Abstract
Nowadays, adulteration of honey is a frequent fraud that is sometimes motivated by the high price of this product in comparison with other sweeteners. Food adulteration is considered a deception to consumers that may have an important impact on people’s health. For this reason, it is important to develop fast, cheap, reliable and easy to use analytical methods for food control. In the present research, a novel method based on headspace-ion mobility spectrometry (HS-IMS) for the detection of adulterated honey by adding high fructose corn syrup (HFCS) has been developed. A Box–Behnken design combined with a response surface method have been used to optimize a procedure to detect adulterated honey. Intermediate precision and repeatability studies have been carried out and coefficients of variance of 4.90% and 4.27%, respectively, have been obtained. The developed method was then tested to detect adulterated honey. For that purpose, pure honey samples were adulterated with HFCS at different percentages (10–50%). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed a tendency of the honey samples to be classified according to the level of adulteration. Nevertheless, a perfect classification was not achieved. On the contrary, a full classification (100%) of all the honey samples was performed by linear discriminant analysis (LDA). This is the first time the technique of HS-IMS has been applied for the determination of adulterated honey with HFCS in an automatic way.
Highlights
Motivated by the high demand for premium products and the elevated prices associated with them, food fraud is a common practice in the market [1]
The use of Ion Mobility Spectrometry (IMS) Sum Spectrum (IMSSS), which it is the sum of intensities of all volatile compounds across the chromatographic profile, was studied
Once this method was optimized, IMSSS in combination with chemometrics, it was used to discriminate the different levels of honey adulteration by High fructose corn syrup (HFCS)
Summary
Motivated by the high demand for premium products and the elevated prices associated with them, food fraud is a common practice in the market [1]. The Food Standards Agency (FSA, UK), has defined this action as “deliberately placing food on the market, for financial gain, with the intention of deceiving the consumer” [2]. For this reason, it is of paramount importance to develop a rapid and reliable analytical method that allows us to identify adulterated food by routine control analysis, as it is the case for adulterated honey. Honey is a highly appreciated product for its superior nutritional properties and its numerous beneficial properties as an antioxidant [4,5] and anti-inflammatory [4,6]
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