Sirisomboon, Panmanas
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Preferred name
Sirisomboon, Panmanas
Alternative Name
Sirisomboon, P.
Main Affiliation
Email
panmanas.si@kmitl.ac.th
62 results
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Item type:Publication, Deep neural networks (DNNs) chemical compositions estimation of fresh durian in-line via near infrared spectroscopy(2025-06-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modified specific gravity method for estimation of starch content and dry matter in cassava(2021-07-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluating limit of detection and quantification for higher heating value and ultimate analysis of fast-growing trees and agricultural residues biomass using NIRS(2023-10-09); Shrestha, Bim PrasadAccurate non-destructive assessment of biomass energy properties is essential for optimizing its use as an alternative fuel. In this study, 200 biomass samples were used to determine higher heating value (HHV) and 120 biomass samples for analyzing ultimate analysis parameters using near-infrared spectroscopy within the full wavenumber range of 12489.48 – 3594.87 cm<sup>-1</sup>. The samples were grounded, and five different types of partial least squares regression (PLSR) models were developed using traditional preprocessing, multi-preprocessing (MP) with 5 range, MP with 3 range, genetic algorithm, and successive projection algorithm. Limit of detection (LOD) and quantification (LOQ) were calculated using the best-performing model among five different PLSR models for HHV in kJ/kg, as well as the weight percentage (wt.%) of carbon (C), oxygen (O), hydrogen (H), and nitrogen (N). The LOD and LOQ for HHV were calculated as 622.42 kJ/kg and 1886.13 kJ/kg, respectively. Additionally, LOD and LOQ for ultimate analysis parameters, including C, O, H, and N were calculated as: 3.24 weight percentage (wt.%) and 9.81 wt.% for C, 2.04 wt.% and 6.18 wt.% for O, 0.35 wt.% and 1.05 wt.% for H, and 0.22 wt.% and 0.68 wt.% for N. The LOD and LOQ values for HHV, C, O, and H were lower than the minimum reference values used for model development, demonstrating the models’ high sensitivity and potential to reliably detect and precisely quantify these parameters. However, the LOD and LOQ values exceeded the minimum reference value used during model development for the N, indicating that the selected models have certain limitations in assessing the N content in biomass. The sample range should be expanded for wt.% of N to enhance the model’s performance, surpassing the LOD and LOQ values. This will improve the overall sensitivity of the model for reliable detection and quantification of N content in biomass samples. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of baby corn husk as a biological sustainable feedstock for the production of cellulase and xylanase by Lentinus squarrosulus Mont.(2023-02-01); ; In an effort to use baby corn husk (BCH) as a sustainable feedstock for cellulase and xylanase production by the Lentinus squarrosulus Mont. isolate LS-YA (LSM-LS-YA), a suitable pretreatment method and fermentation strategies were developed. BCH pretreated with 1 M sodium hydroxide for 90 min, an alkaline pretreatment, exemplified an appropriate pretreatment method. In a 10-L external Venturi injector bioreactor, the highest cellulase and xylanase production was 4.12 ± 0.36 unit/mL and 6.15 ± 0.36 unit/mL, respectively, when 1 g/L diammonium hydrogen phosphate was used as the nitrogen source and the aeration rate was controlled at 0.2 vvm. This study provides an informative perspective on the production of cellulase and xylanase from agricultural lignocellulosic materials, which could reduce agricultural waste while supporting a zero-waste circular economy, and this fermentation process would be applicable to larger-scale production. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of the Pomelo Peel Essential Oils at Different Storage Durations Using a Visible and Near-Infrared Spectroscopic on Intact Fruit(2024-08-01); ; ; Shrestha, Bim PrasadPomelo fruit pulp mainly is consumed fresh and with very little processing, and its peels are discarded as biological waste, which can cause the environmental problems. The peels contain several bioactive chemical compounds, especially essential oils (EOs). The content of a specific EO is important for the extraction process in industry and in research units such as breeding research. The explanation of the biosynthesis pathway for EO generation and change was included. The chemical bond vibration affected the prediction of EO constituents was comprehensively explained by regression coefficient plots and x-loading plots. Visible and near-infrared spectroscopy (VIS/NIRS) is a prominent rapid technique used for fruit quality assessment. This research work was focused on evaluating the use of VIS/NIRS to predict the composition of EOs found in the peel of the pomelo fruit (Citrus maxima (J. Burm.) Merr. cv Kao Nam Pueng) following storage. The composition of the peel oil was analyzed by gas chromatography–mass spectrometry (GC-MS) at storage durations of 0, 15, 30, 45, 60, 75, 90, 105 and 120 days (at 10 °C and 70% relative humidity). The relationship between the NIR spectral data and the major EO components found in the peel, including nootkatone, geranial, β-phellandrene and limonene, were established using the raw spectral data in conjunction with partial least squares (PLS) regression. Preprocessing of the raw spectra was performed using multiplicative scatter correction (MSC) or second derivative preprocessing. The PLS model of nootkatone with full MSC had the highest correlation coefficient between the predicted and reference values (r = 0.82), with a standard error of prediction (SEP) of 0.11% and bias of 0.01%, while the models of geranial, β-phellandrene and limonene provided too low r values of 0.75, 0.75 and 0.67, respectively. The nootkatone model is only appropriate for use in screening and some other approximate calibrations, though this is the first report of the use of NIR spectroscopy on intact fruit measurement for its peel EO constituents during cold storage. