Qualitative and Quantitative Analyses of Egg Yolks Adulterated With
Sudan Red Ⅰ Based on Near-Infrared Spectroscopy
YIN Wei-jian1, WEN Yu-kuan1, DONG Gui-mei1, YANG Ren-jie1, LI Liu-an2, YU Xiao-xue2, YU Ya-ping1*
1. College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China
2. College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, Tianjin 300392, China
Abstract：Sudan Red Ⅰ is an illegal food colorant that can enhance the color intensity of egg yolks. Rapid detection of Sudan Red Ⅰ in egg yolks is of great significance. In this study, a near-infrared spectrometer was used to collect spectral data from 60 unadulterated egg yolk samples and 102 adulterated samples containing Sudan Red Ⅰ at concentrations ranging from 0.5 to 20 mg·(100 g)-1. After spectral analysis and data preprocessing, the sample dataset was divided into training and test subsets at a 3∶1 ratio. Qualitative and quantitative models were then built to detect Sudan Red Ⅰ in egg yolks. The models were evaluated using prediction accuracy, calibration, and prediction R? coefficients (R2c/R2p), and root mean square errors (RMSEC/RMSEP). For qualitative analysis, the Partial Least Squares Discriminant Analysis (PLS-DA) algorithm was used to classify egg samples as adulterated with Sudan Red Ⅰ. After data preprocessing using the Standard Normal Variate (SNV) transformation, the model achieved optimal performance, with accuracy rates of 98.3% for the training set and 97.6% for the test set. For quantitative analysis, the Competitive Adaptive Reweighted Sampling (CARS) method was first used to select characteristic wavelengths from the spectral data. Then, regression models were established using the linear Partial Least Squares Regression (PLSR) and the nonlinear Back-Propagation Artificial Neural Network (BP-ANN) algorithms to predict Sudan Red Ⅰ content. The PLSR model showed better performance, with R2c of 0.98, R2p of 0.98, RMSEC of 0.79, and RMSEP of 0.80. The results demonstrate that near-infrared spectroscopy enables rapid and convenient detection of Sudan Red Ⅰ in egg yolks.
Key words：Egg yolks; Sudan Red Ⅰ; Near-infrared spectroscopy; Selection of characteristic wavelengths; Partial least squares algorithm; BP artificial neural network
尹伟鉴，温裕宽，董桂梅，杨仁杰，李留安，于晓雪，于亚萍. 基于近红外光谱鸡蛋蛋黄掺杂苏丹红Ⅰ定性与定量分析[J]. 光谱学与光谱分析, 2025, 45(12): 3415-3421.
YIN Wei-jian, WEN Yu-kuan, DONG Gui-mei, YANG Ren-jie, LI Liu-an, YU Xiao-xue, YU Ya-ping. Qualitative and Quantitative Analyses of Egg Yolks Adulterated With
Sudan Red Ⅰ Based on Near-Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(12): 3415-3421.
[1] Roseland J M, Somanchi M, Bahadur R, et al. Journal of Food Composition and Analysis, 2020, 86: 103379.
[2] Réhault-Godbert S, Guyot N, Nys Y. Nutrients, 2019, 11(3): 684.
[3] Rebane R, Leito I, Yurchenko S, et al. Journal of Chromatography A, 2010, 1217(17): 2747.
[4] Khodabakhshian R, Bayati M R, Emadi B. Vibrational Spectroscopy, 2022, 120: 103372.
[5] TANG Jun, ZENG Kai, LIU Zhi-bin(唐 俊, 曾 凯, 刘志斌). Food Research and Development(食品研究与开发), 2019, 40(11): 174.
[6] MacArthur R L, Teye E, Darkwa S. Vibrational Spectroscopy, 2020, 110: 103129.
[7] XU Zhi-xiang, WANG Shuo, FANG Guo-zhen(徐志祥, 王 硕, 方国臻). Chinese Condiment（中国调味品）, 2006, (9): 49.
[8] CHANG Zhong-jie, WANG Jun, XIAO Jing, et al（常重杰, 王 君, 肖 静, 等）. Journal of Hydroecology（水生态学杂志）, 2012, 33(4): 113.
[9] FENG Yin-jie, ZHOU Xiao-qing, QIAO Yong-sheng, et al(冯寅洁, 周小清, 乔勇升, 等). Science and Technology of Food Industry(食品工业科技), 2020, 41(5): 232.
[10] Adjei J K, Ahormegah V, Boateng A K, et al. Heliyon, 2020, 6(10): e05243.
[11] CHEN Jing, LIU Zhao-jin, AN Bao-chao, et al(陈 静, 刘召金, 安宝超, 等). Chinese Journal of Analytical Chemistry(分析化学), 2013, 41(9): 1418.
[12] Arrizabalaga-Larrañaga A, Epigmenio-Chamú S, Santos F J, et al. Analytica Chimica Acta, 2021, 1164: 338519.
[13] LIU Jun, GONG Zhen-bin(刘 珺, 弓振斌). Chinese Journal of Chromatography(色谱), 2012, 30(6): 624.
[14] Di Anibal C V, Marsal L F, Callao M P, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2012, 87：135.
[15] Di Anibal C, Rodriguez M S, Albertengo L. Food Analytical Methods, 2014, 7(5): 1090.
[16] Haughey S A, Galvin-King P, Ho Y-C, et al. Food Control, 2015, 48：75.
[17] ZHANG Wei-wei, LIU Ling-ling, WU Yan-wen, et al(张玮玮, 刘玲玲, 武彦文, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(4): 958.
[18] Kar S, Tudu B, Bandyopadhyay R. Journal of Food Science and Technology, 2024, 61(10): 1955.
[19] Chen Q, Guo Z, Zhao J, et al. Journal of Pharmaceutical and Biomedical Analysis, 2012, 60：92.
[20] Cen H, He Y. Trends in Food Science & Technology, 2007, 18(2): 72.
[21] Rukundo I R, Danao M-G C, Weller C L, et al. Journal of Near Infrared Spectroscopy, 2020, 28(2): 81.
[22] Bian X, Wang K, Tan E, et al. Chemometrics and Intelligent Laboratory Systems, 2020, 197: 103916.
[23] Li H, Liang Y, Xu Q, et al. Analytica Chimica Acta, 2009, 648(1): 77.
[24] PENG Hai-gen, JIN Ying, ZHAN You-guo, et al(彭海根, 金 楹, 詹莜国, 等). Journal of Instrumental Analysis(分析测试学报), 2020, 39(10): 1305.
[25] NIE Li-xing, DAI Zhong, MA Shuang-cheng, et al(聂黎行, 戴 忠, 马双成, 等). Chinese Journal of Experimental Traditional Medical Formulae(中国实验方剂学杂志), 2017, 23(11): 45.
[26] Xing Z, Du C W, Shen Y Z, et al. Computers and Electronics in Agriculture, 2021, 191: 106549.
[27] Trentanni Hansen G J, Almonacid J, Albertergo L et al. Food Additives & Contaminants: Part A, 2019, 36(8): 1163.
[28] Zhao Q N, Lv X Z, Jia Y X, et al. Poultry Science, 2018, 97(6): 2239.
