DSpace
 
  Repositório Institucional da Universidade de Aveiro > CESAM - centro de estudos do ambiente e do mar > CESAM - Artigos >
 Near infrared reflectance spectroscopy (NIRS) for predicting glucocorticoid metabolites in lyophilised and oven-dried faeces of red deer
Please use this identifier to cite or link to this item http://hdl.handle.net/10773/23176

title: Near infrared reflectance spectroscopy (NIRS) for predicting glucocorticoid metabolites in lyophilised and oven-dried faeces of red deer
authors: Santos, João P. V.
Vicente, Joaquín
Villamuelas, Miriam
Albanell, Elena
Serrano, Emmanuel
Carvalho, João
Fonseca, Carlos
Gortázar, Christian
López-Olvera, Jorge Ramón
keywords: Cervus elaphus
Faecal indicators
Glucocorticoid metabolites
NIRS
Stress
issue date: 2014
publisher: Elsevier
abstract: Interest in measuring faecal glucocorticoid metabolites (FGM) as indicators of physiological homeostasis and performance in wildlife is increasing. However, current reference techniques, specifically enzyme immunoassays (EIAs) and radioimmunoassays (RIAs), are expensive, time-consuming, reagent-based, and the samples are destroyed during their application. Conversely, near infrared reflectance spectroscopy (NIRS) is a rapid, reagent-free and non-destructive technique, which, once calibrated by standard laboratory methods, can be used at a low cost. The objectives of this study were to evaluate the feasibility of using NIRS to predict glucocorticoid metabolite concentrations in red deer (Cervus elaphus) faeces, as well as the effect of lyophilisation and oven drying on FGM quantification. Seventy-eight fresh faecal samples were collected directly from the rectum of hunter-harvested red deer and then divided into two equal portions; one portion of each individual sample was lyophilised and the other portion was oven-dried. After dehydration, all faecal samples were ground and then analysed by RIA (standard laboratory technique) and scanned with an NIR spectrophotometer. Modified partial least squares regression was used to generate NIRS calibration equations for both lyophilised and oven-dried samples and a cross-validation procedure was employed for their optimisation. Near infrared reflectance spectroscopy proved to be a feasible, acceptably accurate and reliable technique for predicting FGM concentrations in red deer faeces subjected either to lyophilisation or to oven drying. Calibration and cross-validation results indicated that predictive equations for lyophilised faeces were slightly more precise and robust than for the oven-dried ones (lyophilised: R2 = 0.90, r2cv = 0.81, RPD = 2.72; oven-dried: R2 = 0.88, r2cv = 0.79, RPD = 2.26; CV: cross-validation, RPD: ratio of performance to deviation). Nevertheless, oven-dried faeces may be used as an alternative to lyophilised ones to quantify FGM levels accurately, provided that an appropriate combination of dehydration time and temperature is used during the desiccation process. High degrees of association and statistically significant positive correlations (p < 0.001) were found between the lyophilised and oven-dried samples regarding their FGM content, both for RIA assays and NIRS analyses. This study provides a new approach for assessing stress levels in free-ranging populations and has practical implications concerning wildlife monitoring as it makes it possible to improve the efficiency and reduce the cost and time constraints of current analytical techniques.
URI: http://hdl.handle.net/10773/23176
ISSN: 1470-160X
publisher version/DOI: http://dx.doi.org/10.1016/j.ecolind.2014.05.021
source: Ecological Indicators
appears in collectionsBIO - Artigos
CESAM - Artigos

files in this item

file description sizeformat
Santos et al. - 2014 - Near infrared reflectance spectroscopy (NIRS) for .pdf969.21 kBAdobe PDFview/open
Restrict Access. You can Request a copy!
statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! RCAAP OpenAIRE DeGóis
ria-repositorio@ua.pt - Copyright ©   Universidade de Aveiro - RIA Statistics - Powered by MIT's DSpace software, Version 1.6.2