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Revealing the Power of Deep Learning in Quality Assessment of Mango and Mangosteen Purée Using NIR Spectral Data(2025-09-01); ; ; The quality control of fruit purée products such as mango and mangosteen is crucial for maintaining consumer satisfaction and meeting industry standards. Traditional destructive techniques for assessing key quality parameters like the soluble solid content (SSC) and titratable acidity (TA) are labor-intensive and time-consuming; prompting the need for rapid, nondestructive alternatives. This study investigated the use of deep learning (DL) models including Simple-CNN, AlexNet, EfficientNetB0, MobileNetV2, and ResNeXt for predicting SSC and TA in mango and mangosteen purée and compared their performance with the conventional chemometric method partial least squares regression (PLSR). Spectral data were preprocessed and evaluated using 10-fold cross-validation. For mango purée, the Simple-CNN model achieved the highest predictive accuracy for both SSC (coefficient of determination of cross-validation ((Formula presented.)) = 0.914, root mean square error of cross-validation (RMSE<inf>CV</inf>) = 0.688, the ratio of prediction to deviation of cross-validation (RPD<inf>CV</inf>) = 3.367) and TA ((Formula presented.) = 0.762, RMSE<inf>CV</inf> = 0.037, RPD<inf>CV</inf> = 2.864), demonstrating a statistically significant improvement over PLSR. For the mangosteen purée, AlexNet exhibited the best SSC prediction performance ((Formula presented.) = 0.702, RMSE<inf>CV</inf> = 0.471, RPD<inf>CV</inf> = 1.666), though the RPD<inf>CV</inf> values (<2.0) indicated limited applicability for precise quantification. TA prediction in mangosteen purée showed low variance in the reference values (standard deviation (SD) = 0.048), which may have restricted model performance. These results highlight the potential of DL for improving NIR-based quality evaluation of fruit purée, while also pointing to the need for further refinement to ensure interpretability, robustness, and practical deployment in industrial quality control. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Total soluble solids, dry matter content prediction and maturity stage classification of durian fruit using long-wavelength NIR reflectance(2023-12-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, NIR Spectroscopy for Non-Destructive Prediction of Greenhouse Gas Emissions and Global Warming Potential by Biomass Combustion(2026-05-01); ; Shrestha, Bim PrasadGreenhouse gas (GHG) emissions from biomass combustion include carbon dioxide (CO<inf>2</inf>), methane (CH<inf>4</inf>) and nitrous oxide (N<inf>2</inf>O), which cause climate change and global warming. By measuring GHG emissions by biomass combustion, a potent protocol for the calculation of global warming potential (GWP), which is how much the global temperature has risen due to combustion processes, can be achieved, contributing to determining the mean reduction in global temperature rise and fostering a transition towards more sustainable energy systems. Additionally, warning can be given of the GHG and GWP risks associated with different species of biomass. This review includes the GHG emissions and GWP of biomass combustion and their measurement and estimation directly through biomass sample combustion, using unmanned aerial vehicles (UAVs) and satellite measurements of radiation interacting with atmospheric gases, or satellite-derived data and calculations according to IPCC guidelines. In addition, the relationship of lignocellulosic compounds and elements in biomass to HHV and GHG emissions is described. The key mechanism of molecular vibration of hydrogen bonds in biomass caused by NIR radiation related to GHG emissions is revealed and recorded regarding the possibility of using NIR spectroscopy for the prediction of GHG emissions and GWP. Calculation examples for sugarcane bagasse and other biomass species are shown. The comparative advantages and limitations of NIR spectroscopy with respect to other methods are included. These factors lead to elucidation of the possibility of using NIR spectroscopy for non-destructive prediction of GHG emissions. In this review, the feasibility of using NIR spectroscopy to evaluate GHG emissions, GWP and emission factors (EFs) as an alternative to IPCC estimation methods related to climate change by biomass combustion is confirmed. NIR spectroscopy is a novel methodology for predicting GHG emissions and GWP directly from intact chip or powder biomass spectral data without explicit gas measurement. This article records the essential spectroscopic knowledge of biomass polymer valorization that is of value in polymer science. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The influence of processing parameters of parboiled rice on its physiochemical and texture propertiesThe impact of different parboiled rice process conditions on physical (whiteness and yellowness), chemical (amylose and fat contents), and texture (hardness and toughness) properties was studied. The parboiled rice was produced from the Suphanburi 1 variety. The correlation between chemical and texture properties was also analyzed. To study the effect of the soaking process, the time (2, 3, and 6 hr) and temperature (65 and 75°C) of soaking were altered, while the steaming condition was fixed at 100°C for 20 min. To study the effect of the steaming process, the soaking condition was fixed at 65°C for 6 hr while steaming condition was altered, including time (10 and 20 min) and temperature (90 and 100°C). The results show that the different conditions influenced the physical and chemical properties of parboiled rice. The amylose content was negatively correlated (Hardness, r = −0.52) (Toughness, r = −0.38) and fat content was positive low correlated (Hardness, r = 0.20) (Toughness, r = 0.12) with textural properties. Due to the specification of parboiled rice for exportation varying according to customer requirements, the results of this research provided some useful information for parboiled rice factories. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Huanglongbing (HLB) disease detection using drone imagery and deep learning neural networks for early management of HLB(2026-08-01); ;