Utami Dyah Syafitri

Bacillus cereus represents a group of genetically related species, some of which are frequently associated with diarrheal and emetic foodborne illness. Food isolates are often classified as Bacillus cereus sensu lato (s.l.) due to limited information for precise species assignment. Since not all members of this group are pathogenic to humans, characterization is essential to understand their diversity and pathogenic potential. This study evaluated the phenotypic and toxigenic profiles, as well as potential genetic divergence among thirty-three isolates, including two type strains. Isolates were analyzed for motility, hemolysis, and growth at 5°C and 43°C, and further screened using primers targeting diarrheal and emetic toxin genes (nheA, hblD, cytK2, cytK1, and ces), along with 16S rDNA primers. All isolates were motile and produced lecithinase enzyme, but none were able to hydrolyze mannitol. Most isolates, with the exception of two, exhibited hemolytic activity. Genotypic screening revealed that all isolates (100%) carried the nheA and hblD genes; 85% harbored cytK2 gene, while only 18% and 15.2% possessed the cytK1 and ces genes, respectively. Each isolate carried at least two toxin genes, and notably, one isolate harbored all toxin genes. 16S rDNA sequence analysis revealed 98– 100% similarity among isolates. Growth tests and sequence signatures confirmed that all strains belong to the mesophilic group of B. cereus, regardless of their diverse origins. These findings highlight the widespread presence of toxin genes among food isolates and emphasize that any presumptive B. cereus may pose a potential food safety risk.

The genus Cinnamomum (Lauraceae) has a long history of use in traditional medicine and continues to attract scientific interest due to its diverse pharmacological properties. This review summarizes the ethnopharmacological uses of Cinnamomum species, including major investigated species such as C. verum, C. cassia, C. burmannii, C. camphora, and other reported species, plant parts utilized, phytochemical composition, biological activities, underlying mechanisms, and safety considerations. Evidence from the literature indicates that bark and leaves are the plant parts most frequently investigated. The phytochemical profile of Cinnamomum comprises diverse volatile and non-volatile constituents, including bioactive compounds such as cinnamaldehyde, eugenol, cinnamic acid, coumarin, and phenolic compounds, with composition influenced by species, geographical origin, plant part, and extraction method. Chemometric approaches have been applied to characterize metabolite variations and their association with biological activities. Preclinical studies demonstrate antioxidant, anti-inflammatory, antidiabetic, anticancer, antimicrobial, cardioprotective, and neuroprotective effects across several Cinnamomum species. These activities are associated with regulation of oxidative stress, NF-κB/AP-1-mediated inflammatory responses, PI3K/AKT-related metabolic regulation, and apoptosis-related mechanisms. Despite these promising findings, substantial variability in plant materials, extraction procedures, chemical standardization, experimental models, dose selection, and safety profiles across different Cinnamomum species limits comparison among studies and clinical translation. Further research integrating phytochemical characterization, metabolomics and chemometrics, standardized bioactivity assays, toxicological evaluation, and clinical studies is required to establish the efficacy, safety, and quality of Cinnamomum species.

Myristicin is the key marker of nutmeg (Myristica fragrans) quality, while methyl eugenol is an undesirable compound requiring strict monitoring. This study aimed to optimize the extraction conditions of these compounds from nutmeg seeds and mace as a pretreatment step prior to electrochemical quantification. Optimization was performed using Response Surface Methodology (RSM) with a Box–Behnken Design (BBD), considering extraction time (h), ethanol concentration (%), and the sample-to-solvent ratio (g/mL) as independent variables. The analytes were quantified by Gas Chromatography–Mass Spectrometry (GC-MS), and the experimental data were modeled using Design Expert software. Regression models indicated that ethanol concentration and solvent volume significantly influenced extraction yields, while extraction time had a statistically minimal effect (p-values > 0.05, small effect sizes). Nevertheless, practical differences were observed between matrices: nutmeg seeds reached optimal extraction within 2 hours, whereas mace required 17 hours, likely due to its higher essential oil content and fibrous structure, which slows diffusion kinetics. Thus, although time effects were not statistically significant, extended extraction was practically preferred for mace to ensure adequate recovery. Response definitions were specified as follows: Y1 = myristicin concentration (mg/mL), Y2 = methyl eugenol concentration (mg/mL), and % extraction yield. The optimal extraction conditions were achieved with 90% ethanol and 50 mL solvent, maximizing analyte detectability while maintaining reproducibility across seed and mace extracts. These optimized ethanolic matrices are compatible with subsequent electrochemical quantification, given their aqueous/ethanolic composition and anticipated sensitivity of electrochemical detection.

