Different from cooking oils which contain long‐chain fatty acids, virgin coconut oil (VCO) has high medium‐chain fatty acids, making it a potential functional food which can provide some health benefits. In this study, our objective is to investigate the physicochemical properties, antioxidant capacity, and metal contents of the VCO extracted through four different processing methods: chilling and centrifugation; fermentation; direct micro expelling‐oven dried; and direct micro expelling‐sun‐dried processes. We found that the physicochemical properties, including moisture content, refractive index, viscosity, iodine value, saponification value, peroxide value, free fatty acid, and fatty acid content, of all the VCO conform to the Asian and Pacific Coconut Community (APCC) standard. All of the VCO predominantly contains lauric acid which is in the range of 48.40%–52.84% of the fatty acid content. The total phenolic content and DPPH radical‐scavenging activity (IC₅₀) of the VCO was obtained to be in the range of 1.16–12.54 mg gallic acid equivalents (GAE)/g and 7.49–104.52 mg/ml, respectively, and the metal contents in the VCO were within the acceptable range of the recommended APCC limit. These findings ensure good quality and safety assurance of the VCO produced from the coconut grown in Brunei Darussalam through the different processing methods.

Ginger (Zingiber officinale) is widely consumed as an important spice or a common condiment in food and beverages. This study focuses on the determination of pungent and bioactive components in ginger and their antioxidant activity using voltammetric and spectroscopic methods. Gas chromatography-mass spectroscopy analysis revealed that the major components of the pungent compounds were zingerone, shogaols, gingerols, paradols, wikstromol, and carinol. Using spectroscopic methods, the antioxidant capacity of ginger aqueous extract was found to be 16.0 μmol gallic acid equivalent (GAE) per gram of ginger extract, and the total phenolic and flavonoid content was estimated to be 7.8 mg GAE/g ginger extract and 15.4 mg Quercetin equivalent (QE) per gram of ginger extract, respectively. Electroanalytical quantification estimated the antioxidant capacity of the ginger infusion to be 23.5 μmol GAE/g ginger extract, which is slightly higher than that estimated using chemical assay. The results may provide useful information for the development of ginger processing and utilization as a flavoring agent, and for our understanding of ginger as a source of natural antioxidants.

This paper describes the photocatalytic degradation of methylene blue (MB) in aqueous solution in the presence of TiO2 nanoparticles (NPs) in different compositions of anatase and rutile phases under UV light irradiation. The different compositions of crystalline phases of TiO2 NPs were synthesized using sol-gel method at the same temperature for various calcination times. The TiO2 NPs were subjected to crystal phase, vibrational, size, and morphological characterizations. The photocatalytic degradation of MB revealed that anatase TiO2 NPs have a superior catalytic activity in the front of rutile phase. This finding further suggested that low charge resistance and low electron-hole recombination probability lead to the acceleration of the OH• formation on the surface of anatase TiO2 NPs.

Given the vast diversity of bee species and the limited availability of taxonomy experts, bee species identification has become increasingly important, especially with the rise of apiculture practice. This review systematically explores the application of machine learning (ML) techniques in bee species determination, shedding light on the transformative potential of ML in entomology. Conducting a keyword-based search in the Scopus and Web of Science databases with manual screening resulted in 26 relevant publications. Focusing on shallow and deep learning studies, our analysis reveals a significant inclination towards deep learning, particularly post-2020, underscoring its ability to handle complex, high-dimensional data for accurate species identification. Most studies have utilized images of stationary bees for the determination task, despite the high computational demands from image processing, with fewer studies utilizing the sound and movement of the bees. This emerging field faces challenges in terms of dataset scarcity with limited geographical coverage. Additionally, research predominantly focuses on honeybees, with stingless bees receiving less attention, despite their economic potential. This review encapsulates the state of ML applications in bee species determination. It also emphasizes the growing research interest and technological advancements, aiming to inspire future explorations that bridge the gap between computational science and biodiversity conservation.

This present study aimed to investigate the phytochemical content and antioxidant and antidiabetic activities of Curculigo latifolia leaves (CL) and C. latifolia roots (CR) found in Brunei Darussalam. Phytochemical screening showed that CL and CR extracts contain saponins, tannins, glycosides, and terpenoids. CR showed higher total phenolic content (TPC), but lower total flavonoid content (TFC) when compared to CL. The high TPC in CR contributed to its potent radical scavenging activity (RSA) against 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) radicals and strong ferric reducing antioxidant power (FRAP). Additionally, CR exerted significant inhibition of ∝‐glucosidase and ∝‐amylase, suggesting a potential link between the chemical compounds and its antioxidant and antidiabetic effects. In the animal study of antihyperglycemic activity, treatment with 250 mg/kg body weight (b.w.) of the CL extract normalised the blood glucose levels and improved body weight gain of alloxan‐induced diabetic rats within 14 weeks. Furthermore, our investigation into the wound‐healing effects of young C. latifolia leaves (YCL) and matured C. latifolia leaves (MCL) showed a significant reduction in wound size on Day 3, 5, and 7 of the experimental study, indicating its wound‐healing potential. Based on our findings, C. latifolia can be consumed as part of a balanced diet due to its antioxidant and antidiabetic properties.

Heterotrigona itama, a widely reared stingless bee species, produces highly valued honey. These bees naturally secure their colonies within logs, accessed via a single entrance tube, but remain vulnerable to intruders and predators. Guard bees play a critical role in colony defense, exhibiting the ability to discriminate between nestmates and non-nestmates and employing strategies such as pheromone release, buzzing, hissing, and vibrations to alert and recruit hive mates during intrusions. This study investigated the acoustic signals produced by H. itama guard bees during intrusions to determine their potential for intrusion detection. Using a Jetson Nano equipped with a microphone and camera, guard bee sounds were recorded and labeled. After preprocessing the sound data, Mel Frequency Cepstral Coefficients (MFCCs) were extracted as features, and various dimensionality reduction techniques were explored. Among them, Linear Discriminant Analysis (LDA) demonstrated the best performance in improving class separability. The reduced feature set was used to train both Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. KNN outperformed SVM, achieving a Precision of 0.9527, a Recall of 0.9586, and an F1 Score of 0.9556. Additionally, KNN attained an Overall Cross-Validation Accuracy of 95.54% (±0.67%), demonstrating its superior classification performance. These findings confirm that H. itama produces distinct alarm sounds during intrusions, which can be effectively classified using machine learning; thus, demonstrating the feasibility of sound-based intrusion detection as a cost-effective alternative to image-based approaches. Future research should explore real-world implementation under varying environmental conditions and extend the study to other stingless bee species.