High Coefficient Of Determination Research Articles

Relationships between body length, body mass and otolith dimensions in three Sprat species (Teleostei: Ehiravidae) from the Caspian Sea

AbstractReliable and detailed knowledge regarding the relationship between otolith size and the size of fish is important for fisheries management and for ecological studies on predicting fish size and on predator–prey interactions. Therefore, these relationships are estimated for three sprat (genus Clupeonella) species, including C. caspia, C. engrauliformis and C. grimmi from the Caspian Sea. The relative size and mass of the otoliths corrected for body size and mass were also estimated for the three sprat species. After biometry of collected specimens, the Sagittal otoliths were extracted from the cranium of collected specimens and photographed. Photos were used to estimate length and width of otoliths. The results revealed significant relationships between otolith length and width vs. fish length and otolith mass vs. body mass in all three species (r2 > 0.5). The higher coefficient of determination (r2) for relationships between total length (fish) – Otolith length and width was concluded in comparison with relationships between fish mass and otolith mass in the studied sprat species. Otoliths of C. grimmi showed the highest relative length and were significantly (P < 0.05) heavier than sagittal otoliths of two other sprat species. There is no previous report on size–mass relationships between fish and otolith measurements among the three studied sprat species. These estimated equations can be used in back-calculation studies, especially for these three sprats as the dominant prey for piscivorous predators such as Caspian seals and sturgeons, in their habitats.

Unveiling the Genetic Architecture of Semen Traits in Thai Native Roosters: A Comprehensive Analysis Using Random Regression and Spline Function Models

Improving reproductive traits, particularly semen quality and quantity, is crucial for optimizing poultry production and addressing the current limitations in native chicken reproduction. The aim of this study was to develop a genetic model to estimate genetic parameters guiding the selection of individual Thai native roosters. Using data collected from 3475 records of 242 Thai native grandparent roosters aged 1–4 years, we evaluated semen traits (mass movement, semen volume, and sperm concentration) over 54 weeks. A random regression test–day model incorporating five covariance functions, including a linear spline function with four, five, six, and eight knots (SP4, SP5, SP6, and SP8) and second-order Legendre polynomial function (LG2), was used to estimate genetic parameters. The results showed that the SP8 model consistently outperformed the other models across all traits, with the lowest mean square error, highest coefficient of determination, and superior predictive ability. Heritability estimates for mass movement, semen volume, and sperm concentration ranged from 0.10 to 0.25, 0.22 to 0.25, and 0.11 to 0.24, respectively, indicating moderate genetic influence on these traits. Genetic correlations between semen volume and sperm concentration were highest in the SP8 model, highlighting a strong genetic association between these traits. The SP8 model also revealed a high genetic correlation between mass movement and semen volume, supporting the potential for selecting mass movement as a predictor of semen volume. In conclusion, this study highlights the effectiveness of random regression models with linear spline functions to evaluate the genetic parameters of semen traits in native Thai roosters. The SP8 model is a robust tool for breeders to enhance the reproductive performance of native Thai chickens, contributing to sustainable poultry production systems.

A comprehensive multivariate approach for GxE interaction analysis in early maturing rice varieties

The genotype evaluation process requires analysis of GxE interactions to ascertain the responsiveness of a genotype to various environments, including the development of early maturing rice. However, the concept of interaction is relatively specific to grain yield. In contrast, grain yield is highly polygenic, so assessment should be carried out with multivariate approaches. Therefore, multivariate assessment in evaluating GxE interactions should be developed, especially for early maturing rice genotypes. The study aimed to develop a comprehensive multivariate approach to improve the comprehensiveness and responsiveness of GxE interaction analysis. The study was conducted in Bone and Soppeng districts, South Sulawesi, Indonesia, in two seasons. The study used a randomized complete block design, where replications were nested across two seasons and locations. Two check varieties and five early maturing varieties were replicated three times in each environment. Based on this study, a new approach to GxE interaction analysis based on multiple regression index analysis, BLUP analysis, factor analysis, and path analysis was considered adequate, especially for evaluating early maturing rice. This approach combined days to harvest, biological yield, and grain yield in multiple linear regression with weighting based on the combination of all analyses. The effectiveness of the GxE interaction assessment was reflected by high coefficient of determination (R2) and gradient (b) values above 0.8 and 0.9, respectively. Inpari 13 (R2 = 0.9; b=1.05), Cakrabuana (R2 = 0.98; b=0.99), and Padjajaran (R2 = 0.95; b=1.07) also have good grain yield with days to harvesting consideration, namely 7.83 ton ha-1, 98.12 days; 7.37 ton ha-1, 95.52 days; and 7.29 ton ha-1, 97.23 days, respectively. Therefore, this index approach can be recommended in GxE interaction analysis to evaluate early maturing rice genotypes. Furthermore, Inpari 13, Cakrabuana, and Padjajaran are recommended as adaptive early maturing varieties.

