Prediction Residual Error Sum Of Squares Research Articles

Growth and carbon sequestration in biomass of Cordia alliodora in Andean agroforestry systems with coffee

Timber production and carbon sequestration in trees in agroforestry systems (AFS) are key to productivity and climate change mitigation. There are no studies about dynamics of growth and carbon sequestration of Cordia alliodora during all plantation cycle. The objective of this study was to develop models for diametric growth and carbon sequestration in aboveground biomass of C. alliodora in AFS with coffee in Líbano, Tolima, Colombia. Nonlinear models of growth and carbon sequestration in aboveground biomass of C. alliodora in AFS with coffee were developed. A total of 90 trees, ranging in age from 1 to 19 years, were randomly selected in farms and measured (diameter at breast height -dbh- and total height -h) in AFS with a basal area of C. alliodora between 0.22 and 17.8 m2/ha. Timber volume and aboveground biomass were estimated with allometric models, while carbon was estimated by multiplying aboveground biomass by 0.47. The best-fit models were selected according to the coefficient of determination (R2), Akaike's information criterion (AIC), predicted residual error sum of squares (PRESS), biological logic and a residual analysis. The highest growth rate of this species was reached at 4–6 years for dbh and h (3.6 cm/year and 2.9 m/year, respectively) and at 20 years for timber and carbon (0.60 m3/tree/year and 88.9 kg C/tree/year, respectively). In 20 years, a C. alliodora tree would store 1.1 Mg C and a AFS with 60 trees/ha would sequester between 260 Mg CO2/ha in aboveground biomass. The results show that C. alliodora trees could be maintained in the field for more than 20 years, thus increasing the volume per individual and carbon sequestration for a longer time. This demonstrates the importance of this species mainly when timber production and carbon sequestration are priorities for its profitability.

Modeling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion

Fires in buildings are significant public safety hazards and can result in fatalities and substantial financial losses. Studies have shown that the socioeconomic makeup of a region can impact the occurrence of building fires. However, existing models based on the classical stepwise regression procedure have limitations. This paper proposes a more accurate predictive model of building fire rates using a set of socioeconomic variables. To improve the model’s forecasting ability, a backward elimination by robust final predictor error (RFPE) criterion is introduced. The proposed approach is applied to census and fire incident data from the South East Queensland region of Australia. A cross-validation procedure is used to assess the model’s accuracy, and comparative analyses are conducted using other elimination criteria such as p-value, Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and predicted residual error sum of squares (PRESS). The results demonstrate that the RFPE criterion is a more accurate predictive model based on several goodness-of-fit measures. Overall, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modeling of building fires.

Prediction Equation for Physical Activity Energy Expenditure in 11-13-Year-Old Sri Lankan Children.

This study aimed to develop a regression equation to predict physical activity energy expenditure (PAEE) using accelerometry. Children aged 11-13 years were recruited and randomly assigned to validation (n = 54) and cross-validation (n = 25) groups. The doubly labelled water (DLW) technique was used to assess energy expenditure and accelerometers were worn by participants across the same period. A preliminary equation was developed using stepwise multiple regression analysis with sex, height, weight, body mass index, fat-free mass, fat mass and counts per minute (CPM) as independent variables. Goodness-of-fit statistics were used to select the best prediction variables. The PRESS (predicted residual error sum of squares) statistical method was used to validate the final prediction equation. The preliminary equation was cross-validated on an independent group and no significant (p > 0.05) difference was observed in the PAEE estimated from the two methods. Independent variables of the final prediction equation (PAEE = [0.001CPM] - 0.112) accounted for 70.6% of the variance. The new equation developed to predict PAEE from accelerometry was found to be valid for use in Sri Lankan children.

