Phetpan, Kittisak

Preferred name

Phetpan, Kittisak

Main Affiliation

KMITL Prince of Chumphon Campus

kittisak.ph@kmitl.ac.th

Now showing 1 - 10 of 25

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Rapid and accurate classification of Aspergillus ochraceous contamination in Robusta green coffee bean through near-infrared spectral analysis using machine learning
(2023-03-01)
Ruttanadech, Nuttapong
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Phetpan, Kittisak
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Srisang, Naruebodee
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Srisang, Siriwan
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Chungcharoen, Thatchapol
Near-infrared (NIR) spectral-based classification of Aspergillus ochraceous contamination in the Robusta green coffee bean was investigated. Six different learning algorithms, including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbors (KNN), decision tree (Tree), Naive Bayes (NB), and quadratic discriminant analysis (QDA), were applied for the investigating purpose. Four classes of fungal contamination on coffee beans, non-fungal contaminated beans on day 1 and day 3 (NCB-D1 and NCB-D3) and fungal contaminated beans on day 1 and day 3 (CB-D1 and CB-D3), were set for the classification intention. Based on the 6 learning algorithms, the Tree approach was optimal, displaying a training accuracy of 97.5%. As proven by the testing dataset, the classification accuracy of the Tree was also at 97.5%. With this number, the Tree could correctly classify 100% between the contaminated and non-contaminated coffee beans. These findings exhibit the potential of the NIR spectroscopy accompanied by machine learning for the early detection of fungal contamination in green coffee beans.
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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
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Phetpan, Kittisak
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Al Riza, Dimas Firmanda
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Lim, Chin Hock
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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 (R2<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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Enhanced biodiesel purification using coffee husk bioadsorbents: The role of pyrolysis temperature, KOH activation, and adsorption efficiency
(2025-05-01)
Chungcharoen, Thatchapol
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Limmun, Warunee
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Srisang, Siriwan
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Phetpan, Kittisak
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Ruttanadech, Nuttapong
This study evaluates the performance of bioadsorbents derived from coffee husk pyrolyzed at temperatures of 600, 700, and 800 °C (CH600, CH700, and CH800), along with activated CH700 (ACH700), in biodiesel purification. The results indicate that CH700 significantly enhances biodiesel purity, with optimal purification conditions achieved at a dosage of 2 wt% CH700, a stirring rate of 400 rpm, and a contact time of 45 min. CH700 demonstrated modest performance, achieving approximately 20 % removal of methanol and water. However, after activation with potassium hydroxide (KOH), ACH700 demonstrated improved efficiency, achieving 96.92 % methanol removal and 39.46 % water removal. ACH700 also refined biodiesel quality to meet EN14214 standards and maintained a higher biodiesel yield compared to other adsorbents. The bioadsorption process is influenced by the chemical interactions between the surface functional groups of the bioadsorbent and the contaminants, which is further enhanced by the optimized pore structure of ACH700. The use of ACH700 represents a novel and highly effective approach to biodiesel purification, combining both technical efficiency and economic feasibility. Furthermore, the valorization of agricultural waste adds significant environmental benefits, reinforcing the potential of ACH700 for large-scale biodiesel production.
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Near-infrared hyperspectral imaging combined with machine learning for physicochemical-based quality evaluation of durian pulp
(2023-06-01)
Sharma, Sneha
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Sirisomboon, Panmanas
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K.C, Sumesh
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Terdwongworakul, Anupun
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Phetpan, Kittisak
This research reports on the application of near-infrared hyperspectral imaging (NIR-HSI) system for predicting the physicochemical properties; dry matter (DM), total soluble solids (TSS), and fat content (FC) of durian. Partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and 1D convolution neural network (CNN) models: custom, U-Net, and VGG19; were developed to predict DM, TSS, and FC of durian pulp. Feature wavelengths were selected using a genetic algorithm (GA) and successive projection algorithm (SPA). The selected wavelengths were then validated based on the algorithms for regression model development. GA-PLSR model was compelling to predict the DM and FC in durian pulp, which obtained the coefficient of determination for the test set (r2) and root mean square error of prediction (RMSEP) of 0.97 and 1.12% for DM and 0.86 and 0.64% for FC, respectively. The GA-PLSR model provided the best result for the TSS prediction with r2, and RMSEP of 0.90 and 1.40%, respectively, whereas the SPA-PLSR model based on only thirteen wavelengths attained fair result with the r2 and RMSEP of 0.79 and 2.03%, respectively. The above results show that the pushbroom NIR-HSI system achieved promising results for estimating DM, TSS, and FC in durian pulp. This research identified the featured wavelengths that can be used to develop a portable and reliable HSI or multispectral system to be installed at durian packaging firms for quality inspection and grading.
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Influence of Germinated Brown Rice Production by Water Spraying Method on Its Qualities
