Mining hyperspectral data for non-destructive and rapid prediction of nitrite content in ham sausages
Keywords:
hyperspectral data, ham sausage, non-destructive and rapid prediction, nitrite, partial least squares (PLS)Abstract
Accurate and rapid determination of nitrite contents is an important step for guaranteeing sausage quality. This study attempted to mine hyperspectral data in the range of 900-1700 nm for non-destructive and rapid prediction of nitrite contents in sausages. The average spectra of 156 samples were collected to relate to the measured nitrite values by partial least squares (PLS) regression. Optimal wavelengths were respectively selected by successive projections algorithm (SPA) and regression coefficients (RC) to simplify the PLS model. The results indicated that PLS model established with 15 optimal wavelengths (900.5 nm, 907.1 nm, 908.8 nm, 912.1 nm, 915.4 nm, 920.3 nm, 922.0 nm, 941.7 nm, 979.6 nm, 1083.2 nm, 1213.2 nm, 1353.0 nm, 1460.2 nm, 1595.6 nm and 1699.9 nm) selected by SPA had better performance with rC, rCV, rP of 0.92, 0.89, 0.89 and RMSEC, RMSECV, RMSEP of 0.41 mg/kg, 0.89 mg/kg, 0.49 mg/kg, respectively, for calibration set, cross-validation and prediction set. It was concluded that hyperspectral data could be mined by PLS & SPA for realizing the rapid evaluation of nitrite content in ham sausages. Keywords: hyperspectral data, ham sausage, non-destructive and rapid prediction, nitrite, partial least squares (PLS) DOI: 10.25165/j.ijabe.20211402.5407 Citation: Zhu Y D, He H J, Jiang S Q, Ma H J, Chen F S, Xu B C, et al. Mining hyperspectral data for non-destructive and rapid prediction of nitrite content in ham sausages. Int J Agric & Biol Eng, 2021; 14(2): 182–187.References
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[34] Feng Y, Sun D. Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of Pseudomonas loads in chicken fillets. Talanta, 2013; 109: 74–83.
[35] Feng C, Makino Y, Yoshimura M, Rodriguez-Pulido F J. Real-time prediction of pre-cooked Japanese sausage color with different storage days using hyperspectral imaging. Journal of the Science of Food and Agriculture, 2018; 98(7): 2564–2572.
[2] Gong A P, Zhu S S, He Y, Zhang C. Grading of Chinese Cantonese sausage using hyperspectral imaging combined with chemometric methods. Sensors, 2017; 17(8): 1706.
[3] Majou D, Christieans S. Mechanisms of the bactericidal effects of nitrate and nitrite in cured meats. Meat Science, 2018; 145: 273–284.
[4] Sudarvizhi A, Pandian K, Oluwafemi O S, Gopinath S C B. Amperometry detection of nitrite in food samples using tetrasulfonated copper phthalocyanine modified glassy carbon electrode. Sensors & Actuators B: Chemical, 2018; 272: 151–159.
[5] Sudarvizhi A, Siddiqha Z A, Pandian K. Single step synthesis of graphene oxide protected silver nanoparticles using aniline as reducing agent and study its application on electrocatalytic detection of nitrite in food samples. J Chem Applied Biochem, 2014; 1: 101.
[6] Kim H S, Hur S J. Changes in the mutagenicity of heterocyclic amines, nitrite, and N-nitroso compound in pork patties during in vitro human digestion. LWT, 2018; 92: 47–53.
[7] Moorcroft M J, Davis J, Compton R G. Detection and determination of nitrate and nitrite: a review. Talanta, 2001; 54(5): 785–803.
[8] Feng C H, Makino Y, Yoshimura M, Thuyet D Q. Hyperspectral imaging in tandem with R statistics and image processing for detection and visualization of pH in Japanese big sausages under different storage conditions. Journal of Food Science, 2018; 83(2): 358–366.
[9] Jiang H, Wang W, Ni X, Zhuang H, Yoon S C, Lawrence K C. Recent advancement in near infrared spectroscopy and hyperspectral imaging techniques for quality and safety assessment of agricultural and food products in the China Agricultural University. NIR news, 2018; 29(8): 19–23.
[10] He H, Wu D, Sun D. Rapid and non-destructive determination of drip loss and pH distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared (Vis-NIR) hyperspectral imaging. Food Chemistry, 2014; 156: 394–401.
[11] Feng C H, Makino Y, Oshita S, Martín J F G. Hyperspectral imaging and multispectral imaging as the novel techniques for detecting defects in raw and processed meat products: Current state-of-the-art research advances. Food Control, 2018; 84: 165–176.
[12] He H, Sun D. Toward enhancement in prediction of Pseudomonas counts distribution in salmon fillets using NIR hyperspectral imaging. LWT-Food Science and Technology, 2015; 62(1): 11–18.
[13] Khoshnoudi-Nia S, Moosavi-Nasab M, Nassiri S M, Azimifar Z. Determination of total viable count in rainbow-trout fish fillets based on hyperspectral imaging system and different variable selection and extraction of reference data methods. Food Analytical Methods, 2018; 11(12): 3481–3494.
