We have now added signposts to the free IAEA training and Excel add-in for chemometrics to our permanent resources lists. You can find details on our e-seminars page. To aid navigation, it is also listed within mitigation tools. The add-in is invaluable to any laboratory building an authenticity classification model based upon multivariate analysis of known reference samples.
Honey is one of the foods susceptible to extension and adulteration with exogenous sugars. Whereas there is a well established test for C4 sugar (cane or corn based sysrups) adulteration using EA-IRMS (elemental analyser -isotope ratio mass spectrometry), detection of C3 sugar syrups (rice and beet sugar) is more difficult. In this study, an approach combining Fourier-transform infrared spectroscopy (FTIR) and chemometrics was developed for a rapid screening tool to detect potential adulteration of honey with either rice or corn syrup. A set of 46 authentic and 39 commercial honey samples were collected and adulterated samples of between 1-16% with rice syrup and 3-15% corn syrup were prepared, and their FTIR spectra determined. A single class soft independent modeling of class analogy (SIMCA) model was developed on the spectra of the set of commercial and authentic honey samples. The SIMCA model was externally validated with a set of calibration-independent authentic and commercial honey control samples, and those spiked with rice and corn syrups in the 1-16% concentration range. The authentic and commercial honey test samples were correctly predicted with a 88.3% classification rate. High accuracy was also found in predicting the rice and corn syrup spiked samples above 7% concentration range, yielding 97.6% and 94.8% correct classification rates, respectively.
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Pistachio is one of the most expensive nuts, and is prone to adulteration because of its high commodity value. The most common adulterants are green pea and peanuts with added colours. Turkish researchers have developed a non-targeted method using portable FT-IR (Fourier Transform infared) and UV–Visible spectrometers. Samples of pistachio granules were adulterated with green pea and peanut at concentrations from 5-40% w/w, and their spectra taken using a portable FT-IR spectrometer and a conventional UV–Vis spectrometer, which were analysed by Soft Independent Modeling of Class Analogy (SIMCA) to generate classification algorithms to authenticate pistachio. Partial Least Square Regression (PLSR) was used to predict the concentrations of adulterants, and both instruments gave excellent predictions of adulterant levels.
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