Compositional and sensory quality of beef and its determination by near infrared




Infrared spectrophotometry, meat, consumers, perception


Introduction. Beef is a food of high biological value for human nutrition; it is a source of fat, protein, vitamins, and minerals. However, aspects associated with the environmental impact of the production system and association with non-communicable diseases have promoted new protein consumption trends (vegetable, insects, lab protein). So, it is necessary to identify and offer information on the attributes of the meat that favor consumer acceptance quickly and reliably through non-destructive and environmentally friendly techniques such as near-infrared. Objective. To review the experiences associated with the implementation of Near-Infrared Spectroscopy (NIRS) as an alternative in the determination of beef quality. Development. Meat attributes with the greatest relationship with consumers can be segmented into appearance, consumption, and confidence. These characteristics involve intrinsic elements of the meat, such as color, fat, and tenderness. Given the limitations in the application of laboratory techniques for the determination of meat attributes, the application of the NIRS is reviewed with successful experiences in characteristics of interest to consumers. Additionally, the application of NIRS in production lines and other applications of interest in the industry, such as detection of meat anomalies and product authentication, is studied. Conclusion. The application of the NIRS allows to determine quickly and reliably attributes of the quality of the meat that have a close relationship with the consumer. This tool allows to obtain results that respond to the production speed of the industry, and to determinate and authenticate aspects related to meat and the production system.


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Aboah, J., & Lees, N. (2020). Consumers use of quality cues for meat purchase: Research trends and future pathways. Meat Science, 166(April), Article 108142.

Adzitey, F., & Nurul, H. (2011). Pale soft exudative (PSE) and dark firm dry (DFD) meats: Causes and measures to reduce these incidences - a mini review. International Food Research Journal, 18(1), 11–20.

Agelet, L. E., & Hurburgh, C. R. (2010). A tutorial on near infrared spectroscopy and its calibration. Critical Reviews in Analytical Chemistry, 40(4), 246–260.

Alander, J. T., Bochko, V., Martinkauppi, B., Saranwong, S., & Mantere, T. (2013). A review of optical nondestructive visual and Near-Infrared methods for food quality and safety. International Journal of Spectroscopy, 2013, Article 341402.

Alomar, D., Gallo, C., Castañeda, M., Fuchslocher, R., Castaneda, M., & Fuchslocher, R. (2003). Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS). Meat Science, 63(4), 441–450.

American Meat Science Association. (2016). Research guidelines for cookery, sensory evaluation, and instrumental tenderness measurements of meat. American Meat Science Association.

Andrés, S., Murray, I., Navajas, E. A., Fisher, A. V., Lambe, N. R., & Bünger, L. (2007). Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy. Meat Science, 76(3), 509-516.

Angulo, J., Nürnberg, K., Mahecha, L., Olivera, M., & Dannenberger, D. (2012). Manual of lipid extraction, methylation and gas chromatography, for the study of different tissues in ruminant research. Biogénesis.

Ardeshiri, A., & Rose, J. M. (2018). How Australian consumers value intrinsic and extrinsic attributes of beef products. Food Quality and Preference, 65(October), 146–163.

Arenas-De-Moreno, L., Jerez-Timaure, N., Valerio-Hernández, J., Huerta-Leidenz, N. O., & Rodas-González, A. (2020). Attitudinal determinants of beef consumption in Venezuela: A retrospective survey. Foods, 9, Article 202.

Association of Official Agricultural Chemists (2000). Fat (crude) or ether extract in meat 960.39-1960. AOAC Int.

Bhat, M. M., Jalal, H., Para, P. A., Bukhari, S. A., Ganguly, S., Bhat, A. A., Wakchaure, R., & Qadri, K. (2015). Fraudulent adulteration / Substitution of meat: A review. Journal of Recent Research and Applied Studies, 2(12), 21–33.

Barragán-Hernández, W., Aalhus, J. L., Penner, G., Dugan, M. E. R., Juárez, M., López-Campos, Ó., Vahmani, P., Segura, J., Angulo, J., & Prieto, N. (2021a). Authentication of barley-finished beef using visible and near infrared spectroscopy (Vis-NIRS) and different discrimination approaches. Meat Science, 172, Article 108342.

