Application of near and mid-infrared spectroscopy to determine cheese quality and authenticity

This paper reviews the current state of development of both near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for process monitoring, quality control, and authenticity determination in cheese processing. Infrared spectroscopy has been identified as an ideal process analytical technology tool, and recent publications have demonstrated the potential of both NIR and MIR spectroscopy, coupled with chemometric techniques, for monitoring coagulation, syneresis, and ripening as well as determination of authenticity, composition, sensory, and rheological parameters. Recent research is reviewed and compared on the basis of experimental design, spectroscopic and chemometric methods employed to assess the potential of infrared spectroscopy as a technology for improving process control and quality in cheese manufacture. Emerging research areas for these technologies, such as cheese authenticity and food chain traceability, are also discussed.

Estudo dos principais fatores que influenciam na solubilização dos hpas em águas

Polycyclic aromatic hydrocarbons (PAHs) constitute a family of compounds characterized by having two or more condensed aromatic rings and for being a class of substances that are widely distributed in the environment as a complex mixture, being very persistent in the environment due to its low solubility in water. The application of chemometric methods to analytical chemistry has provided excellent results in studying the solubility of PAHs in aqueous media in order to understand the mechanisms involved in environmental contamination. The method consists in analyzing the solubilization of PAHs from diesel oil in water varying parameters such as stirring time, volume of oil added and pH, using a full factorial design of two levels and three factors. PAHs were extracted with n-hexane and analyzed by fluorescence spectroscopy because they have molecular characteristics fluorescent due to the large number of condensed rings and links, and gas chromatography coupled to a mass spectrometer (GC-MS). The results of fluorescence analysis showed that only the stirring time and pH influenced the solubility of PAHs in diesel fuel. How is a non-selective technique for the study of fluorescence was performed on form and semi-quantitative. And for the chromatographic analysis the results showed that the solubility of the different PAHs is influenced differently so that you can classify them into groups by the results of the effects

Spectrophotometric Determination of Captopril Through Charge Transfer Complex Formation Using Fractional Factorial and Central Composite Design

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A structure-activity relationship study of quinone compounds with trypanocidal activity

A set of 25 quinone compounds with anti-trypanocidal activity was studied by using the density functional theory (DFT) method in order to calculate atomic and molecular properties to be correlated with the biological activity. The chemometric methods principal component analysis (PCA), hierarchical cluster analysis (HCA), stepwise discriminant analysis (SDA), Kth nearest neighbor (KNN) and soft independent modeling of class analogy (SIMCA) were used to obtain possible relationships between the calculated descriptors and the biological activity studied and to predict the anti-trypanocidal activity of new quinone compounds from a prediction set. Four descriptors were responsible for the separation between the active and inactive compounds: T-5 (torsion angle), QTS1 (sum of absolute values of the atomic charges), VOLS2 (volume of the substituent at region B) and HOMO-1 (energy of the molecular orbital below HOMO). These descriptors give information on the kind of interaction that occurs between the compounds and the biological receptor. The prediction study was done with a set of three new compounds by using the PCA, HCA, SDA, KNN and SIMCA methods and two of them were predicted as active against the Trypanosoma cruzi. (c) 2005 Elsevier SAS. All rights reserved.

Determinação da composição e da octanagem de gasolinas C por espectroscopia de RMN 'INTPOT.1 H' e métodos quimiométricos

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Ocorrência de isoflavonas de soja no ambiente e correlação com atividades estrogênica : estudo de caso da região de Dourados (MS)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Multivariate curve resolution analysis applied to time-resolved synchrotron X-ray Absorption Spectroscopy monitoring of the activation of copper alumina catalyst

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Theoretical models for the antitrypanosomal activity of thiosemicarbazone derivatives

Thiosemicarbazones are cruzain inhibitors which have been identified as potential antitrypanosomal agents. In this work, several molecular properties were calculated at the density functional theory (DFT)/B3LYP/6-311G* level for a set of 44 thiosemicarbazones. Unsupervised and supervised pattern recognition techniques (hierarchical cluster analysis, principal component analysis, kth-nearest neighbors, and soft independent modeling by class analogy) were used to obtain structureactivity relationship models, which are able to classify unknown compounds according to their activities. The chemometric analyses performed here revealed that 12 descriptors can be considered responsible for the discrimination between high and low activity compounds. Classification models were validated with an external test set, showing that predictive classifications were achieved with the selected variable set. The results obtained here are in good agreement with previous findings from the literature, suggesting that our models can be useful on further investigations on the molecular determinants for the antichagasic activity. (C) 2012 Wiley Periodicals, Inc.

