## 4 Application of AI in Food Integrity
### AI and ML in the Detection of Food Fraud  
Food fraud refers to any deliberate misrepresentation or deception related to food products, which includes the substitution, adulteration, or mislabeling of ingredients in order to achieve economic gain. AI and ML models can scrutinize ingredient information, supply chain records, and consumer feedback, to identify patterns and anomalies and detect risky features that indicate potential fraud in the food industry. Not only can AI and ML technology aid in the detection of food fraud but it can also contribute to prevention efforts. For instance, by analyzing historical data and detecting fraudulent patterns, ML algorithms can help food organizations identify vulnerable points in their supply chains and implement measures to prevent fraudulent activities. [PERSON] et al. [108] developed a Deep Neural Network (DNN) model and employed XAI technologies to predict food fraud categories, analyze the outcomes, and explain the forecasts generated by the AI model. [PERSON] and [PERSON] [109] employed Bayesian network (BN) modeling to predict several types of food fraud using notification data from the Rapid Alert System for Food and Feed (RASFF) spanning from 2000 to 2013. The study sought to forecast anticipated forms of food fraud for imported items based on their known product categories and countries of origin, thereby enabling focused enforcement efforts. The model was trained on 749 RASFF fraud warning data classified into six distinct types and further validated with an additional 88 RASFF fraud notifications from the year 2014. The model demonstrated an accuracy of 80% when the fraud type, country, and food category constituted pre-existing knowledge and 52% accuracy when this information was not previously documented in the RASFF database. Such a system could be utilized by risk managers and controllers at border inspection checkpoints to effectively prioritize fraud-type checks while dealing with goods imports. Elsewhere, [PERSON] et al. [77] constructed a BN model to scrutinize all relevant driving factors influencing food fraud cases, utilizing data from RASFF and the European Medicines Agency (EMA). Their BN model attained a 91.5% accuracy in forecasting food fraud categories, providing significant information for risk managers. In their study, [PERSON] et al. [110] utilized deep learning techniques to discriminate between bananas that have ripened naturally and those that have been artificially ripened. According to the authors, a risk exists that bananas that have been artificially ripened may have been treated with compounds that can cause cancer, such as calcium carbide. The deep learning model implemented in the study achieved a classification accuracy of 90%, while accuracies of up to 98.74% and 89.49% were also achievable using the random forest and MLP feed-forward neural network classifiers, respectively. Elsewhere, the work undertaken by [PERSON] and [PERSON] [111] involved the development of a smart electronic nose (SE-Nose) designed to rapidly detect and measure food adulteration, particularly in recognizing the presence of pork in beef. The methodology employed classification models to do qualitative analysis of adulteration and regression models to conduct quantitative analysis. SVM classification and regression models yielded exceptional outcomes, with an accuracy rate of up to 99.996%. Moreover, the SVM models significantly lowered the detection time for identifying pork adulteration in beef to 40 s, which is a 33%-time decrease compared to the other approaches. [PERSON] et al. [112] combined infrared spectroscopy and ML to identify instances of food adulteration. Their proposed system was utilized in two investigations on food adulteration: one using evening primrose oils analyzed with ATR-FTIR spectroscopy and the other including ground nutmeg analyzed with NIR diffuse reflectance spectroscopy.