Journal of Science and Technology Mahasarakham University

Effect of substitution of almond powder with dried watermeal (Wolffia arrhiza (L.)) in macaron shells

2025-03-05T16:01:55+07:00

Watermeal is a highly nutritious alternative food source derived from local plants. This research aimed to study formula and production process of substitution of almond powder with dried watermeal in macaron shells, investigate their physical and chemical qualities, and evaluate consumer acceptance. Three basic macaron formulas were selected and tested for consumer acceptance based on appearance, color, odor, taste, texture and overall liking by 50 consumers using a 9-point hedonic scale. The most accepted basic formula was chosen to determine the optimal ratio of watermeal substitution. Almond powder in the basic formula was substituted with dried watermeal at five levels: 0%, 2.5%, 5%, 7.5% and 10%. Subsequently, these formulations were analyzed for their physical and chemical qualities, as well as consumer acceptance. The formula with the highest consumer acceptance was selected for nutritional analysis to compare with the basic formula. The results indicated that consumers gave the highest acceptance to the 5% substitution of dried watermeal in macaron shells, with average scores for appearance, color, odor, taste, texture and overall liking of 7.4 ± 1.4, 7.4 ± 1.1, 7.2 ± 1.6, 6.7 ± 1.5, 6.6 ± 1.7 and 7.1 ± 1.7, respectively. The physical qualities included a lightness value (L*) of 70.79 ± 0.74, a redness value (a*) of 1.52 ± 0.17 and a yellowness value (b*) of 12.90 ± 0.41. The chemical quality, specifically water activity (aw), was 0.70 ± 0.02. The nutritional composition consisted of 13.02 ± 0.20% moisture, 7.84 ± 0.03% protein, 24.49 ± 0.01% fat, 1.14 ± 0.06% fiber and 1.42 ± 0.01% ash.

Extraction and purification of medium-chain-length polyhydroxyalkanoates produced by Streptomyces sp. using enzymes and an aqueous two-phase system

2025-03-09T14:27:43+07:00

Polyhydroxyalkanoates (PHAs) are biodegradable polymers synthesized by bacteria, exhibiting properties similar to
petrochemical-based plastics while being environmentally friendly. Traditional extraction methods pose environmental
hazards, necessitating cost-effective and eco-friendly alternatives. This study aimed to investigate the extraction and purification of PHA from bacterial cells using enzymes and an aqueous two-phase system (ATPS). The bacteria used in this study were isolated from agricultural soil and cultivated in a nitrogen-limited minimal salts medium without complex carbon sources. Three enzymes—lysozyme, papain, and lipase—at concentrations ranging from 2 to 10 g/g cell, were employed to digest the bacterial cell wall. Subsequently, the purification of PHA was examined using five different ATPSs, and the composition of the extracted PHA was analyzed by gas chromatography-mass spectrometry (GC-MS). The results indicated that the isolated bacterium was identified as the Gram-positive species Streptomyces griseoincarnatus. Among the tested enzymes, papain at 10 g/g cell exhibited the highest efficiency in cell wall digestion, followed by lysozyme and lipase at the same concentration, with relative absorbance reductions of 81.9%, 34.2%, and 26.2%, respectively. The optimal extraction efficiency was achieved when lysozyme (10 g/g cell) was used to pre-digest the cell wall for 8 hours, followed by papain (10 g/g cell) for an additional 12 hours, yielding the highest PHA recovery of 87.2%. The PEG6000/K₂HPO₄ system was found to be the most effective ATPS, increasing the purity of the extracted PHA by 58.8%. The extracted PHA was identified as a medium-chain-length polymer. Therefore, the development of an enzymatic extraction method combined with ATPS purification provides an efficient and environmentally friendly approach for recovering PHA from bacterial cells.

