Asia-Pacific Journal of Science and Technology

Enterobacter species Obtained from Poultry Droppings of Selected Farms in Southwest Nigeria is a Hotspot of Antibiotic-Resistant Genes

Motunrayo J. Olatoye, Olutayo I. Falodun, Yogesh S. Shouche — Tue, 14 Jul 2026 00:00:00 +0700

This study aimed to determine the antibiotic resistance patterns of Enterobacter spp. isolated from poultry droppings in the southwest of Nigeria. Enterobacter species were isolated from poultry droppings of selected farms in the southwest, Nigeria, between February and June, 2019. The isolates were identified by MALDITOF- MS and tested against 20 antibiotics using the Kirby-Bauer disk diffusion method. Genomic DNA and plasmids of the isolates were extracted using the sodium dodecyl sulfate protocol and alkaline lysis method, respectively. Antibiotic resistance genes were detected with specific primers using the polymerase chain reaction. Seventy-two Enterobacter spp. comprising E. cloacae (n=52), E. asburiae (n=12), E. kobei (n=7), and E. ludwigii (n=1) were isolated. Antibiotic resistance patterns revealed that all the isolates were resistant to amoxicillin, cefpodoxime, and cefixime, while high resistance to cefotaxime (98.6%), ceftriaxone (86.1%), and cefuroxime (84.7%) was observed. All Enterobacter species were multiple antibiotic-resistant, and plasmids were detected in 43.1% of the isolates, and 52.8% harbored at least one antibiotic resistance gene. This study showed that multidrug-resistant Enterobacter spp. are present in poultry droppings in southwest Nigeria, and also revealed that antibiotic resistance genes were plasmid and chromosomally borne in the Enterobacter species, thus, might be posing a serious health threat to humans and animals.

Optimization of fucoidan extraction from brown algae (Sargassum swartzii) using response surface methodology and assessment of its antioxidant activity

Tue, 14 Jul 2026 00:00:00 +0700

Fucoidan is among the sulfated polysaccharides that originate from the cell walls of brown algae. It is recognized as the most vital factor in pharmaceuticals and food due to its many beneficial properties, such as anticancer, anti-inflammatory, antioxidant, etc. Applying modern extraction techniques and optimizing extraction conditions have recently received special attention in order to achieve high extraction efficiency while being environmentally and health-friendly. This study used the Response Surface Methodology (RSM) to optimize the extraction conditions of fucoidan from the brown algae Sargassum swartzii collected from Nam Du archipelago with the support of ultrasound. The effects of various temperatures, including 60°C, 70°C, 80°C (A), and duration of time 30, 45, 60 min (B) were studied through the central composite design model. The antioxidant activity of fucoidan extracted under optimal conditions was also investigated. The tests revealed that the optimal conditions for fucoidan extraction were at 75°C for 50 min, corresponding to the extraction efficiency (crude fucoidan content) of 2.59%; the sulfate and total polyphenol contents were 29.11% and 1.87%, respectively. The analysis of the structural characteristics of fucoidan was taken using Fourier-transform infrared spectroscopy (FT-IR). In addition, fucoidan from Sargassum swartzii exhibited biological activity in antioxidant properties, DPPH and ABTS free radical scavenging efficiency of 51.04% at 1.250 mg/mL and 72.47% at 0.5 mg/mL, respectively. These findings can be applied to improve the field of pharmaceuticals and functional food.

