Publikasi Ilmiah & Artikel Jurnal

Breath odor contains volatile compounds that can reflect the body’s metabolic condition, including those associated with fasting. This study aims to classify breath odor patterns in fasting and non-fasting individuals using an electronic nose (e-nose) combined with the Principal Component Analysis (PCA) method. The e-nose system was developed using an array of five MQ gas sensors (MQ-2, MQ-3, MQ-4, MQ-5, and MQ-6) to detect breath samples from 100 respondents, consisting of 50 fasting and 50 non-fasting individuals. The sensor responses were recorded and analyzed using PCA for dimensionality reduction and data pattern visualization. The results show that the first two principal components account for 81.2% of the total data variance, with contributions of 56.1% from PC1 and 25.1% from PC2. The PCA score plot demonstrates a relatively clear separation between the breath odor patterns of the fasting and non-fasting groups. These findings indicate that the developed e-nose system has potential as a rapid and non-invasive method for breath-based classification.

Background Halal authentication is critical in Muslim-majority markets, where pork contamination raises significant religious and regulatory concerns. Biomolecular techniques have been widely employed for halal detection in foods and drugs; however, challenges remain in sample handling, method validation, data interpretation, and technical limitations. Objective This study developed a modified loop-mediated isothermal amplification (LAMP) assay for detecting Sus scrofa DNA across various meat matrices at various dilutions. Methodology Specific modified LAMP primers were designed using the D-loop mitochondrial region. DNA was extracted from pork meat and nonpork meat using modified protocols. The LAMP assay’s sensitivity was tested down from 10-fold to 100,000-fold dilution. PCR reactions were done on the same sample with various dilutions as confirmation. Results Our modified LAMP with specific primers detected Sus scrofa DNA qualitatively and quantitatively with high specificity and no crossreactivity as sensitively as PCR reactions. It detected qualitatively and quantitatively Sus scrofa DNA up to 100,000x dilution (0.00286 ng/ μL), exceeding PCR’s detection limit. Visual detection with HNB dye showed clear color changes in the LAMP reaction, easing the rapid qualitative detection of Sus scrofa DNA, making LAMP an effortless and rapid screening for non-halal food or drugs. Subsequently, our results have shown that the sensitivity and the specificity of LAMP reactions compared to the PCR reactions are 100% and 100%, respectively. Thus, compared to PCR, LAMP was faster, highly sensitive, highly accurate, and field-deployable in the point-of-care setting. Conclusion LAMP is a sensitive, cost-effective, and equipment-independent technique suitable for qualitative adulteration detection in field and low-resource settings at the point-of-care.

Rapid and reliable identification of gelatin origin is critically important for food authentication and halal verification. This study proposes a label-free optical sensing approach based on surface plasmon resonance (SPR) for discriminating bovine, porcine, and fish gelatin. Gelatin solutions (1–8% w/v) were analyzed using a prism-coupled attenuated total reflection (ATR)-SPR configuration, where resonance angle shifts were extracted from reflectance minima. Complementary finite-difference time-domain (FDTD) simulations were performed to validate experimental trends and elucidate field interactions at the metal–dielectric interface. Results show distinct refractive index (RI) profiles and concentration-dependent resonance shifts for each gelatin type, enabling clear differentiation without chemical labeling. At higher concentrations (7–8%), fish gelatin exhibits the highest RI, followed by porcine and bovine gelatin. Porcine gelatin demonstrates the most linear and sensitive response, with a strong correlation (R² = 0.9412) between concentration and resonance angle shift. Simulation results are in good agreement with experimental data, confirming the sensitivity of SPR to subtle RI variations. The novelty of this work lies in the integration of ATR-SPR and numerical modeling for rapid, non-destructive, and label-free identification of gelatin sources across multiple animal origins. This approach offers a promising platform for real-time food authentication, quality control, and halal assurance, with potential for portable and on-site sensing applications.

Rapid and objective assessment of fish freshness is essential for ensuring food safety and maintaining product quality throughout the seafood supply chain. Conventional methods for evaluating fish freshness, such as sensory inspection, microbiological analysis, and chemical indicators, are often time-consuming, destructive, and unsuitable for rapid monitoring. Therefore, the development of rapid and non-destructive analytical approaches is increasingly important for food quality control. This study aimed to evaluate the potential of an integrated E-nose and Fourier Transform Infrared (FTIR) spectroscopic system combined with PCA for classifying freshness degradation in freshwater Clarias gariepinus during storage. A total of 40 homogenized fish samples were prepared and divided into four storage groups (0, 12, 24, and 36 hours). Volatile compounds released from the samples were measured using an E-nose equipped with five metal oxide semiconductor sensors (MQ-3, MQ-4, MQ-136, MQ-137, and TGS-822). Each sample measurement was repeated five times, and the averaged sensor responses were used for subsequent analysis. Chemical structural changes in the samples were further investigated using FTIR spectroscopy within the wavenumber range of 4000–400 cm⁻¹. PCA was applied to both E-nose and FTIR datasets to reduce dimensionality and visualize clustering patterns among storage conditions. The PCA results of E-nose data showed that PC1 and PC2 explained 98.5% and 0.12% of the total variance, respectively, with the MQ-3 sensor exhibiting the highest sensitivity to volatile changes during storage. The PCA score plot revealed clear separation among samples stored at different time intervals. FTIR analysis also demonstrated progressive spectral variations associated with protein degradation and lipid oxidation, particularly in O–H, Amide I–II, C–H, and C=O functional groups. PCA of FTIR spectra explained 63.1% and 26.2% of the variance for PC1 and PC2, respectively, indicating consistent classification patterns. These results suggest that the integration of E-nose sensing and FTIR spectroscopy combined with PCA provides complementary information for monitoring fish freshness changes during storage.