Direct Determination of Sugar Cane Quality Parameters by X-ray Spectrometry and Multivariate Analysis

Current methods for quality control of sugar cane are performed in extracted juice using several methodologies, often requiring appreciable time and chemicals (eventually toxic), making the methods not green and expensive. The present study proposes the use of X-ray spectrometry together with chemometric methods as an innovative and alternative technique for determining sugar cane quality parameters, specifically sucrose concentration, POL, and fiber content. Measurements in stem, leaf, and juice were performed, and those applied directly in stem provided the best results. Prediction models for sugar cane stem determinations with a single 60 s irradiation using portable X-ray fluorescence equipment allows estimating the % sucrose, % fiber, and POL simultaneously. Average relative deviations in the prediction step of around 8% are acceptable if considering that field measurements were done. These results may indicate the best period to cut a particular crop as well as for evaluating the quality of sugar cane for the sugar and alcohol industries.

Biochemistry in healthy and neoplastic human tissues: metabolic alteration revealed by HR-MAS nuclear magnetic resonance spectroscopy

This thesis is focused on the metabolomic study of human cancer tissues by ex vivo High Resolution-Magic Angle Spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy. This new technique allows for the acquisition of spectra directly on intact tissues (biopsy or surgery), and it has become very important for integrated metabonomics studies. The objective is to identify metabolites that can be used as markers for the discrimination of the different types of cancer, for the grading, and for the assessment of the evolution of the tumour. Furthermore, an attempt to recognize metabolites, that although involved in the metabolism of tumoral tissues in low concentration, can be important modulators of neoplastic proliferation, was performed. In addition, NMR data was integrated with statistical techniques in order to obtain semi-quantitative information about the metabolite markers. In the case of gliomas, the NMR study was correlated with gene expression of neoplastic tissues. Chapter 1 begins with a general description of a new “omics” study, the metabolomics. The study of metabolism can contribute significantly to biomedical research and, ultimately, to clinical medical practice. This rapidly developing discipline involves the study of the metabolome: the total repertoire of small molecules present in cells, tissues, organs, and biological fluids. Metabolomic approaches are becoming increasingly popular in disease diagnosis and will play an important role on improving our understanding of cancer mechanism. Chapter 2 addresses in more detail the basis of NMR Spectroscopy, presenting the new HR-MAS NMR tool, that is gaining importance in the examination of tumour tissues, and in the assessment of tumour grade. Some advanced chemometric methods were used in an attempt to enhance the interpretation and quantitative information of the HR-MAS NMR data are and presented in chapter 3. Chemometric methods seem to have a high potential in the study of human diseases, as it permits the extraction of new and relevant information from spectroscopic data, allowing a better interpretation of the results. Chapter 4 reports results obtained from HR-MAS NMR analyses performed on different brain tumours: medulloblastoma, meningioms and gliomas. The medulloblastoma study is a case report of primitive neuroectodermal tumor (PNET) localised in the cerebellar region by Magnetic Resonance Imaging (MRI) in a 3-year-old child. In vivo single voxel 1H MRS shows high specificity in detecting the main metabolic alterations in the primitive cerebellar lesion; which consist of very high amounts of the choline-containing compounds and of very low levels of creatine derivatives and N-acetylaspartate. Ex vivo HR-MAS NMR, performed at 9.4 Tesla on the neoplastic specimen collected during surgery, allows the unambiguous identification of several metabolites giving a more in-depth evaluation of the metabolic pattern of the lesion. The ex vivo HR-MAS NMR spectra show higher detail than that obtained in vivo. In addition, the spectroscopic data appear to correlate with some morphological features of the medulloblastoma. The present study shows that ex vivo HR-MAS 1H NMR is able to strongly improve the clinical possibility of in vivo MRS and can be used in conjunction with in vivo spectroscopy for clinical purposes. Three histological subtypes of meningiomas (meningothelial, fibrous and oncocytic) were analysed both by in vivo and ex vivo MRS experiments. The ex vivo HR-MAS investigations are very helpful for the assignment of the in vivo resonances of human meningiomas and for the validation of the quantification procedure of in vivo MR spectra. By using one- and two dimensional experiments, several metabolites in different histological subtypes of meningiomas, were identified. The spectroscopic data confirmed the presence of the typical metabolites of these benign neoplasms and, at the same time, that meningomas with different morphological characteristics have different metabolic profiles, particularly regarding macromolecules and lipids. The profile of total choline metabolites (tCho) and the expression of the Kennedy pathway genes in biopsies of human gliomas were also investigated using HR-MAS NMR, and microfluidic genomic cards. 