Classification Of Frankfurters By Ft-raman Spectroscopy And Chemometric Methods.

Frankfurters are widely consumed all over the world, and the production requires a wide range of meat and non-meat ingredients. Due to these characteristics, frankfurters are products that can be easily adulterated with lower value meats, and the presence of undeclared species. Adulterations are often still difficult to detect, due the fact that the adulterant components are usually very similar to the authentic product. In this work, FT-Raman spectroscopy was employed as a rapid technique for assessing the quality of frankfurters. Based on information provided by the Raman spectra, a multivariate classification model was developed to identify the frankfurter type. The aim was to study three types of frankfurters (chicken, turkey and mixed meat) according to their Raman spectra, based on the fatty vibrational bands. Classification model was built using partial least square discriminant analysis (PLS-DA) and the performance model was evaluated in terms of sensitivity, specificity, accuracy, efficiency and Matthews's correlation coefficient. The PLS-DA models give sensitivity and specificity values on the test set in the ranges of 88%-100%, showing good performance of the classification models. The work shows the Raman spectroscopy with chemometric tools can be used as an analytical tool in quality control of frankfurters.

Authenticity study of Phyllanthus species by NMR and FT-IR Techniques coupled with chemometric methods

The importance of medicinal plants and their use in industrial applications is increasing worldwide, especially in Brazil. Phyllanthus species, popularly known as quebra-pedras in Brazil, are used in folk medicine for treating urinary infections and renal calculus. This paper reports an authenticity study, based on herbal drugs from Phyllanthus species, involving commercial and authentic samples using spectroscopic techniques: FT-IR, ¹H HR-MAS NMR and ¹H NMR in solution, combined with chemometric analysis. The spectroscopic techniques evaluated, coupled with chemometric methods, have great potential in the investigation of complex matrices. Furthermore, several metabolites were identified by the NMR techniques.

Chemometric Studies on Natural Products as Potential Inhibitors of the NADH Oxidase from Trypanosoma cruzi Using the VolSurf Approach

Natural products have widespread biological activities, including inhibition of mitochondrial enzyme systems. Some of these activities, for example cytotoxicity, may be the result of alteration of cellular bioenergetics. Based on previous computer-aided drug design (CADD) studies and considering reported data on structure-activity relationships (SAR), an assumption regarding the mechanism of action of natural products against parasitic infections involves the NADH-oxidase inhibition. In this study, chemometric tools, such as: Principal Component Analysis (PCA), Consensus PCA (CPCA), and partial least squares regression (PLS), were applied to a set of forty natural compounds, acting as NADH-oxidase inhibitors. The calculations were performed using the VolSurf+ program. The formalisms employed generated good exploratory and predictive results. The independent variables or descriptors having a hydrophobic profile were strongly correlated to the biological data.

Monitoring the authenticity of Brazilian UHT milk: A chemometric approach

In this work, chemometric methods are reported as potential tools for monitoring the authenticity of Brazilian ultra-high temperature (UHT) milk processed in industrial plants located in different regions of the country. A total of 100 samples were submitted to the qualitative analysis of adulterants such as starch, chlorine, formal. hydrogen peroxide and urine. Except for starch, all the samples reported, at least, the presence of one adulterant. The use of chemometric methodologies such as the Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) enabled the verification of the occurrence of certain adulterations in specific regions. The proposed multivariate approaches may allow the sanitary agency authorities to optimise materials, human and financial resources, as they associate the occurrence of adulterations to the geographical location of the industrial plants. (c) 2010 Elsevier Ltd. All rights reserved.

Quantification of chlorpheniramine maleate enantiomers by ultraviolet spectroscopy and chemometric methods

Chlorpheniramine maleate (CLOR) enantiomers were quantified by ultraviolet spectroscopy and partial least squares regression. The CLOR enantiomers were prepared as inclusion complexes with beta-cyclodextrin and 1-butanol with mole fractions in the range from 50 to 100%. For the multivariate calibration the outliers were detected and excluded and variable selection was performed by interval partial least squares and a genetic algorithm. Figures of merit showed results for accuracy of 3.63 and 2.83% (S)-CLOR for root mean square errors of calibration and prediction, respectively. The ellipse confidence region included the point for the intercept and the slope of 1 and 0, respectively. Precision and analytical sensitivity were 0.57 and 0.50% (S)-CLOR, respectively. The sensitivity, selectivity, adjustment, and signal-to-noise ratio were also determined. The model was validated by a paired t test with the results obtained by high-performance liquid chromatography proposed by the European pharmacopoeia and circular dichroism spectroscopy. The results showed there was no significant difference between the methods at the 95% confidence level, indicating that the proposed method can be used as an alternative to standard procedures for chiral analysis.

