Quick assessment of chicken spoilage based on hyperspectral NIR spectra combined with partial least squares regression
Keywords:
hyperspectral NIR spectra, chicken, dominant spoilage, partial least squares regression, quick assessmentAbstract
Pseudomonas spp. and Enterobacteriaceae are dominant spoilage bacteria in chicken during cold storage (0°C-4°C). In this study, high resolution spectra in the range of 900-1700 nm were acquired and preprocessed using Savitzky-Golay convolution smoothing (SGCS), standard normal variate (SNV) and multiplicative scatter correction (MSC), respectively, and then mined using partial least squares (PLS) algorithm to relate to the total counts of Pseudomonas spp. and Enterobacteriaceae (PEC) of fresh chicken breasts to predict PEC rapidly. The results showed that with full 900-1700 nm range wavelength, MSC-PLS model built with MSC spectra performed better than PLS models with other spectra (RAW-PLS, SGCS-PLS, SNV-PLS), with correlation coefficient (RP) of 0.954, root mean square error of prediction (RMSEP) of 0.396 log10 CFU/g and residual predictive deviation (RPD) of 3.33 in prediction set. Based on the 12 optimal wavelengths (902.2 nm, 905.5 nm, 923.6 nm, 938.4 nm, 946.7 nm, 1025.7 nm, 1124.4 nm, 1211.6 nm, 1269.2 nm, 1653.7 nm, 1691.8 nm and 1693.4 nm) selected from MSC spectra by successive projections algorithm (SPA), SPA-MSC-PLS model had RP of 0.954, RMSEP of 0.397 log10 CFU/g and RPD of 3.32, similar to MSC-PLS model. The overall study indicated that NIR spectra combined with PLS algorithm could be used to detect the PEC of chicken flesh in a rapid and non-destructive way. Keywords: hyperspectral NIR spectra, chicken, dominant spoilage, partial least squares regression, quick assessment DOI: 10.25165/j.ijabe.20211401.5726 Citation: Jiang S Q, He H J, Ma H J, Chen F S, Xu B C, Liu H, et al. Quick assessment of chicken spoilage based on hyperspectral NIR spectra combined with partial least squares regression. Int J Agric & Biol Eng, 2021; 14(1): 243–250.References
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[21] de Lima T K, Musso M, Menezes D B. Using Raman spectroscopy and an exponential equation approach to detect adulteration of olive oil with rapeseed and corn oil. Food Chemistry, 2020; 333: 127454. doi: 10.1016/j.foodchem.2020.127454.
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[27] Jia B, Yoon S C, Zhuang H, Wang W, Li C. Prediction of pH of fresh chicken breast fillets by VNIR hyperspectral imaging. Journal of Food Engineering, 2017; 208: 57–65.
[28] Xiong Z, Sun D W, Pu H, Xie A, Han Z, Luo M. Non-destructive prediction of thiobarbituric acid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chemistry, 2015; 179: 175–181.
[29] Massaoudi M, Refaat S S, Abu-Rub H, Chihi I, Oueslati F S. PLS-CNN-BiLSTM: An end-to-end algorithm-based Savitzky-Golay smoothing and evolution strategy for load forecasting. Energies, 2020; 13(20): 5464. doi: 10.3390/en13205464.
[30] Xia Z Z, Yang J, Wang J, Wang S P, Liu Y. Optimizing rice near-infrared models using fractional order Savitzky–Golay derivation (FOSGD) combined with competitive adaptive reweighted sampling (CARS). Applied Spectroscopy, 2020; 74(4): 417–426.
[31] Mishra P, Nordon A, Roger J M. Improved prediction of tablet properties with near-infrared spectroscopy by a fusion of scatter correction techniques. Journal of Pharmaceutical and Biomedical Analysis, 2020; 192: 113684. doi: 10.1016/j.jpba.2020.113684.
[32] Mishra P, Polder G, Gowen A, Rutledge D N, Roger J M. Utilising variable sorting for normalisation to correct illumination effects in close-range spectral images of potato plants. Biosystems Engineering, 2020; 197: 318–323.
[33] Wu Y, Peng S, Xie Q, Han Q J, Zhang G W, Sun H G. An improved weighted multiplicative scatter correction algorithm with the use of variable selection: Application to near-infrared spectra. Chemometrics and Intelligent Laboratory Systems, 2019; 185: 114–121.
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[35] He H J, Wu D, Sun D W. Nondestructive spectroscopic and imaging techniques for quality evaluation and assessment of fish and fish products. Critical Reviews in Food Science and Nutrition, 2015; 55(6): 864–886.
