Development of a processing factor prediction model for pesticides in processed tomato foods using elastic net regularization.

A novel regularized elastic net regression model was developed to predict processing factor (PF) for pesticide residues, which represents a change in the residue levels during food processing. The PF values for tomato juice, wet pomace and dry pomace in the evaluations and reports published by the Joint FAO/WHO Meeting on Pesticide Residues significantly correlated with the physicochemical properties of pesticides, and subsequently the correlation was observed in the present tomato processing study. The elastic net regression model predicted the PF values using the physicochemical properties as predictor variables for both training and test data within a 2-fold range for 80-100% of the pesticides tested in the tomato processing study while overcoming multicollinearity.

Development of an analytical method for determination of total ethofumesate residues in foods by gas chromatography-tandem mass spectrometry.

Analytical method was developed for determining the total residue of ethofumesate (ET) herbicide using GC-MS/MS. The ET residues were analyzed as a sum of ET, 2-keto-ethofumesate (KET), and open-ring-2-keto-ethofumesate (OKET) and its conjugate. The extracted samples were partitioned with hexane and NaOH solution.

Determination of the total tulathromycin residues in bovine muscle, fat, and liver by liquid chromatography-tandem mass spectrometry.

A reliable and accurate liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based method was developed to quantify total tulathromycin residues in bovine tissues. Specifically, the above method relied on the quantification of CP-60,300, a marker produced by tulathromycin hydrolysis, for which maximum residue limits (MRLs) were established by the European Union and several other countries. Sample preparation and LC-MS/MS conditions were thoroughly optimized to allow for accurate quantification.