Barragán-Hernández, W., Mahecha-Ledesma, L., Angulo-Arizala, J., & Olivera-Angel, M. (2020a). Near-infrared spectroscopy as a beef quality tool to predict consumer acceptance. Foods, 9(8), 1–15.

Barragán Hernández, W. A., Mahecha-Ledesma, L., Burgos-Paz, W. O., Olivera-Angel, M. M., & Angulo-Arizala, J. (2020b). Using near-infrared spectroscopy to determine intramuscular fat and fatty acids of beef applying different prediction approach. Journal of Animal Science, 98(11), Article 342.

Barragán-Hernández, W., Mahecha-Ledesma, L., Olivera-Angel, M., & Angulo-Arizala, J. (2021b). ¿Cómo los consumidores valoran atributos de calidad de carne bovina y su disposición a pago? Revista Biotecnología en el Sector Agropecuario y Agroindustrial, 19(1), 1–15.

Carpenter, C. E., Cornforth, D. P., & Whittier, D. (2001). Consumer preferences for beef color and packaging did not affect eating satisfaction. Meat Science, 57(4), 359–363.

Cecchinato, A., de Marchi, M., Penasa, M., Casellas, J., Schiavon, S., & Bittante, G. (2012). Genetic analysis of beef fatty acid composition predicted by near-infrared spectroscopy. Journal of Animal Science, 90(2), 429-438.

Chapman, J., Elbourne, A., Truong, V. K., & Cozzolino, D. (2020). Shining light into meat – a review on the recent advances in in vivo and carcass applications of near infrared spectroscopy. International Journal of Food Science and Technology, 55(3), 935–941.

Cho, S. H., Kim, J., Park, B. Y., Seong, P. N., Kang, G. H., Kim, J. H., Jung, S. G., Im, S. K., & Kim, D. (2010). Assessment of meat quality properties and development of a palatability prediction model for Korean Hanwoo steer beef. Meat Science, 86(1), 236–242.

Cocking, C., Walton, J., Kehoe, L., Cashman, K. D., & Flynn, A. (2020). The role of meat in the European diet: current state of knowledge on dietary recommendations, intakes and contribution to energy and nutrient intakes and status. Nutrition Research Reviews, 33(2), 181–189.

Cozzolino, D. (2016). 7 – Near infrared spectroscopy and food authenticity. In M. Espiñeira, & F. J. Santaclara (Eds.), Advances in Food Traceability Techniques and Technologies (pp. 119–136). Woodhead Publishing.

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.

Cozzolino, D., & Murray, I. (2004). Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. LWT - Food Science and Technology, 37(4), 447–452.

Damez, J. L., & Clerjon, S. (2008). Meat quality assessment using biophysical methods related to meat structure. Meat Science, 80(1), 132–149.

De-Marchi, M., Berzaghi, P., Boukha, A., Mirisola, M., & Gallo, L. (2007). Use of near infrared spectroscopy for assessment of beef quality traits. Italian Journal of Animal Science, 6(Suppl. 1), 421–423.

de-Olivera, R., (2014). Modelos de calibração multivariada por NIRS para a predição de características de qualidade da carne bovina [Tese de Doutorado, não publicada]. Universidade Federal de Goiás.

Doosti, A., Ghasemi Dehkordi, P., & Rahimi, E. (2014). Molecular assay to fraud identification of meat products. Journal of Food Science and Technology, 51(1), 148–152.

Drey, L., Legako, J., Brooks, J., Miller, M., & O’quinn, T. (2017). The contribution of tenderness, juiciness, and flavor to overall consumer beef eating experience. Meat and Muscle Biology, 1(3), 13–13.

Ekmekcioglu, C., Wallner, P., Kundi, M., Weisz, U., Haas, W., & Hutter, H. P. (2018). Red meat, diseases, and healthy alternatives: A critical review. Critical Reviews in Food Science and Nutrition, 58(2), 247–261.

Flowers, S., Hamblen, H., Leal-Gutiérrez, J. D., Elzo, M. A., Johnson, D. D., & Mateescu, R. G. (2018a). Fatty acid profile, mineral content, and palatability of beef from a multibreed Angus-Brahman population. Journal of Animal Science, 96(10), 4264–4275.

