Root-mean Error Research Articles

Adaptation of the Food Literacy (FOODLIT) Tool for Turkish Adults: A Validity and Reliability Study.

Food literacy is associated with sustainable food systems and encourages individuals to adopt healthy eating habits. However, there is no validated method that can be used to measure food literacy related to sustainable food systems of Turkish adults. This research aimed to assess the validity and reliability of the Turkish adaptation of the "Food Literacy (FOODLIT) Tool" for Turkish adults. The study involved 328 people aged 19 to 58 years. The FOODLIT-Tool is a five-point Likert-type scale consisting of 24 items and five factors ("culinary competencies", "production and quality", "selection and planning", "environmentally safe" and "origin"). The Cronbach's alpha coefficient was applied to assess internal consistency reliability, showing an excellent scale coefficient of 0.927. The model was evaluated with a confirmatory factor analysis (CFA). The findings of the CFA suggested that the fit indices were acceptable (χ2/df = 1.257, comparative fit index: 0.991, goodness-of-fit index: 0.977, normed fit index: 0.990 and root mean error of approximation: 0.028). Furthermore, there was a positive relationship between the FOODLIT-Tool score and the "Sustainable and Healthy Eating Behaviors Scale" (SHEB) score (r = 0.518, p < 0.001). Our study shows that the Turkish version of the FOODLIT-Tool integrated with sustainable food systems is a valid and reliable measurement tool for assessing the food literacy of Turkish adults.

Lyapunov-based neural network model predictive control using metaheuristic optimization approach

This research introduces a new technique to control constrained nonlinear systems, named Lyapunov-based neural network model predictive control using a metaheuristic optimization approach. This controller utilizes a feedforward neural network model as a prediction model and employs the driving training based optimization algorithm to resolve the related constrained optimization problem. The proposed controller relies on the simplicity and accuracy of the feedforward neural network model and the convergence speed of the driving training based optimization algorithm. The closed-loop stability of the developed controller is ensured by including the Lyapunov function as a constraint in the cost function. The efficiency of the suggested controller is illustrated by controlling the angular speed of three-phase squirrel cage induction motor. The reached results are contrasted to those of other methods, specifically the fuzzy logic controller optimized by teaching learning-based optimization algorithm, the optimized PID with particle swarm optimization algorithm, the neural network model predictive controller based on particle swarm optimization algorithm, and the neural network model predictive controller using driving training based optimization algorithm. This comparative study showcase that the suggested controller provides good accuracy, quickness and robustness due to the obtained values of the mean absolute error, mean square error root mean square error, enhancement percentage, and computing time in the different simulation cases, and it can be efficiently utilized to control constrained nonlinear systems with fast dynamics.

Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms

Artificial intelligence algorithms have recently demonstrated their efficacy in accurately predicting concrete properties by optimizing mixing proportions and overcoming design limitations. In this regard, foam concrete (FC) production presents a unique challenge, necessitating extensive experimental trials to attain specific properties such as compressive strength (CS). In this context, linear regression (LR), support vector regression (SVR), a multilayer-perceptron artificial neural network (MLP-ANN), and Gaussian process regression (GPR) algorithms, were used to predict the CS of FC. 261 experimental results were utilized, incorporating input variables such as density, water-to-cement ratio, and fine aggregate-to-cement ratio. During the training phase, 75 % of the experimental dataset was utilized. The experimental data is then validated using metrics such as coefficient of determination (R2), root mean square error, and root mean error. In comparison, the GPR algorithm reveals high-accuracy towards the estimation of CS, as proved by its high R2-value, which equals 0.98, while the R2 for ANN, SVR, and LR are 0.97, 0.90, and 0.89, respectively. Additionally, parametric and sensitivity analyses were used to assess the performance of the GPR and LR algorithms. Results revealed that density exerted the most significant influence on CS, with the GPR model showing a pronounced negative impact of fine aggregate-to-cement ratio on CS, particularly in low-density FC, contrasting with the LR model. This study confirmed that the GPR algorithm provided reliable accuracy in predicting the CS of FC. Therefore, it is recommended to utilize the prediction algorithms within the range of input variables employed in this investigation for optimal results.

Adaptive Weighted Error-Correction Method Based on the Error Distribution Characteristics of Multi-Channel Alignment.

