Pornchaloempong, Pimpen
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Preferred name
Pornchaloempong, Pimpen
Alternative Name
Pornchaloempong, P.
Main Affiliation
Email
pimpen.po@kmitl.ac.th
39 results
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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, Physical properties of green soybean: Criteria for sorting(2007-03-01); ; Romphophak, TeeranudThe objective of this research was to explore the possibility in developing a criteria for green soybean (Glycine max variety AGS 292) sorter. Green soybean could be classified into two main categories by its usage; the perfect pods (3-seed pod and 2-big seed pod) contained 47%, the rest was imperfect pods including 2-small seed pod, 1-seed pod, atrophied pod, twisted pod, and defected pod. Some important physical characteristics were investigated including width, length, thickness, pod weight, pod projected area, apparent density, bulk density, and seed firmness. The results illustrated that the perfect pods had length, pod weight, projected area and seed firmness significantly larger than the imperfect pods. All pod groups had apparent density equal or lower than water. Among all pod groups, the 2-big seed pods had the highest bulk density, thickest pod, and the firmest seed. The application of these physical properties for green soybean sorting was also proposed. © 2006 Elsevier Ltd. All rights reserved. - 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, Business viability and carbon footprint of Thai-grown Nam Dok Mai mango powdered drink mix(2020-05-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Numerical simulation of conduction heating in conically shaped bodies(2003-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Integrating Vis-SWNIR spectrometer in a conveyor system for in-line measurement of dry matter content and soluble solids content of durian pulp(2021-11-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Preliminary Life Cycle Analysis of Greenhouse Gas Emissions at Transportation Phase of a Beverage Drink for Green Logistics(2021-05-12); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Moisture content prediction in durian husk biomass via near infrared spectroscopy coupled with aquaphotomics and explainable machine learning(2025-12-15); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Shelf-life extension of Thai green papaya salad dressing by hurdle technology(2024-09-01); ; ; Srisawat, KraisuwitGreen papaya salad or Som Tum is the most popular spicy mixed salads in Thailand due to its unique rich flavor. Green papaya salad dressing (GPSD) is made from various ingredients such as fresh chili pepper, fresh garlic, rind tamarind, fish sauce and lime oil, including the limitation in controlling the taste and flavor of salad dressing and its poor shelf-life. In this study, a convenient ready-to-eat GPSD was developed. Hurdle technology was applied to extend shelf-life of the GPSD based on monitoring of microbial contamination and food pathogens throughout the process. Hurdle technology able to decrease total plate count (TPC) from 5.6 ± 0.2 to 1 ± 0.3 log CFU/g and yeast and mold (Y&M) from 4.2 ± 0.3 to <1 log CFU/g. After 12 weeks of storage at 5 ± 2 °C, slightly increase of TPC was detected as 1.5 ± 0.2 log CFU/g and no changes were found for Y&M and other pathogens. At week 12, GPSD stored at 32 ± 2 °C was found to have higher TPC and Y&M (3.7 ± 0.3 and 2.4 ± 0.3 log CFU/g, respectively). Therefore, a combination of hurdles that combines low a<inf>w</inf>, low pH, heat treatment, low temperature after hot filling, and chilled storage could extend the shelf-life of GPSD with satisfy sensorial test result and be suitable for minimally processed salad dressing.