Beef marbling measurement using spectral imaging: A multiple linear regression approach
DOI:
https://doi.org/10.57188/manglar.2023.038Resumo
This study aimed at measuring beef marbling scores in an objective and simple manner through spectral imaging and multiple linear regression (MLR). Beef marbling prediction by hyperspectral imaging and partial least squares regression (PLSR) was analyzed to calibrate and evaluate an MLR model with a few selected wavelengths. Data came from 44 beef samples and consisted of their spectral signatures (75 wavelengths) from hyperspectral reflectance images (400-1000 nm) and their marbling scores assigned by evaluators. The wavelengths that presented regression coefficients with the highest absolute values in the PLSR model, were used to calibrate the MLR model by a backward stepwise approach (p < 0.05). The coefficient of determination for prediction (R2p) and the standard error of prediction (SEP) were evaluated. The MLR model was suitable for practical use because it required only 12 wavelengths for reliable predictions (R2p = 0.824 > 0.8; SEP = 11.4% < 15%). A model is proposed for the objective and simple measurement of beef marbling score using multispectral imaging technology.
Downloads
Referências
Alomar, D., Gallo, C., Castaneda, M., & Fuchslocher, R. (2003). Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS). Meat science, 63(4), 441-450. https://doi.org/10.1016/S0309-1740(02)00101-8
Aredo, V., Velásquez, L., & Siche, R. (2017). Prediction of beef marbling using hyperspectral imaging (HSI) and partial least squares regression (RMCP). Scientia Agropecuaria, 8(2), 169-174. http://dx.doi.org/10.17268/sci.agropecu.2017.02.09
Aredo, V., Velásquez, L., Carranza-Cabrera, J., & Siche, R. (2019). Predicting of the Quality Attributes of Orange Fruit Using Hyperspectral Images. Journal of food quality and hazards control, 6, 82-92. http://dx.doi.org/10.18502/jfqhc.6.3.1381
Çetin, N., Karaman, K., Kavuncuoğlu, E., Yıldırım, B., & Jahanbakhshi, A. (2022). Using hyperspectral imaging technology and machine learning algorithms for assessing internal quality parameters of apple fruits. Chemometrics and Intelligent Laboratory Systems, 230, 104650. https://doi.org/10.1016/j.chemolab.2022.104650
Bro, R., & Smilde, A. K. (2014). Principal component analysis. Analytical methods, 6(9), 2812-2831. https://doi.org/10.1039/C3AY41907J
Cheng, W., Cheng, J. H., Sun, D. W., & Pu, H. (2015). Marbling analysis for evaluating meat quality: Methods and techniques. Comprehensive Reviews in Food Science and Food Safety, 14(5), 523-535. https://doi.org/10.1111/1541-4337.12149
Cozzolino, D., & Murray, I. (2002). Effect of sample presentation and animal muscle species on the analysis of meat by near infrared reflectance spectroscopy. Journal of Near Infrared Spectroscopy, 10(1), 37-44.
Dong, J., Guo, W., Wang, Z., Liu, D., & Zhao, F. (2016). Nondestructive determination of soluble solids content of ‘Fuji’apples produced in different areas and bagged with different materials during ripening. Food Analytical Methods, 9, 1087-1095. https://doi.org/10.1007/s12161-015-0278-4
Echegaray, N., Hassoun, A., Jagtap, S., Tetteh-Caesar, M., Kumar, M., Tomasevic, I., ... , & Lorenzo, J. M. (2022). Meat 4.0: Principles and applications of Industry 4.0 technologies in the meat industry. Applied Sciences, 12(14), 6986. https://doi.org/10.3390/app12146986
ElMasry, G., Wang, N., ElSayed, A., & Ngadi, M. (2007). Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of food engineering, 81(1), 98-107. https://doi.org/10.1016/j.jfoodeng.2006.10.016
Gagaoua, M., Duffy, G., Álvarez García, C., Burgess, C., Hamill, R., Crofton, E. C., ... & Troy, D. (2022). Current research and emerging tools to improve fresh red meat quality. Irish Journal of Agricultural and Food Research, 61(1), 145-167. https://doi.org/10.15212/ijafr-2020-0141
Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica chimica acta, 185, 1-17. https://doi.org/10.1016/0003-2670(86)80028-9
