Phanomsophon, Thitima
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Preferred name
Phanomsophon, Thitima
Alternative Name
Phanomsophon, T.
Main Affiliation
Email
thitima.ph@kmitl.ac.th
14 results
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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, Huanglongbing (HLB) disease detection using drone imagery and deep learning neural networks for early management of HLB(2026-08-01); ; - 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, Classification of N, P, and K concentrations in durian (Durio zibethinus murray cv. mon thong) leaves using near-infrared spectroscopy(2022-08-24); ; A good durian fruit should be supported by sufficient nutrient concentrations. If farmers can quickly determine the nutrient concentration in leaves, they can control fertilisation. Near-infrared (NIR) spectroscopy is a rapid non-destructive test for estimating nutrient concentration. Measuring nutrient concentrations requires chemical analysis and is a time-consuming process. The aim of this paper is a preliminary test for classifying the N, P, and K concentration levels to determine the possibility of creating models in the future to help farmers obtain information on durian tree nutrient requirements quickly. The models reported in this paper were created by PLS-DA, which had higher accuracy than SVM and the SIMCA method. Fresh durian leaf samples were used, and the spectra provided greater accuracy than the dried ground leaf sample spectra. The classification models had an accuracy of 88.89, 81.25, and 86.36% for predicting N, P, and K, respectively. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Predicting biomass global warming potential with FT-NIR spectroscopy(2025-12-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Primary assessment of macronutrients in durian (CV Monthong) leaves using near infrared spectroscopy with wavelength selection(2024-01-05); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modeling Textural Properties of Cooked Germinated Brown Rice Using the near-Infrared Spectra of Whole Grain(2023-12-01); - 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, Non-Destructive Quality Evaluation of Tropical Fruit (Mango and Mangosteen) Purée Using Near-Infrared Spectroscopy Combined with Partial Least Squares Regression(2022-12-01); ;