## 4 Application of AI in Food Integrity
### AI and ML in Food Pathogen Detection, Classification, and Virulence/Resistance Prediction  
The utilization of AI and ML methodologies in foodborne pathogen source fingerprinting, prediction of antibiotic resistance, and rapid detection and assessment of foodborne outbreaks and associated risks has been facilitated by the emergence of pathogen genomes and novel data streams, including textual data [14]. [PERSON] et al. [88] developed a classification system based on ML for predicting microorganisms that cause foodborne illnesses. The study attempted to examine connections between several factors (such as area, time, and exposed food) and foodborne disease pathogens using case data from the National Foodborne Disease Surveillance Reporting System. Multiple ML models were used to categorize these infections and the best model (i.e., the gradient boost decision tree) predicted _Salmonella_, _Norovirus_, _E. coli_, and _Vibrio parahaemolyticus_ with a 69% accuracy rate. [PERSON] et al. [89] used Raman spectroscopy in conjunction with ML to rapidly identify food-borne pathogens at the single-cell level. They collected 15,890 single-cell Raman spectra from 23 common strains from 7 different genera. Individual bacterial cells were analyzed and distinguished at the serotype level using a decision tree technique. The adopted methodology demonstrated an average correct prediction rate of 86.23% on an independent test set. The approach showed great promise for the quick diagnosis of pathogenic bacteria, demonstrating the ability to swiftly identify food-borne pathogens at the single-cell level in contaminated food. In a different study, [PERSON] et al. [90] compared ML and rule-based techniques for predicting antimicrobial resistance profiles in Gram-negative _bacilli_, specifically _Enterobacteriaceae_ isolates, including _E. coli_. Three curated antibiotic resistance sequencing databases were compared to determine resistance genes of 78 previously known whole genome-sequenced (_Enterobacteriaceae_) isolates. The rule-based system used hard-coded resistance profiles based on curated information for identified genes, whereas the ML approach treated all resistance genes equally and predicted based on algorithmically deduced patterns in the data. Both classifiers performed similarly, agreeing with phenotypic antibiotic susceptibility testing (AST) for over 90% of the isolates tested. [PERSON] et al. [91] studied the prospective detection of foodborne illness outbreaks using ML algorithms. The research looked at the performance of Hidden Markov Models (HMMs) for syndromic surveillance monitoring and disease outbreak detection under two different data collection regimes. A sentinel population was used in one regime, with smartphone-based software tracking the location of food consumption and subclinical reporting. To compare the results with the HMM, the researchers used an SVM technique. According to the findings, depending purely on clinical data has a low potential for automatic epidemic detection. However, when guided by subclinical reporting, the use of HMMs indicated remarkable potential for detecting foodborne illness outbreaks, even from a relatively small sentinel group representing 4% of the population. [PERSON] et al. [92] investigated the persistence of _Listeria monocytogenes_ in retail delicatessen environments using expert elicitation and ML. Using retail delicatessen operations as a template, the study extracted parameters utilized by food safety specialists in estimating bacterial persistence in the environment. Likewise, with the Delphi approach, the researchers undertook an expert elicitation with 10 food safety experts from academia, business, and government to classify _L. monocytogenes_ persistence based on environmental sampling results from 30 retail delicatessen stores gathered over six months. Variations in random forest, SVM, logistic regression, and linear regression were used to model the results.