Jia, W., van Ruth, S., Scollan, N., & Koidis, A. (2022). Hyperspectral imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends. Current Research in Food Science, 5, 1017-1027. https://doi.org/10.1016/j.crfs.2022.05.016
JMGA (Japan Meat Grading Association). (2000). Beef Carcass Grading Standards. Tokyo. Japan. Retrieved from http://wagyu.org/uploads/page/JMGA%20Meat%20Grading%20Brochure_english.pdf
Khaled, A. Y., Parrish, C. A., & Adedeji, A. 2021. Emerging nondestructive approaches for meat quality and safety evaluation—A review. Comprehensive Reviews in Food Science and Food Safety, 20(4), 3438-3463. https://doi.org/10.1111/1541-4337.12781
Khan, A., Munir, M. T., Yu, W., & Young, B. (2020). Wavelength selection for rapid identification of different particle size fractions of milk powder using hyperspectral imaging. Sensors, 20(16), 4645. https://doi.org/10.3390/s20164645
Li, Y., Shan, J., Peng, Y., & Gao, X. (2011). Nondestructive assessment of beef-marbling grade using hyperspectral imaging technology. In 2011 international conference on new technology of agricultural (pp. 779-783). IEEE. https://doi.org/10.1109/ICAE.2011.5943908
Liu, J., Ellies-Oury, M. P., Stoyanchev, T., & Hocquette, J. F. (2022). Consumer perception of beef quality and how to control, improve and predict it? Focus on eating quality. Foods, 11(12), 1732. https://doi.org/10.3390/foods11121732
Munera, S., Amigo, J. M., Aleixos, N., Talens, P., Cubero, S., & Blasco, J. (2018). Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine. Food Control, 86, 1-10. https://doi.org/10.1016/j.foodcont.2017.10.037
Nychas, G. J., Sims, E., Tsakanikas, P., & Mohareb, F. (2021). Data Science in the Food Industry. Annual Review of Biomedical Data Science, 4, 341-367. https://doi.org/10.1146/annurev-biodatasci-020221-123602
Özdoğan, G., Lin, X., & Sun, D. W. (2021). Rapid and noninvasive sensory analyses of food products by hyperspectral imaging: Recent application developments. Trends in Food Science & Technology, 111, 151-165. https://doi.org/10.1016/j.tifs.2021.02.044
Pereira, P. M. C. C., & Vicente, A. F. R. B. (2013). Meat nutritional composition and nutritive role in the human diet. Meat science, 93(3), 586-592. https://doi.org/10.1016/j.meatsci.2012.09.018
Pinto, D. L., Selli, A., Tulpan, D., Andrietta, L. T., Garbossa, P. L. M., Vander Voort, G., ... & Ventura, R. V. (2023). Image feature extraction via local binary patterns for marbling score classification in beef cattle using tree-based algorithms. Livestock Science, 267, 105152. https://doi.org/10.1016/j.livsci.2022.105152
Siche, R., Vejarano, R., Aredo, V., Velasquez, L., Saldana, E., & Quevedo, R. (2016). Evaluation of food quality and safety with hyperspectral imaging (HSI). Food Engineering Reviews, 8(3), 306-322. https://doi.org/10.1007/s12393-015-9137-8
Su, W. H., & Sun, D. W. (2018). Multispectral imaging for plant food quality analysis and visualization. Comprehensive reviews in food science and food safety, 17(1), 220-239. https://doi.org/10.1111/1541-4337.12317
Velásquez, L., Cruz-Tirado, J. P., Siche, R., & Quevedo, R. (2017). An application based on the decision tree to classify the marbling of beef by hyperspectral imaging. Meat Science, 133, 43-50. https://doi.org/10.1016/j.meatsci.2017.06.002
Vidal, P. O., Cardoso, R. D. C. V., Nunes, I. L., & Lima, W. K. D. S. (2022). Quality and Safety of Fresh Beef in Retail: A Review. Journal of Food Protection, 85(3), 435-447. https://doi.org/10.4315/JFP-21-294
Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems, 58(2), 109-130. https://doi.org/10.1016/S0169-7439(01)00155-1
Xie, C., Chu, B., & He, Y. (2018). Prediction of banana color and firmness using a novel wavelengths selection method of hyperspectral imaging. Food chemistry, 245, 132-140. https://doi.org/10.1016/j.foodchem.2017.10.079
Downloads
Publicado
Edição
Secção
Licença
Direitos de Autor (c) 2023 Victor Aredo, Lía Velásquez, Nikol Siche
Este trabalho encontra-se publicado com a Licença Internacional Creative Commons Atribuição 4.0.
Manglar is an open access journal distributed under the terms and conditions of Creative Commons Attribution 4.0 International license
