Relative Root Research Articles

Sustainable chemometric methods boosted by Latin hypercube technique for quantifying the recently FDA-approved combination of bupivacaine and meloxicam in the presence of bupivacaine carcinogenic impurity: Comprehensive greenness, blueness, and whiteness assessments

Addressing sustainability requires developing analytical methods that minimize hazards, waste, and energy utilization per the principles of green and white chemistry. Herein, we present a sustainable spectrophotometric chemometrics technique for quantifying the recently approved Bupivacaine (BUP) and Meloxicam (MEL) combination and the potential BUP carcinogen, 2,6-dimethylaniline (DMA). Our models, including partial least squares (PLS), principal component regression (PCR), genetic algorithm-PLS (GA-PLS), and GA-PCR, were established through a comprehensive experimental design involving 25 mixtures as the calibration set. A key innovation is the utilization of the Latin Hypercube Sampling (LHS) technique, which enables the creation of a robust validation set for evaluating the performance and generalizability of these models. The GA-PLS model demonstrated excellent accuracy, with recovery percentages (R%) from 98 to 102% for all analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.097, 0.050, and 0.112) and (0.119, 0.044 and 0.131) for BUP, DMA, and MEL, respectively. The model also showed a negligible bias-corrected mean square error of prediction (BCMSEP) of (-0.014, −0.003, and 0.018), with relative root mean square error of prediction (RRMSEP) reaching (0.992, 0.752, and 0.659), and limits of detection (LOD) reaching (0.153, 0.081, and 0227) for BUP, DMA, and MEL, respectively. Comprehensive greenness, blueness, and whiteness assessments were performed and compared between the suggested and reported methods. This research pioneers a green–blue-white alternative to conventional approaches, serving as a model for developing sustainable techniques that reduce the usage of resources and chemical waste while meeting the global appeal for environmentally accountable solutions.

Alteration of aluminum tolerance mediated by insertion of same transposable element at different sites in the upstream region of HvAACT1 in Japanese barley accessions

ABSTRACT Aluminum (Al) toxicity is a major limiting factor for crop production in acid soils. Barley is highly sensitive to Al, so the genetic improvement of its Al tolerance is required. However, useful breeding materials and genetic factors for improving its tolerance are remain largely unclear. Here, we compared the Al tolerance of ‘Minorimugi’ and ‘Fibersnow,’ both six-rowed hulled barley cultivars grown mainly in northern Japan, using relative root length (RRL) as an index, and found that Minorimugi had higher tolerance. Quantitative trait locus (QTL) analysis using recombinant inbred lines (RILs) derived from the two cultivars detected a major QTL at the HvAACT1 (Al-ACTIVATED CITRATE TRANSPORTER 1) locus that explained approximately 36% of the variance. The PCR amplification analysis revealed a 1-kb transposable element (TE) insertion at −1.9 kb in the upstream region of HvAACT1 in Minorimugi that enhances HvAACT1 expression. The TE has the same nucleotide sequence as one previously found at −4.8 kb in the upstream region of HvAACT1 in ‘Murasakimochi,’ which is an Al tolerant cultivar. In the Japanese barley population, the Murasakimochi-type HvAACT1 upstream allele is shared mainly among barley accessions developed in the Shikoku area, while the Minorimugi-type allele is shared mainly among accessions developed in the Hokuriku and Nagano area. This geographic difference indicates that both alleles are shared among different subpopulations in Japanese barley, which may be advantageous for growth in acid soils. Our results provide information about a new allele of the HvAACT1 upstream region and potential breeding materials for improving the Al tolerance of barley.

Machine Vision for Knot Detection and Location in Chinese Fir Lumber

Abstract In order to be utilized in the design of a wood building, the lumber must pass grade. Machine-vision inspection grading offers higher efficiency and accuracy than traditional manual visual grading. In this paper, a fast and accurate method for identifying defects in large-size structural lumber based on machine vision of Fujian Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) constructional lumber (FCF CL) is proposed. Specifically, the grey matrix of the captured images on the surface of the sawn timber is initially scanned and the pixel weights on the edges of the image greyness variables are calculated. A matrix-valued torus was formed by fitting the knot edge profile and analyzing changes in the gradient values at the knot's edge, as well as calculating the directional derivative's rate of change. The knot three-dimensional mapping curves were projected onto the plane to form horizontal rise contours. Observe from the contour map of the whole large-size sawn timber, and extract the positional information of the knot where there is a trough (groove). The test results show that the rRMSE (Relative Root Mean Square Error) measured at the x axis position of knots is within 0.49 percent; the rRMSE measured at the y axis is 0.35 percent, which has high detection accuracy and meets the production requirements. We also investigated the effect of knots in different positions on the modulus of elasticity and the bending strength of FCF CL, with a view to establishing a link between machine-vision knot detection and mechanical properties of sawn timber in our next work.