Using convolutional neural network combined with multi-scale channel attention module to predict soil properties from visible and near-infrared spectral data

With the increasing demand for precision agriculture and sustainable land management, it is crucial to quickly and accurately predict soil properties. However, there are still challenges in predicting large-scale soil properties. This study proposes a new convolutional neural network architecture (MCA-CNN) combined with visible and near-infrared spectroscopy for predicting soil properties. The architecture introduces a multi-scale channel attention mechanism, aiming to more effectively capture complex features in the spectrum. The performance of the MCA-CNN model was tested on a large-scale dataset and compared with partial least squares (PLS), extreme gradient boosting tree (XGBoost), and conventional convolutional neural network (CNN) models. The predictive performance of different models in organic carbon (OC), nitrogen (N), pH, Clay, Salt, and Sand was analyzed. The results showed that the MCA-CNN model had the best predictive performance on all soil properties, achieving the lowest root mean square error (RMSE) and the highest coefficient of determination (R2), the R2 of OC, N, pH, Clay, Silt, and Sand are 0.95, 0.93, 0.95, 0.80, 0.58, 0.64, respectively. The RMSE of OC, N, pH, Clay, Silt, and Sand are 16.60 g kg−1, 0.96 g kg−1, 0.31, 4.89 %, 8.27 %, and 11.61 %, respectively. Further research has found that soil types (mineral soil and organic soil) affect the predictive performance of OC. It is necessary to distinguish between mineral soil and organic soil when predicting soil OC content. In addition, the effectiveness of the model transfer strategy was explored for situations with small soil sample sizes. By fine-tuning the pre-trained model, the performance of the model can be significantly improved, which provides a feasible solution for predicting soil properties in situations of data scarcity. In summary, this study not only confirms the potential application of deep learning in the field of soil science, but also provides new technological avenues for future soil management and agricultural practices.

Design of experiments with the support of machine learning for process parameter optimization of all‐small‐molecule organic solar cells

AbstractTraditionally, squaraine dyes have been studied and employed in biomedical research due to their excellent optical properties, and the molecules are being adopted in different research fields such as organic solar cells. In this study, we investigate correlations between solar cell performance and processing parameters of all‐small‐molecule bulk heterojunction solar cells comprising squaraine (SQ) as electron donor (D) and non‐fullerene small molecules (e.g., ITIC) as electron acceptor (A) with the help of machine learning (ML) and design of experiment (DoE) methods. Among the five predictive ML models tested with the selected parameters, the eXtreme gradient boosting model shows the satisfactory results with quite high coefficient of determination of 0.999 and 0.997 in training and testing sets, respectively. By measuring the contribution of each input variable to solar cell efficiency, four process parameters, that is, the total concentration, the ratio of D/A, the rotational speed of spin coating, and the annealing temperature, are found to be the key features strongly correlated to solar cell efficiency. From contour plots in DoE, the highest solar cell efficiency of approximately 5% can be predicted under the conditions of 15 mg mL−1 in solution concentration, a 1:2 mix ratio of D and A, rotational speeds ranging from 800 to 900 rpm, and annealing temperatures within 100–110°C. Using the suggested parameter conditions, we fabricated solar cells, achieving a quite high efficiency of approximately 4%. Besides the global optimization conditions, we also employ the solvent vapor annealing combination to the thermal annealing to facilitate further mobilization of molecules and more optimized microstructure of bulk heterojunction films, resulting in a further enhancement in solar cell efficiency of more than 20%.

