Rapid Colorimetric and Artificial Intelligence-Based Methods for Determining the Microbial Quality of Raw Milk, Processed Milk, and Milk Products

Besides maintaining the microbial quality of milk anddairy foods for food safety and public health, contamination detection by traditional methods has been slow and labor-intensive. Fast and efficient alternative methods are therefore urgently needed. This paper discusses the application of the advanced colorimetric and artificial intelligence (AI)-based approaches of assessing microbial quality in raw milk, processed milk, and different dairy products. Meanwhile, AI models such as machine and deep learning approaches analyze spectral data, images, and sensor outputs to develop accuracy and predictive capability. Therefore, the integrated approaches provide real-time, non-invasive, and economically effective means for microbial assessment, hence an advantage over conventional techniques. Experiments have illustrated that these methods can very efficiently detect microbial contamination to high confidence levels. This technique has a lot of potential in improving quality control among dairy industries for consumer safety and efficient supply chain management.