Abstract

Concentrations of different steroid hormones have been used in cows as a measure of adrenal or gonadal activity and, thus, as indicators of stress or reproductive state. Detecting cortisol and progesterone in cow hair provides a long-term integrative value of retrospective adrenal or gonadal/placental activity, respectively. Current techniques for steroid detection require a hormone-extraction procedure that involves time, several types of equipment, management of reagents, and some assay procedures (which can also be time-consuming and can destroy the samples). In contrast, near-infrared reflectance spectroscopy (NIRS) is a multi-component predictor technique, characterized as rapid, nondestructive for the sample, and reagent-free. However, as a predictor technique, NIRS needs to be calibrated and validated for each matrix, hormone, and species. The main objective of this study was to evaluate the predictive value of the NIRS technique for hair cortisol and progesterone quantification in cows by using specific enzyme immunoassay as a reference method. Hair samples from 52 adult Friesian lactating cows from a commercial dairy farm were used. Reflectance spectra of hair samples were determined with a NIR reflectance spectrophotometer before and after trimming them. Although similar results were obtained, a slightly better relationship between the reference data and NIRS predicted values was found using trimmed samples. Near infrared reflectance spectroscopy demonstrated its ability to predict cortisol and progesterone concentrations with certain accuracy (R²= 0.90 for cortisol and R²= 0.87 for progesterone). Although NIRS is far from being a complete alternative to current methodologies, the proposed equations can offer screening capability. Considering the advantages of both fields, our results open the possibility for future work on the combination of hair steroid measurement and NIRS methodology.

© 2017 The Author(s)

Spatial mapping of proteoglycan content in articular cartilage using near-infrared (NIR) spectroscopy

Isaac O. Afara, Hayley Moody, Sanjleena Singh, Indira Prasadam, and Adekunle Oloyede
Biomed. Opt. Express 6(1) 144-154 (2015)

Quantification of extra-cerebral and cerebral hemoglobin concentrations during physical exercise using time-domain near infrared spectroscopy

Héloïse Auger, Louis Bherer, Étienne Boucher, Richard Hoge, Frédéric Lesage, and Mathieu Dehaes
Biomed. Opt. Express 7(10) 3826-3842 (2016)

Estimation of directional coupling between cortical areas using Near-Infrared Spectroscopy (NIRS)

Chang-Hwan Im, Young-Jin Jung, Seungduk Lee, Dalkwon Koh, Do-Won Kim, and Beop-Min Kim
Opt. Express 18(6) 5730-5739 (2010)

Comprehensive study of solid pharmaceutical tablets in visible, near infrared (NIR), and longwave infrared (LWIR) spectral regions using a rapid simultaneous ultraviolet/visible/NIR (UVN) + LWIR laser-induced breakdown spectroscopy linear arrays detection system and a fast acousto-optic tunable filter NIR spectrometer

Clayton S.C Yang, Feng Jin, Siva R. Swaminathan, Sita Patel, Evan D. Ramer, Sudhir B. Trivedi, Ei E. Brown, Uwe Hommerich, and Alan C. Samuels
Opt. Express 25(22) 26885-26897 (2017)

Copper concentrate dual-band joint classification using reflectance hyperspectral images in the VIS-NIR and SWIR bands

Franco Rivas, Francisco Pérez, Claudio Sandoval, Ignacio Sanhueza, Benjamín Sepúlveda, Jorge Yañez, and Sergio Torres
Appl. Opt. 62(12) 2970-2977 (2023)