Development of machine learning-based quantitative structure–activity relationship models for predicting plasma half-lives of drugs in six common food animal species

Abstract Plasma half-life is a crucial pharmacokinetic parameter for estimating extralabel withdrawal intervals of drugs to ensure the safety of food products derived from animals. This study focuses on developing a quantitative structure–activity relationship (QSAR) model incorporating multiple machine learning and artificial intelligence algorithms, and aims to predict the plasma half-lives of drugs in 6 food animals, including cattle, chickens, goats, sheep, swine, and turkeys. By integrating 4 machine learning algorithms with 5 molecular descriptor types, 20 QSAR models were developed using data from the Food Animal Residue Avoidance Databank (FARAD) Comparative Pharmacokinetic Database. The deep neural network (DNN) algorithm demonstrated the best prediction ability of plasma half-lives. The DNN model with all descriptors achieved superior performance with a high coefficient of determination (R2) of 0.82 ± 0.19 in 5-fold cross-validation on the training sets and an R2 of 0.67 on the independent test set, indicating accurate predictions and good generalizability. The final model was converted to a user-friendly web dashboard to facilitate its wide application by the scientific community. This machine learning-based QSAR model serves as a valuable tool for predicting drug plasma half-lives and extralabel withdrawal intervals in 6 common food animals based on physicochemical properties. It also provides a foundation to develop more advanced models to predict the tissue half-life of drugs in food animals.

Assessing cracking resistance and threshold limits of bituminous mixtures with IDEAL-CT and predictive modeling techniques

This paper presents a comprehensive study to establish the threshold limit of CTIndex for the Marshall mixes. The study also aims to assess the impact of different design factors on the cracking resistance of bituminous mixtures using the Indirect Tensile Asphalt Cracking Test (IDEAL-CT) test. This study considers 2 different aggregate sources, 2 different gradations, 5 different types of Design Aggregate Gradation (DAG), 3 binder types, and 5 levels of compactive effort. The test results show that higher cracking resistance can be achieved using a smaller nominal maximum size of the aggregate (NMAS), finer gradation, modified binder, and decreased compactive effort with aggregates having low abrasion and absorptive characteristics. This study also comprehends the influence of volumetric parameters of the bituminous mixes on fracture resistance. It was found that at a particular Optimum Binder Content (OBC), higher bulk specific gravity of the compacted specimen (Gmb) and voids filled with asphalt (VFA) result in reduced CTIndex, specifying poor cracking performance. While higher air voids (AV) and voids in mineral aggregate (VMA) lead to increased CTIndex, indicating better-cracking resistance. Statistical analysis tools were used to evaluate the significance of the influential factors that are affecting the cracking potential of the mix. Different Machine learning models were also developed to predict CTIndex based on the design factors considered in the study. The random forest (RFR) model showed strong accuracy, reflected by low Mean Absolute Error (MAE=3.16), Mean Absolute Percentage Error (MAPE=9.57), Root Mean Square Error (RMSE=4.23), and a high coefficient of determination (R²=0.95) value, notifying a precise fit and reliable predictions. Additionally, a GUI has been also developed to enhance the practical usability of the model for wider usage. Further, the present study proposes the threshold value of CTIndex for the selection of crack-resistant bituminous mixtures. Moreover, the study investigated the correlation between laboratory and field compaction methods and validated the initial threshold specification of CTIndex for the Marshall mixes. Despite of the variations in different compaction methodologies and specimen thickness, a strong positive correlation (R² > 0.76) between laboratory and field cores of BC-1 and DBM-2 indicates that the performance criteria are adequate and justified.

Sediment Transport Dynamics Modeling of Overland Flow on Gentle Slopes Based on Flume Experiments

ABSTRACTSoil erosion is a global environmental issue, and sediment transport capacity (Tc) is critical for developing soil erosion models. This study conducted flume drainage experiments at six flow discharges (0.15, 0.25, 0.35, 0.45, 0.55, and 0.65 L s−1) and eight slope gradients (1.5, 3.0, 4.5, 6.0, 7.5, 9.0, 10.5, and 12.0°) to investigate how the Tc of gentle slopes in the northeastern hilly region of China and water erosion factors are related and to establish a Tc model of overland flow. The study demonstrated a power–law relationship between Tc, flow discharge, and slope gradient, as evidenced by a high coefficient of determination (R2; 0.94) and Nash–Sutcliffe efficiency (NSE; 0.92) values. Additionally, there was a positive correlation between Tc and average flow velocity, with R2 and NSE values of 0.86 and 0.84, respectively. Among the hydrodynamic parameters tested, the average flow velocity was determined to be the most effective Tc predictor, surpassing stream power (R2 > 0.7, NSE > 0.7), shear stress (R2 > 0.6, NSE > 0.6), and unit stream power (R2 < 0.5, NSE < 0.5). These findings lay the foundation for developing process‐based sediment transport models for gentle slopes in the northeastern hilly regions of China.

