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Author: search.filters.author.Kuson, PramoteStart date: 2020

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    Systematic evaluation of spectral preprocessing and machine learning for near-infrared prediction of mechanical stability in complex colloidal systems
    (2026-06-30)
    Suttho, Pisit
    ;
    Phetpan, Kittisak
    ;
    Al Riza, Dimas Firmanda
    ;
    Lim, Chin Hock
    ;
    Kuson, Pramote
    Natural rubber latex (NRL) is a critical industrial material, with concentrated rubber latex (CRL) serving as a major export product. Among its quality parameters, mechanical stability time (MST) is particularly important, reflecting colloidal stability and influencing downstream applications such as glove and balloon manufacturing. Conventional MST testing, however, relies on reagents, manual agitation, and visual assessment, making it labor-intensive, operator-dependent, and unsuitable for real-time quality monitoring. Since variations in proteins, lipids, and carbohydrates strongly govern MST, near-infrared (NIR) spectroscopy offers a promising non-destructive alternative by probing their molecular vibrations. This study developed a near-process NIR instrumentation system integrated with machine learning (ML) to predict MST in CRL. Spectral signals were preprocessed using eight techniques and modeled with five supervised regression algorithms. The best-performing configuration, Savitzky-Golay second derivative and orthogonal signal correction coupled with partial least squares regression, yielded high predictive accuracy, with coefficient of determination for prediction (R<sup>2</sup><inf>p</inf>) of 0.94 and ratio of performance to deviation (RPD) of 4.2. This performance demonstrates the system's ability to extract chemically relevant information governing latex stability. The proposed NIR-ML framework provides a rapid, reagent-free, and scalable alternative to conventional MST testing, addressing the limitations of existing methods and supporting industrial quality monitoring. This approach is also transferable to the analysis of complex colloidal systems across diverse applications. Furthermore, the study provides mechanistic insight into how spectral preprocessing enhances the extraction of chemically meaningful information, establishing a physically interpretable framework for NIR-based analysis of such complex systems.
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    Transflection Near-infrared Spectroscopy Combined with Machine Learning for Mechanical Stability Time Evaluation in Concentrated Rubber Latex
    (2023-01-01)
    Suttho, Pisit
    ;
    Phetpan, Kittisak
    ;
    Sirisomboon, Panmanas
    ;
    Lim, Chin Hock
    ;
    Ruttanadech, Nuttapong
    This study aims to apply near-infrared spectroscopy (NIRS) in transflection mode combined with a machine learning approach to evaluate the mechanical stability time (MST) in Para concentrated rubber latex. Four supervised learning algorithms, including principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR) and random forest regression (RFR), were employed to relate the NIR spectra with the MST degree of the latex samples. A comparison of predictive performance among these different algorithms was performed. The RFR model exhibited the best fitting performance with a coefficient of determination for calibration (R2) and root mean square error of calibration (RMSEC) of 0.95 and 37 seconds, respectively. In addition, the RFR-based model outperformed all others with its predictive performance, presenting coefficient of determination for prediction (r2) and root mean square error of prediction (RMSEP) of 0.64 and 91 seconds, respectively. Based on these results, this study could imply that the relationship between the NIR spectra and the change in the MST degree of the samples tends to be nonlinear.