## 2 Materials and Methods
### Architecture Design and Implementation
#### 2.1.3 Construction of Shelf-Life Prediction Model  
To predict the shelf-life of strawberries, a BP neural network was used to build the shelf-life prediction model in this paper. Four key parameters (temperature, relative humidity, oxygen, and carbon dioxide) would impact the strawberry quality. Thus, they were selected as input nodes of the prediction model to explore the potential coupling relationship between environmental parameters and shelf life. In this way, we could achieve quality prediction effectively. The BP neural network is an artificial intelligence modeling method. The essence of the method is a backpropagation network with a multi-layer feed-forward neural network structure. Following the basic principle of positive signal propagation and error back-propagation, this algorithm corrected the connection weights and thresholds between layers based on the error signal. Thus, the network could learn the potential laws behind the sample by iterating layer by layer to minimize the total error [14]. In recent years, BP neural networks have become increasingly widely used in the field of food shelf-life prediction. The advantages of BP neural networks are as follows [15]:  
* Flexibility. BP neural network has strong flexibility. It could learn the coupling relationship between different variables by choosing the input and output signals, based on the requirement for different research fields. The BP neural network algorithm has an outstanding advantage in achieving comprehensive analysis with a multi-index. Thus, the algorithm is more applicable in the field of food shelf-life prediction research, compared with the traditional quality modeling methods such as zero-order or first-order reaction kinetic models and the Arrhenius equation. * Nonlinear mapping capability. BP neural networks can behave similarly to many characteristics of the brain and the information processing of the human nervous system. For this reason, the network can handle huge chunks of data. Moreover, the method's multi-layer feed-forward network structure can learn complex relationships  
Figure 3: The monitoring center interface of the cold chain transportation (CCT) monitoring platform. among variables. Therefore, the BP neural network is suitable for nonlinear problems. In the field of food shelf-life prediction, a BP neural network can explore the full potential relationship between environmental parameters and shelf-life and predict food shelf-life effectively. * Generalization capability. The BP neural network has a strong self-learning ability to improve the prediction accuracy continuously in the training process. The trained BP neural network can predict non-training samples effectively through learning the non-linear mapping relationship among the limited number of training examples. Moreover, the BP neural network can also improve network performance by changing the type or number of samples, and it is suitable for food shelf-life prediction. The shelf-life prediction model based on a BP neural network used four environmental parameters (temperature, humidity, oxygen, and carbon dioxide concentration) as input signals for shelf-life prediction, and the environmental parameters were received by the environmental data acquisition unit of CCT-QPS. The process of constructing the BP neural network was as follows [16,17]:  
1. Construct the neural network structure. The artificial neural network (ANN) is usually made up of an input layer, a hidden layer, and an output layer, and the number of the network layers depends on the number of the hidden layers. When the number of hidden layers is 1, any function that contains the continuous mapping from one finite space to another finite space can be fitted by the network. Theoretically, the more layers, the stronger the fitting ability of the network is. However, with the network being multi-layered, there comes a problem of overfitting [18].