Estimating equilibrium scour depth around non-circular bridge piers using interpretable hybrid machine learning models

Scouring at bridge piers is a crucial issue that risks bridge collapses, causing economic losses and endangering public safety. Classic models struggle to accurately estimate equilibrium scour depth (dse) due to the complex scouring mechanism. This study combines Bayesian optimization (BO) with support vector machine (SVM) and extreme gradient boosting (XGBoost) to improve dse estimates. These models predict dse around round-nosed, square, and sharp-nosed bridge piers using field and laboratory data. The results demonstrate that the BO algorithm can effectively optimize the hyperparameters of SVM and XGBoost and ameliorate their dse estimates. For the square, sharp-nosed, and round-nosed piers, BO–XGBoost achieves the lowest mean absolute error (MAE) of 0.37 m, 0.35 m, and 0.36 m, the lowest root mean square error (RMSE) of 0.52 m, 0.60 m, and 0.56 m, and the highest coefficient of determination (R2) of 0.85, 0.80, and 0.84, respectively. Accurate estimation of dse is essential to balance safety and affordability of the bridge designs. Underestimating dse can lead to bridge failure, posing significant safety risks. Conversely, overestimating dse can result in unnecessary construction expenses. Finally, the impact of independent variables on dse estimates is analyzed using Shapley additive explanations (SHAP).

Adaptive strategies and thermal perceptions in intermittently used congregational Spaces: A case study

In spaces with considerable volumes that are sporadically used and typically not operating at full capacity, existing thermal comfort models face limitations. This research aims to reveal disparities between these models and survey-based comfort assessments. Comfort calculations were conducted using conventional thermal comfort models based on measured parameters and compared with survey data. Notable differences prompted the formulation of a more robust correlation, accounting for 83.4 % of the variations in Predicted Mean Votes (PMV). A regression model derived from survey data shows a high coefficient of determination (R2 = 0.84), supporting its applicability for large intermittently used spaces like mosques. Addressing limitations of existing models, this research introduces a practical and simplified approach to predicting thermal comfort, enhancing assessments in these distinctive spaces. The study also examines mosque indoor air quality dynamics. Despite sporadic ventilation, CO2 levels rose from 1500 ppm to 2500 ppm during Friday prayers, posing potential health risks. Before prayers, indoor CO2 and humidity met ASHRAE Standard 62, ISO 7730, and EN15231 values. To mitigate health concerns from elevated CO2 levels, improved ventilation systems are necessary. Comfort temperatures were only achieved during prayer periods.

Phenol identification and mapping of rangeland species at laboratory and landscape scales using PRISMA hyperspectral data

ABSTRACT Secondary metabolites have high ecological and economic values. Measuring the amounts of these metabolites in plants through laboratory analysis is time-consuming and expensive. On the other hand, the laboratory results cannot be generalized to a wide area of rangelands with diverse climates, topography, and soil. Therefore, the present study aims to evaluate the potential of PRISMA hyperspectral data to estimate the rangeland phenol concentration at landscape scale in western Isfahan province, Iran. Phenol concentration and spectral signatures were extracted from 42 plant individuals, including Astragalus adscendens, Astragalus verus, Daphne mucronata, Anabasis aphylla, and Phlomis olivieri using laboratory methods and spectroscopy in the range of 350 to 2500 nm. Phenol-sensitive bands were identified by establishing a relationship between phenol concentration and all spectral bands by the Partial Least Square Regression (PLSR) method. The map of phenol spatial changes was then produced with the Bayesian Regularized Artificial Neural Network Model (BRNN), Random Forest (RF), Generalized Linear Model (GAM), Generalized Additive Model (GLM), and Gaussian Process Regression (GPR) in rangelands. The results showed that the phenol concentration in different individuals was between 3.65 and 20.13 (mgGAE/gDW), and species spectral absorptions ranged from 1.650 to 1.660. The very high coefficient of determination of phenol concentration with spectral data (R2 = 0.94) confirmed the use of these data in phenol mapping at the landscape scale. The maximum estimated phenol concentration by GLM, GAM, RF, BRNN, and GPR models was 36.40, 26.76, 18.07, 22.74, and 32.92 GAE/g DW, respectively; which RF model had the highest (R2 = 0.955) and GLM had the lowest accuracy (R2 = 0.816). The findings show that hyperspectral data can estimate secondary metabolites at the landscape scale and can supplement laboratory methods in managing rangeland plants.

