Journal of Science and Agricultural Technology

Study on a potentiality of plant aqueous extracts as natural dye fixatives in traditional dyeing processes using natural indigo from Strobilanthes cusia (Nees) Kuntze

ดร.ปัทม์ ปรานอมรกิตติ์ — 2025-07-18

This research investigated the use of five local plants in Northern Thailand which were Camellia sinensis (CS), Moringa oleifera (MO), Oroxylum indicum (OI), Schefflera leucantha (SL), and Tinospora cordifolia (TC) as stabilizing agents for indigo dyed fabric. The selected plants were extracted and examined for phytochemical compositions. It was found that CS-extract revealed the highest content of total phenolic compound, flavonoid and tannin of 2.40±0.46 GAE/mg DW, 3.31±0.29 QE/mg DW, and 0.23±0.01 ECGC/mg DW, respectively. MO total phenolic compound, flavonoid and tannin of 0.96±0.04 GAE/mg DW, 1.81±0.15 QE/mg DW, and 0.15±0.02 ECGC/mg DW, respectively. For antioxidant properties, CS and MO exhibited the most efficiency of 6564.4±55.6 mM Fe²⁺/g DW and 6512.6±220.6 mM Fe²⁺/g DW, respectively. Consequently, these might be responsible for the ability for color fixation. Particularly, MO with the excellent properties resulted in a good color fastness score at 3-4. In contrast, CS seemed to be less fixative ability than MO.

Saliva amylase inhibitory property of certain herbs and spices in Lampang, Thailand

Tanongsak sassa-deepaeng — 2025-07-18

Salivary α-amylase inhibitors (SaAIs) derived from spices and herbs have shown promise for the effective management of type 2 diabetes. This study aimed to identify potential SaAIs from aqueous extracts of 15 commonly used plants. Among these, Albizia lebbeck (L.) Benth. exhibited the highest inhibitory activity, followed by Mimosa pudica L., Ipomoea aquatica Forssk., and Gymnema inodorum (Lour.) Decne. Phytochemical analysis indicated that A. lebbeck demonstrated the strongest amylase inhibition, which was attributed to its high content of phenolics, flavonoids, tannins, and saponins. M. pudica also showed a significant saponin concentration. I. aquatica displayed notable amylase inhibitory activity linked to its phenolic, flavonoid, and protein content, although it also contained a high amount of reducing sugars. G. inodorum exhibited inhibition through a combination of phenolics, flavonoids, saponins, and proteins. The findings provide compelling evidence that dietary intake of A. lebbeck, I. aquatica, and G. inodorum may positively influence glycemic control. These results suggest that all four plants are promising candidates for further in vivo studies and potential drug development in mammalian models and humans.

Optimization condition to produce sweet-noodles in milk using reverse spherification technique

wanvimon pumpho — 2025-07-18

The principle of reverse spherification relies on the formation of calcium alginate, in which sodium alginate undergoes gelation in the presence of calcium ions. In this study, sweet-noodles in milk balls were produced by incorporating calcium lactate into the core solution and submerging it in a sodium alginate bath to form spherical gel structures. Optimization of the production conditions was conducted using Response Surface Methodology (RSM) with a Central Composite Design (CCD), focusing on two independent variables: sodium alginate concentration (X₁: 1.0–1.5%) and soaking time (X₂: 3–5 minutes). Each formulation was evaluated for its physical properties, including weight, size, color (L*, a*, b*), and membrane thickness. The results indicated that increasing both alginate concentration and soaking time enhanced gelation, resulting in increased bead size and film thickness. The optimal condition for producing sweet-noodles in milk balls was found to be 1.50% alginate concentration and 3 minutes of soaking time. Under this condition, the actual bead size was 15.93 mm. The predictive model was further validated, yielding a predicted bead size of 15.43 ± 0.49 mm, confirming the reliability of the optimization model.

The use of artificial intelligence methods to predict income and expense trends from semi-unstructured data for accounting planning for pineapple farmers in Phitsanulok province

Pramote Sittijuk — 2025-07-18

This study explores the use of artificial intelligence (AI) techniques, particularly Natural Language Processing (NLP), to predict income and expense trends for pineapple farmers in Ban Yang Subdistrict, Nakhon Thai District, Phitsanulok Province. The research focuses on extracting and analyzing semi-unstructured accounting data, primarily from PDF files, provided by 30 farmers with prior accounting experience. The data was processed using a custom NLP algorithm to classify financial records into payments and income categories. Time series forecasting models—Prophet, LSTM, and ARIMA—were applied to predict future trends. The results revealed significant differences in model performance, with ARIMA outperforming both LSTM and Prophet. ARIMA exhibited the lowest Mean Absolute Error (MAE) of 34.1084 and the highest R-squared (R²) of 0.9901, indicating superior prediction accuracy and explanatory power. LSTM showed a MAE of 71.0920 and an R² of 0.9511, demonstrating good accuracy but with higher MAE and lower R² than ARIMA. Prophet demonstrated the highest MAE of 603.8044 and the lowest R² of -3.2273, reflecting poor performance. Based on these findings, ARIMA is identified as the most suitable model for this dataset, with potential for further optimization and exploration of additional features to improve predictive accuracy.