Journal of Science and Technology Mahasarakham University

Optimization of crispy pork stick production by microwave heating using the response surface method

Darunee Naksavi — 2026-02-10

The development of crispy pork stick products is a research topic that Songkwang Green Food Co., Ltd. wanted to develop a new processed pork product that is rich in nutrients and can be eaten with breakfast in the hustle and bustle of daily life. This research aimed to determine the optimization conditions of the crispy pork sticks process with the microwave method, which analyzed the nutritional value and shelf life. The central composite design (CCD) was designed with the variables in each factor including 70.0-75.0% pork (w/w), 4.0-8.0% soy protein extract (w/w), and carboxymethyl cellulose 0.18-0.22% (w/w). The lack of fit test showed that the model did not differ statistically significantly (p>0.05), and the value obtained from the prediction equation consisted of 70% pork, 7.57% soy protein extract, and 0.18% carboxymethylcellulose. The product had the energy from fatty acids, 104 kcal, total fat 11.5 g, saturated fat 4.25 g, cholesterol 79.8 mg, protein 52.3 g, total carbohydrate 24.8 g, and dietary fiber 0.61 g / 100 g sample, respectively. The crispy pork sticks packaged in nitrogen-filled aluminum foil bags (30 g) had a shelf life of 18.5 months, and the aw was 0.28 at 35 °C. The entrepreneurs can take the newly developed and commercialized products.

Prevalence and diversity of Wolbachia in wild mosquito (Diptera: Culicidae) populations from the lower central Thailand

Sarunwitch Cheensae — 2026-02-10

Mosquitoes are medically important insects, recognized as vectors for many serious diseases. Previous studies have
shownthat mosquitoesoftenharborendosymbioticbacteria, suchas Wolbachia, withintheir cells.Theseendosymbiotic
bacteriaplay vital roles in mosquitobiology, includingtheevolutionof various mosquitospecies. Consequently, humans have utilized Wolbachia to control mosquito populations, thereby reducing the spread of mosquito-borne diseases. However, data regarding the prevalence of Wolbachia symbiosis in mosquito populations in Thailand remains limited. This study aimed to investigate the prevalence and diversity of Wolbachia in mosquito populations inhabiting the lower central region of Thailand. A total of 144 mosquito samples were collected and identified as belonging to four genera and six species: Aedes sp., Ae. albopictus, Anopheles sp., Culex sp., Cx. quinquefasciatus, and Udaya argyrurus. Three mosquito species tested positive for Wolbachia using PCR assays: Ae. albopictus, Culex sp., and Cx. quinquefasciatus. Phylogenetic analysis of the Wolbachia 16S rRNA gene revealed that all strains belonged to supergroup B, with diversity varying among mosquito species. The findings of this research contribute to a better understanding of mosquito evolution influenced by Wolbachia. Furthermore, this knowledge provides a basis for the effective control of these insect vectors.

Effect of storage conditions on the stability of physical and chemical characteristics of Thai garlic

Wanpiti Thammasri — 2026-02-10

Thai garlic production during periods of market excess will cause the price of garlic to fall, posing a major problem for
farmers. If the quality of garlic can be preserved for a longer period, it will create a business opportunity for farmers to sell garlic for processing into black garlic products, which can increase their income in another way. In this study, the researchers aimed to investigate the effect of storage conditions on the stability of the physical and chemical characteristics of Thai garlic. The objective of this study was to find the optimum storage conditions for 24 samples of early-maturing garlic and medium-maturing garlic, at temperatures of 1 and 4 degrees Celsius and relative humidity of 10-30% and 40-60% for 30 and 60 days. High-Performance Liquid Chromatography (HPLC) is the instrument used to study S-allylcysteine. The study found that the storage conditions of physical characteristics of early-maturing garlic and medium-maturing garlic: storage time of 60 days. The texture of garlic cloves is soft, but the shine is reduced. This does not cause the garlic cloves to become dry or lose moisture. The storage conditions of both garlic varieties on chemical characteristics by the Kruskal-Wallis Test found that at 60 days under 4 degrees Celsius and 40-60% relative humidity, the light garlic variety gave the highest average SAC content of 2.70 mg/g and was the most suitable condition, significantly different from other conditions at the level of 0.05. And which early- medium-maturing garlic gave the highest average value of S-allylcysteine (SAC), averaging 2.70 milligrams per gram. And medium-maturing garlic gave the highest average value of S-allylcysteine (SAC), averaging 0.98 milligrams per gram. The results of this study found that storage conditions of fresh garlic can increase SAC in garlic.

