Determination of the geographical origin of green coffee beans using NIR spectroscopy and multivariate data analysis
By A. Giraudo, S. Grassi, F. Savorani, G. Gavoci, E. Casiraghi, F. Geobaldo. Food Control, 2019
134 citations recorded by the source corpus.
Abstract In this work, near infrared (NIR) spectroscopy and multivariate data analysis were investigated as a fast and non-disruptive method to classify green coffee beans on continents and countries bases. FT-NIR spectra of 191 coffee samples, origin from 2 continents and 9 countries, were acquired by two different laboratories. Laboratory-independent Partial Least Square-Discriminant Analysis and interval PLS-DA models were developed by following a hierarchical approach, i.e. considering at first the continent and then the country of origin as discrimination rule. The best continent-based classification model was able to identify correctly more than 98% in prediction, whereas 100% of them were correctly predicted by the best country-based classification model. The inter-laboratory reliability of the proposed method was confirmed by McNemar test, since no significant differences (P > 0.05) were found. Furthermore, a validation was performed predicting the spectral test set of a laboratory using the model developed by the other one.
Subfields: Analytical Chemistry, Animal Science and Zoology, Biomedical Engineering
View Paper: https://www.semanticscholar.org/paper/55d32672bfbb2f429556ee1b81e330a51fb7f288
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Citations in the corpus
- Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines (2019, methodology): “This approach has been successfully applied to determine the geographical origin of green coffee beans using spectroscopy (Giraudo et al., 2019), to classify the roasted coffee by cup quality using spectroscopy (Craig et al.”