Phetpan, Kittisak
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Phetpan, Kittisak
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kittisak.ph@kmitl.ac.th
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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, 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, 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, Investigation of physiological disorder classification in mangosteen fruit using visible and shortwave near-infrared spectroscopy combined with machine learning(2025-12-01); ; Thongphut, ChitwadeeAccurate classification of physiological disorders in mangosteen fruit is crucial for ensuring production quality, safety, sustainability, and economic viability. This study investigates the application of visible and shortwave near-infrared (Vis/SWNIR) reflectance spectroscopy, combined with machine learning algorithms, to classify three primary disorders: normal fruit (NF), translucent flesh disorder (TFD), and TFD with yellow gummy latex (TFD & YGL). The study specifically examines the effects of light intensity, spectral pretreatments, and machine learning models on classification performance. Spectral data were collected using two light intensities (50 % and 100 % of a 150 W light source) and processed with three pretreatments: standard normal variate (SNV), second derivative Savitzky-Golay (SGD2), and a combination of SNV and SGD2. Random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms were used for classification. The SGD2 method improved differentiation, especially for the TFD & YGL class, in the 700–725 nm wavelength range, which is associated with xanthone content in the fruit's pericarp. Higher light intensity (100 %) significantly improved classification accuracy, achieving an overall accuracy of 0.71 and an average F1 score of 0.61 with the RF model. Despite these improvements, the model struggled to distinguish the TFD class from NF due to their similar spectral profiles. Overall, the Vis/SWNIR spectroscopy and machine learning combination shows strong potential for the non-destructive classification of mangosteen fruit disorders. Both light intensity and spectral pretreatments play critical roles in enhancing performance. Future studies should focus on improving spectral sensitivity to better capture internal fruit characteristics. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Charcoal briquette production from waste in the coffee production process using hydrothermal and torrefaction techniques: A comparative study with carbonization technique(2022-10-20); ; ; This research aimed to study charcoal briquette production from coffee production waste, i.e., coffee parchment (CP) and coffee cherry pulp (CCP). The carbonization technique (CT) was studied in five mixtures (CP and CCP ≈ 0–90%) and three pressures (1000–1600 psi) to determine the appropriate conditions. The torrefaction technique (TT) and hydrothermal technique (HT) were subsequently performed under proper conditions from the CT to investigate further the fit temperatures (200–260 °C) and reaction times (40–120 min). The fuel characteristics were examined regarding the calorific value (CV), proximate and ultimate analyses, mechanical properties, and utilization properties. The results demonstrated the notable influence of interaction between the mixture and pressure factors and individual mixture on the fuel properties, whereas personal pressure have an insignificant effect. The ratio of CP ≈ 90% and binder ≈10% at a pressure of 1600 psi in the CT prepared appropriate fuel properties (calorific value ≈ 27 MJ/kg, fixed carbon content ≈ 65%). Interestingly, almost all conditions of the TT and HT provided greenhouse gas emissions lower than the CT. The TT and HT at 260 °C for 120 min provided high calorific values (25–26 MJ/kg) with other fuel characteristics in the acceptable standard, except for the fixed carbon content.