Machine Learning Modeling Integrating Experimental Analysis for Predicting Compressive Strength of Concrete Containing Different Industrial Byproducts

This study aimed to develop accurate models for estimating the compressive strength (CS) of concrete using a combination of experimental testing and different machine learning (ML) approaches: baseline regression models, boosting model, bagging model, tree-based ensemble models, and average voting regression (VR). The research utilized an extensive experimental dataset with 14 input variables, including cement, limestone powder, fly ash, granulated glass blast furnace slag, silica fume, rice husk ash, marble powder, brick powder, coarse aggregate, fine aggregate, recycled coarse aggregate, water, superplasticizer, and voids in mineral aggregate. To evaluate the performance of each ML model, five metrics were used: mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), coefficient of determination (R2-score), and relative root mean squared error (RRMSE). The comparative analysis revealed that the VR model exhibited the highest effectiveness, displaying a strong correlation between actual and estimated outcomes. The boosting, bagging, and VR models achieved impressive R2-scores in the range of 86.69%–92.43%, with MAE ranging from 3.87 to 4.87, MSE from 21.74 to 38.37, RMSE from 4.66 to 4.87, and RRMSE between 8% and 11%. Particularly, the VR model outperformed all other models with the highest R2-score (92.43%) and the lowest error rate. The developed models demonstrated excellent generalization and prediction capabilities, providing valuable tools for practitioners, researchers, and designers to efficiently evaluate the CS of concrete. By mitigating environmental vulnerabilities and associated impacts, this research can significantly contribute to enhancing the quality and sustainability of concrete construction practices.

SMALL AREA ESTIMATION WITH HIERARCHICAL BAYES FOR CROSS-SECTIONAL AND TIME SERIES SKEWED DATA

Small Area Estimation (SAE) is a method based on modeling for estimating small area parameters, that applies Linear Mixed Model (LMM) as its basic. It is conventionally solved with Empirical Best Linear Unbiased Prediction (EBLUP). The main requirement for LMM to produce high precision estimates is normally distributed. The observation unit is food crop farmer households from Sulawesi Tenggara Province to estimate food and non-food per capita expenditure at the district/city level using SAE that has been positively skewed. Applying EBLUP for positively skewed data will result less accurate estimates. Meanwhile, transformation will be potentially result biased estimates. Therefore, the problem of skewed data and small area level in this research was completed by Hierarchical Bayes (HB) on combination cross-sectional and time series under skew-normal distribution assumption. The results obtained were skew-normal SAE HB model was significantly reducing Relative Root Mean Squared Error (RRMSE) than the direct estimation. It indicates that SAE modeling is able to provide a shrinkage effect on the direct estimation results. But, there is slightly different interpretating between direct estimation and skew-normal SAE HB. It is possible because the modeling used assumption that the autocorrelation coefficient is equal to 1 or known as the random walk effect. However, in reality, Susenas is not a panel data, so unit of observation for each time period may be different. Therefore, further research should be compared it with the skew-normal or another skewed distribution that assumes the autocorrelation coefficient is unknown and should be estimated in the model.

Evaluating Oil Price Forecasts: A Meta-analysis

Oil price forecasts have traditionally attracted the interest of both the empirical literature and policy makers, although research efforts have been intensified in the last 15 years. The present study investigates the forecasting characteristics that have the greatest impact on the accuracy level of such forecasts. To achieve this, we employ a meta-analysis approach of more than 6,000 observations of relative root mean squared errors (RRMSEs) which are pooled within a Bayesian Model Averaging (BMA) method. The findings indicate that forecasting frameworks such as MIDAS and combined forecasts tend to report significantly lower forecast errors. In addition, the choice of the oil price benchmark is an important factor, with the Brent price to offer lower forecast errors. Furthermore, the short-run horizons tend to produce more accurate forecasts and the same holds for the real, instead of the nominal oil prices. A number of robustness tests confirms the validity of these results. Overall, the findings of this study serve as a guide for future oil price forecasting exercises.

