Characterization of the antibiotic doripenem using physicochemical methods: chromatography, spectrophotometry, spectroscopy and thermal analysis

Doripenem was characterized through physicochemical and spectroscopic techniques, as well as thermal analysis. TLC (Rf = 0.62) and HPLC (rt = 7.4 min) were found to be adequate to identify the drug. UV and infrared spectra showed similar profile between doripenem bulk and standard. The ¹H and 13C NMR analysis revealed chemical shifts that allowed identifying the drug. Thermal analysis demonstrated three steps with mass loss, at 128, 178 and 276 ºC. The work was successfully applied to qualitative analysis of doripenem, showing the reported methods can be used for physicochemical characterization of doripenem

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Predicting rapeseed oil content with near-infrared spectroscopy

The objective of this work was to establish a calibration equation and to estimate the efficiency of near-infrared reflectance (NIR) spectroscopy for evaluating rapeseed oil content in Southern Brazil. Spectral data from 124 half-sib families were correlated with oil contents determined by the chemical method. The accuracy of the equation was verified by coefficient of determination (R²) of 0.92, error of calibration (SEC) of 0.78, and error of performance (SEP) of 1.22. The oil content of ten genotypes, which were not included in the calibration with NIR, was similar to the one obtained by the standard chemical method. NIR spectroscopy is adequate to differentiate oil content of rapeseed genotypes.

Fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy and chemometric techniques for the determination of adulteration in petrodiesel/biodiesel blends

We propose an analytical method based on fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy to detect the adulteration of petrodiesel and petrodiesel/palm biodiesel blends with African crude palm oil. The infrared spectral fingerprints from the sample analysis were used to perform principal components analysis (PCA) and to construct a prediction model using partial least squares (PLS) regression. The PCA results separated the samples into three groups, allowing identification of those subjected to adulteration with palm oil. The obtained model shows a good predictive capacity for determining the concentration of palm oil in petrodiesel/biodiesel blends. Advantages of the proposed method include cost-effectiveness and speed; it is also environmentally friendly.

Application of differential scanning calorimetry in hair samples as a possible tool in Forensic Science

The Differential Scanning Calorimetry (DSC) was used to study the thermal behavior of hair samples and to verify the possibility of identifying an individual based on DSC curves from a data bank. Hair samples of students and officials from Instituto de Química de Araraquara, UNESP were obtained to build up a data bank. Thus to sought an individual, under incognito participant of this data bank, was identified using DSC curves.

Multivariate Analysis for Animal Selection in Experimental Research

Background: Several researchers seek methods for the selection of homogeneous groups of animals in experimental studies, a fact justified because homogeneity is an indispensable prerequisite for casualization of treatments. The lack of robust methods that comply with statistical and biological principles is the reason why researchers use empirical or subjective methods, influencing their results. Objective: To develop a multivariate statistical model for the selection of a homogeneous group of animals for experimental research and to elaborate a computational package to use it. Methods: The set of echocardiographic data of 115 male Wistar rats with supravalvular aortic stenosis (AoS) was used as an example of model development. Initially, the data were standardized, and became dimensionless. Then, the variance matrix of the set was submitted to principal components analysis (PCA), aiming at reducing the parametric space and at retaining the relevant variability. That technique established a new Cartesian system into which the animals were allocated, and finally the confidence region (ellipsoid) was built for the profile of the animals’ homogeneous responses. The animals located inside the ellipsoid were considered as belonging to the homogeneous batch; those outside the ellipsoid were considered spurious. Results: The PCA established eight descriptive axes that represented the accumulated variance of the data set in 88.71%. The allocation of the animals in the new system and the construction of the confidence region revealed six spurious animals as compared to the homogeneous batch of 109 animals. Conclusion: The biometric criterion presented proved to be effective, because it considers the animal as a whole, analyzing jointly all parameters measured, in addition to having a small discard rate.

Record of postmortem injuries caused by the Neotropical social wasp Agelaia fulvofasciata (Degeer) (Hymenoptera, Vespidae) on pig carcasses in the Eastern Amazon region: implications in forensic taphonomy

ABSTRACTPostmortem injuries are a source of misinterpretations in forensic analysis and therefore are subject matter of taphonomic interest. Many types of injuries can cause different artifacts, which deserve attention of the forensic pathologists when evaluating corpses, either at the crime scene or during an autopsy. Insects can be important biotaphonomic agents and their activity may result in artifacts that resemble antemortem injuries. Here, we describe postmortem injuries caused by the Neotropical wasp Agelaia fulvofasciata (Degeer, 1773) on domestic pig carcasses weighting 15 kg. The specimens showed extensive injuries to the lower lip, similar to lacerations, and some minor lesions on the snout and anus. In addition, we observed the same wasp species preying on larvae of Sarcophagidae (Peckia sp.). Besides causing postmortem injuries, the ability of this species to detect carcasses in the early and fresh decomposition stages should be noted. Thus, future applications aiming criminal, any biotaphonomic events caused by carrion insects need to be disclosed.