Flowers, S., McFadden, B., Carr, C., & Mateescu, R. (2018b). Understanding beef nutritional attributes contributes to consumers’ willingness-to-pay for a healthier product. Meat and Muscle Biology, 2(2), 15–16.

Flowers, S., McFadden, B. R., Carr, C. C., & Mateescu, R. G. (2019). Consumer preferences for beef with improved nutrient profile. Journal of Animal Science, 97(12), 4699–4709.

Font-i-Furnols, M., Fulladosa, E., Prevolnik Povše, M., & Čandek-Potokar, M. (2015). Future trends in non-invasive technologies suitable for quality determinations In M. Font-i-Furnols, M. Čandek-Potokar, C. Maltin, & M. Prevolnik Povše (Eds.), A Handbook of Reference Methods for Meat Quality Assessment (pp. 90-104). European Cooperation in Science and Technology.

Font-i-Furnols, M., & Guerrero, L. (2014). Consumer preference, behavior and perception about meat and meat products: An overview. Meat Science, 98(3), 361–371.

Giaretta, E., Mordenti, A., Palmonari, A., Brogna, N., Canestrari, G., Belloni, P., Cavallini, D., Mammi, L., Cabbri, R., & Formigoni, A. (2019). NIRs calibration models for chemical composition and fatty acid families of raw and freezedried beef: a comparison. Journal of Food Composition and Analysis, 83, Article 103257.

Girolami, A., Napolitano, F., Faraone, D., & Braghieri, A. (2013). Measurement of meat color using a computer vision system. Meat Science, 93(1), 111–118.

Gonzalez, J. M., & Phelps, K. J. (2018). United States beef quality as chronicled by the national beef quality audits, beef consumer satisfaction projects, and national beef tenderness surveys - A review. Asian-Australasian Journal of Animal Sciences, 31(7), 1036–1042.

Grunert, K. G., Bredahl, L., & Brunsø, K. (2004). Consumer perception of meat quality and implications for product development in the meat sector - A review. Meat Science, 66(2), 259–272.

Henchion, M. M., McCarthy, M., & Resconi, V. C. (2017). Beef quality attributes: A systematic review of consumer perspectives. Meat Science, 128, 1–7.

Hocquette, J. F., Cassar-Malek, I., Jurie, C., Bauchart, D., Picard, B., & Renand, G. (2012). Relationships between muscle growth potential, intramuscular fat content and different indicators of muscle fibre types in young Charolais bulls. Animal Science Journal, 83(11), 750–758.

Hocquette, J. F., Ellies-Oury, M. P., Legrand, I., Pethick, D., Gardner, G., Wierzbicki, J., & Polkinghorne, R. J. (2020). Research in beef tenderness and palatability in the era of Big Data. Meat and Muscle Biology, 4(2), Article 9488.

Honikel, K. O. (1998). Reference methods for the assessment of physical characteristics of meat. Meat Science, 49(4), 447–457.

Honikel, K. O., Kim, C. J., Hamm, R., & Roncales, P. (1986). Sarcomere shortening of prerigor muscles and its influence on drip loss. Meat Science, 16(4), 267–282.

International Organization for Standardization (1999). Meat and meat products: Measurement of pH 2917:1999. ISO.

Ijaz, M., Li, X., Zhang, D., Hussain, Z., Ren, C., Bai, Y., & Zheng, X. (2020). Association between meat color of DFD beef and other quality attributes. Meat Science, 161, Article 107954.

Joo, S. T., Kim, G. D., Hwang, Y. H., & Ryu, Y. C. (2013). Control of fresh meat quality through manipulation of muscle fiber characteristics. Meat Science, 95(4), 828–836.

Joseph, P., Searing, A., Watson, C., & McKeague, J. (2020). Alternative proteins: Market research on consumer trends and emerging landscape. Meat and Muscle Biology, 4(2), Article 16.

Juárez, M., Aldai, N., López-Campos, Ó., Dugan, M. E. R., Uttaro, B., & Aalhus, J. L. (2012). Beef texture and juiciness In Y. H. Hui (Ed.), Handbook of meat and processing (2nd Ed., pp. 177–206). CRC Press.