Comparative leaf epidermal anatomy of some species of the family Rubiaceae in Roi Et Rajabhat University

Sutarat Khonkayan — 2026-02-11

Anatomical characteristics of plants are important data for taxonomic studies. Additionally, there is little basic data
available to be used for identifying species in the Rubiaceae family. The objective of this research is to identify the
characteristics of leaf epidermal tissue that can be used as a taxonomic tool for species identification. Leaf epidermal
characteristics were examined in 20 species across 14 genera of the Rubiaceae family found at Roi Et Rajabhat
University. The species investigated included two species each of and Spermacoce, each of Gardenia, Hedyotis,
Ixora, Morinda, Oldenlandia and Spermacoce and one species each of Catunaregam, Cynanchum, Hamelia, Mitragyna, Mitracarpus, Mussaenda, Paederia and Rothmannia. Specimens were prepared using leaf epidermal peeling and clearing techniques and stained with 1% safranin. The generalized anatomical characteristics of the family were as follows: 1) Epidermal cell shapes were polygonal, irregular and jigsaw- like; 2) Stomata were of the paracytic type; 3) Trichome types included multicellular hairs, unicellular hairs and peltate hairs; 4) The cuticle patterns were either smooth, papillose or striate; and 5) Inclusion types included druse crystals, prismatic crystals, raphide crystals, sand crystals, styloid crystals, oil bodies and red staining inclusions cell. These leaf features have significant taxonomic value and can be effectively used to identify specific species or genera within the Rubiaceae family.

Development of skin serum products from tangor extract (Citrus nobilis Lour)

Saengsit Kritsadee — 2026-02-11

Tangor (Citrus nobilis Lour), locally known as “Som Kaew,” is a Geographical Indication (GI) plant of Samut Songkhram
Province, characterized by a thicker peel than common oranges. Currently, facial skin care products, particularly in
the form of serums, are highly popular. Objective: To develop a serum from Tangor peel extract and evaluate its
effectiveness in moisturizing the skin. Methods: Key substances were extracted from Tangor peels using ethanol for 7 days and concentrated via a rotary evaporator. The antioxidant activity of the extract was assessed using the DPPH method. The extract was formulated into three serum recipes. Stability was tested through 7 heating-cooling cycles at 5°C and 45°C. Safety was evaluated using a skin patch test, and skin moisturizing efficacy was measured using a Corneometer before and after 28 days of use. Statistical analysis included percentage, mean, standard deviation, and paired-sample t-test. Results: The Tangor peel extract exhibited antioxidant activity with an EC50 value of 0.17 mg/mL, compared to 0.01 mg/mL for the BHT standard. Stability tests revealed that Serum Formula 3 remained a clear yellow liquid with no significant change in pH and no phase separation. After 28 days of using Serum Formula 3, the average skin moisture level was 46.702 ± 12.003 (p < 0.05). Conclusion: The serum containing Tangor peel extract (Formula 3) demonstrated physical and chemical stability and provided the highest moisturizing effect. It has the potential to be developed into effective skin care products.

