Jongyingcharoen, Jiraporn
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Jongyingcharoen, Jiraporn
Alternative Name
Jongyingcharoen, Jiraporn Sripinyowanich
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Email
jiraporn.jo@kmitl.ac.th
3 results
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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, 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, Postharvest detection of anthracnose (Colletotrichum asianum) on mango fruit (Mangifera indica L. cv Namdokmai Sithong) using near-infrared response(2026-12-01);