Deep convolutional neural networks for aged microplastics identification by Fourier transform infrared spectra classification

Infrared (IR) spectroscopy is a powerful technique for detecting and identifying Microplastics (MPs) in the environment. However, the aging of MPs presents a challenge in accurately identification and classification. To address this challenge, a classification model based on deep convolutional neural networks (CNNs) was developed using infrared spectra results. Particularly, original infrared (IR) spectra were used as the sample dataset, therefore, relevant spectral details were preserved and additional noise or distortions were not introduced. The Adam (Adaptive moment estimation) algorithm was employed to accelerate gradient descent and weight update, the Dropout function was implemented to prevent overfitting and enhance the generalization performance of the network. An activation function ReLu (Rectified Linear Unit) was also utilized to simplify the co-adaptation relationship among neurons and prevent gradient disappearance. The performance of the CNN model in MPs classification was evaluated based on accuracy and robustness, and compared with other machine learning techniques. CNN model demonstrated superior capabilities in feature extraction and recognition, and greatly simplified the pre-processing procedure. The identification results of aged commercial microplastic samples showed accuracies of 40 % for Artificial Neural Network, 60 % for Random Forest, 80 % for Deep Neural Network, and 100 % for CNN, respectively. The CNN architecture developed in this work also demonstrates versatility by being suitable for both limited data cases and potential expansion to include more discrete data in the future.