[36] Malley D F, Mcclure C, Martin P D, Buckley K, Mccaughey W P. Compositional analysis of cattle manure during composting using a field-portable near-infrared spectrometer. Communications in Soil Science and Plant Analysis, 2015; 36(4-6): 455–475.
[37] Yun Y H, Li H D, Deng B C, Cao D S. An overview of variable selection methods in multivariate analysis of near-infrared spectra. TrAC Trends in Analytical Chemistry, 2019; 113: 102–115.
[38] Li J B, Wang Q Y, Xu L, Tian X, Xia Y, Fan S X. Comparison and optimization of models for determination of sugar content in pear by portable Vis-NIR spectroscopy coupled with wavelength selection algorithm. Food Analytical Methods, 2019; 12(1): 12–22.
[39] Liu D, Sun D W, Qu J, Zeng X A, Pu H, Ma J. Feasibility of using hyperspectral imaging to predict moisture content of porcine meat during salting process. Food Chemistry, 2014; 152: 197–204.
[40] Ye S, Wang D, Min S. Successive projections algorithm combined with uninformative variable elimination for spectral variable selection. Chemometrics and Intelligent Laboratory Systems, 2008; 91(2): 194–199.
[41] Moreira E D T, Pontes M J C, Galvão, R K H, Araújo M C U. Near infrared reflectance spectrometry classification of cigarettes using the successive projections algorithm for variable selection. Talanta, 2009; 79(5): 1260–1264.
[42] Wang H, He H, Ma H, Chen F, Zhu R. LW-NIR hyperspectral imaging for rapid prediction of TVC in chicken flesh. Int J Agric & Biol Eng, 2019; 12(3): 180–186.
[43] Karlsdottir M G, Arason S, Kristinsson H G, Sveinsdottir K. The application of near infrared spectroscopy to study lipid characteristics and deterioration of frozen lean fish muscles. Food Chemistry, 2014; 159(159): 420–427.
[2] Fu X, Chen J. A review of hyperspectral imaging for chicken meat safety and quality evaluation: application, hardware, and software. Comprehensive Reviews in Food Science and Food Safety, 2019; 18(2): 535–547.
[3] Saenz-García C E, Castañeda-Serrano P, Mercado Silva E M, Alvarado C Z, Nava G M. Insights into the identification of the specific spoilage organisms in chicken meat. Foods, 2020; 9(2): 225. doi: 10.3390/foods902025.
[4] Xu Y, Kutsanedzie F Y H, Sun H, Wang M X, Chen Q S, Guo Z M, et al. Rapid Pseudomonas species identification from chicken by integrating colorimetric sensors with near-infrared spectroscopy. Food Analytical Methods, 2018; 11(4): 1199–1208.
[5] Rouger A, Moriceau N, Prévost H, Remenant B, Zagorec M. Diversity of bacterial communities in French chicken cuts stored under modified atmosphere packaging. Food Microbiology, 2018; 70: 7–16.
[6] Katiyo W, Kock H L D, Coorey R, Buys E M. Sensory implications of chicken meat spoilage in relation to microbial and physicochemical characteristics during refrigerated storage. LWT-Food Science and Technology, 2020; 128: 109468. doi: 10.1016/j.lwt.2020.109468.
[7] Herbert U, Albrecht A, Kreyenschmidt J. Definition of predictor variables for MAP poultry filets stored under different temperature conditions. Poult Science, 2015; 94(3): 424–432.
[8] Sterniša M, Klančnik A, Smole Možina S. Spoilage Pseudomonas biofilm with Escherichia coli protection in fish meat at 5°C. Journal of the Science of Food and Agriculture, 2019; 99(10): 4635–4641.
[9] Díaz-Jiménez D, García-Meniño I, Fernández J, García V, Mora A. Chicken and turkey meat: Consumer exposure to multidrug-resistant Enterobacteriaceae including mcr-carriers, uropathogenic E. coli and high-risk lineages such as ST131. International Journal of Food Microbiology, 2020; 331: 108750. doi: 10.1016/j.ijfoodmicro.2020. 108750.
[10] Shi J Y, Zhang F, Wu S B, Guo Z M, Huang X W, Hu X T, et al. Noise-free microbial colony counting method based on hyperspectral features of agar plates. Food Chemistry, 2019; 274: 925–932.