Khan, M. I., Jo, C., & Tariq, M. R. (2015). Meat flavor precursors and factors influencing flavor precursors-A systematic review. Meat Science, 110, 278–284.

King, D. A., Wheeler, T. L., Shackelford, S. D., & Koohmaraie, M. (2009). Fresh meat texture and tenderness. In J. P. Kerry, & D. Ledward (Eds.), Improving the sensory and nutritional quality of fresh meat (pp. 61-88). Woodhead Publishing Limited.

Li, X., Feng, F., Gao, R., Wang, L., Qian, Y., Li, C., & Zhou, G. (2015). Application of near infrared reflectance (NIR) spectroscopy to identify potential PSE meat. Journal of the Science of Food and Agriculture, 96(9), 3148-3156.

Li, X., Jensen, K. L., Clark, C. D., & Lambert, D. M. (2016). Consumer willingness to pay for beef grown using climate friendly production practices. Food Policy, 64, 93–106.

Liu, J., Ellies-Oury, M. P., Chriki, S., Legrand, I., Pogorzelski, G., Wierzbicki, J., Farmer, L., Troy, D., Polkinghorne, R., & Hocquette, J. F. (2020). Contributions of tenderness, juiciness and flavor liking to overall liking of beef in Europe. Meat Science, 168(April), Article 108190.

Løvland, A., & Wold, J. P. (2020). NIR spectroscopic techniques for quality and process control in the meat industry. Meat and Muscle Biology, 4(2), Article 10020.

Lusk, J. L. (2019). Consumer beliefs about healthy foods and diets. PLoS ONE, 14(10), Article e0223098.

Magalhães, A. F., Teixeira, G. H., Ríos, A. C., Silva, D. B., Mota, L. F., Muniz, M. M., de-Morais, C. de L., de-Lima, K. M., Júnior, L. C., Baldi, F., Carvalheiro, R., de-Oliveira, H., Chardulo, L. A., & de-Albuquerque, L. G. (2018). Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy. Journal of Animal Science, 96(10), 4229–4237.

Maltin, C., Balcerzak, D., Tilley, R., & Delday, M. (2003). Determinants of meat quality: tenderness. Proceedings of the Nutrition Society, 62, 337–347.

Mamani-Linares, L. W., Gallo, C., & Alomar, D. (2012). Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy. Meat Science, 90(2), 378–385.

Mancini, R. A. (2009). Meat color. In J. P. Kerry, & D. Ledward (Eds.), Improving the sensory and nutritional quality of fresh meat (pp. 89–110). Woodhead Publishing Limited.

Mancini, R. A., & Hunt, M. C. (2005). Current research in meat color. Meat Science, 71(1), 100–121.

Manley, M., & Baeten, V. (2018). Spectroscopic technique: Near Infrared Spectroscopy (NIR). In D. W. Sun (Ed.), Modern Techniques for Food Authentication (2nd ed., pp. 51–102). Elsevier Inc.

McCarthy, S. N., Henchion, M., White, A., Brandon, K., & Allen, P. (2017). Evaluation of beef eating quality by Irish consumers. Meat Science, 132(April), 118–124.

Merlino, V. M., Borra, D., Girgenti, V., Dal Vecchio, A., & Massaglia, S. (2018). Beef meat preferences of consumers from Northwest Italy: Analysis of choice attributes. Meat Science, 143(September), 119–128.

Meyerding, S. G. H., Gentz, M., Altmann, B., & Meier-Dinkel, L. (2018). Beef quality labels: A combination of sensory acceptance test, stated willingness to pay, and choice-based conjoint analysis. Appetite, 127, 324-333.

Miller, R. (2020). Drivers of consumer liking for beef, pork, and lamb: A review. Foods, 9(4), Article 428.

Miranda-de-la-Lama, G. C., Estévez-Moreno, L. X., Sepúlveda, W. S., Estrada-Chavero, M. C., Rayas-Amor, A. A., Villarroel, M., & María, G. A. (2016). Mexican consumers’ perceptions and attitudes towards farm animal welfare and willingness to pay for welfare friendly meat products. Meat Science, 125, 106-113.

Mitsumoto, M., & Ozawa, S. (1991). Near-infrared spectroscopy determination of physical and chemical characteristics in beef cuts. Journal of Food Science 56(6), 1493–1496.

