| 3-CA | 3-Chloroaniline |
| AI | Artificial Intelligence |
| ANN | Artificial Neural Networks |
| BHC | Benzenehexachloride |
| CAC | Codex Alimentarius Commission |
| CNN | Convolutional Neural Networks |
| DDT | Dichlorodiphenyltrichloroethane |
| DL | Deep Learning |
| DOMs | Degrees of Milling |
| DTs | Digital Twins |
| EFSA | European Food Safety Authority |
| EPA | United States Environmental Protection Agency |
| EU | European Union |
| EWDs | Electrolyzed Water Devices |
| FAO | Food and Agriculture Organization of the United Nations |
| GAP | Good Agricultural Practices |
| GC-MS/MS | Gas Chromatography coupled with tandem Mass Spectrometry |
| IPM | Integrated Pest Management |
| IRAC | Insecticide Resistance Action Committee |
| JMRP | Joint FAO/WHO Meeting on Pesticide Residues |
| LAB | Lactic Acid Bacteria |
| LC-MS/MS | Liquid Chromatography coupled with tandem Mass Spectrometry |
| Log Kow | Octanol-Water Partition Coefficient |
| LOQ | Limit of Quantification |
| ML | Machine Learning |
| MRLs | Maximum Residue Limits |
| NIPH | Mexican National Institute of Public Health |
| OD600 | Optical Density at 600 nm |
| PAN | Pesticide Action Network |
| PFAS | Per- and polyfluoroalkyl subs |
| PFs | Processing Factors |
| PHI | Pre-harvest Interval |
| PRIMo | Pesticide Residues Intake Model |
| PVPP | Polyvinylpolypyrrolidone |
| SUD | Sustainable Use of pesticides Directive |
| SVM | Support Vector Machine |
| UAVs | Unmanned Aerial Vehicles |
| UV | Ultraviolet |
| WHO | World Health |