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Your search keyword '"Teixeira, G."' showing total 6 results
Start Over Author "Teixeira, G." Publisher oxford university press (oup)
6 results on '"Teixeira, G."'

1. A dark siren measurement of the Hubble constant using gravitational wave events from the first three LIGO/Virgo observing runs and DELVE

Author

Alfradique, V, primary, Bom, C R, additional, Palmese, A, additional, Teixeira, G, additional, Santana-Silva, L, additional, Drlica-Wagner, A, additional, Riley, A H, additional, Martínez-Vázquez, C E, additional, Sand, D J, additional, Stringfellow, G S, additional, Medina, G E, additional, Carballo-Bello, J A, additional, Choi, Y, additional, Esteves, J, additional, Limberg, G, additional, Mutlu-Pakdil, B, additional, Noël, N E D, additional, Pace, A B, additional, Sakowska, J D, additional, and Wu, J F, additional

Published
2024
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2. Data Release 2 of S-PLUS: Accurate template-fitting based photometry covering ∼1000 deg2 in 12 optical filters

Author

Almeida-Fernandes, F, primary, SamPedro, L, additional, Herpich, F R, additional, Molino, A, additional, Barbosa, C E, additional, Buzzo, M L, additional, Overzier, R A, additional, de Lima, E V R, additional, Nakazono, L M I, additional, Oliveira Schwarz, G B, additional, Perottoni, H D, additional, Bolutavicius, G F, additional, Gutiérrez-Soto, L A, additional, Santos-Silva, T, additional, Vitorelli, A Z, additional, Werle, A, additional, Whitten, D D, additional, Costa Duarte, M V, additional, Bom, C R, additional, Coelho, P, additional, Sodré, L, additional, Placco, V M, additional, Teixeira, G S M, additional, Alonso-García, J, additional, Barbosa, C L, additional, Beers, T C, additional, Bonatto, C J, additional, Chies-Santos, A L, additional, Hartmann, E A, additional, Lopes de Oliveira, R, additional, Navarete, F, additional, Kanaan, A, additional, Ribeiro, T, additional, Schoenell, W, additional, and Mendes de Oliveira, C, additional

Published
2022
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3. P–073 High-intensity interval training modulates the effects of hypertension on inflammatory mediators in testis in adult spontaneously hypertensive rats

Author

Giometti, I, primary, Veras, A, additional, Pacagnelli, F, additional, Schaffer, L, additional, Oliveira, M, additional, Nespolo, R, additional, Santos, A, additional, Souza, F, additional, Castilho, C, additional, Favareto, A P, additional, Mendes, L, additional, and Teixeira, G, additional

Published
2021
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4. Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy

Author

Magalhães, A F B, Teixeira, G H de A, Ríos, A C H, Silva, D B dos S, Mota, L F M, Muniz, M M M, Medeiros-De-morais, Camilo De lelis, de Lima, K M G, Cunha Júnior, L C, Baldi, F, Carvalheiro, R, Oliveira, H N de, Chardulo, L A L, Albuquerque, L G de, Magalhães, A F B, Teixeira, G H de A, Ríos, A C H, Silva, D B dos S, Mota, L F M, Muniz, M M M, Medeiros-De-morais, Camilo De lelis, de Lima, K M G, Cunha Júnior, L C, Baldi, F, Carvalheiro, R, Oliveira, H N de, Chardulo, L A L, and Albuquerque, L G de

Abstract

The main definition for meat quality should include factors that affect consumer appreciation of the product. Physical laboratory analyses are necessary to identify factors that affect meat quality and specific equipment is used for this purpose, which is expensive and destructive, and the analyses are usually time consuming. An alternative method to performing several beef analyses is near-infrared reflectance spectroscopy (NIRS), which permits to reduce costs and to obtain faster, simpler, and non-destructive measurements. The objective of this study was to evaluate the feasibility of NIRS to predict shear force (WBSF), marbling, and color (*a=redness; b*=yellowness and L*=lightness) in meat samples of uncastrated male Nelore cattle, that were approximately 2-year-old. Samples of longissimus thoracis (n=644) were collected and spectra were obtained prior to meat quality analysis. Multivariate calibration was performed by partial least squares regression. Several preprocessing techniques were evaluated alone and in combination: raw data, reduction of spectral range, multiplicative scatter correction, and 1st derivative. Accuracies of the calibration models were evaluated using the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), coefficient of determination in the calibration (R²C) and prediction (R²P) groups. Among the different preprocessing techniques, the reduction of spectral range provided the best prediction accuracy for all traits. The NIRS showed a better performance to predict WBSF (RMSEP = 1.42kg, R²P = 0.40) and b* color (RMSEP = 1.21, R²P = 0.44), while its ability to accurately predict L* (RMSEP = 1.98, R²P = 0.16) and a* (RMSEP = 1.42, R²P = 0.17) was limited. NIRS was unsuitable to predict subjective meat quality traits such as marbling in Nelore cattle.

Published
2018

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