Simple method for determination of cocaine and main metabolites in urine by CE coupled to MS

in this work, a simple method for the simultaneous determination of cocaine (COC) and five COC metabolites (benzoylecgonine, cocaethylene (CET), anhydroecgonine, anhydroecgonine methyl ester and ecgonine methyl ester) in human urine using CE coupled to MS via electrospray ionization (CE-ESI-MS) was developed and validated. Formic acid at 1 mol/L concentration was used as electrolyte whereas formic acid at 0.05 mol/L concentration in 1:1 methanol:water composed the coaxial sheath liquid at the ESI nozzle. The developed method presented good linearity in the dynamic range from 250 ng/mL to 5000 ng/mL (coefficient of determination greater than 0.98 for all compounds). LODs (signal-to-noise ratio of 3) were 100 ng/mL for COC and CET and 250 ng/mL for the other studied metabolites whereas LOQ`s (signal-to-noise ratio of 10) were 250 ng/mL for COC and CET and 500 ng/mL for all other compounds. Intra-day precision and recovery tests estimated at three different concentration levels (500, 1500 and 5000 ng/mL) provided RSD lower than 10% (except anhydroecgonine, 18% RSD) and recoveries from 83-109% for all analytes. The method was successfully applied to real cases. For the positive urine samples, the presence of COC and its` metabolites was further confirmed by MS/MS experiments.

Experimental study of the complex reflection coefficient of shear waves from the solid-liquid interface

The determination of the reflection coefficient of shear waves reflected from a solid-liquid interface is an important method in order to study the viscoelastic properties of liquids at high frequency. The reflection coefficient is a complex number. While the magnitude measurement is relatively easy and precise, the phase measurement is very difficult due to its strong temperature dependence. For that reason, most authors choose a simplified method in order to obtain the viscoelastic properties of liquids from the measured coefficient. In this simplified method, inconsistent viscosity results are obtained because pure viscous behavior is assumed and the phase is not measured. This work deals with an effort to improve the experimental technique required to measure both the magnitude and phase of the reflection coefficient and it intends to report realistic values for oils in a wide range of viscosity (0.092 - 6.7 Pa.s). Moreover, a device calibration process is investigated in order to monitor the dynamic viscosity of the liquid.

Determination of ethyl carbamate in cachaça produced from copper stills by HPLC

Ethyl carbamate (EC) is a common substance in fermented foods and drinks, and its quantification is important because of its carcinogenic nature and its usually presence in alcoholic beverages. The present work involved the development and validation of an analytical method for the evaluation of EC in cachaça by HPLC-FLD after previous derivatization with xanthydrol. The method presented a mean recovery of 94.88%, an intra-day precision of 4.19% (30.0 μg L−1) and 3.32% (75.0 μg L−1), a coefficient of determination (r2) equal to 0.9985, and limits of detection and quantification equal to 6.39 and 21.32 μg L−1, respectively. The results show that the analytical method is accurate, reproducible and linear over the concentration range from 5.0 to 160 μg of EC per litre. The method was applied to the analysis of EC in cachaça, the analyses being rapid and efficient.

A simple and portable instrument for determination of captopril in pharmaceutical formulations

Objective: This article describes the application and the performance of a cheap, simple and portable device that can be used for colorimetric quantitative determination of captopril (CPT) in pharmaceutical preparations. Methods: The sensor is a light detector resistor (LDR) placed into a black PTFE cell and coupled to a low cost multimeter (Ohmmeter). The instrument has been tested and is easy and fast to use. The quantitative study is based mainly on reduction of ammonium molybdate by captopril, in the presence of sulphuric acid, producing a green-yellow compound (max 407 nm). The calibration curves were obtained by plotting the electric resistance of the LDR against the CPT concentration on the range of 4.60 x 10-4 to 1.84 x 10-3 mol l-1 with a good coefficient of determination (R2 = 0.9962). Results: Statistical analysis of the obtained results showed no significant difference between the proposed methodology and the official reported method as evident from the t-test and variance ratio at 95% confidence level. Conclusion: The results of this study demonstrate that the instrument can be used for simple, accurate, precise, fast, in situ and low-cost colorimetric analysis of captopril in pharmaceuticals products.