As process nodes of advanced integrated circuits continue to decrease below 10 nm, the requirement for overlay accuracy is becoming stricter. The alignment sensor measures the position of the alignment mark relative to the wafer; thus, sub-nanometer alignment position accuracy is vital. The Phase Grating Alignment (PGA) method is widely used due to its high precision and stability. However, the alignment error caused by the mark asymmetry is the key obstacle preventing PGA technology from achieving sub-nanometer alignment accuracy. This error can be corrected using many methods, such as process verification and multi-channel weighted methods based on multi-diffraction, multi-wavelength and multi-polarization state alignment sensors. However, the mark asymmetry is unpredictable, complex and difficult to obtain in advance. In this case, the fixed-weight method cannot effectively reduce the alignment error. Therefore, an adaptive weighted method based on the error distribution characteristic of a multi-channel is proposed. Firstly, the simulation result proves that the error distribution characteristic of the multi-alignment result has a strong correlation with the mark asymmetry. Secondly, a concrete method of constructing weight values based on error distribution is described. We assume that the relationship between the weight value of each channel and the deviations of all channels' results is second-order linear. Finally, without other prior process correction in the simulation experiment, the residual error's Root Mean Square (RMS) of fixed weighted method is 14.0 nm, while the RMS of the adaptive weighted method is 0.01 nm, when dealing with five typical types of mark asymmetry. The adaptive weighted method exhibits a more stable error correction effect under unpredictable and complicated mark asymmetry.

Deep learning-based path tracking control using lane detection and traffic sign detection for autonomous driving

Automated vehicles are a significant advancement in transportation technique, which provides safe, sustainable, and reliable transport. Lane detection, maneuver forecasting, and traffic sign recognition are the fundamentals of automated vehicles. Hence, this research focuses on developing a dynamic real-time decision-making system to obtain an effective driving experience in autonomous vehicles with the advancement of deep learning techniques. The deep learning classifier such as deep convolutional neural network (Deep CNN), SegNet and are utilized in this research for traffic signal detection, road segmentation, and lane detection. The main highlight of the research relies on the proposed Finch Hunt optimization, which involves the hyperparameter tuning of a deep learning classifier. The proposed real-time decision-making system achieves 97.44% accuracy, 97.56% of sensitivity, and 97.83% of specificity. Further, the proposed segmentation model achieves the highest clustering accuracy with 90.37% and the proposed lane detection model attains the lowest mean absolute error, mean square error, and root mean error of 17.76%, 11.32%, and 5.66% respectively. The proposed road segmentation model exceeds all the competent models in terms of clustering accuracy. Finally, the proposed model provides a better output for lane detection with minimum error, when compared with the existing model.

Rapid Bathymetry Mapping Based on Shallow Water Cloud Computing in Small Bay Waters: Pilot Project in Pacitan-Indonesia

Mapping coastal areas generally requires large data constellations in time series and requires analysis using complex mathematical and modeling approaches. In shallow-water bathymetric mapping, remote sensing plays an important role in supporting conventional bathymetric mapping, especially in areas that are difficult to access. This method called Satellite Derived Bathymetry (SDB). The cloud computing approach is a solution for mapping shallow water bathymetry rapid and effectively. This study using Google Earth Engine (GEE) to compute remote sensing data for produce near-shore bathymetry. The method of Li et al. (2021) performs bathymetric extraction without using depth samples but uses chlorophyll-A as input for depth extraction parameter calculations. This study examines a small bay in the waters of Pacitan, Anakan Bay, and the waters of Kemujan Island in the Karimunjawa Islands. Within this study area, significant differences in resulting depth are very limited, ranging from 0 to -17.8. The developed model, based on the algorithm proposed by Li et al. (2021), is estimated to be able to provide accurate predictions of up to around 90% in the waters studied, with a root mean error rate (RMSE) of 1.1 meters.

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

PurposeBecause of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.Design/methodology/approachThe proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.FindingsThe predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.Originality/valuePSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

Turkish adaptation, validation and reliability of the US Adult Food Security Survey Module in university students.

