Comparison of data augmentation and classification algorithms based on plastic spectroscopy

Plastic waste management is one of the key issues in global environmental protection. Integrating spectroscopy acquisition devices with deep learning algorithms has emerged as an effective method for rapid plastic classification. However, the challenges in collecting plastic samples and spectroscopy data have resulted in a limited number of data samples and an incomplete comparison of relevant classification algorithms. To address this issue, we propose a plastic spectroscopy generation model and conduct a systematic analysis and comparison of different algorithms' performance from multiple perspectives, based on data augmentation. This paper first performs cubic interpolation, normalization, S-G filtering, linear detrending, and standard normal variate (SNV) transformations as preprocessing methods on plastic spectral data collected from public datasets using techniques such as Fourier Transform Infrared Spectroscopy (FTIR), Raman Spectroscopy (RAMAN), and Laser Induced Breakdown Spectroscopy (LIBS). The results, based on Principal Component Analysis (PCA) visualization, demonstrate that the preprocessing steps help improve classification accuracy. Additionally, PCA loading is used to explain the chemical classification features of each spectral device. Secondly, to tackle the issue of insufficient sample size, we propose a plastic spectroscopy generation model based on C-GAN, which effectively handles multi-class spectroscopy generation. The generated spectra are subjectively validated through difference spectroscopy and t-SNE to confirm their consistency with real spectra, and this conclusion is objectively validated using Maximum Mean Discrepancy (MMD). Finally, we compared the classification accuracy of machine learning algorithms, including Support Vector Machine (SVM), Back Propagation Neural Network (BP), K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT), with deep learning algorithms such as GoogleNet and ResNet under various conditions. The results indicate that after data augmentation using the plastic spectrum generation model, the accuracy of each classification model improved by at least 3% compared to pre-augmentation levels. Notably, for data collected via FTIR, the classification accuracy reached a peak of 0.991 under the 1D-ResNet model when the data were augmented twofold. For small sample datasets, traditional machine learning algorithms, such as SVM and RF, demonstrated high stability and accuracy, with only minimal differences compared to deep learning algorithms. However, on large sample datasets, deep learning algorithms showed a stronger advantage. Regarding data input formats, 1D input models generally outperformed 2D input models. Grad-CAM visualizations further illustrated that the 1D-ResNet model achieved the highest classification accuracy, primarily due to its ability to more accurately identify peak features in the data.