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| Nondestructive Identification of Egg Yolk Color Based on Near Infrared Spectrum and Multivariate Data Processing |
| WEN Yu-kuan1, DONG Gui-mei1, LI Liu-an2, YU Xiao-xue2, YU Ya-ping1* |
1. College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300384, China
2. College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, Tianjin 300384, China
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Abstract Yolk color is an important indicator of egg quality, and consumers prefer to buy eggs with darker yolk color. Currently, the commonly used method involves physically opening the egg to distinguish the yolk color using the Roche fan method, so the research on non-destructive identification of yolk color is significant. This paper mainly studies the non-destructive identification method of yolk color for eggs with different eggshell colors. The data is collected by near-infrared spectroscopy. Then, the qualitative classification prediction model is established by using a chemometry algorithm. The components affecting egg yolk color are analyzed to find the functional groups corresponding to the spectral absorption peak. This study collected the NIR spectral data of 90 pink and 89 white eggs using the Roche fan method to record yolk color and establish qualitative classification models. The samples were divided into correction sets and prediction sets according to 2∶1, and prediction models were established for single-color and mixed-color samples, respectively. Linear (partial least square discriminant PLS-DA, linear discriminant analysis LDA) and nonlinear (convolutional neural network CNN, extreme learning machine ELM) methods were used to establish the classification models along sidevarious pretreatment and wavelength screening methods. CARS feature wavelength screening method was used to screen 176 wavelength points of spectral data. Combining CARS wavelength screening, MSC, and second derivative pretreatment methods for 2 kinds of color eggshell samples, the accuracy of the test set reached 91.67% by the PLS-DA model. In contrast, the LDA model reached 98.11%. For the pink shell eggs, the accuracy of the test set is 100% by the PLS-DA model. For the white shell eggs, the accuracy of the PLS-DA model is 96.67%, while that of the LDA model is 100%. These results demonstrate the efficacy of linear methods in characterizing the egg yolk color from spectra. This method can not only meet the needs of consumers but also play a guiding role in feed feeding and control of farms.
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Received: 2024-05-09
Accepted: 2024-09-18
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Corresponding Authors:
YU Ya-ping
E-mail: yaping261@163.com
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[1] ZHENG Yan, XU Qing-cui, FAN Li-xia, et al（郑 颜, 胥清翠, 范丽霞, 等）. Food and Nutrition in China(中国食物与营养), 2022, 28(1): 45.
[2] Rakonjac S, Bogosavljevic B S, Škrbic Z, et al. Biotechnology in Animal Husbandry, 2018, 34(3): 335.
[3] Pal M, Molnár J. International Journal of Food Science and Agriculture, 2021, 5(1): 180.
[4] Drabik K, Karwowska M, Wengerska K, et al. Animals, 2021, 11(5): 1204.
[5] Cendron F, Currò S, Rizzi C, et al. Animals, 2022, 13(1): 77.
[6] Antova G A, Gerzilov V T, Petkova Z Y, et al. Journal of the Science of Food and Agriculture, 2019, 99(13): 5890.
[7] Nuningtyas Y F, Chang H L, Lin M J, et al. Earth and Environmental Science, 2020, 478(1): 012050.
[8] Saleh A A, Gawish E, Mahmoud S F, et al. Sustainability, 2021, 13(8): 4503.
[9] Grčevic M, Kralik Z, Kralik G, et al. Journal of the Science of Food and Agriculture, 2019, 99(5): 2292.
[10] Milovanovic B, Tomovic V, Djekic I, et al. Journal of Food Measurement and Characterization, 2021, 15(6): 5097.
[11] Bovšková H, Míková K, Panovská Z. Czech Journal of Food Sciences, 2014, 32(3): 213.
[12] Joubrane K, Mnayer D, Hamieh T, et al. American Journal of Analytical Chemistry, 2019, 10(10): 488.
[13] Aboonajmi M, Saberi A, Abbasian Najafabadi T, et al. International Journal of Food Properties, 2016, 19(5): 1163.
[14] Chen H, Tan C, Lin Z. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 206: 484.
[15] Abdel-Nour N, Ngadi M, Prasher S, et al. Food and Bioprocess Technology, 2011, 4: 731.
[16] Roger J M, Mallet A, Marini F. Molecules, 2022, 27(20): 6795.
[17] Mishra P, Biancolillo A, Roger J M, et al. Trends in Analytical Chemistry, 2020, 132: 116045.
[18] Lee L C, Liong C Y, Jemain A A. Chemometrics and Intelligent Laboratory Systems, 2017, 163: 64.
[19] Yun Y H, Li H D, Deng B C, et al. TrAC Trends in Analytical Chemistry, 2019, 113: 102.
[20] Fu J, Yu H D, Chen Z, et al. Infrared Physics & Technology, 2022, 125: 104231.
[21] Ng W, Minasny B, Malone B P, et al. Computers and Electronics in Agriculture, 2019, 158: 201.
[22] Chu X L, Yuan H F, Lu W Z. Progress in Chemistry, 2004, 16(4): 528.
[23] Brasil Y L, Cruz-tirado J P, Barbin D F. Food Control, 2022, 131: 108418.
[24] Cruz-tirado J P, Lucimar Da Silva Medeiros M, Barbin D F. Journal of Food Engineering, 2021, 306: 110643.
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