To evaluate the validity and reliability of the Turkish version of the US Adult Food Security Survey Module (AFSSM). A cross-sectional study collected data from 117 university students. The AFSSM questionnaire was completed by all participants. Psychometric evaluation for scale, content, construct, and convergent validity and reliability of the scale was tested. The construct validity was assessed by exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) on data collected from university students. Cronbach's α (internal consistency) and composite reliability were used to assess the reliability (P < 0·05). Students were recruited from the university. This research was conducted with volunteer university students with a mean age of 22·74 ± 4·19 years. Three factors were extracted from eight items through EFA: (1) inadequate nutrition, (2) economic concern and (3) hunger. These factors accounted for 77·4 % of the total variance, and factor loadings ranged from 0·755 to 0·953. Cronbach's α was 0·769. The results of the CFA suggested the fit indices were acceptable (χ2/sd = 0·235, root mean error of approximation: 0·034, goodness-of-fit index: 0·994, comparative fit index: 0·992 and normed fit index: 0·986). This is the first study that validates and reports the Turkish version of AFSSM in university students, and the results of our study show that the Turkish AFSSM is a valid and reliable tool for determining food security in university students. AFSSM can be used by researchers to examine the food security of university students.

FACTORS ASSOCIATED WITH PARTICIPATION IN FUNCTION-FOCUSED CARE AMONG HOSPITALIZED PATIENTS LIVING WITH DEMENTIA

Abstract Function focused care is an approach used to increase physical activity in hospitalized older adults living with dementia. This study tested the factors associated with participating in function focused care among patients living with dementia. This was a cross-sectional descriptive study using baseline data from the first 365 participants from the first 10 hospitals in the study entitled, Testing the Effectiveness of Function Focused Care for Acute Care Using the Evidence Integration Triangle. Structural equation modeling was used for model testing. The mean age of the participants was 83.2 (SD=8.0) and the majority were women (64%) and white (69%). Fifteen of the 27 hypothesized paths were significant and explained 25% of the variance in performance of function focused care. Cognition, quality of care interactions, behavioral and psychological symptoms associated with dementia, physical resilience, and pain were all indirectly associated with performance of function focused care through function and/or pain. Tethers, function, and quality of care interactions were all directly associated with function focused care. The x2 / df was 5.37, the normed fit index was .87 and the Root Mean Error Square of Approximation was .12. For hospitalized patients living with dementia the focus of care should be on treating pain and behavioral symptoms, decreasing tethers, and improving quality of care interactions so as to optimize resilience, function and participation in function focused care.

Detection and Quantification of Bisphenol A in Surface Water Using Absorbance-Transmittance and Fluorescence Excitation-Emission Matrices (A-TEEM) Coupled with Multiway Techniques.

In the present protocol, we determined the presence and concentrations of bisphenol A (BPA) spiked in surface water samples using EEM fluorescence spectroscopy in conjunction with modelling using partial least squares (PLS) and parallel factor (PARAFAC). PARAFAC modelling of the EEM fluorescence data obtained from surface water samples contaminated with BPA unraveled four fluorophores including BPA. The best outcomes were obtained for BPA concentration (R2 = 0.996; standard deviation to prediction error's root mean square ratio (RPD) = 3.41; and a Pearson's r value of 0.998). With these values of R2 and Pearson's r, the PLS model showed a strong correlation between the predicted and measured BPA concentrations. The detection and quantification limits of the method were 3.512 and 11.708 micro molar (µM), respectively. In conclusion, BPA can be precisely detected and its concentration in surface water predicted using the PARAFAC and PLS models developed in this study and fluorescence EEM data collected from BPA-contaminated water. It is necessary to spatially relate surface water contamination data with other datasets in order to connect drinking water quality issues with health, environmental restoration, and environmental justice concerns.

Validity and reliability of Turkish version of the Adult Eating Behaviour Questionnaire for adult participants.

The aim of this study is to assess the validity and reliability of the Adult Eating Behaviour Questionnaire (AEBQ-TR) for adults. Hunot et al. (2016) developed the original questionnaire, which was modified and translated into Turkish. On data collected from adults, construct validity was assessed using exploratory factor analysis and confirmatory factor analysis. Pearson's and Cronbach's correlation coefficients were used to evaluate reliability and validity (P < 0·05). This research was carried out in Ankara, Turkey. A total of 311 adults from Ankara (148 men and 163 women) took part in the study. Seventy-two of these adults take the retest. In the present study, 311 adults with a mean age of 29·3 ± 11·3 years participated. Factor loadings ranged from 0·404 to 0·907. In general, food approach and food avoidance scales showed a positive correlation within themselves. According to the results of confirmatory factor analysis and goodness-of-fit indicators, the seven-factor model showed a better model fit in the Turkish data (chi-square/degrees of freedom = 2·137, root mean error of approximation: 0·061, comparative fit index: 0·884, and normed fit index: 0·850). Higher BMI was associated with higher Emotional Over-eating, higher Enjoyment of the Food, lower Food Satiety and lower Emotional Under-eating. The Turkish AEBQ is a valid and reliable tool for 20- to 65-year adults to determine appetitive properties related to the aetiology of weight change and especially obesity risk. Besides, AEBQ testing is required for validation in early and late adulthood.