Prevalence and genetic variation of Feline Parvovirus (FPV) infection in the central and eastern regions of Thailand

2025-04-21T23:19:34+07:00

Feline Parvovirus (FPV) causes severe bloody diarrhea and feline enteritis, with a relatively high mortality rate, especially in unvaccinated kittens. Additionally, previous studies have indicated that cats are susceptible to infection by both FPV and Canine Parvovirus Type 2 (CPV-2). This study aimed to determine the prevalence of FPV, also known as feline distemper or feline panleukopenia, in the central and eastern regions of Thailand. Cat sample collection was conducted from November 2022 to April 2024, spanning a total period of 1.5 years, to investigate the prevalence of the disease and analyze the genetic variation of FPV. The findings revealed that the prevalence of FPV infection was 14.29%. The highest prevalence was observed in November, December, January, and February, which correspond to the winter season. In contrast, some cases were detected during the rainy season, while the lowest prevalence was recorded during the summer. These findings align with the natural infection patterns and past transmission trends of the virus. The correlation analysis using Spearman’s rank correlation method indicated a strong relationship between gastrointestinal illness in cats and feline enteritis caused by FPV. The correlation coefficient was 0.84 at a significance level of p ≤ 0.05. In addition, a comparative evaluation of diagnostic methods for FPV was conducted between a commercial rapid immunochromatographic test (IC Test) and polymerase chain reaction (PCR) detection of the VP2 gene of FPV/CPV-2. The sensitivity of the rapid IC test was 62.96% (17/27), while the positive predictive value (PPV), which is the proportion of true positive cases among all positive test results, was 65.38% (17/26). This indicated that the commercial rapid IC test detected 62.96% of positive cases, with a 65.38% accuracy rate compared to the gold
standard PCR method. Moreover, the nucleotide sequence analysis of the VP2 gene in both FPV (236 bp) and
CPV-2 (529 bp) revealed that all cases contained only FPV-specific genetic material. This suggested that at present,
no genetic variation leading to cross-species transmission has occurred among the feline population in the central and
eastern regions of Thailand.

Quantity and harvesting value of wedge clams (Donax spp.) at Muang Ngam Beach, Muang Nga m Subdistrict, Singha nakhon District, Songkhla Province

2023-06-09T14:01:53+07:00

Wedge clams in the genus Donax are crucial bivalves in the sandy beach ecosystem. They play a vital role in the
local food chain and provide a source of income for coastal communities. This research aimed to estimate the direct
income value and potential harvesting value of wedge clams. Economic data for direct income were collected through interviews with local harvesters, while potential value was assessed using biological surveys during spring and neap tides. The results identified three species: Donax faba, D. cuneatus, and D. incarnatus. Locals typically harvested clams based on size rather than species. The net income from harvesting amounted to 154,359.38 baht/year (averaging 9,079.96 baht/person/year). Regarding potential quantity, average densities during spring tide for D. faba and D. incarnatus were 11.6 ± 1.85 and 6.0 ± 1.21 ind/m², respectively. During neap tide, densities were 2.13 ± 0.76 for
D. cuneatus and 0.67 ± 0.28 for D. incarnatus. The estimated annual potential harvesting value for this beach area
was 716,420.94 baht/year or 247.05 baht/m²/year. These findings highlight the importance of wedge clams to coastal
communities and the necessity of their conservation for sustainable utilization.

Effects of Indian almond ieaf aqueous extract on Staphylococcus aureus in relation to concentration and exposure time

2025-05-23T09:45:00+07:00

Staphylococcus aureus is a pathogenic bacterium that causes skin infections in both pets and humans with the
increasing trend of bacterial resistance to antibiotics, prompting interest in alternative antibacterial agents from natural sources. Therefore, this study aims to the efficacy of aqueous extract from Indian almond (Terminalia catappa Linn.) leaves, focusing on determining the minimum bactericidal concentration (MBC) and the time required for effective killing S. aureus. The experiment was conducted by preparing an aqueous extract of Indian almond leaves through boiling, followed by extended immersion for 7 days. The MBC and Time-dependent killing was performed by macrodilution method. The results show that the ferment extract of Indian almond leaf extract contained 0.76 mg/ml of tannin. The MBC of ferment extract against S. aureus was between 0.35 to 0.4 mg/ml. In addition, time-killing assay demonstrated a concentration and time-dependent bactericidal effect. At 0.35 mg/ml of aqueous extract, colony counts of S. aureus statistically significantlydecreased from 115 at 2 hours to 0 at 24 hours (rs = -1.000, p-value < 0.001). At 0.4 mg/ml, bacterial colony elimination occurred rapidly, with colony counts reduced significantly to 0 at 8 hours (rs = -0.975, p-value = 0.02). These findings suggest that the aqueous Indian almond leaf extract may serve as a natural alternative to reduce antibiotic use against S. aureus infection.

