Application of near infrared spectroscopy combined with chemometrics to authenticate local cultivar flour content in the production of protected geographical indication “Galician bread”
The Protected Geographical Indication (PGI) “Galician Bread” safeguards the traditional production of Galician wheat bread, recognized for its distinctive quality, which requires the use of at least 25 % flour from the local wheat cultivars ‘Caaveiro’ and ‘Callobre’. The aim of this study was to eva...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Santiago de Compostela (USC) |
| Repositorio: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| Idioma: | inglés |
| OAI Identifier: | oai:minerva.usc.gal:10347/43428 |
| Acceso en línea: | https://hdl.handle.net/10347/43428 |
| Access Level: | acceso abierto |
| Palabra clave: | Food authentication Near infrared spectroscopy Chemometrics Galician bread Local wheat cultivars Protected geographical indication |
| Sumario: | The Protected Geographical Indication (PGI) “Galician Bread” safeguards the traditional production of Galician wheat bread, recognized for its distinctive quality, which requires the use of at least 25 % flour from the local wheat cultivars ‘Caaveiro’ and ‘Callobre’. The aim of this study was to evaluate the potential of near-infrared spectroscopy (NIR) to ensure the authenticity, through quantitative analysis, of this high-value food product. A total of 160 mixtures samples of local wheat cultivars and commercial flours were prepared, ranging from 0 to 100 % of ‘Caaveiro’ or ‘Callobre’ content. Spectral data, acquired in the range 900–1700 nm, was analyzed with chemometric tools, using principal components analysis (PCA) and partial least squares regression (PLSR). The mathematical models were developed using different preprocessing techniques, with the most effective model employing a combination of de-trending (DT) and multiplicative scatter correction (MSC). The optimal model achieved high predictive accuracy, with a prediction determination coefficient (R2P) of 0.965, a root mean square error of prediction (RMSEP) of 5.561 % and a residual predictive deviation (RPD) of 5.292. The results demonstrated the effectiveness of NIR combined with chemometrics as a rapid, non-destructive and reliable method for predicting the content of local cultivars in wheat flour. This approach not only supports the authenticity of PGI “Galician Bread” by quantifying the presence of ‘Caaveiro’ or ‘Callobre’ flours in wheat mixtures, but also enables routine quality control and offers a valuable solution for both producers and regulatory bodies to ensure the traceability and integrity of the product. |
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