Non-invasive method to measure bulk electrical resistivity of spruce wood – influence of moisture content and anatomical direction

ABSTRACT The correlation between electrical resistance and moisture content (MC) of wood has been studied for many years and is the basis for an important method for determining wood MC in practice. This study presents a non-invasive set-up using plate electrodes with a guard ring covered with silver foam and gold leaves to measure the direct-current (DC) electrical resistivity of spruce wood. The design ensures good electrical contact at a pressure of 0.68 MPa, without impairing the wood surface. Measurements were conducted across various moisture contents up to the fiber saturation point (FSP) for the three anatomical directions at a temperature of 21°C. The regression parameters between electrical resistivity and moisture content were determined with high coefficients of determination (R² = 0,97–0.98). The effect of MC on the electrical resistance was significantly more pronounced in the radial direction, and the electrical resistance in the axial direction was lower than in the radial and tangential direction. Those findings support the basic understanding of the electrical properties of spruce wood and provide a basis for non-invasive volume resistivity measurements of wood.

Fine-tuning weibull distribution parameters in Morocco’s Tarfaya and Tangier wind farms using two-stage swarm optimization

This study assesses the efficacy of particle swarm optimization (PSO) in estimating scale ( c) and shape ( k) parameters of the Weibull distribution model for wind energy forecasting at two key wind farm sites in Morocco— Tarfaya in the south and Tangier in the north, utilizing real wind data from 2022. Employing a novel square frequency error objective function to enhance parameter accuracy, the study adopts a two-stage training approach involving recursive least-square estimation and PSO fine-tuning. Validation with artificial data underscores PSO’s effectiveness under diverse wind conditions. Parameter sensitivity analysis identifies four optimal PSO configurations, with the PSO-4 model exhibiting superior performance. Comparative analysis against traditional and heuristic optimization methods consistently demonstrates PSO-4’s lowest root-mean-square error (RMSE) and mean absolute error (MAE), high coefficients of determination ( R2), and shortest computation time. The research highlights PSO-4 model as a precise and efficient tool for Weibull distribution parameter estimation in wind energy forecasting, showcasing robust convergence across both wind farm sites.

Tailoring molecularly imprinted polymer on titanium-multiwalled carbon nanotube functionalized gold electrode for enhanced chlorophyll determination in microalgae health assessment.

A unique method for determining chlorophyll content in microalgae isdevised employing a gold interdigitated electrode (G-IDE) with a 10-µm gap, augmented by a nano-molecularly imprinted polymer (nano-MIP) and a titanium dioxide/multiwalled carbon nanotube (TiO2/MWCNT) nanocomposite. The nano-MIP, produced using chlorophyll template voids, successfully trapped chlorophyll, while the TiO2/MWCNT nanocomposite, synthesized by the sol-gel technique, exhibited a consistent distribution and anatase crystalline structure. The rebinding of procured chlorophyll powder, which was used as a template for nano-MIP synthesis, was identified with a high determination coefficient (R2 = 0.9857). By combining the TiO2/MWCNT nanocomposite with nano-MIP, the G-IDE sensing method achieved a slightly betterR2 value of 0.9892 for detecting chlorophyll in microalgae. The presented G-IDE sensor showed a significant threefold enhancement in chlorophyll detection compared withcommercially available chlorophyll powder. It had a detection limit of 0.917mL(v/v) and a linear range that spanned from10-6to 1mL. The effectiveness of the sensor in detecting chlorophyll in microalgae was confirmed through validation of its repeatability and reusability.

