Discussion

As shown above, this method combining biosensors and deep learning has many advantages such as high efficiency, low cost, and automation, and is expected to be applied in the field of food quality assessment. However, there are many challenges in these applications. First, in the long run, it is necessary to develop effective sensing systems that are cheap, reliable, and fully functional. Since different foods contain different nutrients, how to choose A suitable biosensor to construct a sensing system is still a problem, which needs to be selected according to the needs of users, or according to the general formula for food quality evaluation. Second, the generation of predictive models requires a large amount of training data. Different models may need to be trained for different types of food. Therefore, the amount of data is huge, which requires specific hardware for data processing, model training and evaluation. Therefore, combining technologies such as cloud computing, 5G networks can effectively address the challenges.