019, 44(12): 144.
[3] FAN Rui, SUN Xiao-kai, YANG Chen, et al(范 睿, 孙晓凯, 杨 晨, 等). Food Industry(食品工业), 2017, 38(16): 253.
[4] WEI Yu-juan, LI Lin, YANG Xiao-ya, et al(魏玉娟，李 琳，杨笑亚，等). China Dairy Industry(中国乳品工业), 2016, 44(10)：48.
[5] Chen H, Tan C, Lin Z, et al. Spectrochim Acta A, 2017, 173: 832.
[6] ZHANG Hang, LIU Guo-hai, JIANG Hui, et al(张 航，刘国海，江 辉，等). Progress in Laser and Optoelectronics(激光与光电子学进展), 2017，(2): 314.
[7] Frimani P, De Luca S, Bucci R, et al. Food Control, 2019, 100: 292.
[8] Tejerina David, Contador Rebeca, Ortiz Alberto. Food Chemistry, 2021, 356: 129733.
[9] Chen Hui, Tan Chao, Lin Zan, et al. Computers in Biology and Medicine, 2013, 43(7): 865.
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