Multicompartment body composition analysis in older adults: a cross-sectional study

BackgroundDuring aging, changes occur in the proportions of muscle, fat, and bone. Body composition (BC) alterations have a great impact on health, quality of life, and functional capacity. Several equations to predict BC using anthropometric measurements have been developed from a bi-compartmental (2-C) approach that determines only fat mass (FM) and fat-free mass (FFM). However, these models have several limitations, when considering constant density, progressive bone demineralization, and changes in the hydration of the FFM, as typical changes during senescence. Thus, the main purpose of this study was to propose and validate a new multi-compartmental anthropometric model to predict fat, bone, and musculature components in older adults of both sexes.MethodsThis cross-sectional study included 100 older adults of both sexes. To determine the dependent variables (fat mass [FM], bone mineral content [BMC], and appendicular lean soft tissue [ALST]) whole total and regional dual-energy X-rayabsorptiometry (DXA) body scans were performed. Twenty-nine anthropometric measures and sex were appointed as independent variables. Models were developed through multivariate linear regression. Finally, the predicted residual error sum of squares (PRESS) statistic was used to measure the effectiveness of the predicted value for each dependent variable.ResultsAn equation was developed to simultaneously predict FM, BMC, and ALST from only four variables: weight, half-arm span (HAS), triceps skinfold (TriSK), and sex. This model showed high coefficients of determination and low estimation errors (FM: R2adj: 0.83 and SEE: 3.16; BMC: R2adj: 0.61 and SEE: 0.30; ALST: R2adj: 0.85 and SEE: 1.65).ConclusionThe equations provide a reliable, practical, and low-cost instrument to monitor changes in body components during the aging process. The internal cross-validation method PRESS presented sufficient reliability in the model as an inexpensive alternative for clinical field use.

Forbidden Knowledge and Specialized Training: A Versatile Solution for the Two Main Sources of Overfitting in Linear Regression

Overfitting in linear regression is broken down into two main causes. First, the formula for the estimator includes “forbidden knowledge” about training observations’ residuals, and it loses this advantage when deployed out-of-sample. Second, the estimator has “specialized training” that makes it particularly capable of explaining movements in the predictors that are idiosyncratic to the training sample. An out-of-sample counterpart is introduced to the popular “leverage” measure of training observations’ importance. A new method is proposed to forecast out-of-sample fit at the time of deployment, when the values for the predictors are known but the true outcome variable is not. In Monte Carlo simulations and in an empirical application using MRI brain scans, the proposed estimator performs comparably to Predicted Residual Error Sum of Squares (PRESS) for the average out-of-sample case and unlike PRESS, also performs consistently across different test samples, even those that differ substantially from the training set.

Reliable Cognitive Decline in Late-Life Major Depression.

Major depression in older adults increases the statistical likelihood of dementia. It is challenging to translate statistical evidence of cognitive decline at the group level into knowledge of individual cognitive outcomes. The objective of the current study is to investigate 2-year reliable cognitive change in late-life depression (LLD), which will enhance understanding of cognitive changes in LLD and provide a means to assess individual change. In a sample of non-depressed cognitively normal older adults or NDCN (n = 113), we used linear regression to predict tests of global cognition, processing speed-executive functioning, and memory administered 1 and 2 years later. Stepwise regression was used to select covariates among demographics and raw test scores (either baseline or year 1) and we cross-validated the final models using the predicted residual error sum of squares (PRESS). We then derived a z-change score from the difference between actual and predicted follow-up scores and investigated the proportion of LLD patients (n = 199) and NDCN adults who experienced reliable "decline" (a z-score < -1.645), "stability" (z-scores between + - 1.645), and "improvement" (z scores > +1.645). A greater proportion LLD compared with NDCN experienced cognitive decline in processing speed/executive functioning and global cognition over 2 years. When compared to NDCN, a greater proportion of LLD also significantly improved on one test of processing speed over 2 years. Older adults with LLD are at risk of meaningful cognitive decline over a relatively short period, particularly in the domain of executive functioning and processing speed. This study provides a series of reliable change equations for common neuropsychological tests that can be applied clinically.

Bone Mineral Content Estimation in People Living with HIV: Prediction and Validation of Sex-Specific Anthropometric Models.