A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R (3) (charge density on substituent at position C(3)), Q (1) (charge on atom C(1)), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active, moderately active, and inactive.

A novel approach for honey pollen profile assessment using an electronic tongue and chemometric tools

Nowadays the main honey producing countries require accurate labeling of honey before commercialization, including floral classification. Traditionally, this classification is made by melissopalynology analysis, an accurate but time-consuming task requiring laborious sample pre-treatment and high-skilled technicians. In this work the potential use of a potentiometric electronic tongue for pollinic assessment is evaluated, using monofloral and polyfloral honeys. The results showed that after splitting honeys according to color (white, amber and dark), the novel methodology enabled quantifying the relative percentage of the main pollens (Castanea sp., Echium sp., Erica sp., Eucaliptus sp., Lavandula sp., Prunus sp., Rubus sp. and Trifolium sp.). Multiple linear regression models were established for each type of pollen, based on the best sensors sub-sets selected using the simulated annealing algorithm. To minimize the overfitting risk, a repeated K-fold cross-validation procedure was implemented, ensuring that at least 10-20% of the honeys were used for internal validation. With this approach, a minimum average determination coefficient of 0.91 ± 0.15 was obtained. Also, the proposed technique enabled the correct classification of 92% and 100% of monofloral and polyfloral honeys, respectively. The quite satisfactory performance of the novel procedure for quantifying the relative pollen frequency may envisage its applicability for honey labeling and geographical origin identification. Nevertheless, this approach is not a full alternative to the traditional melissopalynologic analysis; it may be seen as a practical complementary tool for preliminary honey floral classification, leaving only problematic cases for pollinic evaluation.

UV-visible scanning spectrophotometry and chemometric analysis as tools for carotenoids analysis in cassava genotypes (Manihot esculenta Crantz)

In this study, the metabolomics characterization focusing on the carotenoid composition of ten cassava (Manihot esculenta) genotypes cultivated in southern Brazil by UV-visible scanning spectrophotometry and reverse phase-high performance liquid chromatography was performed. Cassava roots rich in -carotene are an important staple food for populations with risk of vitamin A deficiency. Cassava genotypes with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The data set was used for the construction of a descriptive model by chemometric analysis. The genotypes of yellow-fleshed roots were clustered by the higher concentrations of cis--carotene and lutein. Inversely, cream-fleshed roots genotypes were grouped precisely due to their lower concentrations of these pigments, as samples rich in lycopene (redfleshed) differed among the studied genotypes. The analytical approach (UV-Vis, HPLC, and chemometrics) used showed to be efficient for understanding the chemodiversity of cassava genotypes, allowing to classify them according to important features for human health and nutrition.

The differentiation of fibre- and drug type Cannabis seedlings by gas chromatography/mass spectrometry and chemometric tools

Cannabis cultivation in order to produce drugs is forbidden in Switzerland. Thus, law enforcement authorities regularly ask forensic laboratories to determinate cannabis plant's chemotype from seized material in order to ascertain that the plantation is legal or not. As required by the EU official analysis protocol the THC rate of cannabis is measured from the flowers at maturity. When laboratories are confronted to seedlings, they have to lead the plant to maturity, meaning a time consuming and costly procedure. This study investigated the discrimination of fibre type from drug type Cannabis seedlings by analysing the compounds found in their leaves and using chemometrics tools. 11 legal varieties allowed by the Swiss Federal Office for Agriculture and 13 illegal ones were greenhouse grown and analysed using a gas chromatograph interfaced with a mass spectrometer. Compounds that show high discrimination capabilities in the seedlings have been identified and a support vector machines (SVMs) analysis was used to classify the cannabis samples. The overall set of samples shows a classification rate above 99% with false positive rates less than 2%. This model allows then discrimination between fibre and drug type Cannabis at an early stage of growth. Therefore it is not necessary to wait plants' maturity to quantify their amount of THC in order to determine their chemotype. This procedure could be used for the control of legal (fibre type) and illegal (drug type) Cannabis production.