Computer Vision and Field Validation of an Artificial Intelligence-Based Tomato Grader for Large-Scale Production in Northeastern Thailand

Tue, 14 Jul 2026 00:00:00 +0700

Tomato (Solanum lycopersicum L.) quality grading based on visual inspection often yields inconsistent results and reduces market value. The 'Perfect Gold 111' variety presents distinct morphological traits, including a characteristic green-to-red color transition, specific calyx structure, and defect patterns such as greenback distribution and suberization. These characteristics differ substantially from internationally studied cultivars, rendering generic pre-trained models insufficient for accurate grading under the Thai TACFS 1503–2007 standard. This study developed an automated grading model for 'Perfect Gold 111' tomatoes using a deep learning model based on a flow-based (node-based) architecture integrated with the Robot Operating System (ROS) framework and implemented on the CiRA CORE platform. A total of 220 samples were collected and graded according to the TACFS 1503–2007 standard. Top and side-view images were used to create a dataset comprising 165 tomatoes for training and 55 for testing. Model performance was evaluated using Precision, Recall, F1-Score, and Accuracy, and was compared with manual grading performed by farmers. The AI model achieved 80.00% of accuracy, outperforming farmer grading, which achieved 52.72% accuracy. In addition, the model reduced misclassification among visually similar grades and provided consistent, quantitative assessments of color, shape, and defects. These findings highlight the potential of AI-based grading systems to improve quality consistency, reduce labor, and support automated postharvest sorting for both smallholder and industrial tomato production.

Evaluation of the biochemical constituents and antioxidant potential of Heterotrigona itama bee bread extracted using the bromelain-assisted method

Norhazniza Aziz, Koh Soo Peng, Nur Diyana Alyas, Fadzilah Puteh — Tue, 14 Jul 2026 00:00:00 +0700

Bee bread is a nutrient-dense substance produced through the microbial fermentation of pollen, nectar, and bee secretions, serving as the primary nourishment for stingless bee colonies. This study aimed to evaluate the biochemical constituents of Heterotrigona itama bee bread, specifically investigating how bromelain-assisted extraction influences its antioxidant potential and nutrient solubility. Nutritional profiling of the fresh bee bread revealed a high content of carbohydrates (57.0%) and proteins (23.3%). The results demonstrated that bromelain treatment significantly improved the biochemical profile of the enzymatically-treated bee bread (EBB) compared to the raw (RBB) and water-extracted (WBB) controls. Post-treatment, the total soluble protein content increased substantially from 195.8 mg/mL in RBB to 456.7 mg/mL in EBB. Furthermore, enzymatic hydrolysis significantly retained and enhanced the antioxidant capacity, showing a 26.5% to 32.2% increase in radical-scavenging activity through DPPH and ABTS assays relative to the raw samples. The main phenolic acids found in all samples analyzed using HPLC were gallic acid, protocatechuic acid, 4-hydroxybenzoic acid, caffeic acid, and rutin. Proline was identified as the predominant free amino acid, with all nine essential amino acids present, alongside non-protein amino acids such as gamma-aminobutyric acid (GABA). These findings indicate that bromelain-assisted extraction effectively enhances the nutritional composition and antioxidant potential of H. itama bee bread.

The Efficiency of Hybrid K-Means and Density Peak Clustering Algorithms for Multidimensional Poverty Clustering Using Thai People Map and Analytics Platform (TPMAP) Database

Narintra Mingolo, Weerachai Sarakorn — Tue, 14 Jul 2026 00:00:00 +0700

Multidimensional poverty remains a critical challenge for sustainable development in Thailand, requiring analytical approaches that can reveal complex regional patterns of deprivation. This study evaluated the effectiveness of a hybrid clustering algorithm that integrated the K-Means and Density Peak Clustering (DPC) algorithms to categorize Thai provinces based on multidimensional poverty characteristics. The analysis used data from the Thai People Map and Analytics Platform (TPMAP), incorporating seventeen indicators across five key dimensions: living conditions, health, education, income, and access to basic services. The proposed hybrid method combined the computational efficiency of K-Means with the density-based cluster detection capability of DPC to improve clustering accuracy. The clustering results were evaluated using the Silhouette Score and visualized by scatter plots, bar charts, pie charts, and spatial maps to illustrate regional poverty patterns. The results indicated that the hybrid approach produced more coherent and well-separated clusters compared to the traditional K-Means algorithm, revealing clear inter-regional disparities in multidimensional poverty across Thai provinces. The findings suggested that poverty characteristics differ significantly by dimension and region, highlighting the importance of dimension-specific and regionally targeted policy interventions. This study demonstrated the potential of hybrid clustering algorithms as an effective data-driven tool to support evidence-based policymaking for reducing social inequality.

