TY - JOUR

T1 - Quantifying pesticide residues in food matrices using statistical methods

AU - Hakme, Elena

AU - Koubeissy, Amal

AU - Katsikouli, Panagiota

N1 - Publisher Copyright: © 2024 The Authors

PY - 2024

Y1 - 2024

N2 - Traditional techniques for pesticide residues detection and quantification in food using mass spectrometry often require the analysis of standards for each compound, leading to time-consuming and laborious procedures, especially considering that these methods usually involve hundreds of pesticides to be targeted. This paper presents a novel approach to compound quantitation, where multiple target compounds can be accurately quantified using few predictor compounds, significantly reducing the experimental time and minimizing resource requirements. For this purpose, data on detector response which encompassed calibration slopes of a total of 96 pesticide standards on GC-MS/MS and 66 standards on LC-MS/MS, over a period of 4 years, were collected. Two fundamental statistical techniques, Pearson correlation and linear regression, were used to create a predictive model, which accuracy was further evaluated using R-square, adjusted R-square, and Root Mean Square Error (RMSE). Four predicted compounds for the LC dataset and seven predicted compounds for GC dataset were identified, and various predictor combinations were considered. A linear regression model was developed using predictor combinations to estimate the calibration slope of each of the target compound. This model was applied for the quantitation of several pesticide residues in food samples. The results validated the model's high accuracy.

AB - Traditional techniques for pesticide residues detection and quantification in food using mass spectrometry often require the analysis of standards for each compound, leading to time-consuming and laborious procedures, especially considering that these methods usually involve hundreds of pesticides to be targeted. This paper presents a novel approach to compound quantitation, where multiple target compounds can be accurately quantified using few predictor compounds, significantly reducing the experimental time and minimizing resource requirements. For this purpose, data on detector response which encompassed calibration slopes of a total of 96 pesticide standards on GC-MS/MS and 66 standards on LC-MS/MS, over a period of 4 years, were collected. Two fundamental statistical techniques, Pearson correlation and linear regression, were used to create a predictive model, which accuracy was further evaluated using R-square, adjusted R-square, and Root Mean Square Error (RMSE). Four predicted compounds for the LC dataset and seven predicted compounds for GC dataset were identified, and various predictor combinations were considered. A linear regression model was developed using predictor combinations to estimate the calibration slope of each of the target compound. This model was applied for the quantitation of several pesticide residues in food samples. The results validated the model's high accuracy.

KW - Linear regression

KW - Mass spectrometry

KW - Pesticide residues

KW - Quantitation

KW - Statistical correlation

KW - Sustainability

U2 - 10.1016/j.jfca.2024.106305

DO - 10.1016/j.jfca.2024.106305

M3 - Journal article

AN - SCOPUS:85192756807

VL - 132

JO - Journal of Food Composition and Analysis

JF - Journal of Food Composition and Analysis

SN - 0889-1575

M1 - 106305

ER -