Morsy, N., & Sun, D. W. (2013). Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen-thawed minced beef. Meat Science, 93(2), 292–302.

Mourot, B. P., Gruffat, D., Durand, D., Chesneau, G., Mairesse, G., & Andueza, D. (2015). Breeds and muscle types modulate performance of near-infrared reflectance spectroscopy to predict the fatty acid composition of bovine meat. Meat Science, 99, 104–112.

Ngapo, T. M., Braña Varela, D., & Rubio-Lozano, M. S. (2017). Mexican consumers at the point of meat purchase. Beef choice. Meat Science, 134, 34–43.

Paredi, G., Raboni, S., Bendixen, E., de Almeida, A. M., & Mozzarelli, A. (2012). “Muscle to meat” molecular events and technological transformations: The proteomics insight. Journal of Proteomics, 75(14), 4275–4289.

Patel, N., Toledo-Alvarado, H., Cecchinato, A., & Bittante, G. (2020). Predicting the content of 20 minerals in beef by different portable near-infrared (NIR) spectrometers. Foods, 9(10), Article 1389.

Picard, B., Gagaoua, M., & Gagaoua, M. (2020). Muscle fiber properties in cattle and their relationships with meat qualities: An overview. Journal of Agricultural and Food Chemistry, 68(22), 6021-6039.

Prevolnik, M., Candek-Potokar, M., & Škorjanc, D. (2010). Predicting pork water-holding capacity with NIR spectroscopy in relation to different reference methods. Journal of Food Engineering, 98(3), 347–352.

Prieto, N., Andrés, S., Giráldez, F. J., Mantecón, A. R., & Lavín, P. (2008). Ability of near infrared reflectance spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle meat samples. Meat Science, 79(4), 692–699.

Prieto, N., Ross, D. W., Navajas, E. A., Nute, G. R., Richardson, R. I., Hyslop, J. J., & Roehe, R. (2009). On-line application of visible and near infrared reflectance spectroscopy to predict chemical–physical and sensory characteristics of beef quality. Meat Science, 83(1), 96-103.

Prieto, N., López-Campos, Suman, S. P., Uttaro, B., Rodas-González, A., & Aalhus, J. L. (2018). Exploring innovative possibilities of recovering the value of dark-cutting beef in the Canadian grading system. Meat Science, 137, 77–84.

Prieto, N, López-Campos, Ó., Zijlstra, R. T., Uttaro, B., & Aalhus, J. L. (2014). Discrimination of beef dark cutters using visible and near infrared reflectance spectroscopy. Canadian Journal of Animal Science, 94(3), 445–454.

Prieto, N, Pawluczyk, O., Dugan, M. E. R., & Aalhus, J. L. (2017). A review of the principles and applications of near-infrared spectroscopy to characterize meat, fat, and meat products. Applied Spectroscopy, 71(7), 1403-1426.

Prieto-Benavides, N. (2006). Aplicación de la tecnología NIRS para estimar parámetros indicativos de la calidad de la carne de vacuno [Disertación Doctoral, Universidad de León]. Respositorio del Consejo Superior de Investigaciones Científicas.

Realini, C. E., Kallas, Z., Pérez-Juan, M., Gómez, I., Olleta, J. L., Beriain, M. J., Albertí, P., & Sañudo, C. (2014). Relative importance of cues underlying Spanish consumers’ beef choice and segmentation, and consumer liking of beef enriched with n-3 and CLA fatty acids. Food Quality and Preference, 33, 74–85.

Ripoll, G., Albertí, P., Panea, B., Olleta, J. L., & Sañudo, C. (2008). Near-infrared reflectance spectroscopy for predicting chemical, instrumental and sensory quality of beef. Meat Science, 80(3), 697–702.

Roberts, J. J., & Cozzolino, D. (2016). An overview on the application of chemometrics in food science and technology—An approach to quantitative data analysis. Food Analytical Methods, 9(12), 3258–3267.

Rosenvold, K., Micklander, E., Hansen, P. W., Burling-Claridge, R., Challies, M., Devine, C., & North, M. (2009). Temporal, biochemical and structural factors that influence beef quality measurement using near infrared spectroscopy. Meat Science, 82(3), 379–388.