Water Tolerance and Matrix Morphology of Aqueous-induced Borneol-based In Situ Matrix Formation for Periodontal Pocket Delivery

Tue, 14 Jul 2026 00:00:00 +0700

In situ forming matrix systems (ISMs) have gained increasing attention for localized drug delivery, particularly for periodontal pocket treatment through injection. Crevicular fluid exposure can induce a phase transformation of the ISM from a solution into a matrix. Organic solvents, including ethanol, N-methyl pyrrolidone (NMP), dimethyl sulfoxide (DMSO), and 2-pyrrolidone, are typically used in ISM formulations due to their water miscibility and biocompatibility. In this study, 40% borneol solutions in these solvents were used as the matrix former. The water tolerance, defined as the minimum amount of water needed to induce phase inversion from a solution into a matrix, was investigated employing an aqueous titration technique. The water tolerance levels for borneol were 39.65%, 14.77%, 7.40%, and 6.71% v/v in ethanol, NMP, DMSO, and 2-pyrrolidone, respectively. Borneol in DMSO demonstrated low water tolerance and low viscosity (5.61 ± 0.12 cPs), which facilitates rapid matrix formation and ease of administration into the periodontal pocket. Thus, DMSO was considered a suitable solvent for borneol-based ISM. Further, the morphology of the borneol crystal matrix was evaluated under an inverted stereomicroscope at various borneol concentrations (10%–60%). Formulations with lower borneol concentrations required more time to crystallize than those with higher concentrations. The resulting crystal matrix entrapped the drug, thereby retarding its release. The choice of solvent and borneol concentration significantly affected the matrix formation rate and morphology. Therefore, the appropriate selection of solvent and borneol concentration is crucial for developing an aqueous-induced borneol-based ISM for periodontitis treatment.

Optimization of Preparation Conditions for Jackfruit Seed Starch-Based Biofilms Using the Taguchi Method

Wed, 15 Jul 2026 00:00:00 +0700

This study aimed to enhance the tensile strength of a biofilm prepared from jackfruit seed starch by optimizing the preparation conditions using the Taguchi method. The results demonstrated that the starch and glycerol contents considerably affected the tensile strength of the biofilm. The optimal formulation was found to be 5% starch, 1% glycerol, 0.5% cooking oil, and 2% acetic acid. The biofilm exhibited a tensile strength of 1.62 ± 0.27 N/cm². In addition, the results indicated that the biofilm prepared from jackfruit seed starch exhibited higher tensile strength than biofilms prepared from cassava starch and wheat flour. Furthermore, this study evaluated the application of the biofilm in instant noodle seasoning packaging, demonstrating its potential to replace traditional plastic packaging and thereby reduce environmental pollution. Overall, these findings validate the feasibility of biofilms derived from jackfruit seed starch and support further research in sustainable packaging solutions.

Feasibility of Floating Photovoltaic Systems in Nong Han Lake, Sakon Nakhon Province, Thailand

Rutchanon Pongtong, Napaporn Phuangpornpitak — Wed, 15 Jul 2026 00:00:00 +0700

This study aims to assess the potential and feasibility of developing and installing a floating solar photovoltaic (FPV) power generation system on Nong Han Lake in Sakon Nakhon Province, Thailand. This novel assessment of a large-scale, natural water body fills a research gap, as most previous FPV initiatives in Thailand have focused on artificial reservoirs. The feasibility analysis focuses on both technical and economic aspects. The methodological approach integrates field data, a comprehensive site suitability analysis, and simulations using PVsyst software. The results indicate that an FVP system in the 80-hectare pilot area can support a capacity of 96.34 MWp, generating approximately 132.25 GWh/year, with a performance ratio of 80.64% and a levelized cost of energy of 1.40 THB/kWh, which is significantly lower than the current feed-in tariff. The project demonstrates strong economic viability, featuring a 14-year payback period and an internal rate of return of 7.5–8.5%. This study highlights FPV as a promising renewable energy solution and offers a strategic framework for FPV deployment in unique natural aquatic environments across the region.

