Quantitative analysis of plastic blends based on virtual mid-infrared spectroscopy combined with chemometric methods

• Published in: Talanta (Oxford) Vol. 292; p. 128006
• Main Authors: Yang, Jian, Xu, Yu-Peng, Chu, Xiao-Li
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.09.2025
• Subjects: Chemometrics; Mid-infrared hyperspectral imaging technology; Mid-infrared spectroscopy; neural networks; polyethylene; polypropylenes; polystyrenes; prediction; quantitative analysis; Quantitative models; spectral analysis; spectroscopy; Virtual spectrum; Mid-infrared hyperspectral imaging technology; Virtual spectrum; Quantitative models; Chemometrics; Mid-infrared spectroscopy
• ISSN: 0039-9140; 1873-3573; 1873-3573
• DOI: 10.1016/j.talanta.2025.128006

Developing efficient and accurate quantitative analysis methods for plastic blends holds significant value for resource recycling and environmental monitoring. Mid-infrared (MIR) spectroscopy, combined with chemometric techniques, has demonstrated excellent performance in plastic blend quantification. However, obtaining mid-infrared spectral data for a large number of plastic blends to calibrate the model remains challenging. This study proposes an innovative approach that utilizes pure plastic MIR spectra and the Beer-Lambert law to generate virtual plastic blend spectra. Four experimental groups (A–D) were designed, incorporating both real and virtual spectra to systematically evaluate the method's effectiveness. Experiment group A established a baseline model using real spectra, while experiment groups B, C, and D respectively validated the applicability and generalization capability of models based on virtual spectra, as well as their potential applications in MIR hyperspectral imaging (MIR-HSI), respectively. The study further explores feature band selection, model construction, evaluation, and interpretation. The results demonstrate that this method can efficiently predict the mass percentages of components in ternary plastic blends. In experimental group C, partial least squares regression (PLSR), one-dimensional convolutional neural network (CNN1D), and two-dimensional convolutional neural network based on Gramian Angular Field (GAF-CNN2D) models—trained on 208 virtual plastic blend spectra—were employed to predict the mass percentages of 66 ternary plastic blends composed of polyethylene (PE), polypropylene (PP), and polystyrene (PS). The prediction coefficients of determination (RT2) reached 0.9872, 0.9879, and 0.9944, respectively, indicating exceptional predictive accuracy. Experimental group D further demonstrated that, even under Gaussian noise interference and limited spectral range, the strategy of fusing mid-wave infrared and long-wave infrared bands allowed the PLSR and GAF-CNN2D models to maintain high performance in predicting the mass percentages of 66 ternary blends of PE, PP, and PS, with RT2 values of 0.9852 and 0.9895, respectively. This suggests that the proposed method holds potential for applications in MIR-HSI and is promising for real-time online analysis. Finally, this study proposes a more widely applicable and optimized quantitative analysis application scheme based on virtual plastic blend spectra, aiming to enable rapid and precise determination of unknown plastic blends. [Display omitted] •An innovative MIR virtual spectrum generation method.•Quantitative model for plastic blends based on virtual MIR spectra.•Accurate quantification of plastic blends using CNN and PLSR.•Model evaluation and interpretation of the developed model.•Exploring the potential of the above methods in MIR-HSI.