The generation of predictive models relies heavily on data set collection and model determination. DNN is an end-to-end network. Its internal neural network layers can be divided into three categories: input layer, hidden layer and output layer. The input layer takes the characteristic parameters of the collected food as input. The training process of the DNN model mainly includes forward propagation and back propagation. The forward propagation algorithm is to use several weight coefficient matrices \(\omega^{i}(i=1,2\ldots n)\), bias vector b to perform a series of linear operations \(y^{i}\),\(y^{i}=\omega^{i}\times x^{i}+b^{i}\), At the same time, the activation function is used to complete the transformation from linear to nonlinear, the process is done in the hidden layer. After completing the forward propagation, the difference between the predicted output and the actual output can be used to generate the loss function\(L\left(\theta\right),\ L\left(\theta\right)=\frac{1}{n}\sum_{i=1}^{n}\left(y-\hat{y}\right)\ \). After the loss function is obtained, the weight \(w^{i}\) and the bias vector \(b^{i}\) are continuously updated through the gradient descent method. By repeating this process over and over, the model gets optimal weights and biases. Therefore, this process requires a large amount of data for the model to learn. As shown in Figure 1(A)

Figure 1 (A) Predictive model generation process (B) Convolutional and Pooling Layers (C) Schematic diagram of biosensor components

Using deep neural networks to predict food quality, we no longer pay attention to how the low-level features are transformed into high-level features. We only need to use a large amount of statistical real-time feedback data to train a mathematical model with a specific structure containing unknown parameters. This method takes into account both the detection speed and the comprehensiveness of the evaluation.