Major food adulteration incidents occur with alarming frequency and are episodic, with the latest incident, involving the adulteration of meat from 21 producers in Brazil supplied to 60 other countries, reinforcing this view. Food fraud and counterfeiting involves all types of foods, feed, beverages, and packaging, with the potential for serious health, as well as significant economic and social impacts. In the spirit drinks sector, counterfeiters often ‘recycle’ used genuine packaging, or employ good quality simulants. To prove that suspect products are non-authentic ideally requires accurate, sensitive, analysis of the complex chemical composition while still in its packaging. This has yet to be achieved. Here, we have developed handheld spatially offset Raman spectroscopy (SORS) for the first time in a food or beverage product, and demonstrate the potential for rapid in situ through-container analysis; achieving unequivocal detection of multiple chemical markers known for their use in the adulteration and counterfeiting of Scotch whisky, and other spirit drinks. We demonstrate that it is possible to detect a total of 10 denaturants/additives in extremely low concentrations without any contact with the sample; discriminate between and within multiple well-known Scotch whisky brands, and detect methanol concentrations well below the maximum human tolerable level.
The spirits drinks industry is of significant global economic importance and a major employer worldwide, and the ability to ensure product authenticity and maintain consumer confidence in these high-value products is absolutely essential.
Whisky, as a high value product, is often adulterated, with adverse economic effects for both producers and consumers as well as potential public health impacts. Here we report the use of DAPCI-MS to analyse and chemically profile both genuine and counterfeit whisky samples employing a novel ‘direct from the bottle’ methodology with zero sample pre-treatment, zero solvent requirement and almost no sample usage. 25 samples have been analysed from a collection of blended Scotch whisky (n = 15) and known counterfeit whisky products (n = 10). Principal component analysis has been applied to dimensionally reduce the data and discriminate between sample groups. Additional chemometric modelling, a partial least squares regression, has correctly classified samples with 92% success rate. DAPCI-MS shows promise for simple, fast and accurate counterfeit detection with potential for generic aroma profiling and process quality monitoring applications.
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