APPLICATION OF PLANT SEED BIO-COAGULANT IN THE REMEDIATION OF ENVIRONMENTAL PAINT EFFLUENT

The discharge of industrial effluents into the environment has been a growing concern worldwide due to its adverse impacts on human health and the ecosystem. Paint effluent, in particular, poses a significant threat due to its high chemical oxygen demand (COD), total suspended solids (TSS), and colour. Conventional methods of treating paint effluent discharge require the use of chemical coagulants, which are expensive, non-sustainable, and can generate hazardous waste. The search for alternative and sustainable methods for treating industrial effluent has led to the exploration of natural coagulants derived from plant sources. One such source is watermelon seeds, which contain significant amounts of proteins that possess coagulating properties. This study aimed to investigate the potential of watermelon seed bio-coagulant (WSB) for the remediation of paint effluent discharge. The effects of the watermelon seed bio-coagulant dosage, pH, and agitation rate on the removal efficiency of the treatment process were evaluated. These results revealed that the bio-coagulant was effective. The optimum coagulant dosage, pH, and agitation rate were found to be 300mg/L,4, and a treatment time of 45 min, respectively, with BOD and COD removal efficiencies of 88.47% and 90.41%, respectively. Statistical optimization of the treatment process was performed using the response surface methodology (RSM). The results revealed that the quadratic model developed using RSM was statistically significant with a high coefficient of determination (R2 = 0.9958). The analysis also revealed that bio-coagulant dosage and pH are the major factors affecting the efficacy of the treatment process. The optimized conditions led to removal efficiencies of88.95% and 90.61% for BOD and COD, respectively, indicating that watermelon seed bio-coagulant could be an effective and sustainable alternative for treating paint effluent. The results of this study suggest that the use of watermelon seed bio-coagulant is a promising approach for optimizing the paint effluent treatment process. It is a cost-effective and eco-friendly alternative to conventional coagulants used in wastewater treatment and has demonstrated efficacy in treating industrial wastewater. This study presents a sustainable and eco-friendly approach to the treatment of paint effluent discharge using a natural coagulant derived from a waste product.

Effectiveness of Collars and Hooked-Collars in Mitigating Scour around Different Abutment Shapes

Abutment scour is a major cause of bridge failures worldwide, leading to disruptions, economic losses, and loss of life. The present experimental study examines countermeasures against abutment scour using hooked-collar protections on vertical-wall and wing-wall abutments (at 45° and 60°) under different flow conditions. All 60 experiments were performed under sub-critical flow conditions by investigating scour around an abutment 20 cm long, 20 cm wide, and 25 cm tall. Two distinct values of the Froude number, 0.154 and 0.179, and a sediment particle diameter (d50) of 0.88 mm were used throughout the experimental phase. The resulting equilibrium scour around the abutments was compared to those with collar and hooked-collar protections. It was determined that the maximum abutment scour depth reduction was 83.89% when hooked collars were placed on vertical wall abutments beneath the bed surface level, and for wing-wall abutments at 45° and 60°, it was 74.2% and 73.5%, respectively, at the bed surface level. Regression analysis was conducted to assess the non-dimensional scour depth (Ds/Yf) and scour reduction (RDs/Yf), with a high enough coefficient of determination (R2 values of 0.96 and 0.93, respectively), indicating high confidence in the analysis. The sensitivity analysis findings demonstrate that the width of the collar (Wc) and La are the most influencing factors affecting Ds/Yf and RDs/Yf.

Development of analytical methods and method validation for the qualitative and quantitative determination of Carboxymethyl Cellulose (CMC) in yogurt and ayran

This study presents the development and validation of analytical methods for the qualitative and quantitative determination of Carboxymethyl Cellulose (CMC) in yogurt and ayran. The methods were evaluated for their detection limit (LOD), quantification limit (LOQ), and method detection limit (MDL) across multiple samples, demonstrating robust sensitivity with MDL and LOD values of 0.03% for both yogurt and ayran. Recovery studies showed high accuracy, with recovery rates between 98% and 100%, and consistent recovery within the acceptable range of 97–103%, reinforcing the methods' reliability. The quantitative analysis exhibited strong repeatability and reproducibility, with low relative standard deviations (RSDr and RSDR), confirming minimal intra-day and inter-day variability. The calibration curves for CMC analysis demonstrated excellent linearity with high coefficients of determination (R2 = 0.9985 for yogurt and 0.9951 for ayran), ensuring accurate quantification over a wide concentration range. Measurement uncertainty analysis further validated the methods' precision and accuracy, showing low relative uncertainties for trueness, repeatability, and reproducibility. Robustness evaluations indicated that the methods maintained consistent recovery rates despite variations in pH, heating time, and sample quantity, highlighting their reliability under different conditions. For qualitative analysis, the visual images method showed exceptional performance with perfect sensitivity and specificity, accurately identifying all positive and negative samples without any false positives or negatives. This high level of accuracy makes the visual images method a valuable tool for ensuring product safety and compliance with quality standards in the dairy industry. Overall, the validated analytical methods for CMC detection in yogurt and ayran demonstrated high precision, accuracy, and reliability, making them essential for quality control and regulatory compliance in dairy production.