Comparison of 3D finite element analysis derived stiffness and BMD to determine the failure load of the excised proximal femur

Introduction: Bone mineral density (BMD) is currently the preferred surrogate for bone strength in clinical practice. Finite element analysis (FEA) is a computer simulation technique that can predict the deformation of a structure when a load is applied, providing a measure of stiffness (Nmm−1). Finite element analysis of X-ray images (3D-FEXI) is a FEA technique whose analysis is derived froma single 2D radiographic image. Methods: 18 excised human femora had previously been quantitative computed tomography scanned, from which 2D BMD-equivalent radiographic images were derived, and mechanically tested to failure in a stance-loading configuration. A 3D proximal femur shape was generated from each 2D radiographic image and used to construct 3D-FEA models. Results: The coefficient of determination (R2%) to predict failure load was 54.5% for BMD and 80.4% for 3D-FEXI. Conclusions: This ex vivo study demonstrates that 3D-FEXI derived from a conventional 2D radiographic image has the potential to significantly increase the accuracy of failure load assessment of the proximal femur compared with that currently achieved with BMD. This approach may be readily extended to routine clinical BMD images derived by dual energy X-ray absorptiometry. Crown Copyright © 2009 Published by Elsevier Ltd on behalf of IPEM. All rights reserved

Asphalt concrete surfaces macrotexture determination from still images

Road surface macrotexture is identified as one of the factors contributing to the surface's skid resistance. Existing methods of quantifying the surface macrotexture, such as the sand patch test and the laser profilometer test, are either expensive or intrusive, requiring traffic control. High-resolution cameras have made it possible to acquire good quality images from roads for the automated analysis of texture depth. In this paper, a granulometric method based on image processing is proposed to estimate road surface texture coarseness distribution from their edge profiles. More than 1300 images were acquired from two different sites, extending to a total of 2.96 km. The images were acquired using camera orientations of 60 and 90 degrees. The road surface is modeled as a texture of particles, and the size distribution of these particles is obtained from chord lengths across edge boundaries. The mean size from each distribution is compared with the sensor measured texture depth obtained using a laser profilometer. By tuning the edge detector parameters, a coefficient of determination of up to R2 = 0.94 between the proposed method and the laser profilometer method was obtained. The high correlation is also confirmed by robust calibration parameters that enable the method to be used for unseen data after the method has been calibrated over road surface data with similar surface characteristics and under similar imaging conditions.

Removal of toxic metals in a multi metal system using sorbents for potential application to urban stormwater treatment

In the context of increasing demand for potable water and the depletion of water resources, stormwater is a logical alternative. However, stormwater contains pollutants, among which metals are of particular interest due to their toxicity and persistence in the environment. Hence, it is imperative to remove toxic metals in stormwater to the levels prescribed by drinking water guidelines for potable use. Consequently, various techniques have been proposed, among which sorption using low cost sorbents is economically viable and environmentally benign in comparison to other techniques. However, sorbents show affinity towards certain toxic metals, which results in poor removal of other toxic metals. It was hypothesised in this study that a mixture of sorbents that have different metal affinity patterns can be used for the efficient removal of a range of toxic metals commonly found in stormwater. The performance of six sorbents in the sorption of Al, Cr, Cu, Pb, Ni, Zn and Cd, which are the toxic metals commonly found in urban stormwater, was investigated to select suitable sorbents for creating the mixtures. For this purpose, a multi criteria analytical protocol was developed using the decision making methods: PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluations) and GAIA (Graphical Analysis for Interactive Assistance). Zeolite and seaweed were selected for the creation of trial mixtures based on their metal affinity pattern and the performance on predetermined selection criteria. The metal sorption mechanisms employed by seaweed and zeolite were defined using kinetics, isotherm and thermodynamics parameters, which were determined using the batch sorption experiments. Additionally, the kinetics rate-limiting steps were identified using an innovative approach using GAIA and Spearman correlation techniques developed as part of the study, to overcome the limitation in conventional graphical methods in predicting the degree of contribution of each kinetics step in limiting the overall metal removal rate. The sorption kinetics of zeolite was found to be primarily limited by intraparticle diffusion followed by the sorption reaction steps, which were governed mainly by the hydrated ionic diameter of metals. The isotherm study indicated that the metal sorption mechanism of zeolite was primarily of a physical nature. The thermodynamics study confirmed that the energetically favourable nature of sorption increased in the order of Zn < Cu < Cd < Ni < Pb < Cr < Al, which is in agreement with metal sorption affinity of zeolite. Hence, sorption thermodynamics has an influence on the metal sorption affinity of zeolite. On the other hand, the primary kinetics rate-limiting step of seaweed was the sorption reaction process followed by intraparticle diffusion. The boundary layer diffusion was also found to limit the metal sorption kinetics at low concentration. According to the sorption isotherm study, Cd, Pb, Cr and Al were sorbed by seaweed via ion exchange, whilst sorption of Ni occurred via physisorption. Furthermore, ionic bonding is responsible for the sorption of Zn. The thermodynamics study confirmed that sorption by seaweed was energetically favourable in the order of Zn < Cu < Cd < Cr . Al < Pb < Ni. However, this did not agree with the affinity series derived for seaweed suggesting a limited influence of sorption thermodynamics on metal affinity for seaweed. The investigation of zeolite-seaweed mixtures indicated that mixing sorbents have an effect on the kinetics rates and the sorption affinity. Additionally, the theoretical relationships were derived to predict the boundary layer diffusion rate, intraparticle diffusion rate, the sorption reaction rate and the enthalpy of mixtures based on that of individual sorbents. In general, low coefficient of determination (R2) for the relationships between theoretical and experimental data indicated that the relationships were not statistically significant. This was attributed to the heterogeneity of the properties of sorbents. Nevertheless, in relative terms, the intraparticle diffusion rate, sorption reaction rate and enthalpy of sorption had higher R2 values than the boundary layer diffusion rate suggesting that there was some relationship between the former set of parameters of mixtures and that of sorbents. The mixture, which contained 80% of zeolite and 20% of seaweed, showed similar affinity for the sorption of Cu, Ni, Cd, Cr and Al, which was attributed to approximately similar sorption enthalpy of the metal ions. Therefore, it was concluded that the seaweed-zeolite mixture can be used to obtain the same affinity for various metals present in a multi metal system provided the metal ions have similar enthalpy during sorption by the mixture.