Chemometric analysis using Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) was applied to differentiate the aroma profiles of cooking oil samples using an electronic nose. Five types of oils were tested, namely pure lard, pure olive oil, pure palm oil, lard-olive oil mixture, and lard-palm oil mixture. Each oil type was independently tested 40 times, resulting in a total of 200 measurements. The objective was to develop a method to detect lard adulteration in commercial oils. The LDA analysis revealed that the first discriminant function accounted for 59.6% and the second for 34.5% of the total variance. Olive oil was classified with 100% accuracy, palm oil with 90%, while the remaining 10% overlapped with the lard-olive oil mixture (50 : 50 v/v ratio) group. Pure lard and its mixtures were relatively more difficult to classify accurately. Using the backpropagation ANN method, 94% of the test samples were correctly identified. The ANN model employed 50 training data, 100 testing data, and several data points for cross-validation. The results demonstrated stable weight convergence with outputs closely matching the target values, indicating a low level of overfitting. Optimal performance was achieved at the 185th epoch, with a learning rate of 0.001, 30 neurons in the first hidden layer, 10 neurons in the second hidden layer, and a Mean Squared Error (MSE) of 0.0487. Both methods demonstrated high classification accuracy, with ANN achieving 94% and LDA showing variable performance across oil types.

Photocatalysis remains a promising solution for water treatment due to its efficiency, sustainability, and ability to degrade a wide range of contaminants. This study explores the green synthesis of zinc oxide (ZnO/GS) doped with varying weight percent of nitrogen (N) as a photocatalyst. ZnO/GS was synthesized via co-precipitation with zinc nitrate hexahydrate and papaya leaf extract in an aqueous solution, which will be further doped using urea as nitrogen doping. Characterization techniques, including FESEM, EDX, FTIR, and XRD, were employed to analyze the morphology, structural characteristics, surface, and pore volumes of nitrogen-doped green synthesized zinc oxide (N-ZnO/GS). The photocatalytic performance of these samples was evaluated through the photodegradation of methylene blue dye under visible light irradiation. The sample with 10wt%, which is 1 g of dopant, exhibited the optimal results, indicating a homogeneous surface and high crystallinity of N-ZnO/GS synthesized via co-precipitation, demonstrating the best photodegradation efficiency in the photocatalysis process. The findings suggest potential applications in environmental remediation through the degradation of organic pollutants, enhancing wastewater treatment efficacy.

A solar panel is a device/tool that can convert energy from sunlight into electrical energy. This research aims to design a tool that can help solar panels move in the direction of the sun, determine the energy consumption used to run the tracking system that has been created and choose the output power results of solar panels with the tracking system and the output power of solar panels without a tracking system. This automatic sun tracking system was successfully designed using an Arduino Uno as a microcontroller, an LDR sensor as a light detector, and a servo motor as an actuator or driver of solar panels with a two-axis arrangement, namely the vertical and horizontal axes. The system that has been designed will consume 58.8 Wh of energy during the 7 hours of testing. Through outdoor testing results for five days, the average daily output power on solar panels without tracking is 43.10 W, 47.17 W, 10.06 W, 15.59 W, and 30.81 W, while the average daily output power on solar panels with a tracking system is 53.79 W, 57.27 W, 10.31 W, 16.61 W, and 32.40 W. These results show that using the automatic sun tracking system designed in this research can increase the average output power produced by solar panels.

Refillable drinking water was chosen as an alternative to bottled drinking water because the price is cheaper. Drinking water has benefits for the body, but if the levels of heavy metals contained exceed the Ministry of Health's standards, it will have a negative impact on body health. This research was conducted with the aim of determining the quality of refillable drinking water in terms of heavy metal levels. Atomic absorption spectroscopy was used to test the levels of heavy metals (Pb, Cu, Cd, and Hg) in the samples. Research data shows that refill drinking water samples A1, A2, A3, A4 and A5 contain heavy metals at levels that exceed the Ministry of Health's standards. PC1 and PC2 have the highest eigenvalues, with a proportion of PC1 of 60.86% and PC2 of 22.69%. The two PCs have a cumulative proportion of 83.55% and are considered capable of representing the entire data. PCA method was used to identify patterns and group samples based on heavy metal content, with PC1 and PC2 reflecting 83.55% of the data variability. Pb and Cd are the variables that have the longest resultant lines, which shows that these two variables have a large contribution to the formation of new variables. The three secondary data samples, namely A6, A7, and A8, are control samples because their quality is good. Refill drinking water samples that are almost close to control are samples A1, A4, and A5, while samples A2 and A3 are far from control. The further the sample is from the control, the lower the quality. The results of this study highlight the need for strict supervision of refillable drinking water depots and the implementation of more effective purification methods to reduce heavy metal content exceeding health standards.