People living with HIV (PWH) experience an accelerated reduction in bone mineral content (BMC), and a high risk of osteopenia and osteoporosis. Anthropometry is an accurate and low-cost method that can be used to monitor changes in body composition in PWH. To date, no studies have used anthropometry to estimate BMC in PWH. To propose and validate sex-specific anthropometric models to predict BMC in PWH. This cross-sectional study enrolled 104 PWH (64 males) aged >18 years at a local university hospital. BMC was measured using dual energy X-ray absorptiometry (DXA). Anthropometric measures were collected. We used linear regression analysis to generate the models. Cross-validations were conducted using the “leave one out”, from the predicted residual error sum of squares (PRESS) method. Bland–Altman plots were used to explore distributions of errors. We proposed models with high coefficient of determination and reduced standard error of estimate for males (r2 = 0.70; SEE = 199.97 g; Q2PRESS = 0.67; SEEPRESS = 208.65 g) and females (r2 = 0.65; SEE = 220.96 g; Q2PRESS = 0.62; SEEPRESS = 221.90 g). Our anthropometric predictive models for BMC are valid, practical, and a low-cost alternative to monitoring bone health in PWH.

Higher cerebrospinal fluid biomarkers of neuronal injury in HIV-associated neurocognitive impairment

We evaluated whether biomarkers of age-related neuronal injury and amyloid metabolism are associated with neurocognitive impairment (NCI) in people with and without HIV (PWH, PWoH). This was a cross-sectional study of virally suppressed PWH and PWoH. NCI was assessed using a validated test battery; global deficit scores (GDS) quantified overall performance. Biomarkers in cerebrospinal fluid (CSF) were quantified by immunoassay: neurofilament light (NFL), total Tau (tTau), phosphorylated Tau 181 (pTau181), amyloid beta (Aβ)42, and Aβ40. Factor analysis was used to reduce biomarker dimensionality. Participants were 256 virally suppressed PWH and 42 PWoH, 20.2% female, 17.1% Black, 7.1% Hispanic, 60.2% non-Hispanic White, and 15.6% other race/ethnicities, mean (SD) age 56.7 (6.45) years. Among PWH, the best regression model for CSF showed that higher tTau (β = 0.723, p = 3.79e-5) together with lower pTau181 (β = −0.510, p = 0.0236) best-predicted poor neurocognitive performance. In univariable analysis, only higher tTau was significantly correlated with poor neurocognitive performance (tTau r = 0.214, p = 0.0006; pTau181 r = 0.00248, p = 0.969). Among PWoH, no CSF biomarkers were significantly associated with worse NCI. Predicted residual error sum of squares (PRESS) analysis showed no evidence of overfitting. Poorer neurocognitive performance in aging PWH was associated with higher CSF tTau, a marker of age-related neuronal injury, but not with biomarkers of amyloid metabolism. The findings suggest that HIV might interact with age-related neurodegeneration to contribute to cognitive decline in PWH.

Smart spectrophotometric approach for the simultaneous determination of anti-asthma drugs in inhalation spray using least squares support vector machine and net analyte signal

In this study, easy, fast, inexpensive, and accurate spectrophotometric technique with the help of least square support vector machine (LS-SVM) and net analyte signal (NAS) as chemometrics approaches were developed for the simultaneous determination of salmeterol (SMT) and fluticasone (FLU) in mixtures and anti-asthma inhalation spray. The related parameters of the LS-SVM model (sigma and gamma) were optimized and root mean square error (RMSE) were obtained 0.3075 and 0.4553 for SMT and FLU, respectively. Limit of detection (LOD) and limit of quantification (LOQ) dedicated to the calibration plot of the NAS method were 1.434, 1.089 μg mL−1 and 4.584, 3.399 μg mL−1 for SMT and FLU, respectively. The optimum number of components was evaluated by the minimum predicted residual error sum of squares (PRESS) value, which achieved 1.7738 and 1.0636 for SMT and FLU, respectively. Also, the mean recovery value of test set related to the 25 binary mixtures was 98.45% and 98.98% for SMT and FLU, respectively. Analysis of variance (ANOVA) test represented no significant difference significant between high-performance liquid chromatography (HPLC) (reference technique) and chemometrics methods. The LS-SVM and NAS can be a good alternative to expensive methods such as chromatography in the quality control section of different industries.