[11] Luo F F, Li Z, Dai G, Lu Y Q, He P G, Wang Q J. Simultaneous detection of different bacteria by microchip electrophoresis combined with universal primer-duplex polymerase chain reaction. Journal of Chromatography A, 2020; 1615: 460734. doi: 10.1016/j.chroma.2019.460734.
[12] Ripolles-Avila C, Martínez-Garcia M, Capellas M, Yuste J. From hazard analysis to risk control using rapid methods in microbiology: A practical approach for the food industry. Comprehensive Reviews in Food Science and Food Safety, 2020; 19(4): 1877–1907.
[13] Wang Z L, Cai R, Gao Z P, Yuan Y H. Immunomagnetic separation: An effective pretreatment technology for isolation and enrichment in food microorganisms detection. Comprehensive Reviews in Food Science and Food Safety, 2020; 19(6): 3802–3824.
[14] Kakani V, Nguyen V H, Kumar B P, Kim H, Pasupuleti V R. A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2020; 2: 100033. doi: 10.1016/j.jafr.2020.100033.
[15] Fedorov F S, Yaqin A, Krasnikov D V, Kondrashov V A, Ovchinnikov G, Kostyukevich Y, et al. Detecting cooking state of grilled chicken by electronic nose and computer vision techniques. Food Chemistry, 2020; 345: 128747. doi: 10.1016/j.foodchem.2020.128747.
[16] Zhang H L, Zhang S, Chen Y, Luo W, Huang Y F, Tao D, et al. Non-destructive determination of fat and moisture contents in Salmon (Salmo salar) fillets using near-infrared hyperspectral imaging coupled with spectral and textural features. Journal of Food Composition and Analysis, 2020; 92: 103567. doi: 10.1016/j.jfca.2020.103567.
[17] Anjos O, Caldeira I, Roque R, Pedro S I, Lourenço S, Canas S. Screening of Different ageing technologies of wine spirit by application of near-infrared (NIR) spectroscopy and volatile quantification. Processes, 2020; 8(6): 736. doi: 10.3390/pr8060736.
[18] Jiang H, Liu T, Chen Q S. Quantitative detection of fatty acid value during storage of wheat flour based on a portable near-infrared (NIR) spectroscopy system. Infrared Physics & Technology, 2020; 109: 103423. doi: 10.1016/j.infrared.2020.103423.
[19] Neng J, Zhang Q, Sun P L. Application of surface-enhanced Raman spectroscopy in fast detection of toxic and harmful substances in food. Biosensors and Bioelectronics, 2020: 112480. doi: 10.1016/j.bios.2020. 112480.
[20] Xu Y, Zhong P, Jiang A M, Shen X, Li X M, Xu Z L, et al. Raman spectroscopy coupled with chemometrics for food authentication: A review. TrAC Trends in Analytical Chemistry, 2020: 116017. doi: 10.1016/ j.trac.2020.116017.
[21] de Lima T K, Musso M, Menezes D B. Using Raman spectroscopy and an exponential equation approach to detect adulteration of olive oil with rapeseed and corn oil. Food Chemistry, 2020; 333: 127454. doi: 10.1016/j.foodchem.2020.127454.
[22] López-Maestresalas A, Insausti K, Jarén C, Pérez-Roncal C, Urrutia O, Beriain M J, et al. Detection of minced lamb and beef fraud using NIR spectroscopy. Food Control, 2019; 98: 465–473.
[23] Barbin D F, Badaró A T, Honorato D C B, Ida E Y, Shimokomaki M. Identification of turkey meat and processed products using near infrared spectroscopy. Food Control, 2020; 107: 106816. doi: 10.1016/j.foodcont.2019.106816.
[24] Peyvasteh M, Popov A, Bykov A V, Meglinski I. Meat freshness revealed by visible to near-infrared spectroscopy and principal component analysis. Journal of Physics Communications, 2020; 4(9): 095011. doi: 10.1088/2399-6528/abb322.
[25] Furtado E J G, Bridi A M, Barbin D F, Barata C C P, Peres L M, da Costa Barbon A P A, et al. Prediction of pH and color in pork meat using VIS-NIR near-infrared spectroscopy (NIRS). Food Science and Technology, 2019; 39(2): 88–92.
[26] Alexandrakis D, Downey G, Scannell A G M. Rapid non-destructive detection of spoilage of intact chicken breast muscle using near-infrared and Fourier Transform mid-infrared spectroscopy and multivariate statistics. Food and Bioprocess Technology, 2012; 5: 338–347.
[27] Jia B, Yoon S C, Zhuang H, Wang W, Li C. Prediction of pH of fresh chicken breast fillets by VNIR hyperspectral imaging. Journal of Food Engineering, 2017; 208: 57–65.