1H HR-MAS spectra, allowed the resolution and relative quantification by LCModel of the resonances from choline (Cho), phosphorylcholine (PC) and glycerolphorylcholine (GPC), the three main components of the combined tCho peak observed in gliomas by in vivo 1H MRS spectroscopy. All glioma biopsies depicted an increase in tCho as calculated from the addition of Cho, PC and GPC HR-MAS resonances. However, the increase was constantly derived from augmented GPC in low grade NMR gliomas or increased PC content in the high grade gliomas, respectively. This circumstance allowed the unambiguous discrimination of high and low grade gliomas by 1H HR-MAS, which could not be achieved by calculating the tCho/Cr ratio commonly used by in vivo 1H MR spectroscopy. The expression of the genes involved in choline metabolism was investigated in the same biopsies. The present findings offer a convenient procedure to classify accurately glioma grade using 1H HR-MAS, providing in addition the genetic background for the alterations of choline metabolism observed in high and low gliomas grade. Chapter 5 reports the study on human gastrointestinal tract (stomach and colon) neoplasms. The human healthy gastric mucosa, and the characteristics of the biochemical profile of human gastric adenocarcinoma in comparison with that of healthy gastric mucosa were analyzed using ex vivo HR-MAS NMR. Healthy human mucosa is mainly characterized by the presence of small metabolites (more than 50 identified) and macromolecules. The adenocarcinoma spectra were dominated by the presence of signals due to triglycerides, that are usually very low in healthy gastric mucosa. The use of spin-echo experiments enable us to detect some metabolites in the unhealthy tissues and to determine their variation with respect to the healthy ones. Then, the ex vivo HR-MAS NMR analysis was applied to human gastric tissue, to obtain information on the molecular steps involved in the gastric carcinogenesis. A microscopic investigation was also carried out in order to identify and locate the lipids in the cellular and extra-cellular environments. Correlation of the morphological changes detected by transmission (TEM) and scanning (SEM) electron microscopy, with the metabolic profile of gastric mucosa in healthy, gastric atrophy autoimmune diseases (AAG), Helicobacter pylori-related gastritis and adenocarcinoma subjects, were obtained. These ultrastructural studies of AAG and gastric adenocarcinoma revealed lipid intra- and extra-cellularly accumulation associated with a severe prenecrotic hypoxia and mitochondrial degeneration. A deep insight into the metabolic profile of human healthy and neoplastic colon tissues was gained using ex vivo HR-MAS NMR spectroscopy in combination with multivariate methods: Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). The NMR spectra of healthy tissues highlight different metabolic profiles with respect to those of neoplastic and microscopically normal colon specimens (these last obtained at least 15 cm far from the adenocarcinoma). Furthermore, metabolic variations are detected not only for neoplastic tissues with different histological diagnosis, but also for those classified identical by histological analysis. These findings suggest that the same subclass of colon carcinoma is characterized, at a certain degree, by metabolic heterogeneity. The statistical multivariate approach applied to the NMR data is crucial in order to find metabolic markers of the neoplastic state of colon tissues, and to correctly classify the samples. Significant different levels of choline containing compounds, taurine and myoinositol, were observed. Chapter 6 deals with the metabolic profile of normal and tumoral renal human tissues obtained by ex vivo HR-MAS NMR. The spectra of human normal cortex and medulla show the presence of differently distributed osmolytes as markers of physiological renal condition. The marked decrease or disappearance of these metabolites and the high lipid content (triglycerides and cholesteryl esters) is typical of clear cell renal carcinoma (RCC), while papillary RCC is characterized by the absence of lipids and very high amounts of taurine. This research is a contribution to the biochemical classification of renal neoplastic pathologies, especially for RCCs, which can be evaluated by in vivo MRS for clinical purposes. Moreover, these data help to gain a better knowledge of the molecular processes envolved in the onset of renal carcinogenesis.