Effect of alkali surface treatments on mechanical and morphology of natural galangal fibers

Sukanya Petchsirivej, Pornsiri Wanarattikan, Noppamas Akarachantachote — Wed, 15 Jul 2026 00:00:00 +0700

This study addresses the value-addition to agricultural residues by investigating the effects of alkali treatment on the physical and mechanical properties of Alpinia galanga (L.) Willd (AG) and Alpinia siamensis K. Schum (AS) pseudo-stem fibers. The fibers were extracted from pseudo-stem segments with an initial length of 25-30 cm. The untreated fiber diameters were 131.96 ± 38.42 µm for AG and 172.50 ± 42.94 µm for AS. To evaluate treatment efficiency, both a thermal-assisted at low concentration route and a conventional room-temperature process were compared. Morphological and surface roughness analyses ( and ) revealed that alkali treatments effectively dissolved surface impurities, reduced fiber diameters (reaching minimums of 107.99 ± 20.90 µm for AG at 10% wt. NaOH and 62.75 ± 11.77 µm for AS at 15% wt. NaOH), and induced distinct fibrillation. Regarding mechanical performance, the treated fibers showed enhanced tensile strength and elongation. The maximum tensile strength for AG was 147.67 ± 53.47 MPa (at 5% wt. NaOH), while AS peaked at 215.64 ± 95.57 MPa (at 0.5% wt. NaOH). Although extreme concentrations (15% wt. NaOH) yielded maximum elongation for both AG (7.25 ± 1.74%) and AS (5.93 ± 1.30%), they triggered cellulose degradation and reduced tensile strength. These findings highlight that optimized, low-chemical alkali treatments can successfully tailor galangal fibers into sustainable reinforcing materials, advancing circular economy principles in composite engineering.

Interpretable Machine Learning for Mental Workload Detection: From Binary to Multi-Class with Multimodal Physiology Signals

Ade Hendi, Auditya P. Sutarto, Mega B. Herlambang, Nailul Izzah — Wed, 15 Jul 2026 00:00:00 +0700

Accurate detection of mental workload is essential for developing adaptive human–machine interaction (HMI) systems that respond to cognitive states of users in real time. Therefore, this study aimed to develop as well as evaluate machine learning models for classifying mental workload into binary, namely load vs. no load, and multi-class comprising low, medium, and high levels using multimodal physiological signals. Thirty-four participants completed two cognitively demanding tasks, and heart rate variability (HRV) as well as electrodermal activity (EDA) were recorded during the tasks. Signals were segmented into 1-min non-overlapping windows, with features extracted from the time, frequency, and nonlinear HRV domains, as well as tonic and phasic EDA indices. Moreover, three feature selection strategies, correlation-based, domain-expert, and minimum redundancy maximum relevance (mRMR), were evaluated across multiple classifiers, including gradient boosting, XGBoost, random forest, k-nearest neighbor, and multilayer perceptron. The model performance was evaluated using an 80/20 subject-wise train–test split, with 10-fold cross-validation conducted on the training set. Additionally, oversampling was applied exclusively to the training data to address the class imbalance. The result showed that multimodal fusion of HRV and EDA consistently outperformed single-modality models, achieving the strongest performance with XGBoost and correlation-based feature selection (binary: F1 = 0.787, AUC = 0.847; multi-class: Macro-F1 = 0.722, AUC = 0.862). The Shapley additive explanations (ShAP) analysis identified nonlinear HRV indices and phasic EDA features as the dominant predictors. These outcomes showed the importance of multimodal integration and data-driven feature selection for robust mental workload detection in HMI.