Bitcoin trading indicator: a machine learning driven real time bitcoin trading indicator for the crypto market

As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuation is largely influenced by fresh blocks, news, mining information, support or resistance levels, and public opinion. Therefore, a machine-learning model will be fantastic if it learns from data and tells or indicates if we need to purchase or sell for a little period. In this study, we attempted to create a tool or indicator that can gather tweets in real-time using tweepy and the Twitter application programming interface (API) and report the sentiment at the time. Using the renowned Python module "FBProphet," we developed a model in the second phase that can gather historical price data for the bitcoin to US dollar (BTCUSD) pair and project the price of bitcoin. In order to provide guidance for an intelligent forex trader, we finally merged all of the models into one form. We traded with various models for a very little number of days to validate our bitcoin trading indicator (BTI), and we discovered that the combined version of this tool is more profitable. With the combined version of the instrument, we quickly and with little error root mean square error (RMSE: 1,480.58) generated a profit of $1,000.71 USD.

Densi-melting effect for ultra-precision laser beam figuring with clustered overlapping technology at full-spatial-frequency.

Laser beam figuring (LBF), as a processing technology for ultra-precision figuring, is expected to be a key technology for further improving optics performance. To the best of our knowledge, we firstly demonstrated CO2 LBF for full-spatial-frequency error convergence at negligible stress. We found that controlling the subsidence and surface smoothing caused by material densification and melt under specific parameters range is an effective way to ensure both form error and roughness. Besides, an innovative "densi-melting" effect is further proposed to reveal the physical mechanism and guide the nano-precision figuring control, and the simulated results at different pulse durations fit well with the experiment results. Plus, to suppress the laser scanning ripples (mid-spatial-frequency (MSF) error) and reduce the control data volume, a clustered overlapping processing technology is proposed, where the laser processing in each sub-region is regarded as tool influence function (TIF). Through the overlapping control of TIF figuring depth, we achieved LBF experiments for the form error root mean square (RMS) reduced from 0.009λ to 0.003λ (λ=632.8 nm) without destroying microscale roughness (0.447 nm to 0.453 nm) and nanoscale roughness (0.290 nm to 0.269 nm). The establishment of the densi-melting effect and the clustered overlapping processing technology prove that LBF provides a new high-precision, low-cost manufacturing method for optics.

Combining Digital Covariates and Machine Learning Models to Predict the Spatial Variation of Soil Cation Exchange Capacity

Cation exchange capacity (CEC) is a soil property that significantly determines nutrient availability and effectiveness of fertilizer applied in lands under different managements. CEC’s accurate and high-resolution spatial information is needed for the sustainability of agricultural management on farms in the Nagaland state (northeast India) which are fragmented and intertwined with the forest ecosystem. The current study applied the digital soil mapping (DSM) methodology, based on the CEC values determined in soil samples obtained from 305 points in the region, which is mountainous and difficult to access. Firstly, digital auxiliary data were obtained from three open-access sources, including indices generated from the time series Landsat 8 OLI satellite, topographic variables derived from a digital elevation model (DEM), and the WorldClim dataset. Furthermore, the CEC values and the auxiliary were used data to model Lasso regression (LR), stochastic gradient boosting (GBM), support vector regression (SVR), random forest (RF), and K-nearest neighbors (KNN) machine learning (ML) algorithms were systematically compared in the R-Core Environment Program. Model performance were evaluated with the square root mean error (RMSE), determination coefficient (R2), and mean absolute error (MAE) of 10-fold cross-validation (CV). The lowest RMSE was obtained by the RF algorithm with 4.12 cmolc kg−1, while the others were in the following order: SVR (4.27 cmolc kg−1) &lt;KNN (4.45 cmolc kg−1) &lt;LR (4.67 cmolc kg−1) &lt;GBM (5.07 cmolc kg−1). In particular, WorldClim-based climate covariates such as annual mean temperature (BIO-1), annual precipitation (BIO-12), elevation, and solar radiation were the most important variables in all algorithms. High uncertainty (SD) values have been found in areas with low soil sampling density and this finding is to be considered in future soil surveys.

