Prediction of Polyphenols Using BP Neural Network

BP neural network is a classical artificial neural network model suitable for solving both classification and regression problems. In polyphenol prediction tasks, BP neural networks demonstrate excellent performance characteristics. For polyphenol prediction challenges, researchers typically employ two distinct classification approaches: binary classification and six-class classification. In binary classification tasks, the neural network must distinguish samples into two distinct categories, representing the most fundamental classification form. Six-class classification constitutes a more refined approach, requiring the network to identify six different types of polyphenol compounds. BP neural networks adjust weights through the backpropagation algorithm, with the entire training process divisible into two phases: forward propagation and backward propagation. During forward propagation, input data undergoes nonlinear transformation through hidden layers to generate outputs. In backward propagation, network parameters are adjusted based on output errors, minimizing the loss function through gradient descent methods. In code implementation, this typically involves defining loss functions like Mean Squared Error (MSE) and using optimization algorithms such as Adam or SGD. To enhance model performance, several optimization strategies are commonly employed: data preprocessing requires normalization of polyphenol compound features using techniques like Min-Max scaling or Z-score standardization; network architecture design must consider appropriate numbers of hidden layers and neurons, typically implemented through layer configuration in frameworks like TensorFlow or PyTorch; to prevent overfitting, techniques like dropout or regularization (L1/L2) can be implemented using specific layer types or regularization parameters; learning rate settings directly impact model convergence and are often managed through learning rate schedulers. In practical applications, the advantage of BP neural networks in polyphenol prediction lies in their ability to automatically learn complex nonlinear relationships between features without requiring manual design of sophisticated feature engineering. Compared to traditional methods, neural network approaches typically achieve higher prediction accuracy, with implementation often involving libraries like scikit-learn for baseline comparisons and deep learning frameworks for neural network implementation.