Phetpan, Kittisak
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Phetpan, Kittisak
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kittisak.ph@kmitl.ac.th
9 results
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Item type:Publication, Near-infrared hyperspectral imaging combined with machine learning for physicochemical-based quality evaluation of durian pulp(2023-06-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development of the partial least-squares model to determine the soluble solids content of sugarcane billets on an elevator conveyor(2021-01-01); ; This study aimed to determine the optimum multivariate model for monitoring the soluble solids content (SSC) of sugarcane billets being transferred on a conveyor. The study covered two main issues: the exploration of an appropriate spectral range (450–900 nm versus 700–900 nm) and the assessment of the influence of different levels of cane billets on an elevator via modelling to predict the SSC values. Partial least squares regression (PLSR) was used for model development. Modelling using the range of 450–900 nm employed 4 latent variables (LVs) and showed the coefficient of determination (R<sup>2</sup>) and root mean squares error of prediction (RMSEP) of 0.83 and 0.29 °Brix, respectively. This caused the model established using the range of 700–900 nm, employed 3 LVs and provided the R<sup>2</sup> and RMSEP values of 0.81 and 0.31 °Brix, respectively, seems more appropriate. In case of assessing the different cane levels on the conveyor, the outcomes presented model performance of the full and half cane levels in predicting half and full cane datasets with R<sup>2</sup> and RMSEP of 0.52 and 0.55 °Brix and 0.53 and 0.48 °Brix, respectively. This showed that the different levels affected the SSC predictive accuracy of the model. The combined model was developed to cover variations of this difference and was used to predict two external sets. The predictions of ninety and thirty samples that were collected from the same and different growing seasons as the samples for the modelling presented the R<sup>2</sup>, RMSEP and RPD of 0.70, 0.42 °Brix and 1.83 and 0.56, 0.42 °Brix and 2.00, respectively. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Transflection Near-infrared Spectroscopy Combined with Machine Learning for Mechanical Stability Time Evaluation in Concentrated Rubber Latex(2023-01-01); ; - 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, Filter-based short-wave infrared imaging combined with machine learning for non-destructive quality assessment of durian pulp(2026-09-01); - 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, Evaluation of physiological properties and texture traits of durian pulp using near-infrared spectra of the pulp and intact fruit(2021-04-01); ; This study aimed to investigate the feasibility of non-destructively predicting physiological properties (color, dry matter, and soluble solids) and texture properties (initial firmness, average firmness, rupture force, rupture distance, toughness, average penetrating force, and penetrating energy) of ‘Monthong’ durian using Fourier transform near-infrared spectroscopy of the pulp of the largest locule, the intact fruit at the largest locule, and the stylar end of the intact fruit. Based on partial least squares regression modeling, the internal quality evaluation of durian obtained by scanning the pulp could provide rough screening capability, with coefficient of determination of validation (r<sup>2</sup>), root mean square error of prediction (RMSEP), and the ratio of standard error of validation to standard deviation (RPD) values for the dry matter content, average penetrating force, and rupture force of 0.89, 3.60%, and 3.27; 0.73, 5.53 N, and 1.95; and 0.74, 6.15 N, and 1.96, respectively. Only the dry matter content of the pulp could be reasonably predicted based on scanning the intact durian fruit at the largest locule, with r<sup>2</sup>, RMSEP, and RPD values of 0.79, 5.23%, and 2.18, respectively. This finding could be applied at the first stage of trade between durian agriculturalists and exporters to prevent the exportation of immature durian and would be helpful for the industries producing frozen durian pulp and intact durian fruit for export. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluation of informative spectral wavelengths for estimating soluble solids content in sugarcane billets(2022-02-22); ; This study proposed individual spectral wavelengths significant to estimate soluble solids content (SSC) in sugarcane billets moving on the conveyor. At the same time, an all-in-one quality and yield monitor using those wavelengths for a sugarcane harvester was also proposed. Seven wavelengths, 475, 560, 668, 717, 755, 840, and 890 nm, were arranged into three groups for modeling. Group 1, consisting of 475, 560, 668, 717, and 840 nm, was based on the spectral responses of a commercial multispectral camera, while group 2 (717 and 840 nm) was based on the invisible (RedEdge and near-infrared or NIR) responses of the camera. For group 3, two sugar-related wavelengths at 755 and 890 nm were selected as the candidates for modeling. Partial least squares regression (PLSR) was employed to model those three groups with corresponding soluble solids content (SSC). The results showed that the developed models based on two sugar-related wavelengths at 755 and 890 nm provided the best performance, explaining 80.2 % of the variance in the SSC and displaying a root mean square error of calibration (RMSEC) of 0.32 ºBrix. The predictive performance had the root mean square error of prediction (RMSEP) of 0.33 ºBrix. This finding confirmed the effectiveness of the sugar wavelengths and conveyed the possibility to develop the sugarcane quality and yield monitor. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of near-infrared spectroscopy in detection of steroids adulteration in traditional thai medicines(2022-01-01); This study aimed to focus on applying near-infrared (NIR) spectroscopy to identify the adulteration of traditional Thai medicine products (TTM) with steroids. One hundred and ten samples were prepared with pure TTM and ten different steroid concentrations (0.25-5 mg steroid/g TTM). Fourier transform near-infrared (FT-NIR) spectrometer was used to scan TTM samples. The partial least squares (PLS) regression was used for the NIR spectroscopic model development to predict the level of steroid adulteration in TTM. For classification analysis, the principal component analysis (PCA) was used to discriminate 11 groups of raw TTM spectra (220 spectra). The developed PLS model accompanied by 3 latent variables (LVs) could predict the steroid content in TTM accurately with the coefficient of determination of prediction (r<sup>2</sup>) of 98.20%, root mean square error of prediction (RMSEP) of 0.22 mg steroid/g TTM, and residual prediction deviation (RPD) of 7.46. Furthermore, the PCA approach was possible to discriminate among the groups of TTM. The study showed NIR spectroscopy's capability to be used as a powerful technique to evaluate the steroid adulterated in TTM. This report is useful for food and drug association, patients, pharmaceuticals, and medical sectors.