Efficiency of liquld media culbres on growth and development of Robiquetia succisa (Lindl.) Seidenf. & Garay

Nuengruethai Jacksri — 2026-02-11

The study of efficiency of Murashige and Skoog (1962) liquid medium for in vitro culture of young seedlings of Robiquetia succisa (Lindl.) Seidenf. & Garay in static and shaking culture conditions, supplemented with 150 ml/l coconut water and various concentrations of 6-Benzylaminopurine (BA) and 1-Naphthalene acetic acid (NAA) (0, 0.5, 1.0, and 2.0 mg/l) for 12 weeks. The results indicated that culturing young seedlings in the liquid medium supplemented with 150 ml/l coconut water, using the static culture method, was the most effective in inducing the proliferation of protocorm-like bodies (PLBs) (more than 30 PLBs per explant in average) from the root segments of R. succisa seedlings. Additionally,
R. succisa plantlets developed without planting materials exhibited the highest survival rate of 90% compared to those grown with lump charcoal or sphagnum moss as planting materials when cultivated in a greenhouse for 12 weeks.

Performance comparison of association rule mining algorithms among Apriori, FP-Growth, FP-Max, and H-Mine for market basket analysis

Kritbodin Phiwhorm — 2026-02-11

Association rule mining is a crucial technique for market basket analysis in retail businesses, but it often faces challenges in processing speed and memory usage, particularly with large-scale datasets. This research presents a performance comparison of four algorithms: Apriori, FP-Growth, FP-Max, and H-Mine, using a grocery store dataset for market basket analysis under varying support thresholds. The results showed that the H-Mine algorithm demonstrated superior performance in both execution time and memory usage, attributed to its efficient Hyperlink data structure, followed by FP-Growth and FP-Max algorithms, which employ FP-Tree structure to minimize database scanning. Meanwhile, the Apriori algorithm exhibited the lowest performance.

Analyzing students’ opinions on teaching and learning management using data mining techniques

Tanutchaipong Phetsongkram — 2026-02-11

In universities, the management of teaching and learning plays a crucial role in student learning. Gathering student
feedback is important for improving the quality of teaching and learning, allowing instructors to adjust teaching methods, content, or activities to better suit students. In courses with a large number of students, summarizing all feedback for the purpose of improving teaching can be time-consuming. Therefore, this research aims to develop a model for analyzing student opinions on teaching and learning management using data mining techniques. The research methodology is divided into five steps: 1) Data collection of 3,000 student feedback messages. 2) Data preparation by filtering messages and performing word segmentation, along with selecting key terms using the TF-IDF technique. 3) Modeling to compare the performance of four algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naive Bayes, and Random Forest for classifying positive and negative opinions and categorizing feedback on various aspects such as instructors, course content, and learning support resources. 4) Model performance evaluation using accuracy, precision, recall, and F1-score, and 5) Model application. The research findings indicate that the SVM algorithm has the highest performance in classifying positive and negative opinions, with average values of accuracy, precision, recall, and F1-score at 97.00%, 97.10%, 97.40%, and 97.30%, respectively. For categorizing feedback on various aspects, the KNN algorithm demonstrated the best performance, with average values of accuracy, precision, recall, and F1-score at 91.00%, 91.60%, 91.00%, and 91.10, respectively. The developed model has been deployed as a web application to analyze student feedback, effectively enhancing the quality of teaching and learning management.

An analysis of interaction design factors for online data collection applications using smartphones

Sununthar Vongjaturapat — 2026-02-11

Currently, online data collection for research in various fields may replace traditional paper-and-pencil/paper-and-pen questionnaire methods due to the demonstrated potential of online data collection. This research presents an
investigation into the design factors for interaction in online data collection applications using smartphones. The study
is based on the Task-Technology Fit (TTF) theory and principles of interaction design. The analysis of data using
Structural Equation Modeling and hypothesis testing on a sample of 320 participants from Ramkhamhaeng University
found that the following factors enhance the effectiveness of designing an online data collection application using
smartphones:1) The content of the questions in the application 2) The method of selecting options 3) The presentation of results, and 4) The adjustment of the scale. These factors contribute to the application’s ability to adequately support online surveys conducted via smartphones. Moreover, the research findings indicate that the method of selecting options in online data collection applications displayed on smartphones is the most significant factor compared to other factors within the endogenous latent variables. This indicated the importance of symbols that are easy to understand and of a size appropriate for the size of the smartphone screen.

