Chungcharoen, Thatchapol
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Chungcharoen, Thatchapol
Alternative Name
Chungcharoen, T.
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thatchapol.ch@kmitl.ac.th
10 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, 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, 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, Low-Cost Near-Infrared Spectroscopy for Rapid Prediction of Biodiesel Properties: Acid Value, Density, Viscosity, and Water Content(2026-03-31); ; Partial least squares (PLS) regression, combined with various spectral pre-processing techniques, was employed to compare the performance of two diode array near-infrared (NIR) spectrometers in predicting key biodiesel quality parameters, including acidity, viscosity, density, and water content. An AvaSpec-Mini4096CL NIR spectrometer, operating within the 350–1100 nm wavelength range, was used as the representative shortwave near-infrared (SW-NIR) spectrometer, while a NIRQuest512 spectrometer, covering the 900–1700 nm range, was employed as the longwave near-infrared (LW-NIR) spectrometer. Both spectrometers were equipped with a transflection probe for spectral collection from oil palm-based biodiesel samples. The SW-NIR spectrometer outperformed the LW-NIR spectrometer. The optimal PLS models achieved root mean square errors of prediction (RMSEP) of 0.0037 mg KOH/g for acidity, 0.062 cSt (mm<sup>2</sup>/s) for viscosity, 2.67 kg/m<sup>3</sup> for density, and 59.14 mg/kg for water content, highlighting the potential of compact SW-NIR spectrometers as effective, low-cost tools for rapid biodiesel quality monitoring. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine learning-based prediction of nutritional status in oil palm leaves using proximal multispectral images(2022-07-01); ; ; This study evaluated the application of proximal multispectral images accompanied by 4 machine learning approaches for estimating the nutritional status of oil palm leaves. The image responded for five bands: blue, green, red, red edge, and near-infrared regions with a center wavelength of 475, 560, 668, 717, and 840 nm. Average and standard deviation (SD) values from the leaf pixels of each band were extracted, obtaining 5 average and 5 SD values from 5 bands. Thirty-four vegetation variables were generated based on those average and SD values. In total, forty-four variables consisted of 10 average-and SD-based features, and 34 vegetation variables were used as the input candidates for analyses against 10 target variables: nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), zinc (Zn), boron (B), and chlorophyll (SPAD). No significant input came out for modeling with P and Zn based on the stepwise selection. Therefore, 8 nutritional models were proposed in this study. A training set with 50 samples was used to be modeled for each target, and a test set with 15 samples was employed to evaluate the models' performances. Based on random forest (RF), support vector regression (SVR), partial least square regression (PLSR), and artificial neuron network (ANN) applied to be modeled, the models for chlorophyll, N, and Ca predictions were acceptable for screening, and those for K and Mg predictions were acceptable for rough screening. The chlorophyll model developed based on the RF had the predictive statistics in terms of coefficient of determination for prediction (r<sup>2</sup>), root mean square error of prediction (RMSEP), and standard error of prediction (SEP) of 0.752, 5.46 SPAD, and 5.65 SPAD, respectively. The other 2 screening models developed based on SVR and RF for N and Ca, respectively, gave the performances with the r<sup>2</sup>, RMSEP, and SEP ranging from 0.655 to 0.718, 0.12 to 0.17%, and 0.12 to 0.18%, respectively. In the case of the 2 rough screening models established using the RF algorithm, the predictive statistics ranged from 0.496 to 0.530 for the r<sup>2</sup> and 0.07–0.16% for both RMSEP and SEP. In this study, the Fe, Mn, and B models had poor results presenting the range of r<sup>2</sup>, RMSEP, and SEP of 0.308–0.491, 2.39–72.9 ppm, and 2.45–62.8 ppm, respectively. Based on the results, this study confirmed that the proximal multispectral information of oil palm leaves had enough significance to account for the status of chlorophyll and macro-nutrients: N, K, Ca, and Mg in the leaves. - 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhancing biodiesel yield and purification with a recently developed centrifuge machine: A response surface methodology approach(2024-04-15); ; ; ; Limmun, WanidaBiodiesel production processes, such as gravity settling, have limitations in terms of biodiesel yield, purification efficiency, operating time in the separation process, and more extensive equipment. Therefore, this study has focused on using a recently developed centrifuge machine for biodiesel separation to address these challenges due to its compact design, high efficiency, and simplicity. Additionally, this study aimed to optimize the separation efficiency of glycerol from biodiesel using a centrifuge machine, employing response surface methodology (RSM) with central composite design (CCD). The optimum conditions for separating glycerol from biodiesel via centrifuge machine are a rotation speed of 1800 rpm, a mixture flow rate of 192.25 ml/min, and a temperature of 55 °C, respectively. In optimum conditions, 94.52% separation efficiency was achieved. Biodiesel production can be improved, leading to higher yields and greater purity. The utilization of RSM proved valuable in determining the optimum conditions for separation. Furthermore, the machine successfully separated the biodiesel to meet ASTM D6751 and EN 14,214 standards. The results highlight the potential of the centrifuge machine for efficient and reliable biodiesel production, contributing to the advancement of the biodiesel industry. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The improvement of germination method for producing the germinated brown rice using a water spraying system with a revolved sieve(2024-11-01); ; Fonghiransiri, SurasakWater soaking is an important method in germinated brown rice (GBR) production that causes fermentation, leading to an unpleasant smell of GBR. In this research, a water spraying system with a revolved sieve is applied to produce the GBR. The increased speed and time of spray break led to higher moisture content and water absorption. The spray break of 30 min and revolved speed of 15 rpm provided the shortest time to obtain the paddy with a moisture content of 30% (w.b.). The incubation pattern with a revolved sieve and water spray provided the shortest incubation time for 90% germination. When producing the GBR with a water spraying system with a revolved sieve (GBR-WSSRS), it had a lower number of microorganisms compared to the GBR with a water soaking (GBR-WS), leading to higher scores of overall acceptability. However, the GBR-WSSRS had a lower GABA content than the GBR-WS.