HPLC-DAD method development and validation for the quantification of hydroxymethylfurfural in corn chips by means of response surface optimisation.

In recent years, there has been an increased concern about the presence of toxic compounds derived from the Maillard reaction produced during food cooking at high temperatures. The main toxic compounds derived from this reaction are acrylamide and hydroxymethylfurfural (HMF). The majority of analytical methods require sample treatments using solvents which are highly polluting for the environment. The difficulty of quantifying HMF in complex fried food matrices encourages the development of new analytical methods. This paper provides a rapid, sensitive and environmentally-friendly analytical method for the quantification of HMF in corn chips using HPLC-DAD. Chromatographic separation resulted in a baseline separation for HMF in 3.7 min. Sample treatment for corn chip samples first involved a leaching process using water and afterwards a solid-phase extraction (SPE) using HLB-Oasis polymeric cartridges. Sample treatment optimisation was carried out by means of Box-Behnken fractional factorial design and Response Surface Methodolog y to examine the effects of four variables (sample weight, pH, sonication time and elution volume) on HMF extraction from corn chips. The SPE-HPLC-DAD method was validated. The limits of detection and quantification were 0.82 and 2.20 mg kg-1, respectively. Method precision was evaluated in terms of repeatability and reproducibility as relative standard deviations (RSDs) using three concentration levels. For repeatability, RSD values were 6.9, 3.6 and 2.0%; and for reproducibility 18.8, 7.9 and 2.9%. For a ruggedness study the Yuden test was applied and the result demonstrated the method as robust. The method was successfully applied to different corn chip samples.

Validation and assessment of matrix effect and uncertainty of a gas chromatography coupled to mass spectrometry method for pesticides in papaya and avocado samples.

In this paper a method of using the "quick, easy, cheap, effective, rugged, and safe" (QuEChERS) extraction and gas chromatography coupled to mass spectrometry detection (GC-MS) was developed for the analysis of five frequently applied pesticides in papaya and avocado. The selected pesticides, ametryn, atrazine, carbaryl, carbofuran, and methyl parathion, represent the most commonly used classes (carbamates, organophosphorous, and triazines). Optimum separation achieved the analysis of all pesticides in < 6.5 minutes. Validation using papaya and avocado samples established the proposed method as linear, accurate, and precise. In this sense, the correlation coefficients were > 0.99. The limits of detection (LOD) and quantification (LOQ) in papaya ranged from 0.03 mg/kg to 0.35 mg/kg and from 0.06 mg/kg to 0.75 mg/kg, respectively. Meanwhile for avocado, LOD values varied from 0.14 mg/kg to 0.28 mg/kg and LOQ values ranged from 0.22 mg/kg to 0.40 mg/kg. Recoveries obtained for each pesticide in both matrices ranged between 60.6% and 104.3%. The expanded uncertainty of the method was < 26% for all the pesticides in both fruits. Finally, the method was applied to other fruits.

Quantitation of twelve metals in tequila and mezcal spirits as authenticity parameters.

In this paper the differentiation of silver, gold, aged and extra-aged tequila and mezcal has been carried out according to their metal content. Aluminum, barium, calcium, copper, iron, magnesium, manganese, potassium, sodium, strontium, zinc, and sulfur were determined by inductively coupled plasma optical emission spectrometry. The concentrations found for each element in the samples were used as chemical descriptors for characterization purposes. Principal component analysis, linear discriminant analysis and artificial neural networks were applied to differentiate types of tequila and mezcal. Using probabilistic neural networks 100% of success in the classification was obtained for silver, gold, extra-aged tequila and mezcal. In the case of aged tequila 90% of samples were successfully classified. Sodium, potassium, calcium, sulfur, magnesium, iron, strontium, copper and zinc were the most discriminant elements.