[14] Kamruzzaman M, Makino Y, Oshita S. Online monitoring of red meat color using hyperspectral imaging. Meat Science, 2016; 116: 110–117.
[15] Achata E M, Inguglia E S, Esquerre C A, Tiwari B K, O’Donnell C P. Evaluation of Vis-NIR hyperspectral imaging as a process analytical tool to classify brined pork samples and predict brining salt concentration. Journal of Food Engineering, 2019; 246: 134–140.
[16] Cheng W W, Sun D W, Pu H B, Wei Q Y. Interpretation and rapid detection of secondary structure modification of actomyosin during frozen storage by near-infrared hyperspectral imaging. Journal of Food Engineering, 2019; 246: 200–208.
[17] Feng C H, Makino Y, Yoshimura M, Rodríguez-Pulido F J. Estimation of adenosine triphosphate content in ready-to-eat sausages with different storage days, using hyperspectral imaging coupled with R statistics. Food Chemistry, 2018; 264: 419. doi: 10.1016/j.foodchem.2018.05.029.
[18] Stachniuk A, Szmagara A, Stefaniak E A. Spectrophotometric assessment of the differences between total nitrate/nitrite contents in peel and flesh of cucumbers. Food Analytical Methods, 2018; 11(10): 2969–2977.
[19] Liu Y W, Sun D W, Cheng J H, Han Z. Hyperspectral imaging sensing of changes in moisture content and color of beef during microwave heating process. Food Analytical Methods, 2018; 11(9): 2472–2484.
[20] He J, Chen L, Chu B, Zhang C. Determination of total polysaccharides and total flavonoids in Chrysanthemum morifolium using near-infrared hyperspectral imaging and multivariate analysis. Molecules, 2018; 23(9): 2395. doi: 10.3390/molecules23092395.
[21] Giacomo D R, Stefania D Z. A multivariate regression model for detection of fumonisins content in maize from near infrared spectra. Food Chemistry, 2013; 141(4): 4289–4294.
[22] He H, Wu D, Sun D. Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging. Innovative Food Science & Emerging Technologies, 2013; 18: 237–245.
[23] Siripatrawan U. Hyperspectral imaging for rapid evaluation and visualization of quality deterioration index of vacuum packaged dry-cured sausages. Sensors and Actuators B: Chemical, 2018; 254: 1025–1032.
[24] Cheng J, Sun D. Rapid quantification analysis and visualization of Escherichia coli loads in grass carp fish flesh by hyperspectral imaging method. Food and Bioprocess Technology, 2015; 8(5): 951–959.
[25] 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.
[26] He H, Sun D, Wu D. Rapid and real-time prediction of lactic acid bacteria (LAB) in farmed salmon flesh using near-infrared (NIR) hyperspectral imaging combined with chemometric analysis. Food Research International, 2014; 62: 476–483.
[27] He H, Sun D. Selection of informative spectral wavelength for evaluating and visualising Enterobacteriaceae contamination of salmon flesh. Food Analytical Methods, 2015; 8(10): 2427–2436.
[28] Araújo M C U, Saldanha T C B, Galvão R K H, Yoneyama T, Chame H C, Visani V. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics and Intelligent Laboratory Systems, 2001; 57(2): 65–73.
[29] Wu D, Shi H, Wang S, He Y, Bao Y D, Liu K S. Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system. Analytica Chimica Acta, 2012; 726: 57–66.
[30] Cozzolino D, Murray I. A review on the application of infrared technologies to determine and monitor composition and other quality characteristics in raw fish, fish products, and seafood. Applied Spectroscopy Reviews, 2012; 47(3): 207–218.
[31] Wu D, Wang S J, Wang N F, Nie P C, He Y, Sun D W, et al. Application of time series hyperspectral imaging (TS-HSI) for determining water distribution within beef and spectral kinetic analysis during dehydration. Food and Bioprocess Technology, 2013; 6(11): 2943–2958.
[32] Fernández-Cabanás V M, Polvillo O, Rodríguez-Acuña R, Botella B, Horcada A. Rapid determination of the fatty acid profile in pork dry-cured sausages by NIR spectroscopy. Food Chemistry, 2011; 124(1): 373–378.
[33] Nicolaï B M, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron K I, et al. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 2007; 46(2): 99–118.
[34] Feng Y, Sun D. Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of Pseudomonas loads in chicken fillets. Talanta, 2013; 109: 74–83.
[35] Feng C, Makino Y, Yoshimura M, Rodriguez-Pulido F J. Real-time prediction of pre-cooked Japanese sausage color with different storage days using hyperspectral imaging. Journal of the Science of Food and Agriculture, 2018; 98(7): 2564–2572.
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Published
2021-04-03
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Zhu, Y., He, H., Jiang, S., Ma, H., Chen, F., Xu, B., … Kang, Z. (2021). Mining hyperspectral data for non-destructive and rapid prediction of nitrite content in ham sausages. International Journal of Agricultural and Biological Engineering, 14(2), 182–187. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/5407
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Information Technology, Sensors and Control Systems
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