Enhancing the efficiency of a convolutional neural network model for rice variety classification using a customized prewitt operator

2024-09-16T12:10:46+07:00

Rice variety classification is a crucial process in the agricultural industry, directly impacting product quality and production efficiency. This article presents a method for enhancing rice variety classification efficiency using a Convolutional Neural Network (CNN) integrated with a customized Prewitt operator for precise edge detection from grain images. This technique improves the accuracy of distinguishing between rice varieties with similar characteristics. The experimental results demonstrate that the CNN model combined with the Prewitt operator achieves a classification accuracy of 98.5%. This performance surpasses previous research using other methods, such as Sobel and Canny edge detection, which reported accuracies ranging from 85% to 96%. This article discusses the development stages of the CNN model and the Prewitt operator and provides a comparison with related works, highlighting its effectiveness across diverse environments.

A comparison of independent variable selection in a logistic regression model using Bayesian variable selection and stepwise regression

2025-02-23T12:54:20+07:00

Selecting appropriate independent variables yields a highly efficient model, particularly in regression analysis. This
study aims to examine the selection of independent variables in a logistic regression model using Bayesian variable selection via Gibbs sampling and stepwise regression. It compares these two selection methods under conditions of very low and very high multicollinearity among independent variables. The study is conducted through data simulation and applied to online writing behavior data for diagnosing Alzheimer’s disease. The sample sizes for the simulation were set to 25 and 100, with 100 replications for each case. The performances of Gibbs sampling and stepwise regression were compared based on evaluation criteria, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), number of correctly selected independent variables, average estimated coefficients of correctly selected variables, and accuracy percentage. The results indicate that for both small (n = 25) and large (n = 100) sample sizes, when the multicollinearity among independent variables is very low, Gibbs sampling and stepwise regression show no significant difference in their efficiency in selecting independent variables. However, in cases where
multicollinearity is very high, Gibbs sampling demonstrates superior performance in selecting independent variables
compared to stepwise regression. The findings of this research can be applied to select independent variables in realworld data exhibiting multicollinearity.

Cashew nut size classification using hybrid learning techniques

2025-03-06T12:55:37+07:00

The research problem originated from the limitations of traditional cashew nut size classification methods, which result in high labor costs and inconsistent product quality, affecting the competitiveness of Thailand’s cashew nut industry in the global market. The research objective was to develop a high-precision cashew nut size classification system by integrating deep learning and machine learning techniques. The proposed methodology involves a hybrid cashew nut size classification system. First, a deep learning technique utilizing YOLOv5 was applied to detect cashew nuts from images, with experiments conducted to determine the optimal and most efficient parameters. Subsequently, machine learning using a Support Vector Machine (SVM) was employed for size classification based on dominant morphological features, including width, length, and area. The SVM model was evaluated using four kernel types: linear, radial basis function (RBF), polynomial, and sigmoid. The results indicated that the detection model performed optimally when using 400 epochs and a batch size of 64, achieving a precision of 0.9817 and a recall of 0.9706. Furthermore, the size classification model achieved perfect performance, yielding precision and recall values of 1.0 across all tested kernels. However, the overall system performance still had minor limitations due to occasional errors in the detection step. In conclusion, this system can improve the accuracy of cashew nut size classification and enhance the competitiveness of Thailand’s cashew nut industry in the global market by reducing labor costs and improving product quality consistency. Future developments should focus on refining the detection process to further enhance the overall system efficiency.

Classification of offences in narcotic cases using machine learning

2025-02-06T17:53:19+07:00

The examination of indictments in narcotic cases requires considerable time and attention to detail, posing challenges for judicial officers. This study aimed to reduce such burdens and enhance the efficiency of the justice process. Therefore, the objective of this research was to develop and evaluate the performance of a machine learning model for classifying offences in narcotic cases. The research consisted of five main steps: (1) collecting 1,651 narcotic case samples from the Suphanburi Provincial Court (January 2022 – May 2024), (2) exploring the dataset, (3) preprocessing the data using PyThaiNLP and TF-IDF, (4) developing a two-stage classification model, where stage one classified narcotic types using a multi-class classification with a One-vs-Rest strategy, and stage two classified the nature of the offence using a multi-label classification with a Classifier Chains strategy, and (5) evaluating the model performance using three algorithms (Support Vector Machine, Logistic Regression, and Random Forest) via 5-fold cross-validation. The results indicated that the Random Forest algorithm achieved the highest performance and stability in both stages, with an F1-score of 99.55% for stage one and 97.70% for stage two, along with a Hamming Loss of 1.58%. These findings demonstrate the potential of the proposed model in assisting the classification of offences in narcotic cases.