Characterization of soil chemical properties of strawberry fields using principal component analysis

One of the largest strawberry-producing municipalities of Rio Grande do Sul (RS) is Turuçu, in the South of the State. The strawberry production system adopted by farmers is similar to that used in other regions in Brazil and in the world. The main difference is related to the soil management, which can change the soil chemical properties during the strawberry cycle. This study had the objective of assessing the spatial and temporal distribution of soil fertility parameters using principal component analysis (PCA). Soil sampling was based on topography, dividing the field in three thirds: upper, middle and lower. From each of these thirds, five soil samples were randomly collected in the 0-0.20 m layer, to form a composite sample for each third. Four samples were taken during the strawberry cycle and the following properties were determined: soil organic matter (OM), soil total nitrogen (N), available phosphorus (P) and potassium (K), exchangeable calcium (Ca) and magnesium (Mg), soil pH (pH), cation exchange capacity (CEC) at pH 7.0, soil base (V%) and soil aluminum saturation(m%). No spatial variation was observed for any of the studied soil fertility parameters in the strawberry fields and temporal variation was only detected for available K. Phosphorus and K contents were always high or very high from the beginning of the strawberry cycle, while pH values ranged from very low to very high. Principal component analysis allowed the clustering of all strawberry fields based on variables related to soil acidity and organic matter content.

Classificação de água de coco processada e natural por meio de HCA, PCA e teores de íons metálicos determinados por ICP OES

Inductively Coupled Plasma Optical Emission Spectrometry was used to determine Ca, Mg, Mn, Fe, Zn and Cu in samples of processed and natural coconut water. The sample preparation consisted in a filtration step followed by a dilution. The analysis was made employing optimized instrumental parameters and the results were evaluated using methods of Pattern Recognition. The data showed common concentration values for the analytes present in processed and natural samples. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) indicated that the samples of different kinds were statistically different when the concentrations of all the analytes were considered simultaneously.

Determinação não destrutiva do nitrogênio total em plantas por espectroscopia de reflectância difusa no infravermelho próximo

Diffuse reflectance near-infrared (DR-NIR) spectroscopy associated with partial least squares (PLS) multivariate calibration is proposed for a direct, non-destructive, determination of total nitrogen in wheat leaves. The procedure was developed for an Analytical Instrumental Analysis course, carried out at the Institute of Chemistry of the State University of Campinas. The DR-NIR results are in good agreement with those obtained by the Kjeldhal standard procedure, with a relative error of less than ± 3% and the method may be used for teaching purposes as well as for routine analysis.

Análise por componentes principais de espectros nexafs na especiação do molibdênio em catalisadores de hidrotratamento

Bulk and supported molybdenum based catalysts, modified by nickel, phosphorous or tungsten were studied by NEXAFS spectroscopy at the Mo L III and L II edges. The techniques of principal component analysis (PCA) together with a linear combination analysis (LCA) allowed the detection and quantification of molybdenum atoms in two different coordination states in the oxide form of the catalysts, namely tetrahedral and octahedral coordination.

Experimento didático de quimiometria para análise exploratória de óleos vegetais comestíveis por espectroscopia no infravermelho médio e análise de componentes principais: um tutorial, parte I

This manuscript aims to show the basic concepts and practical application of Principal Component Analysis (PCA) as a tutorial, using Matlab or Octave computing environment for beginners, undergraduate and graduate students. As a practical example it is shown the exploratory analysis of edible vegetable oils by mid infrared spectroscopy.

A quantitative validated method using liquid chromatography and chemometric analysis for evaluation of raw material oF Maytenus ilicifolia (Schrad.) Planch., Celastraceae

The hydroalcoholic extracts prepared from standard leaves of Maytenus ilicifolia and commercial samples of espinheira-santa were evaluated qualitatively (fingerprinting) and quantitatively. In this paper, fingerprinting chromatogram coupled with Principal Component Analysis (PCA) is described for the metabolomic analysis of standard and commercial espinheira-santa samples. The epicatechin standard was used as an external standard for the development and validation of a quantitative method for the analysis in herbal medicines using a photo diode array detector. This method has been applied for quantification of epicatechin in commercialized herbal medicines sold as espinheira-santa in Brazil and in the standard sample of M. ilicifolia.

Oxidação de glicerol sobre nanopartículas de ouro suportadas em carvão ativado: monitoramento quimiométrico da reação por ESI-MS e MIR

A study on the monitoring of glycerol oxidation catalyzed by gold nanoparticles supported on activated carbon under mild conditions by chemometric methods is presented. The reaction was monitored by mass spectrometry-electrospray ionization (ESI-MS) and comparatively by mid infrared spectroscopy (MIR). Concentration profiles of reagent and products were determined by chemometric tools such as Principal Component Analysis (PCA), Evolving Factor Analysis (EFA) and Multivariate Curve Resolution (MCR). The gold nanoparticle catalyst was relatively active in glycerol oxidation, favoring formation of high added value products. It was found that the reaction stabilization was reached at four hours, with approximately 70% glycerol conversion and high selectivity for glycerate.

PCA: uma ferramenta para identificação de traçadores químicos para água de formação e água de injeção associadas à produção de petróleo

This study describes the use of Principal Component Analysis to evaluate the chemical composition of water produced from eight oil wells in three different production areas. A total of 609 samples of produced water, and a reference sample of seawater, were characterized according to their levels of salinity, calcium, magnesium, strontium, barium and sulphate (mg L-1) contents, and analyzed by using PCA with autoscaled data. The method allowed the identification of variables salinity, calcium and strontium as tracers for formation water, and variables magnesium and sulphate as tracers for seawater.