HAttFFNN: Hybridized attention mechanism-based feedforward neural network deep learning model for the plastic material classification of three stage materials on spectroscopic data.

Classification of plastic materials based on spectroscopic data is a very crucial task in a variety of applications, including automated recycling, environmental monitoring, quality control in manufacturing, quality control of products, and analysis of complex material properties. These applications demand high precision in identifying and separating plastic types to enhance sustainability and ensure regulatory compliance. In this work, we presented a novel technique Hybridized Attention mechanism-based Feedforward Neural Network (HAttFFNN) to detect three stage Polyethylene Terephthalate (PET) materials. Dataset used in this methodology is basically comprised of 295,327 samples, and contains the parameters like absorbance, wavelengths, references, samples. We collected the spectral data (900-1700 nm) using the Digital Light Processing (DLP) Near-Infrared (NIR) scan Nano Evaluation Module (EVM). We utilized various preprocessing techniques for better and improved detection result, such as Savitzky-Golay filter, interference, Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC). The preprocessed and organized spectral data is provided to the proposed HAttFFNN model for the detection of three stage PET material. To validate the performance of the proposed model, we experimented various State-Of-The-Art (SOTA) models, Multi-Head Neural Network (MHNN), Virtual Geometry Group (VGG16), One-Dimensional Convolutional Neural Network (1D-CNN), Residual Network (ResNet), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The proposed model outperforms state of the art techniques across all metrics including accuracy, precision, recall, F1 score, and specificity with Stage 1 (PET Clear vs PET Hazard) achieving 99.33% accuracy, Stage 2 (PET vs Others) 99.32%, and Stage 3 (PET Coloured vs PET Transparent) 99.28%, along with consistently high precision, recall, and specificity values for each class. These results confirm that our proposed model, HAttFFNN, is able to achieve higher accuracy in spectroscopic classification domain, especially in complex cases such as differentiating between visually and spectrally similar materials (PET Clear vs PET Hazard, PET vs Others and PET Colored vs PET Transparent) where traditional models often fail. Furthermore, the Root Mean Square Error (RMSE) values 0.1408 for Stage 1, 0.1249 for Stage 2, and 0.1403 for Stage 3, further validate the model's low-error performance, reinforcing its effectiveness as a less error-prone approach for spectrometry-based plastic material classification.