Data clustering analysis to identify high-risk Thai individuals for domestic violence and mental health problems using machine learning

2025-03-17T12:06:13+07:00

Domestic violence and mental health problems remain critical public health concerns in Thailand, with an increasing prevalence observed during economic crises and the COVID-19 pandemic. Despite their significant societal impact, previous research has lacked in-depth analytical approaches utilizing data science to identify high-risk populations. This study aims to classify high-risk Thai individuals vulnerable to mental health issues and domestic violence by applying unsupervised machine learning techniques, specifically K-means, Hierarchical Clustering (HC), and Gaussian Mixture Models (GMMs). Internal evaluation metrics, including the Silhouette Score, Calinski-Harabasz Index, and Davies-Bouldin Index, were used to assess clustering performance. The dataset comprised 1,162 records of domestic violence offenders obtained from the Digital Government Development Agency (DGA) in Thailand. The findings indicate that HC achieved the highest performance (Silhouette Score = 0.429, Calinski-Harabasz Index = 61.790, and Davies-Bouldin Index = 1.034), effectively differentiating risk groups. The high-risk group was predominantly characterized by middle-aged males with mental health issues, substance abuse, and economic stress. This study demonstrates the potential of machine learning for identifying vulnerable populations. It provides insights that can inform the development of targeted prevention strategies, early warning systems, and evidence-based policymaking to mitigate domestic violence and promote sustainable mental well-being.

Pre-trained CNN-based feature extraction for automatic morphological identification of Acanthamoeba spp.

2025-03-06T12:53:05+07:00

Acanthamoeba spp. are free-living protozoa and are known to cause severe infections in humans. Traditional morphological identification relies on assessing the size and shape of the inner (endocyst) and outer (ectocyst) walls of cysts, which are categorized into three groups (GI, GII, GIII). However, this method is time-consuming and requires skilled experts. This study aims to develop an automated image analysis system for the classification of Acanthamoeba spp. cysts by employing a pre-trained convolutional neural network (CNN)-based feature extraction approach combined with a Support Vector Machine (SVM) classifier to increase diagnostic accuracy in pathology and reduce classification errors. The feature extraction is performed using various CNN models pre-trained on ImageNet, including Xception, EfficientNet-B0, EfficientNet-B1, VGG16, ResNet50, ResNet101, MobileNet, Inception-V3, and InceptionResNet-V2, to extract high-level global features from microscope images of cysts. These extracted features are then input into an SVM for classifying the cysts into groups GI, GII, and GIII. Additionally, the performance of this approach is compared with that of conventional feature extraction methods. Experimental results demonstrated that the system employing pre-trained CNNs combined with SVM achieved an accuracy exceeding 95%, with ResNet50 and ResNet101 yielding the best results, respectively. These findings validate the potential of using pre-trained CNN-based feature extraction for the robust and efficient classification of complex morphological cyst groups, thereby offering a promising tool for enhancing clinical diagnostic procedures.

Effects of filter motor speed and air suction fan speed on dust reduction efficiency using a solar-powered air purifier

2025-04-11T09:01:32+07:00

Air pollution is a major problem affecting public health in many areas. Therefore, the development of efficient and sustainable air purifiers is an important approach to reduce such impacts. This research developed a solar-powered air purifier that uses a wet cloth as a filter to capture small dust particles. The objective was to study the effects of filter motor speed and air suction fan speed on the dust reduction efficiency of the air purifier, using a factorial experimental design to analyze the main factors and their interactions. The independent variables were the speeds of the filter motor and the air suction fan, tested at 50%, 75%, and 100%, while the dependent variable was the percentage of dust reduction. The experimental results showed that a higher fan speed increased the air flow rate, causing more dust to be drawn into the system, while the filter motor speed helped reduce dust accumulation on the filter. Optimal speed settings significantly reduced clogging and improved dust filtration efficiency. In addition, the analysis of the main factors and their interactions revealed that the relationship between the two factors significantly affected the efficiency of the air purifier. The optimal settings from the experiment were a filter motor speed of 75% and an air suction fan speed of 75%, which yielded the highest efficiency in reducing dust particles. This study can serve as a guideline for designing and improving air filtration systems to be more efficient by holistically considering the factors affecting the system.