Enhanced exponential ratio-cum-ratio estimator in ranked set sampling using transformed auxiliary information

Estimation of population mean is a determined subject issue in sampling surveys and many efforts have been paid by various researchers to enhance the precision of the estimates by utilizing the correlated auxiliary information. In connection with this, we suggest an improved exponential ratio-cum-ratio estimator using transformed auxiliary variables under ranked set sampling scheme. Theoretical comparison between estimators is made in terms of mean square errors (MSEs), percentage relative efficiencies (PREs), and percentage relative root mean squared error (PRRMSE). The numerical expression for the bias and MSE of the suggested estimator is derived up to first order of approximation. Based on the results of actual data sets and a simulation study, it is found that the suggested estimator perform well as compared to its existing counterparts.

Influence of Iodine Merz–Singh–Kollman Radius on the Calculated Charges and Hydration Free Energies of Iodinated Molecules

Empirical force field methods typically rely on point charges to describe the electrostatic interactions, which is problematic when anisotropy needs to be considered, as in the case of the electrostatic potential of covalently bound halogens that possess a positive site, termed [Formula: see text]-hole, surrounded by a large negative belt. To address this, an off-center point charge (extra point, EP) is usually placed at a given distance from the halogen to emulate the [Formula: see text]-hole and commonly used implementations are based on the restrained electrostatic potential (RESP) procedure to fit atomic charges, being one of the most used charge models. In this context, no specific Merz–Singh–Kollman (MK) radius for iodine is available in the literature, which is an essential parameter in the RESP fitting procedure. In this work, we explored the impact of the iodine MK radius on the obtained RESP charges for a set of 12 iodinated molecules. We verified that the relative root mean square (RRMS) values obtained with and without an EP kept decreasing with increasing radii for most compounds, thus impairing optimization using such a procedure. Nevertheless, the use of an iodine MK radius lower than 2 Å is not advisable since the RRMS kept decreasing considerably until this value was reached. Moreover, the performance of three iodine MK radii was studied with the estimation of the free energy of hydration ([Formula: see text] values using alchemical free energy calculations, which are particularly sensitive to the charges used. Despite the usage of different radii not leading to remarkable differences, our results indicate that using a value of 2.70 Å leads to lower mean absolute errors (MAE) and root mean squared error (RMSE) values when comparing the calculated with the experimental [Formula: see text] values.

Price forecasting through neural networks for crude oil, heating oil, and natural gas

Building price projections of various energy commodities has long been an important endeavor for a wide range of participants in the energy market. We study the forecast problem in this paper by concentrating on four significant energy commodities. Using nonlinear autoregressive neural network models, we investigate the daily prices of WTI and Brent crude oil as well as the monthly prices of Henry Hub natural gas and New York Harbor No. 2 heating oil. We investigate prediction performance resulting from various model configurations, including training techniques, hidden neurons, delays, and data segmentation. Based on the investigation, relatively straightforward models are built that yield quite accurate and reliable performance. Specifically, performance in terms of relative root mean square errors is 1.96%/1.81%/9.75%/21.76%, 1.96%/1.80%/8.76%/14.41%, and 1.87%/1.78%/9.10%/16.97% for model training, validation, and testing, respectively, and the overall relative root mean square error is 1.95%/1.80%/9.51%/20.35% for the whole sample for WTI crude oil/Brent crude oil/New York Harbor No. 2 heating oil/Henry Hub natural gas. The outcomes of this projection might be used in technical analysis or integrated with other fundamental forecasts for policy analysis.

Bayesian Vancomycin Model Selection for Therapeutic Drug Monitoring in Neonates.

Pharmacokinetic models can inform drug dosing of vancomycin in neonates to optimize therapy. However, the model selected needs to describe the intended population to provide appropriate dose recommendations. Our study aims to identify the population pharmacokinetic (PopPK) model(s) with the best performance to predict vancomycin exposure in neonates in our hospital. Relevant published PopPK models for vancomycin in neonates were selected based on demographics and vancomycin dosing strategy. The predictive performance of the models was evaluated in Tucuxi using a local cohort of 69 neonates. Mean absolute error (MAE), relative bias (rBias) and relative root mean square error (rRMSE) were used to quantify the accuracy and precision of the predictive performance of each model for three different approaches: a priori, a posteriori, and Bayesian forecasting for the next course of therapy based on the previous course predictions. A PopPK model was considered clinically acceptable if rBias was between ± 20 and 95% confidence intervals included zero. A total of 25 PopPK models were identified and nine were considered suitable for further evaluation. The model of De Cock et al. 2014 was the only clinically acceptable model based on a priori [MAE 0.35 mg/L, rBias 0.8 % (95% confidence interval (CI) -7.5, 9.1%), and rRMSE 8.9%], a posteriori [MAE 0.037 mg/L, rBias -0.23% (95% CI -1.3, 0.88%), and rRMSE 6.02%] and Bayesian forecasting for the next courses [MAE 0.89 mg/L, rBias 5.45% (95% CI -8.2, 19.1%), and rRMSE 38.3%) approaches. The De Cock model was selected based on a comprehensive approach of model selection to individualize vancomycin dosing in our neonates.

