A fast chemometric procedure based on NIR data for authentication of honey with protected geographical indication.

Abstract In this work, information contained in near infrared (NIR) spectra of honeys with protected geographical indication (PGI) “Mel de Galicia” was processed by means of different chemometric techniques to develop an authentication system for this high quality food product. Honey spectra were obtained in a fast and single way, and they were pretreated by means of standard normal variate transformation in order to remove the influence of particle size, scattering and other factors, and prior to their use as input data. As the first step in chemometric study, display techniques such as principal component analysis and cluster analysis were applied in order to demonstrate that the NIR data contained useful information to develop a pattern recognition classification system to authenticate honeys with PGI. The second step consisted in the application of different pattern recognition techniques (such as D-PLS: Discriminant partial least squares regression; SIMCA: Soft independent modelling of class analogy; KNN: K-nearest neighbours; and MLF-NN: Multilayer feedforward neural networks) to derive diverse models for PGI-honey class with the objective of detecting possible falsification of these high-quality honeys. Amongst all the classification chemometric procedures, SIMCA achieved to be the best PGI-model with 93.3% of sensitivity and 100% of specificity. Therefore, the combination of NIR information data with SIMCA developed a single and fast method in order to differentiate between genuine PGI-Galician honey samples and other commercial honey samples from other origins that, due to their lower price, could be used as substrates for falsification of genuine PGI ones.

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