Raman Spectroscopy as Molybdenum and Tungsten Content Analysis Tool for Mesoporous Silica and Beta Zeolite Catalysts.

Raman spectroscopy was used for the quantitative determination of Mo and W in Mo- and W-supported mesoporous silica (Mo/SBA-15 and W/SBA-15, respectively) and Mo-supported beta zeolite (Mo-BEA). Three Raman quantitative models were developed and optimized for the metal contents of Mo/SBA-15, W/SBA-15, and Mo/BEA. Subsequently, the models were characterized using the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), correlation coefficient, and predicted residual error sum of squares (PRESS) diagnostic function. The calibration range of the models were in the range of approximately 2–40 wt% for the SBA-15 support and 1–21 wt% for the BEA support because the BEA support presented lower Mo absorption than the SBA-15 support. The RMSEC, RMSECV, and RMSEP values were below 1.80% for all developed models. The highest and lowest correlation coefficients corresponded to the W/SBA-15 (0.9984) and Mo/BEA (0.9777) models, respectively. The change in catalyst support affected the mentioned chemometric parameters (Mo/SBA-15 vs. Mo/BEA). Subsequently, Raman spectroscopy combined with the temperature control stage was used to study the calcination of Mo/BEA, Mo/SBA-15, and W/SBA-15 using three-dimensional diagrams, in which the changes in catalyst structure were analyzed as functions of the temperature and time. Raman spectroscopy was determined to be a suitable analytical tool for the quantitative analysis of the metal contents of the catalyst and optimization of the calcination process. Therefore, Raman spectroscopy can be used during catalyst manufacturing.

Framework of model selection criteria approximated genetic programming for optimization function for renewable energy systems

Abstract For the realization of complex renewable energy systems (such as nano-fluids based direct absorption solar collector), an evolutionary system identification method such as genetic programming (GP) can be applied to develop mathematical models/functional relationships between the process parameters. The system complexity is attributed to interaction among the design variables influencing the outputs. There are also uncertainties in the system due to random and unknown variations in the design and response variables. GP suffers from the higher complexity structure of its solutions and non-optimal convergence, which leads to poor fitness values. Therefore, to address these uncertainties and problems, the framework based on the model selection criteria approximated genetic programming (MSC-GP) is proposed for the formulation of geometry design based thermal efficiency and entropy generation optimization function for direct absorption solar collector (DASC) system. In this proposed method, the four mathematical model selection criteria are used as an approximation for objective functions in GP framework for the evaluation of fitting degree and structure of the model. The results based on statistical measures (best fitness, mean fitness, standard deviation of fitness, number of nodes) show that models obtained from the mathematical selection criteria, Predicted Residual error sum of squares (PRESS), have performed the best. Based on Pareto front analysis of PRESS function, it is found that the best objective values and the number of nodes of models (complexity) follows more or less gradually slow increasing trend which is a good symbolic desirable sign of minimal increase of complexity of model with a decrease in objective values as the values of generation increases. The results of the sensitivity analysis show that the main factor affecting the efficiency of DASC is its geometry of the structure. 3-D interaction analysis shows that increasing the thickness, length and reducing the width of the collector can make the system maintain its higher thermal efficiency and a smaller entropy generation, which is useful for the optimized operation of DASC. Non-dominated sorting genetic algorithm-II (NSGA-II) is applied in the acquisition of the optimal geometric settings of DASC system based on the selected models. The optimal settings achieved is 5 cm in length, 5 cm in width, and 2 cm in thickness. Systems when operated using these settings results in a satisfactory performance with 77.8117% in thermal efficiency and 6.0004E+3 in entropy generation).