Calibration Estimation of Population Proportion in Probability Proportional to Size Sampling in the Presence of Non-Response

Objectives: In this article, we addressed the problem of estimation of the finite population proportion under the Probability Proportional to Size (PPS) sampling technique, when the complete information is unavailable due to the presence of non-response. We developed calibrated estimators of the population proportion under PPS sampling in the presence of nonresponse based on the availability of auxiliary information. Methods: The expressions for the mean squared errors of the suggested estimators were developed to the first order of approximation. The developed estimators of the population proportion are compared with the design-based Horvitz-Thompson estimator and Horvitz-Thompson type calibration estimator which were obtained on the complete response units along with the design-based Hansen and Hurwitz type estimator in the presence of non-response. A Simulation study has also been conducted to support the performance of the developed estimators of population proportion with the help of two real datasets, by computing Percentage Absolute Relative Bias (%ARB) and Percentage Relative Root Mean Squared Error (%RRMSE) using R software. Findings: The simulation study supported the performance of the developed estimators of the finite population proportion based on %ARB and %RRMSE. The proposed calibration estimators of population proportion are more efficient than the other considered estimators in the presence of non-response. Novelty: The proposed new calibrated estimators have practical implications in the estimation of finite population proportions. Keywords: Auxiliary information, Calibration Approach, Non­response, Population proportion, PPS sampling

Can Machine Learning Predict Running Kinematics Based on Upper Trunk GPS-Based IMU Acceleration? A Novel Method of Conducting Biomechanical Analysis in the Field Using Artificial Neural Networks

This study aimed to investigate whether running kinematics can be accurately estimated through an artificial neural network (ANN) model containing GPS-based accelerometer variables and anthropometric data. Thirteen male participants with extensive running experience completed treadmill running trials at several speeds. Participants wore a GPS device containing a triaxial accelerometer, and running kinematics were captured by an 18-camera motion capture system for each trial. Multiple multilayer perceptron neural network models were constructed to estimate participants’ 3D running kinematics. The models consisted of the following input variables: 3D peak accelerometer acceleration during foot stance (g), stance time (s), running speed (km/h), participant height (cm), leg length (cm), and mass (kg). Pearson’s correlation coefficient (r), root mean squared error (RMSE), and relative root mean squared error (rRMSE) showed that ANN models provide accurate estimations of joint/segment angles (mean rRMSE = 13.0 ± 4.3%) and peak segment velocities (mean rRMSE = 22.1 ± 14.7%) at key gait phases across foot stance. The highest accuracies were achieved for flexion/extension angles of the thorax, pelvis, and hip, and peak thigh flexion/extension and vertical velocities (rRMSE < 10%). The current findings offer sports science and medical practitioners working with this data a method of conducting field-based analyses of running kinematics using a single IMU.

Prediction of Energy Consumption in a Coal-Fired Boiler Based on MIV-ISAO-LSSVM

Aiming at the problem that the energy consumption of the boiler system varies greatly under the flexible peaking requirements of coal-fired units, an energy consumption prediction model for the boiler system is established based on a Least-Squares Support Vector Machine (LSSVM). First, the Mean Impact Value (MIV) algorithm is used to simplify the input characteristics of the model and determine the key operating parameters that affect energy consumption. Secondly, the Snow Ablation Optimizer (SAO) with tent map, adaptive t-distribution, and the opposites learning mechanism is introduced to determine the parameters in the prediction model. On this basis, based on the operation data of an ultra-supercritical coal-fired unit in Xinjiang, China, the boiler energy consumption dataset under variable load is established based on the theory of fuel specific consumption. The proposed prediction model is used to predict and analyze the boiler energy consumption, and a comparison is made with other common prediction methods. The results show that compared with the LSSVM, BP, and ELM prediction models, the average Relative Root Mean Squared Errors (aRRMSE) of the LSSVM model using ISAO are reduced by 2.13%, 18.12%, and 40.3%, respectively. The prediction model established in this paper has good accuracy. It can predict the energy consumption distribution of the boiler system of the ultra-supercritical coal-fired unit under variable load more accurately.