Low-cost portable photometer using wi-fi–enabled microcontroller and silicon photodiode for iron and phosphorus determination

Miki Kanna, Watcharin Permwong, Sarawut Somnam — Wed, 15 Jul 2026 00:00:00 +0700

A low-cost and portable photometer was developed based on the ESP32 microcontroller integrated with a BPW34 silicon photodiode for the colorimetric determination of iron (Fe) and phosphorus (P) in fertilizer samples. The BPW34 sensor provided broad spectral responsivity in the 400–1100 nm range, rapid response (<100 ns), and low dark current (<2 nA), making it well suited for visible-light detection in colorimetric assays. Calibration studies showed linear responses for Fe(II)–phenanthroline and phosphomolybdate blue complexes within the ranges of 1.14–10.0 mg L⁻¹ and 0.085–1.0 mg L⁻¹, with coefficients of determination (R²) of 0.981 and 0.9966, respectively. The limits of detection (LOD) were 0.34 mg L⁻¹ for Fe and 0.025 mg L⁻¹ for P. Validation against standard methods showed good agreement between the developed photometer and the reference spectrophotometer, with Bland–Altman mean bias 0.00 mg L⁻¹ for Fe and −2.25 mg L⁻¹ for P, along with acceptable spike recoveries of 93–105% (Fe) and 90–112% (P). These results confirm that the developed photometer offers reliable analytical performance and practical applicability for quantitative colorimetric determination of nutrients in fertilizer samples, representing an affordable alternative to conventional spectrophotometric instruments.

Injectability and mechanical properties of borneol-based solvent removal-induced in situ matrix for periodontal pocket delivery

Wed, 15 Jul 2026 00:00:00 +0700

Solvent removal-induced in situ matrices (ISMs) have garnered attention for periodontitis treatment because of their capability to localize antibiotic drug release within the periodontal pocket over an extended duration, effectively targeting and eliminating infecting microbes. In this study, various concentrations of borneol—10%–70%—were used as a matrix agent in ISMs, with a 40% w/w borneol-based ISM selected to load 1% vancomycin HCl. The injectability and mechanical properties of drug-free and vancomycin HCl-loaded borneol ISMs were evaluated with a texture analyzer. Both maximum injection force and energy increased proportionally with borneol concentrations and slightly further with vancomycin HCl. Nevertheless, the injection energy required for borneol-loaded ISMs at 10%–50% was low, from 12.22–25.00 mJ. Consequently, these formulations were considered appropriate and feasible as injectable drug delivery systems, providing ease of administration while promoting patient comfort and compliance. Additionally, a more compact borneol matrix was formed after complete solvent removal; it exhibited plastic deformation, enabling close adaptation to the periodontal pocket geometry.

Deep Learning and Error Correction Code-Based Robust Watermark Extraction from DWT–Chaotic Embedding

Jantana Panyavaraporn, Paramate Horkaew — Thu, 16 Jul 2026 00:00:00 +0700

A blind digital watermarking framework based on Discrete Wavelet Transform (DWT) is proposed for secure image authentication and copyright protection. A binary watermark is embedded within the LH subband, and a chaotic map is used to select embedding positions, ensuring high randomness and security. During the extraction, a convolutional neural network (CNN) is applied to the embedded subband and combined with an error correction code to improve recovery performance under attacks. Experimental results on 200 COCO test images demonstrate that, with a magnitude factor of 0.35, the proposed method achieves an average Peak Signal-to-Noise Ratio (PSNR) of 29.46 dB and an SSIM of 0.9189 on 200 COCO test images. The results indicate that the framework achieves stable watermark recovery under blur, Gaussian noise, JPEG compression, and scaling, with high perceptual image quality.