Development of Population-Specific Prediction Equations for Bioelectrical Impedance Analyses in Vietnamese Children

Abstract Objectives Bioelectrical impedance analysis (BIA) is an accurate, inexpensive and field-friendly methods to assess body composition, but there is limited information on its use and validity for children in low-middle income countries. Our aim was to develop and validate population-specific prediction equations for estimating total fat mass (FM) and fat free-mass (FFM) in Vietnamese children using reactance and resistance from BIA and anthropometric variables. Methods We conducted a cross-sectional survey of 120 children in Thai Nguyen, Vietnam. Body composition was measured using dual energy x-ray absorptiometry (DXA), BIA and anthropometry (height, weight, triceps and subscapular skinfolds, and waist, hip, and mid upper arm circumferences). To develop prediction equations, we split the sample into development (70%) and validation datasets (30%). The model performance was evaluated using PRESS (Predicted residual error sum of squares), RMSE (Root mean squared error), MEA (Mean absolute error) and R,2. Results %MCEPASTEBIN% The development of prediction equations for total FM resulted in seven models. We identified a top performing model with the least number of parameters (age, sex, weight and resistance index), low RMSE (178 and 164 for FM and FFM, respectively), low MAE (136 and 141 for FM and FFM, respectively), high R2 (&amp;gt;.90), and the least difference between predicted and actual values (FM 25 0.03 g and FFM 8 0.01 g). Conclusions We developed valid and highly predictive equations to estimate FM and FFM in Vietnamese children using BIA. These findings have important implications for future research examining the risks associated with overweight and obesity in young children in resource-poor settings. Funding Sources Rollins School of Public Health (RSPH) Dean's Pilot and Innovation Grant, the New Jersey Institute for Food, Nutrition, and Health, and the Nestle Foundation.

Estimating canopy LAI and chlorophyll of tropical forest plantation (North India) using Sentinel-2 data

With the free and full access to images from Sentinel-2 satellite, the interest to use this data for quantitative retrieval of vegetation parameters is ever-increasing. LAI and chlorophyll are two key variables which are desired for studying productivity, nutrient and stress status of vegetation. Studies carried out on croplands using simulated Sentinel-2 MSI and parametric approach have identified vegetation indices (VIs) with high sensitivity to LAI and chlorophyll. To test how Sentinel-2 red-edge based VIs perform for retrieval of LAI and Chlorophyll of tropical mixed forest canopies, this study has been performed. The field measurements of LAI and chlorophyll content were recorded in a total of 28 ESUs (Elementary Sampling Units) in Bhakra range in the Tarai Central Forest Division, Uttarakhand (India). The in-situ measurements were statistically correlated with Sentinel-2VIs and strength of correlation was validated using Predicted Residual Error Sum of Squares (PRESS) statistic. Field LAI corrected for foliage clumpiness effect improved correlation of VIs with LAI. Among all VIs tested, Normalized Difference Index (NDI) offered highest positive correlation (R2 = 0.79, p < 0.05) with LAI while Red-Edge Chlorophyll Index (RECI) (R2 = 0.83, RMSE = 0.24 g/m2, p < 0.05) and Simple Ratio (SR) 740/705 (R2 = 0.79, RMSE = 0.27 g/m2, p < 0.05) were the most closely related to chlorophyll content. VIs with red-edge and NIR combinations offered best results.

How old are you now? A new ageing method for nonadults based on dental wear

AbstractThe main aim of this study is to present a novel method of nonadult (ca. 1–19 years) age‐at‐death estimation using the dental wear of deciduous, mixed deciduous‐permanent, and permanent dentitions, including the incisors, canines, premolars, and first and second molars. The stage‐based method is derived from degrees of dental wear in known‐age (n = 39) and estimated‐age (n = 11) nonadults containing 951 teeth from the predominately 19th century cemetery of Middenbeemster, The Netherlands. The need for such a method is warranted in cases where dental development and/or eruption cannot be assessed for age‐at‐death estimation. As well, by establishing a baseline for normal age‐related nonadult tooth wear, users may better document wear that could be due to extramasticatory behaviours. The regression analysis reveals a strong quadratic correlation—F(2, 47) = 555.1, p &lt; .001, R2 = .95, standard error of the estimate = 1.14, residual sum of squares (RSS) = 68.89, predicted residual error sum of squares (PRESS) = 77.67—between age and wear and multivariate adaptive regression splines (R2 = .95, generalised cross validation = 1.67, RSS = 67.68, PRESS = 89.34), which are used to develop an R‐package that users may employ to estimate age‐at‐death from dental wear. The accuracy of this method (78–98%) is evaluated using leave‐one‐out cross‐validation. Analyses of males versus females, deciduous versus permanent, upper versus lower, and anterior versus posterior teeth revealed no apparent reason to warrant separate methods for these groups of separated dentitions. This method fills a disciplinary gap in the understudied area of deciduous and nonadult dental wear and hopes to stimulate much future research. With the R‐package, we also provide the foundation and framework for the development of additional reference populations across different spatiotemporal contexts, to make the method more widely applicable.