The high economic value and increasing demand for Strobilanthes crispa have created a market incentive to adulterate it with cheaper alternatives, such as Piper aduncum, posing significant risks to consumer safety and product efficacy. This study addresses the critical challenge of multiclass adulteration detection in S. crispa from P. Aduncum using Fourier Transform Infrared (FTIR) spectroscopy combined with optimized ensemble machine learning methods. The high-dimensional spectral data were processed with advanced preprocessing techniques, particularly Standard Normal Variate (SNV) and its combinations (SNV+SG and SG+SNV), which effectively reduced baseline shifts and noise. These enhancements significantly improved spectral quality and model performance. Among the evaluated models, Light Gradient Boosting Machine (LightGBM) achieved the highest classification accuracy of 94.58% ± 2.69. Extreme Gradient Boosting (XGBoost) also demonstrated robust performance, attaining 93.38% ± 2.27 accuracy with SNV+SG preprocessing. Both boosting algorithms proved effective in handling class imbalance, as indicated by high Balanced Accuracy and G-mean values. Statistical analysis using ANOVA confirmed that the choice of model and preprocessing method significantly influenced performance, whereas the Cuckoo Search algorithm's nest had a significant effect on computational time. Feature importance analysis identified an essential subset of spectral features that consistently contributed to the classification performance of XGBoost and LightGBM. Functional group analysis revealed that distinct chemical fingerprints native to S.crispa exhibited strong aromatic N-H stretching and C-H bending. In contrast, impurities such as P.aduncum showed dominant alkyl C-H stretching and C=O stretching. These chemical distinctions provide a reliable foundation for detecting adulteration.

This study aims to develop an electrochemical sensor based on a glassy carbon electrode (GCE) modified with Fe₃O₄ and graphene for the detection of myristicin as a characteristic compound in nutmeg plants. Electrode modification materials were prepared from a combination of graphene and magnetite, synthesized via a hydrothermal method, and further characterized using X-ray diffraction (XRD), scanning electron microscope–energy dispersive spectroscopy (SEM-EDS), and transmission electron microscopy (TEM). The two modifying materials were then optimized, and the optimum conditions were obtained at a w/w ratio of 1:2, which was applied to the GCE surface using the drop-casting technique. The electrochemical performance of the Fe₃O₄/graphene-modified electrode was evaluated under optimum experimental conditions using a Britton–Robinson buffer solution at pH 5. The scan-rate analysis of the electrode to evaluate its electrochemical performance showed an increase in surface area from 0.101 cm² for the bare GCE to 0.534 cm² for the GCE/Fe₃O₄–graphene. Electroanalytical performance was evaluated using differential pulse voltammetry (DPV), which showed a linear response over the concentration range of 1–100 µM, with a limit of detection of 0.19 µM and a limit of quantitation of 0.58 µM. The developed electrode was applied successfully to detect myristicin in nutmeg seed extract samples, and its calculated concentrations were not significantly different from those obtained with the GC-MS method. These results suggest that the developed sensor may have further potential as an alternative detection tool for characterizing electroactive compounds in nutmeg plants.