Application of near infrared (NIR) spectroscopy for determining the thickness of articular cartilage

The determination of the characteristics of articular cartilage such as thickness, stiffness and swelling, especially in the form that can facilitate real-time decisions and diagnostics is still a matter for research and development. This paper correlates near infrared spectroscopy with mechanically measured cartilage thickness to establish a fast, non-destructive, repeatable and precise protocol for determining this tissue property. Statistical correlation was conducted between the thickness of bovine cartilage specimens (n = 97) and regions of their near infrared spectra. Nine regions were established along the full absorption spectrum of each sample and were correlated with the thickness using partial least squares (PLS) regression multivariate analysis. The coefficient of determination (R2) varied between 53 and 93%, with the most predictive region (R2 = 93.1%, p < 0.0001) for cartilage thickness lying in the region (wavenumber) 5350–8850 cm−1. Our results demonstrate that the thickness of articular cartilage can be measured spectroscopically using NIR light. This protocol is potentially beneficial to clinical practice and surgical procedures in the treatment of joint disease such as osteoarthritis.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Modeling meat yield based on measurements of body traits in genetically improved giant freshwater prawn (GFP) Macrobrachium rosenbergii

A single-generation dataset consisting of 1,730 records from a selection program for high growth rate in giant freshwater prawn (GFP, Macrobrachium rosenbergii) was used to derive prediction equations for meat weight and meat yield. Models were based on body traits [body weight, total length and abdominal width (AW)] and carcass measurements (tail weight and exoskeleton-off weight). Lengths and width were adjusted for the systematic effects of selection line, male morphotypes and female reproductive status, and for the covariables of age at slaughter within sex and body weight. Body and meat weights adjusted for the same effects (except body weight) were used to calculate meat yield (expressed as percentage of tail weight/body weight and exoskeleton-off weight/body weight). The edible meat weight and yield in this GFP population ranged from 12 to 15 g and 37 to 45 %, respectively. The simple (Pearson) correlation coefficients between body traits (body weight, total length and AW) and meat weight were moderate to very high and positive (0.75–0.94), but the correlations between body traits and meat yield were negative (−0.47 to −0.74). There were strong linear positive relationships between measurements of body traits and meat weight, whereas relationships of body traits with meat yield were moderate and negative. Step-wise multiple regression analysis showed that the best model to predict meat weight included all body traits, with a coefficient of determination (R 2) of 0.99 and a correlation between observed and predicted values of meat weight of 0.99. The corresponding figures for meat yield were 0.91 and 0.95, respectively. Body weight or length was the best predictor of meat weight, explaining 91–94 % of observed variance when it was fitted alone in the model. By contrast, tail width explained a lower proportion (69–82 %) of total variance in the single trait models. It is concluded that in practical breeding programs, improvement of meat weight can be easily made through indirect selection for body trait combinations. The improvement of meat yield, albeit being more difficult, is possible by genetic means, with 91 % of the variation in the trait explained by the body and carcass traits examined in this study.

Influence of environmental and instrumental variables on the non-invasive prediction of Brix in pineapple using near infrared spectroscopy

The Brix content of pineapple fruit can be non-invasively predicted from the second derivative of near infrared reflectance spectra. Correlations obtained using a NIRSystems 6500 spectrophotometer through multiple linear regression and modified partial least squares analyses using a post-dispersive configuration were comparable with that from a pre-dispersive configuration in terms of accuracy (e.g. coefficient of determination, R2, 0.73; standard error of cross validation, SECV, 1.01°Brix). The effective depth of sample assessed was slightly greater using the post-dispersive technique (about 20 mm for pineapple fruit), as expected in relation to the higher incident light intensity, relative to the pre-dispersive configuration. The effect of such environmental variables as temperature, humidity and external light, and instrumental variables such as the number of scans averaged to form a spectrum, were considered with respect to the accuracy and precision of the measurement of absorbance at 876 nm, as a key term in the calibration for Brix, and predicted Brix. The application of post-dispersive near infrared technology to in-line assessment of intact fruit in a packing shed environment is discussed.