Assessment of Pilots’ Cognitive Competency Using Situation Awareness Recognition Model Based on Visual Characteristics

Visual characteristics have the potential to assess the navigational proficiency of ship pilots. A precise assessment of ship piloting competence is imperative to mitigate human errors in piloting. An exhaustive examination of cognitive capabilities plays a pivotal role in developing an enhanced and refined system for classifying, selecting, and training ship piloting proficiency. Insufficiency in situation awareness (SA), denoting the cognitive underpinning of hazardous behaviors among pilots, may lead to subpar performance in ship pilotage when faced with adverse conditions. To address this issue, we propose an SA recognition model based on the random forest-support vector machine (RF-SVM) algorithm, which utilizes wearable eye-tracking technology to detect pilots’ at-risk cognitive state, specifically low-SA levels. We rectify the relative error (RE) and root mean square error (RMSE) and employ principal component analysis (PCA) to enhance the RF algorithm, optimizing the combination of salient features in greater depth. Through the utilization of these feature combinations, we construct a SVM algorithm using the most suitable variables for SA recognition. Our proposed RF-SVM algorithm is compared to RF or SVM alone, and it achieves the highest accuracy in recognizing at-risk cognitive states under poor visibility conditions (an improvement of 86.79% to 93.43% in accuracy). Taken collectively, the present findings offer vital technical support for developing a technique-based intelligent system for adaptively evaluating the cognitive accomplishment of pilots. Furthermore, they establish the groundwork and framework for the surveillance of cognitive processes and capabilities in marine pilotage operations within China.

An Ensemble-Based Model for Specific Humidity Retrieval from Landsat-8 Satellite Data for South Korea

Specific humidity (SH) which means the amount of water vapor in 1 kg of air, is used as an indicator of energy exchange between the atmosphere and the Earth’s surface. SH is typically computed using microwave satellites. However, the spatial resolution of data for microwave satellite is too low. To overcome this disadvantage, we introduced new methods that applied data collected by the Landsat-8 satellite with high spatial resolution (30 m), a meteorological model, and observation data for South Korea in 2016–2017 to 4 machine learning techniques to develop an optimized technique for computing SH. Among the 4 machine learning techniques, the random forest-based method had the highest accuracy, with a coefficient of determination (R) of 0.98, Root Mean Square Error (RMSE) of 0.001, bias of 0, and Relative Root Mean Square Error (RRMSE) of 11.16%. We applied this model to compute land surface SH using data from 2018 to 2019 and found that it had high accuracy (R = 0.927, RMSE = 0.002, bias = 0, RRMSE = 28.35%). Although the data used in this study were limited, the model was able to accurately represent a small region based on an ensemble of satellite and model data, demonstrating its potential to address important issues related to SH measurements from satellites.

Advancing sustainable air quality through calibration of miniature air quality monitors with SRA-SVR combined model

Effective calibration of miniature air quality monitor measurements is an important task to ensure accurate measurements and guarantee sustainable air quality. The aim of this study is to calibrate the measurement data of miniature air quality monitors using Stepwise Regression Analysis and Support Vector Regression (SRA-SVR) combined model. Firstly, a stepwise regression analysis model is used to find a linear relationship between the measured data from the miniature air quality monitor and the air pollutant concentration. Secondly, support vector regression is used to extract the non-linear relationships which affect the pollutant concentrations hidden in the residuals of the stepwise regression analysis model. Finally, the residual calibration values of the SVR model outputs are added to the SRA model outputs to obtain the final outputs of the SRA-SVR combined model for the pollutants. Mean absolute error, relative mean absolute percent error and root mean square error are used to compare the effectiveness of the SRA-SVR combined model and some other commonly used statistical models for the calibration of miniature air quality monitors. The results show that the SRA-SVR combination model performs optimally on both the training and test sets, regardless of which pollutant and which indicator. The SRA-SVR combined model not only has the advantages of the SRA model’s strong interpretability and the SVR model’s high accuracy, but also has higher accuracy than the single model. By using this model to calibrate the measurements of the miniature air quality monitor, its accuracy can be improved by 61.33%–87.43%.

