Feasibility of a Rapid and non-destructive Methodology for the Study and Discrimination of Pine nuts Using near-infrared Hyperspectral Analysis and Chemometrics
Spanish pine nut is highly appreciated globally for its aroma and taste. Nevertheless, its market is affected by the growing presence of Chinese pine nuts, entailing mislabeling and counterfeits. In this study, near-infrared hyperspectral imaging (940–1625 nm) coupled to chemometrics, was applied, f...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/167966 |
| Acceso en línea: | https://hdl.handle.net/11441/167966 https://doi.org/10.1016/j.jhydrol.2008.07.048 |
| Access Level: | acceso abierto |
| Palabra clave: | Pine nut Near-Infrared chemical imaging PCA MCR SIMCA Labeling |
| Sumario: | Spanish pine nut is highly appreciated globally for its aroma and taste. Nevertheless, its market is affected by the growing presence of Chinese pine nuts, entailing mislabeling and counterfeits. In this study, near-infrared hyperspectral imaging (940–1625 nm) coupled to chemometrics, was applied, for the first time, to perform a spectral study (identification of chemical distribution and composition) of commercial pine nuts labeled on their package as Spanish and Chinese and to develop a single class-modelling classification model. Sixty-three pine nuts from both marketed origin labels and different qualities were analysed. Principal component analysis (PCA) and multivariate curve resolution (MCR) showed the chemical distribution of the major compounds (bands around 1170–1210 nm and 1485–1550 nm, associated with fats and fatty acids and water and proteins, respectively) of each marketed origin. Soft independent modelling of class analogies (SIMCA) classified the samples according to their labeling of origin, in a pixel-based and nut-based approach, obtaining 89–98% and 84–100% of correct prediction, respectively. This preliminary study demonstrated that the proposed methodology could be used as a fast, comprehensive and innovative quality control tool (for characterisation and classification) for the pine nut industry. |
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