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News

Study: Development of a processing factor prediction model for pesticides

22/11/2024 - François-Xavier Branthôme
Study: Development of a processing factor prediction model for pesticides in processed tomato foods using elastic net regularization
A prediction model was developed for processing factor (PF) of pesticide residues. The development of the model was examined for tomato juice, wet and dry pomace. The physicochemical properties of pesticides were used as predicter variables. The established model could predict PF within a 2-fold range for many pesticides.

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. These results suggest that the PF values are predictable at a certain degree of accuracy from the unique sets of physicochemical properties of pesticides using the developed model based on a processing study with representative pesticides.

Agricultural food commodities are not only consumed as raw products, but also as processed products. In general, processing affects the level of residual chemicals, such as pesticides, in the commodities. Changes in the level during processing can be expressed by a processing factor (PF), which is defined as the ratio of the residual concentration in the processed commodity over that in the raw agricultural commodity (RAC). The PF is used as one of the essential parameters for the risk analysis of pesticide residues in processed food commodities.
For risk assessment of pesticide residues, dietary exposure estimates can be refined by the PF values, resulting in a more realistic assessment in cases when commodities are mainly consumed after processing. For example, the international estimated daily intakes (IEDIs) and the international estimated short-term intakes (IESTIs) proposed by the Joint Food and Agriculture Organization of the United Nations use the supervised trials median residue (STMR) and highest residue (HR) of the RAC multiplied by the PF to give the STMR-processing (STMR-P) and HR-processing (HR-P).
The PF is also employed for the risk management of pesticide residues. For example, the PF provides an estimate of pesticide residue levels in the RAC from those in the processed food commodities, which is crucial information on whether the starting material is in compliance with national regulations. Furthermore, the PF is used to establish the maximum residue limits (MRLs) for processed commodities by considering the changes in pesticide residual levels during processing. To date, MRLs are set for a limited number of processed commodities such as fruit juices, vegetable oils, cereal grain milling fractions and by-products derived from fruits and vegetables used for animal feeding purposes by the Codex Alimentarius. Consequently, the PF is an indispensable parameter for risk analysis of pesticide residues in foods.

In this study, a novel PF prediction model was developed based on the physicochemical properties of pesticides using regularized elastic net regression to overcome the problem of multicollinearity. Processed tomato commodities (juice, wet pomace and dry pomace) were selected as the targeted commodities of our study, since they are representative products; however, a PF prediction model has not been established. Significant correlations between the PF values and a set of the physicochemical properties of pesticides in the processed tomato commodities were observed in the published PF data and the processing study conducted in this study. The PF prediction models were established based on these correlations, which was followed by the evaluation of their predictive performance.

Some complementary data
Authors: Yuki Yamasaki, Kosuke Nakamura, Nao Kashiwabara, Shinji Chiba, Hiroshi Akiyama, Tomoaki Tsutsumi. For further detail about this sudy, click the link below:
https://doi.org/10.1016/j.foodchem.2024.138943

Source: sciencedirect.com
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