Comparative analysis of bias correction methods for IMERG V06 precipitation products: case study in Guangxi, China

ABSTRACT This study investigates the effectiveness of three bias correction methods: linear scaling (LS), local intensity scaling (LOCI), and quantile mapping (QM) in correcting the bias of Integrated Multi-SatellitE Retrieval for Global Precipitation Measurement (IMERG) V06 products (IMERG-Early run (IMERG-E), IMERG-Late run (IMERG-L) and IMERG-Final run (IMERG-F)) at different spatial and temporal scales in Guangxi, China. The results reveal that the LS and LOCI are effective in reducing the relative bias (RB) and root mean square error (RMSE) of IMERG products at the regional and monthly scales, while the QM tends to increase the overestimation or underestimation of precipitation. The QM outperforms the other two methods in improving the correlation coefficient (CC) of IMERG products at the monthly and daily scales, especially for stations with low correlation before correction. None of the methods can effectively correct the extreme values of IMERG products at a daily scale, and the correction efficacy of the three methods is limited for IMERG-F, which has a high initial accuracy. The QM has an advantage in enhancing the correlation between IMERG and gauge data. LS and LOCI have great potential in correcting the bias of IMERG near-real-time products.

Phytotoxicological assessment of AMD affected river waters in “Sredna Gora”, Bulgaria to Lepidium sativum L.

The ongoing pollution of water bodies from decommissioned mines and mining waste facilities in Bulgaria is a serious problem, leading to a contamination of river waters with heavy metals. Although the chemical analyses of acid mine drainage (AMD) affected waters provide information about their toxicity, this information is not enough to evaluate the potential impacts on the biota. For this, different biological responses are used to assess the toxicity of the polluted waters.The goal of the research was to determine the phytotoxicity (using Lepidium sativum) of five affected river waters. The lower reaches of the rivers flow though agricultural areas and are often used for irrigation by local farmers, increasing the chance of produce contamination.The effect of the working solutions on seed germination and primary root growth of L. sativum was assessed by the following indices: Relative seed germination (RSG), relative root growth (RRG) and germination index (GI, the product of RSG and RRG).The obtained results have shown the connection between germination/root growth inhibition and concentrations of heavy metals in working solutions, but there is no connection between germination/root growth inhibition and pH levels.

Theoretical approach for the equilibrium scour depth underneath a partially submerged bridge deck

The destructive implication of pressure-flow scour during flood events is a critical issue for researchers throughout the world. The current paper presents two models to estimate the pressure-flow scour depth underneath a partially submerged bridge deck in the equilibrium phase based on the jet flow theory. An estimate of the submergence distance of the jet flow under the bridge deck is the base point of the first model. The second model uses the phenomenological theory of turbulence where the tangential component of jet velocity is scaled to the velocity of the eddy formed under the bridge deck. This theory has already been used by researchers to estimate the scour depth of the jet flow. Dimensionless parameters are constructed in the theoretical framework of both models. The angle and velocity of the combined jet under the bridge deck and the effective depth underneath the bridge deck have been obtained using the relations presented in the previous research. The application of the presented models is limited to cases where the relative opening height of the bridge deck is greater than 0.25. The derived equations of the current study are calibrated based on the data with sufficient time durations. The results show that both models predict fairly well the maximum pressure-flow scour depth. The values of Nash–Sutcliffe efficiency and relative root mean square errors of the second model predictions are more appropriate, compared to the estimates of the first model and estimates obtained by equations presented in the previous studies.

Two new methods for the estimation and interpretation of the range of feasible profiles in multivariate curve resolution and their implications to analytical chemistry

AbstractTwo new models have been recently introduced for studying the remaining rotational ambiguity in the bilinear decomposition of matrix data. One of the models is N‐BANDS, which yields two extreme profiles per sample component, corresponding to maximum or minimum signal contribution function or relative component area under its concentration profile. It is highly useful for computing the relative root mean square error due to rotational ambiguity in estimated analyte concentrations (RMSERA), which numerically quantifies the impact of the phenomenon in terms of prediction uncertainty. Since N‐BANDS successfully consider the presence of instrumental noise in the data, it is extremely useful for the analysis of real data sets. The other model is SW‐N‐BANDS, which is similar to N‐BANDS, but is applied in a sensor wise manner, that is, computing the maximum and minimum intensity value at each sensor. It provides the boundaries of the full set of feasible profiles, and helps to better understand the behavior of a given component under the application of several constraints. Both models are described in light of both simulations and experimental data, illustrating their main characteristics of importance to analytical chemistry studies.