A novel low-cost sensors system for real-time multipollutant indoor air quality monitoring – Development and performance

Indoor air pollution poses a significant health risk, thus demanding effective indoor air quality (IAQ) monitoring strategies. Low-cost sensors (LCS) have gained attraction for IAQ monitoring, but their data accuracy and robustness remain key challenges. This study addresses gaps in the literature by developing and evaluating a novel low-cost monitor prototype for IAQ monitoring. A detailed design methodology based on requirements analysis was used to create two identical low-cost monitors (LCM) prototypes for multipollutant monitoring, including PM2.5, CO2, CO, O3, NO2, temperature and relative humidity. The LCM were deployed in an in-field monitoring campaign alongside research-grade instruments. The uncorrected sensor signals showed linear response compared to research-grade instruments with high Pearson Correlation Coefficients for 1-min mean: PM2.5 (0.97), CO2 (0.81–0.89), CO (0.95–0.98), and O3 (0.80–0.85). The concentration range of pollutants was observed to play a crucial role in the response and accuracy of the LCS. Kalman filter was implemented with optimised parameters to reduce noise from the signal of ozone sensors. Univariate and multivariate linear models were used to field calibrate the sensors. Linear regression models, with high coefficients of determination (>0.8) and low error values, imply that the developed LCM prototypes can be reliably used for indicative monitoring. In most of the cases, univariate linear models using only the sensor response of LCS as explanatory variables showed significant improvement. Future work will involve longer monitoring campaigns in different microenvironments, evaluation of calibration drift and to assess failure episodes in long-term use.

Higuchi fractal dimension and deep learning on near-infrared spectroscopy for determination of free fatty acid (FFA) content in oil palm fruit

Free fatty acid (FFA) content is an essential parameter with a significant influence on the quality of oil palm, with lower levels showing higher quality. The measurement of this parameter is often carried out with conventional methods in chemical laboratories using solvents and reagents, but these techniques have several limitations. Therefore, this study aimed to develop a rapid and non-destructive technique for determining FFA content based on near-infrared (NIR) spectroscopy. This technique comprised the use of Higuchi fractal dimension (HFD) and deep learning to process NIR spectra as a new chemometric analysis. The sample population consisted of 350 oil palm fruits at various maturity ages collected from the Cikabayan Oil Palm Plantation. Each sample had its NIR spectrum acquired with a wavelength of 1000–1500 nm. Good performance was shown by a low root mean squared error (RMSE) value and a high coefficient of determination (R2). Based on numerical analysis, this study proposed HFD with a maximum discrete time interval (kmax) value of 10 and deep learning with long short-term memory (LSTM) as a robust architectural model. The results showed that the proposed technique could predict oil palm fruit FFA levels with RMSE and R2 values of 0.167 and 0.959, respectively. Furthermore, it could identify FFA content at each process step, before and after harvesting. Based on these results, its implementation in oil palm control management was expected to enhance effectiveness and efficiency, leading to the production of high-quality products.

Efficient extraction and HPLC determination of sulfonamide residues in milk samples using acylated graphene oxide-functionalized polyurethane sponges: A tailored approach for efficient analyte recovery

This study reports a novel surface modification of polyurethane sponge (PUS) with acylated graphene oxide (AGO), yielding a versatile sorbent material (AGO@PUS) for the efficient extraction of sulfonamide residues from milk samples. The AGO@PUS combines the extractive properties of AGO with the advantages of PUS, enabling easy, fast, cost-effective, and efficient sample preparation. The material was characterized using FTIR, SEM, XRD and the extraction protocol was optimized for the determination of three sulfonamides (SGD, SDS, and SDZ) by HPLC-UV. The proposed method exhibited excellent linearity (0.01–1000 µg mL−1), low limits of detection (0.04–0.06 µg mL−1) and quantification (0.13–0.21 µg mL−1), and high coefficients of determination (R2 = 0.993–0.997). The maximum recovery rate reached 99.63 %, demonstrating the exceptional sorption capacity of AGO@PUS. Notably, the clear extracts obtained with this material highlight its potential for efficient sample preparation and analysis.