Spectrophotometric and Principal Component Regression Chemometric Method for Simultaneous Determination of Hypertension Drug's in Commercial Pharmaceutical

In this study, the simultaneous determination of Lisinopril (LIS) and Hydrochlorothiazide (HCT) in drug samples by chemometric approaches using UV spectrophotometry has been reported as a simple alternative to using separate models for each component. Spectra of LIS and HCT were recorded at several concentrations within their ranges and were used to compute the calibration mixture between wavelengths 200 and 350 nm at an interval of 5 nm was used for data acquisition. Principal component regression was used for chemometric analysis of data and the parameters of the chemometric procedures were optimized. The analytical performances of this chemometric method was characterized by predicted residual error sum of squares (PRESS), standard error of prediction (SEP) and recoveries (%) and were compared with each other. A series of synthetic solution containing different concentrations of LIS and HCT were used to check the prediction ability of the principal component regression. This method was successfully applied to real samples, with no interference from excipients as indicated by recovery study results. The outcomes acquired in this examination firmly urge us to apply these strategies for a standard investigation and quality control of the two medications. Keywords : Lisinopril, Hydrochlorothiazide, Principal Component Regression DOI : 10.7176/JSTR/5-4-03

Methods for sandy land detection based on multispectral remote sensing data

Sandification is becoming a serious threat to the sustainability of human habitation. The potential of remote sensing in sandy land detection has been previously demonstrated, but transitional sandy land is difficult to detect because of vegetation cover. The aim of this study, therefore, was to propose methods for sandy land detection based on mixed pixel decomposition and soil particle composition to determine the effects of vegetation coverage and transitional sandy land, using Zhenglan Banner of China as the study area and GF-1 multispectral images as the main data. Results showed that the pixel purity index (PPI) is a viable indicator for pure endmember extraction for sandy land detection via remote sensing. A linear spectral unmixing (LSU) model was established to distinguish sandy land coverage from vegetation, alkaline land, etc. Results showed that without considering the vegetation proportion, when the endmember proportion of sandy land accounted for >50% of the total (except for the vegetation), a pixel would be detected as sandy land, its extraction accuracy was verified to be 86.42% by field data. The results derived from soil particle composition showed that silt was the best indicator for sandy land detection, and clay was secondary to it. Through Partial Least Squares Analysis (PLSA), the percentage of silt content was determined as the dependent variable; R676 and R774 were selected as independent variables to establish the inversion model according to the model effect weights and VIP value. The minimum Prediction Residual Error Sum of Squares (PRESS) was 0.824 tested by leave one out cross validation. The threshold of silt content was determined finally as 3.5%, namely, when the silt content was <3.5%, the pixel would be classified as sandy land, its extraction accuracy was 80.86% that was verified using the same field data.

A new model selection criterion for partial least squares regression

Choosing the right number of latent factors to be used in PLS regression (Partial Least Squares Regression) has been a matter of concern among users, academics and researchers. In this paper, we introduce a statistic to select the appropriate number of latent factors according to the model predictive ability. This method is based on the Predicted Residual Error Sum of Squares (PRESS) for PLS regression. Our mathematical development is based on matrix calculations obtained from the orthogonal vectors that compose the matrix of latent factors. Currently, the leave-one-out method is widely used for this, where one observation is left out and then the regression model is estimated. This technique is repeated as many times as the number of observations. The advantage of using the PRESS statistic for PLS regression (P-PLS), developed in this work, is to have the possibility of selecting the best predictive model straightforwardly. Additionally, the P-PLS can be used for analyzing the impact caused by the ith observation on the PLS regression vector of coefficients, as well as for detecting other kinds of data that affect the model.