(2023-01-01)
Chungcharoen, Thatchapol
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Sansiribhanb, Sansanee
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Munsin, Ronnachart
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Thuwapanichayanan, Ratiya
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Palamanit, Arkom
This study was aimed to compare the production time (germination and drying process) and quality of germinated brown rice (GBR) obtained from the water spraying-based GBR production (Sprayed-GBR) system and the water soaking-based GBR production (Soaked-GBR) system. The results showed that the Sprayed-GBR, in the germination process, required 2.5 h for spraying in a water spraying step to obtain paddy with the moisture content of 30% (w.b.) and 26 h in an incubation step to obtain the 90% germination percentage. This led to a shorter germination time compared to the Soaked-GBR, which required a germination time of 50 h for a 90% germination percentage. After germination, the moisture content of the Sprayed-GBR was lower than that of the Soaked-GBR. This provided a shorter drying time in the Sprayed-GBR (27 min) drying process compared with the Soaked-GBR (33 min). For GBR qualities, the Sprayed-GBR could decrease the unpleasant odor problem by providing a smaller number of attached microorganisms after germination (Shade drying), leading to a significantly higher score in the odor and the overall acceptability than the Soaked-GBR. This indicated that the Sprayed-GBR got more consumer acceptance. Moreover, the head rice yield value of the Sprayed-GBR was not different from that of the Soaked-GBR. However, the Sprayed-GBR provided a significantly lower GABA content and a significantly higher percentage of fissured kernels than the Soaked-GBR.
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Development of the partial least-squares model to determine the soluble solids content of sugarcane billets on an elevator conveyor
(2021-01-01)
Udompetaikul, Vasu
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Phetpan, Kittisak
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Sirisomboon, Panmanas
This study aimed to determine the optimum multivariate model for monitoring the soluble solids content (SSC) of sugarcane billets being transferred on a conveyor. The study covered two main issues: the exploration of an appropriate spectral range (450–900 nm versus 700–900 nm) and the assessment of the influence of different levels of cane billets on an elevator via modelling to predict the SSC values. Partial least squares regression (PLSR) was used for model development. Modelling using the range of 450–900 nm employed 4 latent variables (LVs) and showed the coefficient of determination (R2) and root mean squares error of prediction (RMSEP) of 0.83 and 0.29 °Brix, respectively. This caused the model established using the range of 700–900 nm, employed 3 LVs and provided the R2 and RMSEP values of 0.81 and 0.31 °Brix, respectively, seems more appropriate. In case of assessing the different cane levels on the conveyor, the outcomes presented model performance of the full and half cane levels in predicting half and full cane datasets with R2 and RMSEP of 0.52 and 0.55 °Brix and 0.53 and 0.48 °Brix, respectively. This showed that the different levels affected the SSC predictive accuracy of the model. The combined model was developed to cover variations of this difference and was used to predict two external sets. The predictions of ninety and thirty samples that were collected from the same and different growing seasons as the samples for the modelling presented the R2, RMSEP and RPD of 0.70, 0.42 °Brix and 1.83 and 0.56, 0.42 °Brix and 2.00, respectively.
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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
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Chungcharoen, Thatchapol
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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
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Phetpan, Kittisak
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Sirisomboon, Panmanas
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Lim, Chin Hock
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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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Non-linear viscoelastic behavior of cooked white, brown, and germinated brown Thai jasmine rice by large deformation relaxation test
(2017-07-03)
Sirisomboon, Panmanas
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Kaewsorn, Kannapot
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Thanimkarn, Satthawat
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Phetpan, Kittisak
Stress relaxation tests at high strain were conducted on scoops of cooked white, brown, and germinated brown Thai jasmine rice using a King Mongkut’s Institute of Technology Ladkrabang test rig. The diameter of the scoop was 35 mm and the height was 10 mm. Non-linear modeling, consisting of four relaxation models, was applied to the data obtained for each type of rice. The modeling methods included Peleg and Normand’s; Yadav, Roopa, and Bhattacharya’s; Jaya and Durance’s; and Myhan, Markowski, and Daszkiewicz’s. The cooked white rice showed greater tenderness compared to the others. The toughness of the three types of cooked rice was not found to be different. The Myhan et al. model was the most accurate in describing the non-linear viscoelastic behavior of all types of cooked rice. The cooked brown rice showed the highest initial decay rate, but the lowest relaxation, lowest elasticity, and greatest viscosity. In contrast, the cooked white rice had opposite characteristics.
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Evaluation of the moisture content of tapioca starch using near-infrared spectroscopy
(2015-03-25)
Phetpan, Kittisak
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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 (R2), 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.

Phetpan, Kittisak

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