Feasibility study on the use of visible-near-infrared spectroscopy for the screening of individual and total glucosinolate contents in broccoli
The potential of visible-near-infrared spectroscopy to determine selected individual and total glucosinolates in broccoli has been evaluated. Modified partial least-squares regression was used to develop quantitative models to predict glucosinolate contents. Both the whole spectrum and different spe...
| Autores: | , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2012 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/137696 |
| Acceso en línea: | https://hdl.handle.net/11441/137696 https://doi.org/10.1021/jf3018113 |
| Access Level: | acceso abierto |
| Palabra clave: | Broccoli Chemometrics Glucosinolates Near-infrared spectroscopy Visible spectroscopy |
| Sumario: | The potential of visible-near-infrared spectroscopy to determine selected individual and total glucosinolates in broccoli has been evaluated. Modified partial least-squares regression was used to develop quantitative models to predict glucosinolate contents. Both the whole spectrum and different spectral regions were separately evaluated to develop the quantitative models; in all cases the best results were obtained using the near-infrared zone between 2000 and 2498 nm. These models have been externally validated for the screening of glucoraphanin, glucobrassicin, 4-methoxyglucobrassicin, neoglucobrassicin, and total glucosinolates contents. In addition, discriminant partial least-squares was used to distinguish between two possible broccoli cultivars and showed a high degree of accuracy. In the case of the qualitative analysis, best results were obtained using the whole spectrum (i.e., 400-2498 nm) with a correct classification rate of 100% in external validation being obtained. |
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