Multivariate Analysis in Food Production: Thermal Regulation Discriminant Analysis in Meat Quality
DOI:
https://doi.org/10.5327/fst.522Palavras-chave:
carcass quality, dimensionality reduction, food industry impact, quail productionResumo
The objective of this research was to reduce the dimensionality of the original set of variables by eliminating redundant information and enabling the recommendation of variables to be evaluated in future experiments. A total of 240 European quail chicks (Coturnix coturnix Japonica), aged 1 day and with an average weight of 8 ± 0.50 g, were used. It was observed that weight gain (32.75%), liver (37.19%), and intestine (33.54%) exhibited the highest coefficients of variation. On the other hand, cloacal temperature had the lowest coefficient of variation (0.88%), indicating low variability in respiratory rate (16.23%) and surface temperature (3.55%). In total, the three main components together explain 74.65% of the data variation, indicating that, among the 16 variables analyzed, 13 were significant in explaining the observed variability. The variables heart weight, liver, and gizzard were not considered relevant for this study, as their impact on the multivariate analysis was minimal. These results have direct implications for the food industry, as they reveal factors that affect meat quality and consumer acceptance.
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Abouelezz, F. M. K. (2017). Evaluation of spirulina algae (Spirulina platensis) as a feed supplement for Japanese quail: nutiritional effects on growth performance, egg production, egg quality, blood metabolites, sperm-egg penetration and fertility. Poultry Science, 37(3), 707–719. https://doi.org/10.21608/epsj.2017.7535
Boiago, M. M., Dilkin, J. D., Kolm, M. A., Barreta, M., Souza, C. F., Baldissera, M. D., Santos, I. D., Wagner, R., Tavernari, F. C., Silva, M. L. B., Zampar, A., Stivanin, T. E., & Silva, A. S. (2019). Spirulina platensis in Japanese quail feeding alters fatty acid profiles and improves egg quality: Benefits to consumers. Journal of Food Biochemistry, 43(7), Article 12860. https://doi.org/10.1111/jfbc.12860
Carrillo, S., Bahena, A., Casas, M., Carranco, M. E., Calvo, C. C., Ávila, E., & Pérez-Gil, F. (2012). El alga Sargassum spp. como alternativa para reducir el contenido de colesterol en el huevo. Revista Cubana de Ciencia Agrícola, 46(2), 181–186. https://www.redalyc.org/articulo.oa?id=193024447011
Cheong, D. S. W., Kasim, A., Sazili, A. Q., Omar, H., & Teoh, J. Y. (2015). Effect of supplementing spirulina on live performance, carcass composition and meat quality of Japanese quail. Wailailak Journal of Science and Technology, 13(2), 77–84. https://wjst.wu.ac.th/index.php/wjst/article/view/1396
Chisti, Y. (2007). Biodiesel from microalgae. Biotechnology Advances, 25(3), 294–306. https://doi.org/10.1016/j.biotechadv.2007.02.001
Costa, G. B., Felix, M. R. L., Simioni, C., Ramlov, F., Oliveira, E. R., Pereira, D. T., Maraschin, M., Chow, F., Horta, P. A., Lalau, C. M, Costa, C. H., Matias, W. G., Bouzon, Z. L., & Schmidt, É. C. (2016). Effects of copper and lead exposure on the ecophysiology of the brown seaweed Sargassum cymosum. Protoplasma, 253, 111–125. https://doi.org/10.1007/s00709-015-0795-4
Couce, M. L., & Pipaon, M. S. (2021). Bone mineralization and calcium phosphorus metabolism. Nutrients, 13(11), Article 3692. https://doi.org/10.3390/nu13113692
Dillon, W. R., & Goldstein, M. (1984). Multivariate Analysis: Methods and Applications. Wiley.
