## 4 Application of AI in Food Integrity
### AI and ML for Chemical and Biochemical Food Hazard Prediction and Analysis  
AI and ML systems have the capability to thoroughly examine chemical and biochemical data pertaining to the composition of food and the presence of contaminants and other unwanted substances in order to forecast future risks. In the study by [PERSON] et al. [96], various ML models were used to classify and predict aflatoxin B1 (AFB1) levels in maize kernels. The hyperspectral images of 240 maize kernels that were exposed to six varying levels (25, 40, 70, 200, 300, and 500 ppb) of AFB1 were preprocessed and used to generalize a number of ML models, including partial least square discriminant analysis (PLS-DA) and KNN. The choice of the wavelength (508 nm) was determined by analyzing the loadings of PCA in order to differentiate between noncontaminated and contaminated maize kernels. A classification accuracy of 94.7% was attained by employing PLS-DA with standard normal variate (SNV) pre-processed data. Moreover, the KNN model using the raw data showed the highest efficiency, with an accuracy of 98.2%. In their study on the detection of pesticide residue levels in crops, [PERSON] et al. [97] employed visible/near-infrared (Vis-NIR) (376-1044 nm) and NIR (915-1699 nm) hyperspectral imaging systems (HSIs) together with ML to detect the levels of pesticide residues in grapes. Three grape cultivars were exposed to four degrees of pesticide application. Logistic regression, SVM, random forest, convolutional neural network (CNN), and residual neural network (ResNet) were utilized to build classification models for the pesticide residue levels in the crop. ResNet proved to be the most effective model for Vis-NIR spectra, achieving an accuracy rate above 93%. With regards to the NIR spectra data, logistic regression had the highest performance, surpassing 97% accuracy, while SVM, CNN, and ResNet also demonstrated comparable and satisfactory prediction outcomes. [PERSON] et al. [98] employed ML to optimize pesticide spray on crops. The algorithm pinpoints the areas on the crop that do not necessitate additional pesticide spraying, enabling farmers to exclusively administer pesticides to the required regions. This system reduced pesticide repetition by at least 20% compared to the standard method (the exit concept). Inferences from the study indicated that implementation of the concept has the potential to decrease the usage of pesticides in India by 72.5%. In a different study, [PERSON] et al. [99] adopted ML models to predict pesticide dissipation half-life intervals. Four machine learning models, namely gradient boosting regression tree (GBRT), random forest, support vector classifier (SVC), and logistic regression, were developed to predict pesticide dissipation half-life intervals using temperature, plant component class, extended connectivity fingerprints (ECFP), and plant type as model inputs. The GBRT-ECFP model exhibited superior performance compared to the other models. In their study, [PERSON] and [PERSON] [100] presented a novel approach using Explainable Artificial Intelligence (XAI) and Faster RCNN to digitize food product information, retrieve it, evaluate the composition of the foods, and identify the hazardous ingredients that may compromise food integrity. Linear and non-linear ML models were employed by [PERSON] et al. [101] to ascertain the levels of heavy metals present in turbid muscle and liver tissues. The models were constructed using data that were obtained from the scientific literature and included eleven heavy metals (As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Ni, and Zn) that were found in the liver and muscle tissues of specimens of turbid. Over 70% prediction accuracy was attained by the non-linear tree-based random forest prediction models for As, Cd, Cu, K, Mg, and Zn in muscle tissue and As, Ca, Cd, Mg, and Fe in turbid liver tissue. The adopted multiple linear regression (MLR) and random forest models were found to be appropriate for predicting the levels of heavy metals in the liver and muscle of turbid.