Phetpan, Kittisak
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Phetpan, Kittisak
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kittisak.ph@kmitl.ac.th
19 results
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Item type:Publication, Rapid and accurate classification of Aspergillus ochraceous contamination in Robusta green coffee bean through near-infrared spectral analysis using machine learning(2023-03-01); ; ; 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Systematic evaluation of spectral preprocessing and machine learning for near-infrared prediction of mechanical stability in complex colloidal systems(2026-06-30); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhanced biodiesel purification using coffee husk bioadsorbents: The role of pyrolysis temperature, KOH activation, and adsorption efficiency(2025-05-01); ; ; ; Ruttanadech, NuttapongThis 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Near-infrared hyperspectral imaging combined with machine learning for physicochemical-based quality evaluation of durian pulp(2023-06-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Influence of Germinated Brown Rice Production by Water Spraying Method on Its Qualities(2023-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparison between Linear and Nonlinear Machine-Learning Algorithms for Predicting the Properties of Biodiesel Using Near-infrared Spectra(2023-01-01); 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Transflection Near-infrared Spectroscopy Combined with Machine Learning for Mechanical Stability Time Evaluation in Concentrated Rubber Latex(2023-01-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data(2024-01-01); ; Posom, JetsadaClassification of the crosslink density level of para rubber medical gloves by using near-infrared spectral data combined with machine learning is the first time reported in this paper. The spectra of medical glove samples with different crosslink densities acquired by an ultra-compact portable MicroNIR spectrometer were correlated with their crosslink density levels, which were referencely evaluated by the toluene swell index (TSI). The machine learning protocols used to classify the 3 groups of TSI were specified as less than 80% TSI, 80–88% TSI, and more than 88% TSI. The 80–88% TSI group was the group in which the compounded latex was suitable for medical glove production, which made the glove specification comply with the requirements of customers as indicated by the tensile test. The results show that when comparing the algorithms used for modeling, the linear discriminant analysis (LDA) developed by 2nd derivative spectra with 15 k-best selected wavelengths fairly accurately predicted the class but was most reliable among other algorithms, i.e., artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (kNN), due to higher prediction accuracy, precision, recall, and F1-score of the same value of 0.76 and no overfitting or underfitting prediction. This developed model can be implemented in the glove factory for screening purposes in the production line. However, deep learning modeling should be explored with a larger sample number required for better model performance. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design, calibration, and validation of an inline green coffee moisture estimation system using time-domain reflectometry(2023-03-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Filter-based short-wave infrared imaging combined with machine learning for non-destructive quality assessment of durian pulp(2026-09-01);