Age-Dependent Bromelain Enzyme Activity in Pineapple Rhizomes (“Smooth Cayenne” cultivar) Predicted by Fourier-Transform Near-Infrared Spectroscopy

Thu, 16 Jul 2026 00:00:00 +0700

Bromelain is a proteolytic enzyme complex derived from pineapple with extensive medicinal properties. The purpose of this study was to use near-infrared (NIR) spectroscopy to predict bromelain enzyme activity in “Smooth Cayenne” pineapple rhizomes. Samples from 18- and 24-month-old rhizomes were analyzed. A total of 574 NIR spectra (derived from 106 rhizomes, each divided into three anatomical sections and scanned twice) were used to develop the predictive models, with 459 spectra assigned to the calibration set and 115 spectra to the prediction set. NIR spectra were measured with an Fourier-transform NIR spectrometer in diffuse reflection mode at a wavenumber of 11000–4000 cm^–1 (909–2500 nm); then, bromelain enzyme activity was measured. A prediction model was established by partial least squares regression and validated by the test set validation method. A calibration equation was used to predict the activity of bromelain enzyme in a prediction set. The results showed bromelain enzyme activity of 0.0328 ± 0.0193 and 0.0293 ± 0.0182 CDU/mL in 18- and 24-month-old pineapple rhizomes, respectively. The calibration equation to predict bromelain enzyme activity based on spectral scanning of the middle of 18-month-old pineapple rhizomes had a correlation coefficient of 0.85, a root mean square error of prediction of 0.0097 CDU/mL, a bias of 0.0036 CDU/mL, and a ratio of prediction to deviation of 1.7. Although the model can be used for screening and predicting the approximate bromelain enzyme activity in 18-month-old pineapple rhizomes, it needs to be improved to provide more accurate predictions.

Rice straw decomposition as a source of nutrients and bacteria with potential antagonistic traits: Insights from two localities in Iloilo, Philippines

Thu, 16 Jul 2026 00:00:00 +0700

Rice straw is an abundant agricultural byproduct that contributes to soil fertility and supports diverse microbial communities. While organic decomposition is well-documented, the specific microbial dynamics and antagonistic potential inherent in the resulting decomposed rice straw remain under-explored. This study aimed to (1) evaluate the physicochemical characteristics of decomposed rice straw after three months of decomposition from two locations in Iloilo, Philippines (Zarraga and San Miguel), and (2) evaluate the antagonistic activity of bacterial isolates against Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 25923 using agar plug method. Physicochemical analysis showed higher phosphorus (0.21 ± 0.00%), potassium (0.43 ± 0.01%), and organic carbon (14.52 ± 0.88%) in Zarraga compared to San Miguel (0.11 ± 0.00%, 0.18 ± 0.02%, and 3.97 ± 0.21%, respectively; p < 0.05). Viable counts were also greater in Zarraga (11.69 ± 0.19 log CFU/g) than in San Miguel (9.67 ± 0.20 log CFU/g; p < 0.05). Among 30 isolates obtained, four inhibited S. aureus, with the strongest inhibition observed in San Miguel isolates SM9 (20.67 ± 1.15 mm) and SM2 (18.67 ± 1.53 mm). No isolates inhibited E. coli. These results may suggest that nutrient-rich conditions in Zarraga supported higher bacterial abundance, whereas the unique physicochemical conditions in San Miguel could favor isolates with stronger antagonistic activity. Overall, the findings indicate that decomposed rice straw provides both nutrients and microbial communities with potential antagonistic traits. Further molecular and functional characterization is needed to identify these isolates and evaluate their relevance for agricultural and ecological potential.

Enhancing Germination and Early Growth of Millets (Sorghum bicolor L. and Pennisetum glaucum L.) Using Plasma-Activated Water

