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| Development of Predictive Models for Fatty Acids Content in Multiple Pork Muscle Sites Using Spectroscopic Techniques |
| ZHANG Sheng-jie1, ZHAI Chen2*, XING Wei-hai2, FENG Xiao-hui2, YANG Ying-kang2, YANG You-you2, SHI Chao3, YANG Zhou2, WANG Wen-xiu1* |
1. College of Food Science and Technology, Hebei Agricultural University, Baoding 071000, China
2. State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
3. Beijing Jitian Instrument Co., Ltd., Beijing 100016, China
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Abstract The fatty acids in pork are not only a concern for consumer health but also significantly impact the sensory quality, processing performance, and market competitiveness of the meat. With the progress in nutritional research and the development of the food industry, the rapid detection of fatty acid content in pork has become a crucial direction for enhancing meat quality and meeting the diverse demands of consumers. This study aimed to develop an on-site rapid detection method for fatty acids in multiple muscle tissues of three-way crossbred pigs, based on spectroscopic technologies. Portable near-infrared (NIR) and Raman spectrometers were used to collect spectral data from 14 muscle parts (e.g., longissimus dorsi, psoas major, and trapezius) of 10 three-way crossbred pigs at slaughterhouses. Simultaneously, gas chromatography was employed for the accurate quantification of 28 fatty acids and fatty acid groups. After preprocessing the spectra with a second derivative and standard normal variate (SNV) transformation, competitive adaptive reweighted sampling (CARS) was applied to select feature wavelengths, followed by the establishment of partial least squares (PLS) quantitative prediction models. The results demonstrated that models based on Raman spectral data outperformed those using NIR spectroscopy. Consequently, Raman spectroscopy was utilized to develop 28 prediction models for key indicators, including total fatty acids (FA), saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), C16:0, C18:0, and C18:1. Among these, 25 models achieved a prediction coefficient of determination (R2) greater than 0.85 and a residual predictive deviation (RPD) exceeding 2.5. Research indicates that establishing a prediction model for fatty acid content in multi-site muscle tissues using Raman spectroscopy is feasible. The development of this method provides technical support for predicting various fatty acid contents in pork at slaughter sites.
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Received: 2025-04-15
Accepted: 2025-08-28
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Corresponding Authors:
ZHAI Chen, WANG Wen-xiu
E-mail: zhaichen@caas.cn；cauwwx@hebau.edu.cn
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[1] Barnard N D, Nicholson A, Howard J L. Preventive Medicine, 1995, 24(6): 646.
[2] Mohd Hilmi Tan M I S, Jamlos M F, Omar A F, et al. Sensors, 2021, 21: 3052.
[3] Olsen E F, Baustad C, Egelandsdal B. Meat Science, 2010, 85(1): 1.
[4] Gjerlaug-Enger E, Kongsro J, Aass L, et al. Animal, 2011, 5(11): 1829.
[5] Zhou L J, Wu H, Li J T, et al. Meat Science, 2012, 90: 658.
[6] Andueza D, Listrat A, Durand D, et al. Meat Science, 2019, 158: 107910.
[7] Olsen E F, Rukke E O, Egelandsdal B, et al. Applied Spectroscopy, 2008, 62(9): 968.
[8] Lam S, Rolland D, Zawadski S, et al. Foods, 2023, 12: 1629.
[9] Afseth N K, Dankel K, Andersen P V, et al. Foods, 2022, 11: 962.
[10] Nilsson P M, Tuomilehto J, Rydén L. European Journal of Preventive Cardiology, 2019, 26(2S): 33.
[11] Hernández-Jiménez M, Revilla I, Hernández-Ramos P, et al. Food and Bioprocess Technology, 2025, 18: 737.
[12] Cozzolino D, Murray I. Journal of Near Infrared Spectroscopy, 2002, 10(1): 37.
[13] Barlocco N, Vadell A, Ballesteros F, et al. Animal Science, 2006, 82(1): 111.
[14] Berhe D T, Eskildsen C E, Lametsch R, et al. Meat Science, 2016, 111: 18.
[15] Zhang W, Ma J, Sun D W. Critical Reviews in Food Science and Nutrition, 2021, 61(16): 2623.
[16] Czamara K, Majzner K, Pacia M Z, et al. Journal of Raman Spectroscopy, 2015, 46(1): 4.
[17] Kim J E, Dunville K, Li J, et al. Endocrinology and Metabolism (Seoul), 2017, 32(2): 257.
[18] Jin X, Li S, Zhang W, et al. Applied Sciences, 2020, 10: 1520.
[19] ZHANG Zhen, WANG Ji-fen, LIU Jin-tong, et al(张 震, 王继芬, 刘津彤, 等). Journal of Instrumental Analysis(分析测试学报), 2023, 42(9): 1181.
[20] Wold S, Sjöström M, Eriksson L. Chemometrics and Intelligent Laboratory Systems, 2001, 58(2): 109.
[21] Li H D, Liang Y Z, Xu Q S, et al. Analytica Chimica Acta, 2009, 648(1): 77.
[22] BAI Jing, ZANG Ming-wu, QIAO Xiao-ling, et al(白 京, 臧明伍, 乔晓玲, 等). Food Science(食品科学), 2023, 44(8): 301.
[23] García Martím J F. Sensors, 2022, 22(8): 2831.
[24] Lintvedt T A, Andersen P V, Afseth N K, et al. Talanta, 2023, 254: 124113.
[25] Fisher R A. Biometrika, 1915, 10(4): 507.
[26] Fisher R A. Metron, 1921, 1(4): 3.
[27] Shoko P T, Landry J D, Blanch E W, et al. Journal of Food Composition and Analysis, 2025, 139: 107069.
[28] Fowler S M, Ponnampalam E N, Schmidt H, et al. Meat Science, 2015, 110: 70. |
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