## 4 Application of AI in Food Integrity
### AI and ML for Chemical and Biochemical Food Hazard Prediction and Analysis
Using a different approach, [PERSON] et al. [102] estimated heavy metal concentrations in winter wheat leaves from a typical sewage irrigation area based on canopy reflectance spectra and ML modeling. Spectral data from winter wheat canopies were collected at 61 sampling stations in Longkou City, Shandong province, China, and the Pb, Zn, Cd, Cr, Ni, and Hg contents were measured. Back-propagation neural network (BPNN), partial least squares regression, and stepwise multiple linear regression methods were used to build eight estimation models, which were trained with reflectance spectra, a first-order derivative of spectral reflectance (FDR), a second-order derivative of spectral reflectance (FDR), and spectral parameters (SPs). The study revealed that the BPNN model with SPs performed best for Pb, Zn, and Cd, while the BPNN model with FDR performed best for Cr, Ni, and Hg. AI and ML in the Formulation of New Recipe and Personalized Nutrition for Improved Quality and Safety  
Beyond the prediction of food safety hazards, AI and ML are driving product development innovation, for improved food quality and nutritional health. AI-based algorithms benefit from the fundamental understanding of the intricacies and interrelationships of the various food components and ingredients and vast datasets containing consumer preferences, market trends, and nutritional profiles and are able to suggest varied ingredient combinations and design tailored food products for consumer-specific needs and tastes. AI-driven product development improves not only customer satisfaction and food safety/quality but also market competitiveness by knowing consumer preferences and market dynamics and responding to changing trends. For example, [PERSON] et al. [103] assembled various culinary recipes and food chemical information to create FlavorGraph, a large-scale food-compound network graph. The food-specific metapath graph embedding method was combined with a chemical structure learning layer to create an intricate representation of food vectors for FlavorGraph. The food representation vectors were then used to make meal pairing recommendations and predict new food-compound relationships. FlavorGraph, according to the authors, can be used to better comprehend the cooking and medicinal uses of food. Furthermore, the deep learning algorithms described in the study can be used as the foundation for meal pairing and food-relationship prediction problems. [PERSON] and colleagues [104] proposed a deep learning solution for health-based medical datasets, which autonomously determines the appropriate food and makes recommendations for patients based on their specific diseases and other features such as age, gender, weight, calories, protein, fat, sodium, fiber, and cholesterol. Their study implemented several ML and deep learning algorithms such as logistic regression, naive Bayes, recurrent neural network (RNN), MLP, gated recurrent units (GRU), and long short-term memory (LSTM) in accomplishing the research goal. The medical dataset, which was gathered from the internet and hospitals, included information from 30 patients with 13 attributes related to various diseases and 1000 products, each with an 8-feature set. The performance of the different ML and deep learning models was evaluated and it was observed that the LSTM model surpassed other methods in terms of prediction accuracy (97.74%), recall, precision, and F1 measures. [PERSON] and [PERSON] [105] developed a diet recommendation system by employing hybrid collaborative filtering learning methods. The nutrition support system was created by utilizing KNN and collaborative filtering models to recommend well-balanced diets for thyroid patients that fulfill their specific nutrient needs. The system manages the patient's dietary intake by providing the necessary nutrients required for those with thyroid issues. [PERSON] and colleagues [106] created a diabetes management system that uses several ML and AI algorithms to improve diabetes control based on patient calorie intake through food consumption.