Association rules of factors influencing postpartum depression

Artitayaporn Rojarath — 2026-02-11

Postpartum depression is a mental health condition that results from the abrupt hormonal fluctuations occurring during the postnatal period. This condition significantly affects the psychological well-being of mothers and directly impacts the well-being of the infant, as well as the broader familial and social environment. This study aims to generate
association rules and identify relevant factors that serve as preliminary data for the development of a screening tool
for postpartum depression. This research employs the Apriori algorithm to identify association rules within a dataset sourced from the Kaggle platform. The dataset comprises 1,503 instances and includes ten relevant variables, which are age, feelings of sadness or tearfulness, fatigue, sleep disturbances, difficulties with concentration or decision-making, eating disturbances, feelings of guilt, social withdrawal, suicide attempts, and anxiety, which serves as the key response variable. Relying on data from a single source in the experiment presents limitations regarding the generalizability of the findings to a wider population. The performance of the association rules is evaluated by applying
a minimum support threshold of 0.2 and a minimum confidence threshold of 0.2. The experimental results reveal 18
significant association rules. The most frequently occurring factors that exhibit strong relationships with the target
variable are difficulty concentrating or making decisions, suicide attempts, and feelings of sadness or tearfulness.
These three factors significantly affect the mental health of postpartum mothers. The findings of this study highlight
the importance of supporting the mental health of postpartum mothers, understanding their emotions and thoughts,
and preventing potential risks associated with these factors. The findings of this research apply to the design of a
screening model for postpartum depression and the development of appropriate counseling and guidance strategies
aimed at reducing risks and enhancing the long-term quality of life for both mothers and their infants.

Enhanced prediction of slope stability and failure distance using hyperparameter tuning and polynomial features

Pattanasak Chaipanna — 2026-02-12

This study presents an extensive analysis of slope stability using various machine learning (ML) models, focusing on
hyperparameter tuning and feature importance and model validation for predicting factor safety (FS), slope failure
distance relative to the height of the slope, and the safety level of the slope. The input parameters include cohesion
(C), internal friction angle (Phi), slope angle (Slope), and height of the slope (H). The performance of each model was
assessed using Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and R-squared (R²) for
regression tasks, while classification tasks were evaluated using accuracy, precision, recall, F1-score, and Area Under
the Curve (AUC). The analysis demonstrates the efficacy of different ML models. The model that performed well in both regression and classification is the Random Forest. The use of polynomial features significantly improved the
performance of linear methods, while hyperparameter tuning greatly enhanced the performance of Support Vector and MLP models.

Application of LIDAR technology for monitoring and assessing particulate matter at Kanchanaburi Rajabhat University

Artit Ruangsri — 2026-02-12

Air pollution is a significant global environmental issue, profoundly impacting human health, ecosystems, and sustainable economic development. Particularly in densely populated educational institutions, standard measurement methods often face limitations in terms of frequency and spatial coverage. This study, therefore, investigates the consistency of particulate matter (PM_2.5 and PM₁₀) measurements between a LIDAR system installed at Kanchanaburi Rajabhat University and data from a Beta Attenuation Monitor operated by the Pollution Control Department located in Pak Phraek Subdistrict, 15 kilometers away. Data were collected from five key points within the university over four months, alongside environmental factors and traffic volume. The findings reveal that the area near the main entrance recorded the highest particulate levels (PM_2.5: 28.5 ± 3.2, PM₁₀: 52.3 ± 5.1 μg/m³), while green spaces showed the lowest levels (PM_2.5: 17.3 ± 2.0, PM₁₀: 32.8 ± 3.5 μg/m³). Correlation analysis indicates a positive relationship between traffic volume and particulate levels (r = 0.71), whereas wind speed exhibits a negative correlation (r = -0.58). This research contributes to the development of guidelines for effectively applying LIDAR technology in monitoring and managing air quality in educational institutions.