[28] Xiong Z, Sun D W, Pu H, Xie A, Han Z, Luo M. Non-destructive prediction of thiobarbituric acid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chemistry, 2015; 179: 175–181.
[29] Massaoudi M, Refaat S S, Abu-Rub H, Chihi I, Oueslati F S. PLS-CNN-BiLSTM: An end-to-end algorithm-based Savitzky-Golay smoothing and evolution strategy for load forecasting. Energies, 2020; 13(20): 5464. doi: 10.3390/en13205464.
[30] Xia Z Z, Yang J, Wang J, Wang S P, Liu Y. Optimizing rice near-infrared models using fractional order Savitzky–Golay derivation (FOSGD) combined with competitive adaptive reweighted sampling (CARS). Applied Spectroscopy, 2020; 74(4): 417–426.
[31] Mishra P, Nordon A, Roger J M. Improved prediction of tablet properties with near-infrared spectroscopy by a fusion of scatter correction techniques. Journal of Pharmaceutical and Biomedical Analysis, 2020; 192: 113684. doi: 10.1016/j.jpba.2020.113684.
[32] Mishra P, Polder G, Gowen A, Rutledge D N, Roger J M. Utilising variable sorting for normalisation to correct illumination effects in close-range spectral images of potato plants. Biosystems Engineering, 2020; 197: 318–323.
[33] Wu Y, Peng S, Xie Q, Han Q J, Zhang G W, Sun H G. An improved weighted multiplicative scatter correction algorithm with the use of variable selection: Application to near-infrared spectra. Chemometrics and Intelligent Laboratory Systems, 2019; 185: 114–121.
[34] El Jabri M, Sanchez M P, Trossat P, Laithier C, Wolf V, Grosperrin P, et al. Comparison of Bayesian and partial least squares regression methods for mid-infrared prediction of cheese-making properties in Montbéliarde cows. Journal of Dairy Science, 2019; 102(8): 6943–6958.
[35] He H J, Wu D, Sun D W. Nondestructive spectroscopic and imaging techniques for quality evaluation and assessment of fish and fish products. Critical Reviews in Food Science and Nutrition, 2015; 55(6): 864–886.
[36] Malley D F, Mcclure C, Martin P D, Buckley K, Mccaughey W P. Compositional analysis of cattle manure during composting using a field-portable near-infrared spectrometer. Communications in Soil Science and Plant Analysis, 2015; 36(4-6): 455–475.
[37] Yun Y H, Li H D, Deng B C, Cao D S. An overview of variable selection methods in multivariate analysis of near-infrared spectra. TrAC Trends in Analytical Chemistry, 2019; 113: 102–115.
[38] Li J B, Wang Q Y, Xu L, Tian X, Xia Y, Fan S X. Comparison and optimization of models for determination of sugar content in pear by portable Vis-NIR spectroscopy coupled with wavelength selection algorithm. Food Analytical Methods, 2019; 12(1): 12–22.
[39] Liu D, Sun D W, Qu J, Zeng X A, Pu H, Ma J. Feasibility of using hyperspectral imaging to predict moisture content of porcine meat during salting process. Food Chemistry, 2014; 152: 197–204.
[40] Ye S, Wang D, Min S. Successive projections algorithm combined with uninformative variable elimination for spectral variable selection. Chemometrics and Intelligent Laboratory Systems, 2008; 91(2): 194–199.
[41] Moreira E D T, Pontes M J C, Galvão, R K H, Araújo M C U. Near infrared reflectance spectrometry classification of cigarettes using the successive projections algorithm for variable selection. Talanta, 2009; 79(5): 1260–1264.
[42] Wang H, He H, Ma H, Chen F, Zhu R. LW-NIR hyperspectral imaging for rapid prediction of TVC in chicken flesh. Int J Agric & Biol Eng, 2019; 12(3): 180–186.
[43] Karlsdottir M G, Arason S, Kristinsson H G, Sveinsdottir K. The application of near infrared spectroscopy to study lipid characteristics and deterioration of frozen lean fish muscles. Food Chemistry, 2014; 159(159): 420–427.
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2021-02-10
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Jiang, S., He, H., Ma, H., Chen, F., Xu, B., Liu, H., … Zhao, S. (2021). Quick assessment of chicken spoilage based on hyperspectral NIR spectra combined with partial least squares regression. International Journal of Agricultural and Biological Engineering, 14(1), 243–250. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/5726
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Agro-product and Food Processing Systems
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