Ekiz, B., Baygul, O., Yalcintan, H., & Ozcan, M. (2020). Comparison of the decision tree, artificial neural network and multiple regression methods for prediction of carcass tissues composition of goat kids. Meat Science, 161, Article 108011. https://doi.org/10.1016/j.meatsci.2019.108011
Ferreira, T. S., Lana, S. R. V., Lana, G. R. Q., Madalena, J. A., Silva, L. C. L., & Torres, E. C. (2019). Resíduo de acerola em dietas para codornas. Arquivo Brasileiro Medicina Veterinária e Zootecnia, 71(1), 259–266. https://doi.org/10.1590/1678-4162-9965
Gatrell, S., Lum K., Kim, J., & Lei, X. G. (2014). Nonruminant Nutrition Symposium: Potential of defatted microalgae from the biofuel industry as an ingredient to replace corn and soybean meal in swine and poultry diets. Journal of Animal Science, 92(4), 1306–1314. https://doi.org/10.2527/jas.2013-7250
Guedes, D. G. P., Ribeiro, M. N., & Carvalho, F. F. R. (2018). Multivariate techniques in the analysis of carcass traits of Morada Nova breed sheep. Ciência Rural, 48(9), Article e20170746. https://doi.org/10.1590/0103-8478cr20170746
Hajati, H., Zaghari, M., & Oliveira, H. C. (2020). Arthrospira (Spirulina) platensis can be considered as a probiotic alternative to reduce heat stress in laying Japanese quails. Brazilian Journal of Poultry Science, 22(1), 1–8. https://doi.org/10.1590/1806-9061-2018-0977
Jolliffe, I. T. (1972). Discarding Variables in a Principal Component Analysis. I: Artificial Data. Journal of the Royal Statistical Society, 21(2), 160–173. https://doi.org/10.2307/2346488
Jolliffe, I. T. (1973). Discarding Variables in a Principal Component Analysis. II: Real Data. Journal of the Royal Statistical Society, 22(1), 21–31. https://doi.org/10.2307/2346300
Khattree, R., & Naik, D. N. (2000). Multivariate Data Reduction and Discrimination with SAS Software. Wiley-SAS.
Liao, T., Gan, M., Zhu, Y., Lei, Y., Yang, Y., Zheng, Q., Niu, L., Zhao, Y., Chen, L., Wu, Y., Zhou, L., Xue, J., Zhou, X., Wang, Y., Shen, L., & Zhu, L. (2025). Carcass and meat quality characteristics and changes of lean and fat pigs after the growth turning point. Foods, 14(15), Article 2719. https://doi.org/10.3390/foods14152719
Maciel, M. P., Saraiva, E. P., Aguiar, É. F., Ribeiro, P. A. P., Passos, D. P., & Silva, J. B. (2010). Effect of using organic microminerals on performance and external quality of eggs of commercial laying hens at the end of laying. Revista Brasileira de Zootecnia, 39(2), 344–348. https://doi.org/10.1590/S1516-35982010000200017
Melo, T. V., Ferreira, R. A., Oliveira, V. C., Carneiro, J. B. A., Moura, A. M. A., Silva, C. S., & Nery, V. L. H. (2008a). Calidad del huevo de codornices utilizando harina de algas marinas y fosfato monoamónico. Archivos de Zootecnia, 57(219), 313–319. https://www.redalyc.org/pdf/495/49515005004.pdf
Melo, T. V., Ferreira, R. A., Carneiro, J. B. A., Oliveira, V. C., Moura, A. M. A, Silva, C. S., & Nery, V. L. H. (2008b). Rendimiento de codornices japonesas utilizando harina de algas marinas y fosfato monoamónico. Archivos de Zootecnia, 57(219), 381–384. https://www.redalyc.org/pdf/495/49515005018.pdf
Petrolli, T. G., Petrolli, O. J., Pereira, A. S. C., Zotti, C. A., Romani, J., Villani, R., Leite, F., & Zanandréa, F. M. (2019). Effects of the Dietary Supplementation with a Microalga Extract on Broiler Performance and Fatty-Acid Meat Profile. Brazilian Journal of Poultry Science, 21(3), 1–8. https://doi.org/10.1590/1806-9061-2018-0958
Qadri, S. S. N., Biswas, A., Mandal, A. B., Kumawat, M., Saxena, R., & Nasir, A. M. (2019). Production performance, immune response and carcass traits of broiler chickens fed diet incorporated with Kappaphycus alvarezii. Journal of Applied Phycology, 31, 753–760. https://doi.org/10.1007/s10811-018-1498-y
Richards, S. A. (1971). The significance of changes in the temperature of the skin and body core of the chicken in the regulation of heat loss. Journal of Physiology, 216(1), 1–10. https://doi.org/10.1113/jphysiol.1971.sp009505
Sakomura, N. K., & Rostagno, H. S. (2007). Métodos de pesquisa em nutrição de monogástricos. FUNEP.
Xiang, R., Zhao, X., Sha, L., Tang, M., Wu, X., Zhang, L., Hou, J., Deng, Q., Qu, Y., Zhu, J., Qin, C., Xiao, C., Xiao, J., Zhong, Y., Yang, B., Song, X., Zhou, J., Han, T., Zheng, S., ... Jiang, X. (2025). Mapping the role of macro and micronutrients in bone mineral density: A comprehensive Mendelian randomization study. European Journal of Nutrition, 64, Article 156. https://doi.org/10.1007/s00394-025-03665-2