Development of the Nondestructive Leaf Area Estimation Model for Valeriana (Valeriana jatamansi Jones)

ABSTRACTLeaf area (LA) is an important parameter related to plant growth and physiology. An allometric model was developed to estimate the LA of endangered medicinal plant Valeriana jatamansi using linear measurements such as leaf length (L) and width (W). LA and other leaf dimensions were measured using a laser leaf area meter. Leaves from seven accessions of valeriana were collected from the experimental site during 2015. Different regression models were developed between LA and other leaf components, viz. L, W, etc. The linear model having LW as an independent variable (y = 0.487 + 0.644 LW) provided the best estimation [coefficients of determination (R2) = 0.974, root mean square error (RMSE) = 2.222, coefficient of variation (CV) = 4.529]. Validation of the selected model showed a higher correlation between the actual leaf area (ALA) and the predicted leaf area (PLA) [R2 = 0.956, RMSE = 2.310, CV = 5.319, predicted residual error sum of squares (PRESS) = 1067.352].

Utility of novel body indices in predicting fat mass in elite athletes

ObjectiveRecently, new body indices, including body adiposity (BAI), a body shape (ABSI), and body roundness (BRI) indices have been developed to estimate adiposity. The aim of this study was to compare percent fat mass (%FM) with novel indices in an elite athlete population. MethodsUsing a cross-sectional design, %FM in 159 male and 50 female athletes using a four-component model was assessed. The %FM was compared with body mass index (BMI), BAI, ABSI, BRI, and with other %FM field methods (bioimpedance spectroscopy and skinfold prediction equation). These associations were determined using multilinear regression analysis, which resulted in predictive models of %FM in athletes. Cross-validation was performed using the prediction residual error sum of squares (PRESS) statistics method. ResultsAlthough higher associations than other indices were observed, BRI still presented low coefficients of determination (men: R2 = 0.36; women: R2 = 0.25) when comparing with other field methods (R2 range, 0.33–0.75). Using BAI as the independent variable, the R2 was 0.07 for men and 0.14 for women. ABSI did not result in a significant association with %FM in women (R2 = 0.05) while in men a significant association was found (R2 = 0.22). The BMI model resulted in a R2 = 0.20 for men and R2 = 0.22 for women. Waist circumference and the sum of skinfolds were the anthropometric variables with the highest association with adiposity. New alternatives were presented with higher coefficients of determination (PRESS R2 ranged from 0.47 to 0.71). ConclusionsThe newly developed body indices are limited in predicting %FM in elite athletes, particularly when compared with other commonly and readily available field methods like bioimpedance analysis or skinfold prediction models.

Chemometrics-Assisted UV Spectrophotometric Method for Determination of Metformin Hydrochloride and Glyburide in Pharmaceutical Tablets

This study aims to estimate simultaneously metformin hydrochloride (MET) and glyburide (GLY) in a multicomponent tablets dosage form by spectrophotometric method using chemometric approaches such as principal component regression (PCR) and partial least-squares regression (PLS). Because of highly overlapped in UV spectra and difference proportions of two active ingredients, the conventional univariate calibration methods was not allowed without previous separation. The linearity ranges used to construct the calibration matrix were selected in the ranges from 40.00 to 200.00 mg L-1for MET and from 1.00 to 10.00 mg L-1for GLY. The absorbances were measured in the wavelength range of 200-400 nm, using ethanol as solvent. The resulting UV spectra were subjected to PCR and PLS algorithms and the optimum numbers of principal components (PCs) were selected according to prediction residual error sum of squares (PRESS) values of leave-one out cross-validation. The number of PCs for MET and GLY were found to be 5, 3 by PCR and 5, 3 by PLS, respectively. A set of synthetic mixtures was employed to verify the models and the performance of the models were shown in the values of the root mean square error in prediction (RMSEP). RMSEP values of MET and GLY were 1.806, 0.256 for PCR and 1.802, 0.185 for PLS, respectively. The suitable calibration models were applied to the analysis of these compounds in pharmaceutical formulation.