Vis-NIRS As an Auxiliary Tool in the Classification of Bovine Carcasses

This work aimed to evaluate the use of Visible and Near-infrared Spectroscopy (Vis-NIRS) as a tool in the classification of bovine carcasses. A total of 133 animals (77 females, 29 males surgically castrated and 27 males immunologically castrated) were used. Vis-NIRS spectra were collected in a chilling room 24 h postmortem directly on the hanging carcasses over the longissimus thoracis between the surface of the 5th and 6th ribs. The data were evaluated by principal component analysis (PCA) and the partial least squares regression (PLSR) method. For the prediction of sex, the best model was the Standard Normal Variate (SNV) because it presented a relatively high coefficient of determination for prediction, presenting a percentage of correctness of 75.51% and an error of 24.49%. Regarding age, none of the models were able to differentiate the samples through Vis-NIRS. The findings confirm that Vis-NIRS prediction models are a valuable tool for differentiating carcasses based on sex. To further enhance the precision of these predictions, we recommend using Vis-NIRS equipment with the full infrared wavelength range to collect and predict sex and age in intact beef samples.

References

Reddy B, Sivakumar A, Jeong D, Woo Y, Park S, Lee S . Beef quality traits of heifer in comparison with steer, bull and cow at various feeding environments. Anim Sci J. 2014; 86(1):1-16. DOI: 10.1111/asj.12266. View

Shackelford S, Wheeler T, Koohmaraie M . Development of optimal protocol for visible and near-infrared reflectance spectroscopic evaluation of meat quality. Meat Sci. 2011; 68(3):371-81. DOI: 10.1016/j.meatsci.2004.01.013. View

De Marchi M, Penasa M, Cecchinato A, Bittante G . The relevance of different near infrared technologies and sample treatments for predicting meat quality traits in commercial beef cuts. Meat Sci. 2012; 93(2):329-35. DOI: 10.1016/j.meatsci.2012.09.013. View

Prieto N, Andres S, Giraldez F, Mantecon A, Lavin P . Discrimination of adult steers (oxen) and young cattle ground meat samples by near infrared reflectance spectroscopy (NIRS). Meat Sci. 2011; 79(1):198-201. DOI: 10.1016/j.meatsci.2007.08.001. View

Watanabe G, Motoyama M, Nakajima I, Sasaki K . Relationship between water-holding capacity and intramuscular fat content in Japanese commercial pork loin. Asian-Australas J Anim Sci. 2017; 31(6):914-918. PMC: 5933991. DOI: 10.5713/ajas.17.0640. View

Geesink G, Schreutelkamp F, Frankhuizen R, Vedder H, Faber N, Kranen R . Prediction of pork quality attributes from near infrared reflectance spectra. Meat Sci. 2011; 65(1):661-8. DOI: 10.1016/S0309-1740(02)00269-3. View

Prieto N, Lopez-Campos O, Aalhus J, Dugan M, Juarez M, Uttaro B . Use of near infrared spectroscopy for estimating meat chemical composition, quality traits and fatty acid content from cattle fed sunflower or flaxseed. Meat Sci. 2014; 98(2):279-88. DOI: 10.1016/j.meatsci.2014.06.005. View

Prieto N, Pawluczyk O, Dugan M, Aalhus J . A Review of the Principles and Applications of Near-Infrared Spectroscopy to Characterize Meat, Fat, and Meat Products. Appl Spectrosc. 2017; 71(7):1403-1426. DOI: 10.1177/0003702817709299. View

Pogorzelska-Przybylek P, Nogalski Z, Sobczuk-Szul M, Momot M . The effect of gender status on the growth performance, carcass and meat quality traits of young crossbred Holstein-Friesian×Limousin cattle. Anim Biosci. 2020; 34(5):914-921. PMC: 8100472. DOI: 10.5713/ajas.20.0085. View

10.

Prieto N, Roehe R, Lavin P, Batten G, Andres S . Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review. Meat Sci. 2010; 83(2):175-86. DOI: 10.1016/j.meatsci.2009.04.016. View

11.

Shackelford S, Wheeler T, Koohmaraie M . On-line classification of US Select beef carcasses for longissimus tenderness using visible and near-infrared reflectance spectroscopy. Meat Sci. 2011; 69(3):409-15. DOI: 10.1016/j.meatsci.2004.08.011. View

12.

Park S, Beak S, Jung D, Kim S, Jeong I, Piao M . Genetic, management, and nutritional factors affecting intramuscular fat deposition in beef cattle - A review. Asian-Australas J Anim Sci. 2018; 31(7):1043-1061. PMC: 6039335. DOI: 10.5713/ajas.18.0310. View

13.

Downey G, Beauchene D . Discrimination between fresh and frozen-then-thawed beef m. longissimus dorsi by combined visible-near infrared reflectance spectroscopy: A feasibility study. Meat Sci. 2011; 45(3):353-63. DOI: 10.1016/s0309-1740(96)00127-1. View

14.

Kamruzzaman M . Optical sensing as analytical tools for meat tenderness measurements - A review. Meat Sci. 2022; 195:109007. DOI: 10.1016/j.meatsci.2022.109007. View

15.

Ballin N, Vogensen F, Karlsson A . Species determination - Can we detect and quantify meat adulteration?. Meat Sci. 2010; 83(2):165-74. DOI: 10.1016/j.meatsci.2009.06.003. View

16.

Dixit Y, Hitchman S, Hicks T, Lim P, Wong C, Holibar L . Non-invasive spectroscopic and imaging systems for prediction of beef quality in a meat processing pilot plant. Meat Sci. 2020; 181:108410. DOI: 10.1016/j.meatsci.2020.108410. View

17.

Bonin M, da Luz E Silva S, Bunger L, Ross D, Feijo G, da Costa Gomes R . Predicting the shear value and intramuscular fat in meat from Nellore cattle using Vis-NIR spectroscopy. Meat Sci. 2020; 163:108077. DOI: 10.1016/j.meatsci.2020.108077. View

18.

Kapper C, Klont R, Verdonk J, Urlings H . Prediction of pork quality with near infrared spectroscopy (NIRS): 1. Feasibility and robustness of NIRS measurements at laboratory scale. Meat Sci. 2012; 91(3):294-9. DOI: 10.1016/j.meatsci.2012.02.005. View

19.

Balage J, da Luz E Silva S, Gomide C, Bonin M, Figueira A . Predicting pork quality using Vis/NIR spectroscopy. Meat Sci. 2015; 108:37-43. DOI: 10.1016/j.meatsci.2015.04.018. View

20.

Li Y, Wang H, Yang Z, Wang X, Wang W, Hui T . Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review. Foods. 2024; 13(10). PMC: 11120403. DOI: 10.3390/foods13101512. View