In this study, we propose an innovative method called the integrated GPCA MIGA, which integrates the multiple imputation genetic algorithm (MIGA) and generalized principal component analysis (GPCA) to perform missing value imputation and data dimensionality reduction simultaneously. The approximated original data produced by GPCA serves as the basis for MIGA to update missing values in the next iteration. At the same time, GPCA refines the low-dimensional representation using the latest imputation results from MIGA, thereby balancing the accuracy of missing value imputation and the stability of dimensionality reduction. The objective of this study is to evaluate the performance of the integrated GPCA-MIGA and analyze trends in human development at the district/city level in Indonesia. The findings of this study show that the integrated GPCA-MIGA effectively reduces the dimensionality of data containing missing values compared to other methods. The integrated GPCA-MIGA method was applied to human development data. The results were then visualized using a biplot, which revealed that human development trends in Jayawijaya from 2019 to 2022 indicate progress in school enrollment rates for ages 16–18 years.

In a sensory test, the response is a Likert scale, which belongs to the ordinal scale. The ordinal response can be analyzed using a linear model approach; however, this approach can be misleading. This research aims to compare three different methods for ordinal response: the average score, the second-order Scheffe model, and the ordinal logistic model. The case study focused on the response to the taste of cookies resulting from the mixture experiment. The mixture experiment is one type of experimental design which is commonly used for product formulation. The research involved three ingredients with different lower bonds. The D-optimal design which also the {3,2} simplex-lattice design was chosen for the experiment. The three methods were conducted, and they all yielded the same results for the optimum composition; however, the ordinal model provided more information about the data's characteristics. The optimal formulation of each ingredient was 10%, 20%, 70%.

A root detector is a non-destructive technology developed to indicate the radial distribution of tree roots, which are not often visible on the surface. This study aims to assess the accuracy of the root detector in estimating the radial distribution of both exposed and buried tree roots. Six Agathis loranthifolia Salisb. trees were selected, three with exposed roots and three with buried roots. The Fakopp® root detector, an acoustic-based tool, was used in this study. Root estimation was based on a combination of threshold values (> 400 m•s-1), average values, and the peak of the sound wave velocity. Soil excavation was manually conducted at a depth of 30 cm within a 100 cm radius of the tree trunk. The results showed that under similar soil conditions, the root detector achieved an accuracy of over 80% in detecting the actual radial root distribution, as validated by the excavation method. Root diameter exhibited the strongest correlation with sound velocity in detecting lateral roots. However, root depth and inclination angle contributed to detection inaccuracies in estimating the radial distribution of lateral roots.

Mortar has been widely applied as a coating for various purposes on building infrastructures, including basements, water tanks, retaining walls, dams and building exteriors, where resistance to moisture and water is highly required. Various chemicals have been added to mortar to reduce its water permeability. However, they raise environmental problems, therefore, environmentally friendly additives are indispensable. The purpose of this study was to enhance the water resistance, compressive strength, and crack recovery ability of mortar with the addition of hydrochar from biomass. Hydrochar was prepared from the hydrothermal carbonization of coconut coir fiber at 150°C for 4 hours. The hydrochar was added to the mortar at different particle sizes and ratios to cement. Mortars were made by mixing cement, sand, water, superplasticizer, and hydrochar; and they were tested after 28 days. The results showed that the addition of 1 wt% hydrochar with a particle size of 150-250 µm reduced the water permeability of mortar, as evidenced by a 36.45% decrease in secondary water absorption and a 14.94% reduction in percentage of void. In addition, the compressive strength increased by 10.29% compared to the control mixture. Hydrochar can absorb water bubbles in the mortar mixture; however, excessive hydrochar causes the mortar to be more porous. The addition of hydrochar in mortar demonstrated superior crack recovery abilities because it functions as a connection medium to accelerate the recovery.

Perbandingan Metode Regresi Multilevel dan Beta Generalized Linear Mixed Models pada Data Longitudinal Capaian IPK Mahasiswa