Recently, the application of biosensors in food safety assessment has gained considerable research attention. Nevertheless, the evaluation of biosensors' sensitivity, accuracy, and efficiency is still ongoing. The advent of machine learning has enhanced the application of biosensors in food security assessment, yielding improved results. Machine learning has been preliminarily applied in combination with different biosensors in food safety assessment, with positive results. This review offers a comprehensive summary of the diverse machine learning methods employed in biosensors for food safety. Initially, the primary machine learning methods were outlined, and the integrated application of biosensors and machine learning in food safety was thoroughly examined. Lastly, the challenges and limitations of machine learning and biosensors in the realm of food safety were underscored, and potential solutions were explored. The review's findings demonstrated that algorithms grounded in machine learning can aid in the early detection of food safety issues. Furthermore, preliminary research suggests that biosensors could be optimized through machine learning for real-time, multifaceted analyses of food safety variables and their interactions. The potential of machine learning and biosensors in real-time monitoring of food quality has been discussed.

A highly selective and sensitive method for Cd(II) detection was developed based on aptamer and gold nanoparticles (AuNPs) combined with a colorimetric smartphone readout. The experimental conditions such as reaction time of polydiene dimethyl ammonium chloride (PDDA) and AuNPs, PDDA dose, time of aptamer and PDDA incubation, and aptamer concentration were optimized. Under the optimized conditions, the color and red(R) value of the solution was concentration-dependent on Cd(II). The proposed method exhibited a linear range of 1–400 ng/mL (r² = 0.9794) with a limit of detection (LOD) of 1 ng/mL. This method had been successfully applied to test and quantify Cd(II) in water and rice samples, and the results were in full agreement with those from the atomic absorption spectrometer. Therefore, low-cost colorimetry demonstrated its potential for practical application in visual or quantitative detection with a smartphone. This approach can be readily applied to other analytes.