Advanced chemometric methods: Applicability on industrial data

Recent years have produced great advances in the instrumentation technology. The amount of available data has been increasing due to the simplicity, speed and accuracy of current spectroscopic instruments. Most of these data are, however, meaningless without a proper analysis. This has been one of the reasons for the overgrowing success of multivariate handling of such data. Industrial data is commonly not designed data; in other words, there is no exact experimental design, but rather the data have been collected as a routine procedure during an industrial process. This makes certain demands on the multivariate modeling, as the selection of samples and variables can have an enormous effect. Common approaches in the modeling of industrial data are PCA (principal component analysis) and PLS (projection to latent structures or partial least squares) but there are also other methods that should be considered. The more advanced methods include multi block modeling and nonlinear modeling. In this thesis it is shown that the results of data analysis vary according to the modeling approach used, thus making the selection of the modeling approach dependent on the purpose of the model. If the model is intended to provide accurate predictions, the approach should be different than in the case where the purpose of modeling is mostly to obtain information about the variables and the process. For industrial applicability it is essential that the methods are robust and sufficiently simple to apply. In this way the methods and the results can be compared and an approach selected that is suitable for the intended purpose. Differences in data analysis methods are compared with data from different fields of industry in this thesis. In the first two papers, the multi block method is considered for data originating from the oil and fertilizer industries. The results are compared to those from PLS and priority PLS. The third paper considers applicability of multivariate models to process control for a reactive crystallization process. In the fourth paper, nonlinear modeling is examined with a data set from the oil industry. The response has a nonlinear relation to the descriptor matrix, and the results are compared between linear modeling, polynomial PLS and nonlinear modeling using nonlinear score vectors.

Simultaneous spectrophotometric determination of ezetimibe and simvastatin in pharmaceutical preparations using chemometric techniques

Two spectrophotometric methods are described for the simultaneous determination of ezetimibe (EZE) and simvastatin (SIM) in pharmaceutical preparations. The obtained data was evaluated by using two different chemometric techniques, Principal Component Regression (PCR) and Partial Least-Squares (PLS-1). In these techniques, the concentration data matrix was prepared by using the mixtures containing these drugs in methanol. The absorbance data matrix corresponding to the concentration data matrix was obtained by the measurements of absorbances in the range of 240 - 300 nm in the intervals with Δλ = 1 nm at 61 wavelengths in their zero order spectra, then, calibration or regression was obtained by using the absorbance data matrix and concentration data matrix for the prediction of the unknown concentrations of EZE and SIM in their mixture. The procedure did not require any separation step. The linear range was found to be 5 - 20 µg mL-1 for EZE and SIM in both methods. The accuracy and precision of the methods were assessed. These methods were successfully applied to a pharmaceutical preparation, tablet; and the results were compared with each other.

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.

Authenticity study of Phyllanthus species by NMR and FT-IR Techniques coupled with chemometric methods

The importance of medicinal plants and their use in industrial applications is increasing worldwide, especially in Brazil. Phyllanthus species, popularly known as "quebra-pedras" in Brazil, are used in folk medicine for treating urinary infections and renal calculus. This paper reports an authenticity study, based on herbal drugs from Phyllanthus species, involving commercial and authentic samples using spectroscopic techniques: FT-IR, ¹H HR-MAS NMR and ¹H NMR in solution, combined with chemometric analysis. The spectroscopic techniques evaluated, coupled with chemometric methods, have great potential in the investigation of complex matrices. Furthermore, several metabolites were identified by the NMR techniques.

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.

Grape juice quality control by means of ¹H nmr spectroscopy and chemometric analyses

This work shows the application of ¹H NMR spectroscopy and chemometrics for quality control of grape juice. A wide range of quality assurance parameters were assessed by single ¹H NMR experiments acquired directly from juice. The investigation revealed that conditions and time of storage should be revised and indicated on all labels. The sterilization process of homemade grape juices was efficient, making it possible to store them for long periods without additives. Furthermore, chemometric analysis classified the best commercial grape juices to be similar to homemade grape juices, indicating that this approach can be used to determine the authenticity after adulteration.