# Advances in Fermentation Technology: A Focus on Health and Safety
## 3 The Effectiveness of Innovative Methods in Fermented Food Safety Assurance
### Artificial Intelligence and Machine Learning in Smart Fermentation Systems  
Smart fermentation systems leverage machine learning (ML) and Internet of Things (IoT) technologies to optimise fermentation processes. ML applications in metabolic engineering systems enable efficient strain development and optimisation of metabolic flux [124]. These systems use advanced algorithms to analyse and optimise fermentation parameters, including temperature, humidity, and microbial composition. IoT technologies allow real-time monitoring of critical parameters and remote automation of production processes. Integrating constraint-based modelling with ML enhances predictability and efficiency in fermentation analysis [82; 125]. Furthermore, ML techniques are increasingly applied across various biorefinery systems, including fermentation, to handle complex scientific tasks and support resource efficiency in the circular bioeconomy [126]. These advancements in smart fermentation systems represent a significant leap forward, offering greater control and efficiency in fermentation. Integrating artificial intelligence (AI) and ML in food fermentation has significantly improved quality control and process optimisation. AI-based systems can enhance operational efficiency, predict sales, and improve food safety [127]. For example, an in-line monitoring strategy employing Raman spectroscopy to assess ethanol production through yeast fermentation (_Saccharomyces cerevisiae_) was introduced. By applying machine learning techniques for feature selection, this study effectively reduced the dimensionality of the Raman spectral data. The results demonstrated a significant decrease in model training time, exceeding 90%, while also improving the predictive accuracy for glycerol and cell concentration by 14.20% and 17.10%, respectively, measured at the root mean square error(RMSE) level. Additionally, the refined model, following hyperparameter optimization, shows enhancements in RMSE for ethanol, glycerol, glucose, and biomass, with 9.73%, 4.33%, 22.22%, and 13.79%, respectively [128]. In beer production, AI technologies like electronic noses and robotics have shown high accuracy in identifying fermentation types and predicting consumer acceptability. The research discussed the incorporation and application of AI technology utilising low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. The findings from the machine learning models demonstrated impressive accuracy (97%) in distinguishing between fermentation types (Model 1) based on data from the e-nose. Moreover, the models successfully predicted consumer acceptability using near-infrared data (Model 2; R = 0.90) and e-nose data (Model 3; R = 0.95) and analysed the physicochemical properties and colour metrics of beers sourced from e-nose data. Integrating RoboBEER with an electronic nose and advanced AI provides brewers with a sophisticated yet accessible solution for assessing the fermentation process. This approach enables precise evaluation of beer quality, pinpointing defects, ensuring traceability, and confirming product authenticity while maintaining cost-effectiveness [129]. The IoT enables real-time monitoring of fermentation parameters, remote automation, and predictive analysis of fermented food products [82]. Omics tools and bioinformatics approaches can characterise and trace fermented foods, predict metabolic outcomes, and enhance nutritional value while preserving traditional methods [130]. These technologies offer opportunities for improving food quality, safety, and production efficiency across various food industry sectors, from smart farming to restaurant management [127]. Predictive microbiology models integrated with sensor technologies offer promising advancements in food safety and fermentation control. These models can accurately predict microbial growth in various food products, enabling timely interventions and optimised processing conditions [131,132].