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Author: search.filters.author.Phetpan, KittisakSubject: search.filters.subject.near-infrared spectroscopy

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    Comparison between Linear and Nonlinear Machine-Learning Algorithms for Predicting the Properties of Biodiesel Using Near-infrared Spectra
    (2023-01-01)
    Thongphut, Chitwadee
    ;
    Chungcharoen, Thatchapol
    ;
    Phetpan, Kittisak
    This study points out the application of nearinfrared (NIR) spectra combined with machine-learning approaches to evaluate biodiesel properties. The performance comparison between partial least squares regression (PLSR)-based linear and support vector regression (SVR)-based nonlinear machine-learning algorithms for predicting the biodiesel properties is the main objective of this paper. The models were built for four biodiesel properties: pH, viscosity, density, and water content. As a result, the PLSR had better performance than the SVR. An effective model of each biodiesel property prediction exhibited the coefficient of determination for the prediction (r2) and root mean square of prediction (RMSEP) of 0.89 and 0.01 mg KOH.g-1, 0.75 and 0.07 cSt, 0.84 and 2.77 kg.m-3, and 0.75 and 79.33 mg.kg-1 for pH, viscosity, density, and water content, respectively.
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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.
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    Evaluation of the moisture content of tapioca starch using near-infrared spectroscopy
    (2015-03-25)
    Phetpan, Kittisak
    ;
    Sirisomboon, Panmanas
    The purpose of this study was to develop a calibration model to evaluate the moisture content of tapioca starch using the near-infrared (NIR) spectral data in conjunction with partial least square (PLS) regression. The prediction ability was assessed using a separate prediction data set. Three groups of tapioca starch samples were used in this study: tapioca starch cake, dried tapioca starch and combined tapioca starch. The optimum model obtained from the baseline-offset spectra of dried tapioca starch samples at the outlet of the factory drying process provided a coefficient of determination (R<sup>2</sup>), standard error of prediction (SEP), bias and residual prediction deviation (RPD) of 0.974, 0.16%, -0.092% and 7.4, respectively. The NIR spectroscopy protocol developed in this study could be a rapid method for evaluation of the moisture content of the tapioca starch in factory laboratories. It indicated the possibility of real-time online monitoring and control of the tapioca starch cake feeder in the drying process. In addition, it was determined that there was a stronger infl uence of the NIR absorption of both water and starch on the prediction of moisture content of the model.