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Glucosinolate assessment in Brassica oleracea leaves by near-infrared spectroscopy

Published online by Cambridge University Press:  28 June 2005

R. FONT
Affiliation:
Department of Agronomy and Plant Breeding, Institute of Sustainable Agriculture (CSIC), Alameda del Obispo s/n, 14080 Córdoba, Spain
M. DEL RÍO-CELESTINO
Affiliation:
Department of Agronomy and Plant Breeding, Institute of Sustainable Agriculture (CSIC), Alameda del Obispo s/n, 14080 Córdoba, Spain
E. ROSA
Affiliation:
CECEA-Centro de Ciência e Engenharia Agrícola, Departamento de Fitotecnia e Engenharia Rural, Universidade de Trás-os-Montes e Alto Douro, Apartado 1013, 5001-911 Vila Real, Portugal
A. AIRES
Affiliation:
CECEA-Centro de Ciência e Engenharia Agrícola, Departamento de Fitotecnia e Engenharia Rural, Universidade de Trás-os-Montes e Alto Douro, Apartado 1013, 5001-911 Vila Real, Portugal
A. DE HARO-BAILÓN
Affiliation:
Department of Agronomy and Plant Breeding, Institute of Sustainable Agriculture (CSIC), Alameda del Obispo s/n, 14080 Córdoba, Spain

Abstract

Glucosinolates and their hydrolysis products are of great concern because they are responsible for many of both the beneficial and harmful properties of glucosinolate-containing plants. Cabbage (Brassica oleracea L.) is a species highly consumed as a leaf vegetable in many countries showing high qualitative and quantitative differences in glucosinolate composition among cultivars. The standard analytical techniques for determining glucosinolate composition using conventional methods lead to high costs, labour input and delays, all of which affect both the availability of data and the taking of swift decisions. In contrast, near-infrared spectroscopy (NIRS) has emerged as a rapid and cost-effective technique of analysis for many agro-food products. In this work we test the potential of NIRS for screening the total glucosinolates (t-GSL), gluconapin (GNA), gluconasturtiin (GNAST) and neoglucobrassicin (NGBS) contents of cabbage leaf cultivars coming from Portugal and Spain. NIRS calibrations resulted in coefficients of determination and standard deviation to standard error of cross-validation ratio of 0·83 and 2·38 for t-GSL; 0·70 and 1·85 for GNA; 0·62 and 1·63 for GNAST; and 0·60 and 1·58 for NGBS, respectively. An examination of the loadings of the equation for t-GSL suggested that O-H groups of water, C-H combinations of the methylene group and also N-H groups of amides were the molecular associations most strongly used in modelling total glucosinolates. It was concluded that NIRS shows a high potential as an analytical method for total and individual glucosinolate routine analysis in cabbage.

Type
Research Article
Copyright
© 2005 Cambridge University Press

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