Sakadevan, K., & Nguyen, M. L. (2017). Livestock production and its impact on nutrient pollution and greenhouse gas emissions. Advances in Agronomy, 141, 147–184.

Savoia, S., Albera, A., Brugiapaglia, A., Di Stasio, L., Ferragina, A., Cecchinato, A., & Bittante, G. (2020). Prediction of meat quality traits in the abattoir using portable and hand-held near-infrared spectrometers. Meat Science, 161(March), Article 108017.

Scollan, N. D., Dannenberger, D., Nuernberg, K., Richardson, I., MacKintosh, S., Hocquette, J. F., & Moloney, A. P. (2014). Enhancing the nutritional and health value of beef lipids and their relationship with meat quality. Meat Science, 97(3), 384–394.

Scollan, N. D., Price, E. M., Morgan, S. A., Huws, S. A., & Shingfield, K. J. (2017). Can we improve the nutritional quality of meat? Proceedingsof the Nutrition Society, 76(4), 603–618.

Sierra, V., Aldai, N., Castro, P., Osoro, K., Coto-Montes, A., & Oliván, M. (2008). Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy. Meat Science, 78(3), 248–255.

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104(July), 333–339.

Su, H., Sha, K., Zhang, L., Zhang, Q., Xu, Y., Zhang, R., & Sun, B. (2014). Development of near infrared reflectance spectroscopy to predict chemical composition with a wide range of variability in beef. Meat Science, 98(2), 110-114.

Su, H., Zhang, S., Li, H., Xie, P., & Sun, B. (2018). Using near-infrared reflectance spectroscopy to predict physical parameters of beef. Spectroscopy Letters, 51(4), 163–168.

Tøgersen, G., Isaksson,T., Nilsen, B. N., Bakker, E. A., & Hildrum, K. I. (1999). On-line NIR analysis of fat, water and protein in industrial scale ground meat batches. Meat Science, 51(1), 97-102. https//

Ton, S., De Marchi, M., Manfrin, D., Meneghesso, M., Cassandro, M., & Penasa, M. (2015). Use of near infrared technology to predict fatty acid groups in commercial ground meat products. Poljoprivreda, 21(Supplement 1), 232-236.

Troy, D. J., & Kerry, J. P. (2010). Consumer perception and the role of science in the meat industry. Meat Science, 86(1), 214–226.

Van-Loo, E. J., Caputo, V., & Lusk, J. L. (2020). Consumer preferences for farm-raised meat, lab-grown meat, and plant-based meat alternatives: Does information or brand matter? Food Policy, 95, Article 101931.

Warriss, P. D. (2000). Meat science: An introductory text (2nd Ed). CABI.

Weeranantanaphan, J., Downey, G., Allen, P., & Sun, D. W. (2011). A review of near infrared spectroscopy in muscle food analysis: 2005-2010. Journal of Near Infrared Spectroscopy, 19(2), 61–104.

Willett, W., Rockström, J., Loken, B., Springmann, M., Lang, T., Vermeulen, S., Garnett, T., Tilman, D., DeClerck, F., Wood, A., Jonell, M., Clark, M., Gordon, L. J., Fanzo, J., Hawkes, C., Zurayk, R., Rivera, J. A., De-Vries, W., Sibanda, L. M., … Murray, C. J. L. (2019). Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. The Lancet, 393(10170), 447–492.

Workman, J. (2016). The concise handbook of analytical spectroscopy : theory, applications, and reference materials. World Scientific Publishing.

Yang, S. H., Suhandoko, A. A., & Chen, D. (2020). Impact of nutritional information on consumers’ willingness to pay for meat products in traditional wet markets of taiwan. Foods, 9(8), Article 1086.

Young, O., West, J., Hart, A., & van Otterdijk, F. F. (2004). A method for early determination of meat ultimate pH. Meat Science, 66(2), 493–498.



How to Cite

Barragán-Hernández, W. A., Mahecha-Ledesma, L., Olivera-Angel, M., & Angulo-Arizala, J. (2021). Compositional and sensory quality of beef and its determination by near infrared. Agronomía Mesoamericana, 32(3), 1000–1018.

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