Trajectory prediction of underwater launch equipment based on varying flow coefficient

In the underwater launch systems, a real-time prediction of the trajectory of the equipment out of the tube can provide accurate motion feedback for the closed-loop control. In this paper, using an analytical approach, a hydrodynamic mathematical model is developed for the open pipeline underwater launch system, along with an analysis of the coupling of pressure-flow variables influence mechanism on the trajectory. A time-varying coefficient-based prediction model for the equipment trajectory of the underwater launch process is developed for the compressible fluid and the strong transient characteristics of the fluid during the underwater launch process. Through the incorporation of the time-varying component of inertia into the flow coefficient, the interlinking relationship between the fluid, the pipe structure, and the equipment motion is clarified. This addresses the problem that the traditional steady-state hydrodynamic pipeline model cannot accurately describe the strong transient launch process. The experimental results show that, under typical firing conditions, the maximum error between the equipment motion velocity predicted by the mathematical model based on the time-varying flow coefficient and the measured velocity is 8.48%, with an error root mean square of 0.3035, proving the accuracy of the model based on the time-varying flow coefficient.

Prediction of the Premium Production of Some Insurance Companies Operating in Turkey with Artificial Neural Networks

The insurance sector can be seen as a sector that directly affects the country's economy and development with its ability to fund financial markets and meet risks. In this respect, estimating the premium size, which is the main factor that constitutes the volume of the insurance sector, as accurately and reliably as possible, indirectly means predicting the risks that may arise in terms of the economy and development of the country and taking precautions. necessary measures. In this study, premium productions of some insurance companies operating in Turkey were estimated with different artificial neural networks and their results were evaluated comparatively. In this context, two different artificial neural networks (ANNs), feed forward and feedback, were used as the estimation tools for insurance premium production. Two training algorithms and two different activation functions were run in the structure of the ANNs used. Thus, eight different estimation tools were created for insurance companies' premium production. The estimation performances of ANNs were evaluated on test sets by using error criteria such as Root Mean Square Error, Mean Absolute Percentage Error, and Median Absolute Percentage Error. In terms of the MdAPE criterion in our best-performing algorithms, in the analysis of a total of 36 data sets, 18 quarters of 18 months in total, the predictions for only 6 data sets were estimated with an error of more than 10%, and 5 of them were around 10% or just above, which is still acceptable. have an acceptable level of error.

Optimal ECG Signal Denoising Using DWT with Enhanced African Vulture Optimization

Cardiovascular diseases (CVDs) are the world's leading cause of death; therefore cardiac health of the human heart has been a fascinating topic for decades. The electrocardiogram (ECG) signal is a comprehensive non-invasive method for determining cardiac health. Various health practitioners use the ECG signal to ascertain critical information about the human heart. In this paper, the noisy ECG signal is denoised based on Discrete Wavelet Transform (DWT) optimized with the Enhanced African Vulture Optimization (AVO) algorithm and adaptive switching mean filter (ASMF) is proposed. Initially, the input ECG signals are obtained from the MIT-BIH ARR dataset and white Gaussian noise is added to the obtained ECG signals. Then the corrupted ECG signals are denoised using Discrete Wavelet Transform (DWT) in which the threshold is optimized with an Enhanced African Vulture Optimization (AVO) algorithm to obtain the optimum threshold. The AVO algorithm is enhanced by Whale Optimization Algorithm (WOA). Additionally, ASMF is tuned by the Enhanced AVO algorithm. The experiments are conducted on the MIT-BIH dataset and the proposed filter built using the EAVO algorithm, attains a significant enhancement in reliable parameters, according to the testing results in terms of SNR, mean difference (MD), mean square error (MSE), normalized root mean squared error (NRMSE), peak reconstruction error (PRE), maximum error (ME), and normalized root mean error (NRME) with existing algorithms namely, PSO, AOA, MVO, etc.