Punit Kumar, Abhisek Kumar Singh — Thu, 16 Jul 2026 00:00:00 +0700

The application of non-thermal plasma in agriculture has emerged as an eco-friendly technology to enhance seed vigor and crop productivity. In this study, the effects of plasma activated water (PAW) generated through atmospheric pressure gliding arc discharge (GAD) plasma were evaluated on two staple millets, Jowar (Sorghum bicolor L.) and Bajra (Pennisetum glaucum L.). PAW treatments of varying activation times (5, 10, 15, and 20 min) were tested against a control (untreated water). Physicochemical analysis revealed a decrease in pH (from ~5.8 to ~3.7), along with significant increases in electrical conductivity, oxidation - reduction potential, and nitrate concentration (up to ~240 mg L⁻¹) with increasing activation time. Biological assays demonstrated that PAW significantly enhanced seed performance and early growth in both crops. For Sorghum bicolor L., 10 min PAW treatment was identified as the optimum, resulting in an increase in germination from 92% (control) to 98%, a reduction in mean germination time, and ~25–30% improvement in shoot length. For Pennisetum glaucum L., 15 min PAW treatment was optimal, yielding germination enhancement from 89% to 97%, ~32% higher water imbibition, and up to ~35–38% increases in shoot and root lengths. Chlorophyll content also improved significantly, with increases of ~19% in sorghum (10 min PAW) and ~23% in pearl millet (15 min PAW). Results suggest that PAW can serve as a sustainable pre-sowing and irrigation technology to improve seed vigor and early growth of millets, contributing to food security and resilience in semi-arid agroecosystems.

Enhancing Growth and Bioactive Compound Contents in Brahmi (Bacopa monnieri (L.) Wettst.) Irrigated with Canteen Wastewater

Jirarat Thipnang, Nipaporn Taweejun, Narumol Piwpuan — Fri, 17 Jul 2026 00:00:00 +0700

A medicinal plant, Bacopa monnieri, was grown under different canteen wastewater concentrations for five weeks. Their growth and certain secondary metabolite contents were measured, together with several water and soil parameters. Plants irrigated with 50%, 75% and 100% wastewater had higher biomass than those irrigated with tap water and plant growth appearing to be optimal under 50% wastewater treatment according to shoot fresh weight and root dry weight. The highest secondary metabolites measured as total phenolic contents (1.18±0.11 mg GAE/g dry plant) and total flavonoid content (8.79±1.13 mg QE/g dry plant) were observed in plants irrigated with 100% wastewater. This may be due to the plants irrigated with 100% wastewater were experienced to higher stress factors, such as increased potassium (K) and electrical conductivity (EC), than the plants in other treatments. These results suggest that canteen wastewater could be a suitable option for irrigation, potentially integrating wastewater management with medicinal plant cultivation.

Exploration of Patchouli (Pogostemon cablin Benth.) Indigenous Rhizosphere Bacteria as Biofertilizer Candidates in Dissolving Phosphate and Producing Growth Regulator

Fri, 17 Jul 2026 00:00:00 +0700

Patchouli is a plant that has high economic value due to it is extensively application in many different industries. Despite its economic importance, the production of Patchouli has struggled to meet the demands of these industries. Addressing this challenge, one potential approach involves harnessing the capabilities of indigenous rhizosphere bacteria to enhance Patchouli growth. This study aims to investigate indigenous rhizosphere bacteria associated with Patchouli, focusing on their ability to solubilize phosphate and produce indole-3-acetic acid (IAA) and gibberellins. The research commenced with the isolation of microbes, which were subsequently evaluated for their phosphate-solubilizing and growth regulator-producing capacities. The results revealed the selection of ten isolates using Pikovskaya media, characterized by morphological variations, with 60% identified as gram-negative bacteria. Notably, three isolates (N1R1, N1R3, N1R7) exhibited superior phosphate-solubilizing abilities, with the highest average clear zone diameter recorded at 10.0 mm. Moreover, isolate N1R1 demonstrated the highest IAA production at 73.03 µg/ml, while isolate N1R7 exhibited the most substantial gibberellin (GA3) production at 98.18 µg/ml. Molecular identification indicated that N1R1 resembled Providencia rettgeri, N1R3 resembled Enterobacter sp., and N1R7 resembled Erwinia sp. In conclusion, three indigenous rhizosphere bacteria associated with Patchouli exhibit promising potential for biofertilizer development, given their proficiency in phosphate solubilization and growth regulator production.