NMR, Metabonomics and Molecular Profiles: Applications to the Quality Assessment of Foodstuffs

Nuclear Magnetic Resonance (NMR) is a branch of spectroscopy that is based on the fact that many atomic nuclei may be oriented by a strong magnetic field and will absorb radiofrequency radiation at characteristic frequencies. The parameters that can be measured on the resulting spectral lines (line positions, intensities, line widths, multiplicities and transients in time-dependent experi-ments) can be interpreted in terms of molecular structure, conformation, molecular motion and other rate processes. In this way, high resolution (HR) NMR allows performing qualitative and quantitative analysis of samples in solution, in order to determine the structure of molecules in solution and not only. In the past, high-field NMR spectroscopy has mainly concerned with the elucidation of chemical structure in solution, but today is emerging as a powerful exploratory tool for probing biochemical and physical processes. It represents a versatile tool for the analysis of foods. In literature many NMR studies have been reported on different type of food such as wine, olive oil, coffee, fruit juices, milk, meat, egg, starch granules, flour, etc using different NMR techniques. Traditionally, univariate analytical methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due especially to the complexity of an NMR spectrum. In fact, the lat-ter is composed of different signals belonging to different mole-cules, but it is also true that the same molecules can be represented by different signals, generally strongly correlated. The univariate methods, in this case, takes in account only one or a few variables, causing a loss of information. Thus, when dealing with complex samples like foodstuff, univariate analysis of spectra data results not enough powerful. Spectra need to be considered in their wholeness and, for analysing them, it must be taken in consideration the whole data matrix: chemometric methods are designed to treat such multivariate data. Multivariate data analysis is used for a number of distinct, differ-ent purposes and the aims can be divided into three main groups: • data description (explorative data structure modelling of any ge-neric n-dimensional data matrix, PCA for example); • regression and prediction (PLS); • classification and prediction of class belongings for new samples (LDA and PLS-DA and ECVA). The aim of this PhD thesis was to verify the possibility of identify-ing and classifying plants or foodstuffs, in different classes, based on the concerted variation in metabolite levels, detected by NMR spectra and using the multivariate data analysis as a tool to inter-pret NMR information. It is important to underline that the results obtained are useful to point out the metabolic consequences of a specific modification on foodstuffs, avoiding the use of a targeted analysis for the different metabolites. The data analysis is performed by applying chemomet-ric multivariate techniques to the NMR dataset of spectra acquired. The research work presented in this thesis is the result of a three years PhD study. This thesis reports the main results obtained from these two main activities: A1) Evaluation of a data pre-processing system in order to mini-mize unwanted sources of variations, due to different instrumental set up, manual spectra processing and to sample preparations arte-facts; A2) Application of multivariate chemiometric models in data analy-sis.

Intra-population variation and geographic correlation in Canthon humectus hidalgoensis using FTIR-ATR spectroscopy

In some cases external morphology is not sufficient to discern between populations of a species, as occurs in the dung beetle Canthon humectus hidalgoensis Bates; and much less to determine phenotypic distances between them. FTIR-ATR spectroscopy show several advantages over other identification techniques (e.g. morphological, genetic, and cuticular hydrocarbons analysis) due to the non-invasive manner of the sample preparation, the relative speed of sample analysis and the low-cost of this technology. The infrared spectrum obtained is recognized to give a unique ‘fingerprint’ because vibrational spectra are specific and unique to the molecular nature of the sample. In our study, results showed that proteins, amino acids and aromatic ethers of insect exocuticle have promising discriminative power to discern between different populations of C. h. hidalgoensis. Furthermore, the correlation between geographic distances between populations and the chemical distances obtained by proteins + amino acids + aromatic ethers was statistically significant, showing that the spectral and spatial information available of the taxa together with appropriated chemometric methods may help to a better understanding of the identity, structure, dynamics and diversity of insect populations.

Integrated Omics for the global mapping of biomolecules in LDL

Circulating low density lipoproteins (LDL) are thought to play a crucial role in the onset and development of atherosclerosis, though the detailed molecular mechanisms responsible for their biological effects remain controversial. The complexity of biomolecules (lipids, glycans and protein) and structural features (isoforms and chemical modifications) found in LDL particles hampers the complete understanding of the mechanism underlying its atherogenicity. For this reason the screening of LDL for features discriminative of a particular pathology in search of biomarkers is of high importance. Three major biomolecule classes (lipids, protein and glycans) in LDL particles were screened using mass spectrometry coupled to liquid chromatography. Dual-polarity screening resulted in good lipidome coverage, identifying over 300 lipid species from 12 lipid sub-classes. Multivariate analysis was used to investigate potential discriminators in the individual lipid sub-classes for different study groups (age, gender, pathology). Additionally, the high protein sequence coverage of ApoB-100 routinely achieved (≥70%) assisted in the search for protein modifications correlating to aging and pathology. The large size and complexity of the datasets required the use of chemometric methods (Partial Least Square-Discriminant Analysis, PLS-DA) for their analysis and for the identification of ions that discriminate between study groups. The peptide profile from enzymatically digested ApoB-100 can be correlated with the high structural complexity of lipids associated with ApoB-100 using exploratory data analysis. In addition, using targeted scanning modes, glycosylation sites within neutral and acidic sugar residues in ApoB-100 are also being explored. Together or individually, knowledge of the profiles and modifications of the major biomolecules in LDL particles will contribute towards an in-depth understanding, will help to map the structural features that contribute to the atherogenicity of LDL, and may allow identification of reliable, pathology-specific biomarkers. This research was supported by a Marie Curie Intra-European Fellowship within the 7th European Community Framework Program (IEF 255076). Work of A. Rudnitskaya was supported by Portuguese Science and Technology Foundation, through the European Social Fund (ESF) and "Programa Operacional Potencial Humano - POPH".

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.

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.