Optimal ECG Signal Denoising Using DWT with Enhanced African Vulture Optimization

Cardiovascular diseases (CVDs) are the world's leading cause of death; therefore cardiac health of the human heart has been a fascinating topic for decades. The electrocardiogram (ECG) signal is a comprehensive non-invasive method for determining cardiac health. Various health practitioners use the ECG signal to ascertain critical information about the human heart. In this paper, the noisy ECG signal is denoised based on Discrete Wavelet Transform (DWT) optimized with the Enhanced African Vulture Optimization (AVO) algorithm and adaptive switching mean filter (ASMF) is proposed. Initially, the input ECG signals are obtained from the MIT-BIH ARR dataset and white Gaussian noise is added to the obtained ECG signals. Then the corrupted ECG signals are denoised using Discrete Wavelet Transform (DWT) in which the threshold is optimized with an Enhanced African Vulture Optimization (AVO) algorithm to obtain the optimum threshold. The AVO algorithm is enhanced by Whale Optimization Algorithm (WOA). Additionally, ASMF is tuned by the Enhanced AVO algorithm. The experiments are conducted on the MIT-BIH dataset and the proposed filter built using the EAVO algorithm, attains a significant enhancement in reliable parameters, according to the testing results in terms of SNR, mean difference (MD), mean square error (MSE), normalized root mean squared error (NRMSE), peak reconstruction error (PRE), maximum error (ME), and normalized root mean error (NRME) with existing algorithms namely, PSO, AOA, MVO, etc.

Consensual Regression of Lasso-Sparse PLS models for Near-Infrared Spectra of Food

In some cases, near-infrared spectra (NIRS) make the prediction of quantitative models unreliable, and the choice of a suitable number of latent variables (LVs) for partial least square (PLS) is difficult. In this case, a strategy of fusing member models with important information is gradually becoming valued in recent research. In this work, a series of PLS regression models were developed with an increasing number of LVs as member models. Then, the least absolute shrinkage and selection operator (Lasso) was employed as the model’s selection access to sparse uninformative ones among these PLS member models. Deviation weighted fusion (DW-F), partial least squares regression coefficient fusion (PLS-F), and ridge regression coefficient fusion (RR-F) were comparatively used further to fuse the above sparsed member models, respectively. Three spectral datasets, including six attributes in NIR data of corn, apple, and marzipan, respectively, were applied in order to validate the feasibility of this fusion algorithm. Six fusion models of the above attributes performed better than the general optimal PLS model, with a noticeable enhancement of root mean errors squared of prediction (RMSEP) arriving at its highest at 80%. It also reduced more than half of the spectral bands; the DW-F especially showed its excellent fusing capacity and obtained the best performance. Results show that the preferred strategy of DW-F model combined with Lasso selection can make full use of spectral information, and significantly improve the prediction accuracy of fusion models.

Comparative Chemometric Manipulations of UV-Spectrophotometric Data for the Efficient Resolution and Determination of Overlapping Signals of Cyclizine and Its Impurities in Its Pharmaceutical Preparations.

Cyclizine (CYZ), a commonly used antiemetic drug, has two pharmacopeial toxic impurities, 1-methylpiperazine (MPZ) and diphenylmethanol (DPM). When CYZ parenteral formulations are administered intravenously, both impurities are poisonous, toxic, and harmful to the human body. Cyclizine was determined along with its hazardous impurities MPZ and DPM by green multivariate calibration using UV-spectroscopic data. Three multivariate algorithms were used to resolve and quantify overlapped spectral signals: principal component regression (PCR), partial least squares (PLS), and synergistic intervals partial least squares (siPLS). A concentration set containing 16 distinct combinations of CYZ, MPZ, and DPM was randomly prepared, and the absorbance values of the concentration set were determined using the 376 point-wavelength set with an interval of 0.2 nm between 200 and 275 nm. Good linear correlations were established for CYZ, MPZ, and DPM in the concentration ranges of 5.00-25.0, 0.50-2.50, and 0.50-2.50 µg/mL, respectively. The ideal spectral range and associated combinations were chosen based on the lowest root mean error of prediction (RMSEP) and correlation coefficient values (r). The siPLS approach performed better than the PCR and PLS models. The combination of four subintervals, 1, 3, 4, and 7, demonstrated the greatest effect, with RMSEP values of 0.0272, 0.0053, and 0.0315 for CYZ, MPZ, and DPM, respectively, and correlation coefficients of 0.9991, 0.9999, and 0.9997, in order. Various assessment tools were used to evaluate and measure the greenness profile of the established methods. The proposed methods were validated using internal and external validation sets. The three methods were effectively used to determine CYZ in its pure form and parenteral formulations, as well as its toxic impurities. The acquired results were compared statistically to those obtained using the reported HPLC method. Cyclizine and its toxic impurities can be determined spectrophotometrically by using the three developed chemometric models.