Novel method based on ion mobility spectroscopy for the quantification of adulterants in honeys. (August 2020)

• Record Type: Journal Article
• Authors: Aliaño-González, María José; Ferreiro-González, Marta; Espada-Bellido, Estrella; Barbero, Gerardo F.; Palma, Miguel
• Is Part Of: Food control. Volume 114(2020)
• Publication Date: 2020-08
• Subjects: Honey -- Adulteration -- Sweeteners -- Ion mobility spectrometry -- Sum spectrum -- Chemometrics -- Quantification; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2020.107236
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: According to European Union Regulation, honey is a food product whose composition cannot be modified. However, high-quality honey is often adulterated by adding sweeteners of other sugar compounds. This paper studies the suitability of Ion Mobility Spectra from generated headspace as a method for the detection and discrimination of honey adulterated by different substances. A Box-Behnken design in conjunction with a response surface methodology was employed to optimize five different variables related to headspace generation (incubation temperature, incubation time, injection volume, weight of the samples and pre-heating time). The resulting model showed a regression coefficient of R 2 = 88.07%, it is therefore suitable for a reliable selection of the experimental variables. Repeatability and intermediate precision were also evaluated, and coefficients of variation below 5% were obtained (CV of 4.6% and 4.2% respectively). The developed method has been applied to different samples resulting for the mixture of honey and other sweeteners at different percentages (5%–50%) in an attempt to mimic the adulterated products that are more commonly found in the market. A thorough and exact classification (100%) with regards to the presence/absence of adulterant as well as the type of adulterant used has been achieved. A Partial Least Squares regression model was completed in order to determine the percentage of the different adulterants. The prediction error was below 4% in all the cases. These results demonstrate the applicability of the developed method for the detection and quantification of adulterated honey with different adulterant contents. Highlights: Optimization study for the discrimination between pure honey and five adulterants. Application to adulterated samples with five different adulterants (5%–50%). Full discrimination among different adulterants has been achieved. Individual PLS models for a good prediction of the percentage of adulteration in honey. Ion Mobility Spectra with chemometrics can be used for honey quality control.