Non-invasive assessment of pineapple and mango fruit quality using near infra-red spectroscopy

The potential of near infra-red (NIR) spectroscopy for non-invasive measurement of fruit quality of pineapple (Ananas comosus var. Smooth Cayenne) and mango (Magnifera indica var. Kensington) fruit was assessed. A remote reflectance fibre optic probe, placed in contact with the fruit skin surface in a light-proof box, was used to deliver monochromatic light to the fruit, and to collect NIR reflectance spectra (760–2500 nm). The probe illuminated and collected reflected radiation from an area of about 16 cm2. The NIR spectral attributes were correlated with pineapple juice Brix and with mango flesh dry matter (DM) measured from fruit flesh directly underlying the scanned area. The highest correlations for both fruit were found using the second derivative of the spectra (d2 log 1/R) and an additive calibration equation. Multiple linear regression (MLR) on pineapple fruit spectra (n = 85) gave a calibration equation using d2 log 1/R at wavelengths of 866, 760, 1232 and 832 nm with a multiple coefficient of determination (R2) of 0.75, and a standard error of calibration (SEC) of 1.21 °Brix. Modified partial least squares (MPLS) regression analysis yielded a calibration equation with R2 = 0.91, SEC = 0.69, and a standard error of cross validation (SECV) of 1.09 oBrix. For mango, MLR gave a calibration equation using d2 log 1/R at 904, 872, 1660 and 1516 nm with R2 = 0.90, and SEC = 0.85% DM and a bias of 0.39. Using MPLS analysis, a calibration equation with R2 = 0.98, SEC = 0.54 and SECV = 1.19 was obtained. We conclude that NIR technology offers the potential to assess fruit sweetness in intact whole pineapple and DM in mango fruit, respectively, to within 1° Brix and 1% DM, and could be used for the grading of fruit in fruit packing sheds.

Effect of timing of a deficit-irrigation allocation on corn evapotranspiration, yield, water use efficiency and dry mass

Water regulations have decreased irrigation water supplies in Nebraska and some other areas of the USA Great Plains. When available water is not enough to meet crop water requirements during the entire growing cycle, it becomes critical to know the proper irrigation timing that would maximize yields and profits. This study evaluated the effect of timing of a deficit-irrigation allocation (150 mm) on crop evapotranspiration (ETc), yield, water use efficiency (WUE = yield/ETc), irrigation water use efficiency (IWUE = yield/irrigation), and dry mass (DM) of corn (Zea mays L.) irrigated with subsurface drip irrigation in the semiarid climate of North Platte, NE. During 2005 and 2006, a total of sixteen irrigation treatments (eight each year) were evaluated, which received different percentages of the water allocation during July, August, and September. During both years, all treatments resulted in no crop stress during the vegetative period and stress during the reproductive stages, which affected ETc, DM, yield, WUE and IWUE. Among treatments, ETc varied by 7.2 and 18.8%; yield by 17 and 33%; WUE by 12 and 22%, and IWUE by 18 and 33% in 2005 and 2006, respectively. Yield and WUE both increased linearly with ETc and with ETc/ETp (ETp = seasonal ETc with no water stress), and WUE increased linearly with yield. The yield response factor (ky) averaged 1.50 over the two seasons. Irrigation timing affected the DM of the plant, grain, and cob, but not that of the stover. It also affected the percent of DM partitioned to the grain (harvest index), which increased linearly with ETc and averaged 56.2% over the two seasons, but did not affect the percent allocated to the cob or stover. Irrigation applied in July had the highest positive coefficient of determination (R2) with yield. This high positive correlation decreased considerably for irrigation applied in August, and became negative for irrigation applied in September. The best positive correlation between the soil water deficit factor (Ks) and yield occurred during weeks 12-14 from crop emergence, during the "milk" and "dough" growth stages. Yield was poorly correlated to stress during weeks 15 and 16, and the correlation became negative after week 17. Dividing the 150 mm allocation about evenly among July, August and September was a good strategy resulting in the highest yields in 2005, but not in 2006. Applying a larger proportion of the allocation in July was a good strategy during both years, and the opposite resulted when applying a large proportion of the allocation in September. The different results obtained between years indicate that flexible irrigation scheduling techniques should be adopted, rather than relying on fixed timing strategies.