Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS) can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA) was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis) and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

Serum metabolic profiling of human gastric cancer based on gas chromatography/mass spectrometry

Research on molecular mechanisms of carcinogenesis plays an important role in diagnosing and treating gastric cancer. Metabolic profiling may offer the opportunity to understand the molecular mechanism of carcinogenesis and help to non-invasively identify the potential biomarkers for the early diagnosis of human gastric cancer. The aims of this study were to explore the underlying metabolic mechanisms of gastric cancer and to identify biomarkers associated with morbidity. Gas chromatography/mass spectrometry (GC/MS) was used to analyze the serum metabolites of 30 Chinese gastric cancer patients and 30 healthy controls. Diagnostic models for gastric cancer were constructed using orthogonal partial least squares discriminant analysis (OPLS-DA). Acquired metabolomic data were analyzed by the nonparametric Wilcoxon test to find serum metabolic biomarkers for gastric cancer. The OPLS-DA model showed adequate discrimination between cancer and non-cancer cohorts while the model failed to discriminate different pathological stages (I-IV) of gastric cancer patients. A total of 44 endogenous metabolites such as amino acids, organic acids, carbohydrates, fatty acids, and steroids were detected, of which 18 differential metabolites were identified with significant differences. A total of 13 variables were obtained for their greatest contribution in the discriminating OPLS-DA model [variable importance in the projection (VIP) value >1.0], among which 11 metabolites were identified using both VIP values (VIP >1) and the Wilcoxon test. These metabolites potentially revealed perturbations of glycolysis and of amino acid, fatty acid, cholesterol, and nucleotide metabolism of gastric cancer patients. These results suggest that gastric cancer serum metabolic profiling has great potential in detecting this disease and helping to understand its metabolic mechanisms.

Evaluation of noni (Morinda citrifolia) volatile profile by dynamic headspace and gas chromatography-mass spectrometry

Noni is a fruit that has interested the scientific community due to its medicinal and functional activities. Different products that contain noni are already in the market, but their consumption could be impaired by their distinctive unpleasant aroma and flavor. The aim of this work was to evaluate the noni pulp volatile profile by dynamic headspace and gas chromatography-mass spectrometry. Thirty seven volatile compounds were detected, mainly alcohols (63.3%), esters (26.9%), cetones (7.4%), and acids (1.2%).

(E)-2-Nonenal determination in brazilian beers using headspace solid-phase microextraction and gas chromatographic coupled mass spectrometry (HS-SPME-GC-MS)

(E)-2-nonenal is considered an important off-flavor of beer, related to the flavor of beer staling. In this study, a new method for determination of (E)-2-nonenal in beer using headspace solid-phase microextraction and gas chromatographic coupled mass spectrometry (HS-SPME-GC-MS) was developed and applied in Brazilian beer samples. The extractions were carried out in CAR-PDMS (carboxen-polydimethylsiloxane) fiber and the best results were found with 15 minutes of equilibrium and 90 minutes of extraction at 50 °C. The method was linear in the range from 0.02 to 4.0 μg.L-1 with correlation coefficient of 0.9994. The limits of detection and quantification were 0.01 and 0.02 μg.L-1, respectively. 96.5% of recovery and 4% precision (RSD) were obtained in the fortification of beer samples with 2.0 μg.L-1 of (E)-2-nonenal. The developed method proved to be simple, efficient and highly sensitive to the determination of this analyte being easily applied in the quality control of the brewery. (E)-2-nonenal was found in all beer samples analyzed with levels between 0.17 and 0.42 μg.L-1.

Monitoring of wine aging process by electrospray ionization mass spectrometry

The characterization of wine samples by direct insertion electrospray ionization mass spectrometry (ESI-MS), without pre-treatment or chromatographic separation, in a process denominated fingerprinting, has been applied to several samples of wine produced with grapes of the Pinot noir, Merlot and Cabernet Sauvignon varieties from the state o Rio Grande do Sul, in Brazil. The ESI-MS fingerprints of the samples detected changes which occurred during the aging process in the three grape varieties. Principal Component Analysis (PCA) of the negative ion mode fingerprints was used to group the samples, pinpoint the main changes in their composition, and indicate marker ions for each group of samples.

Development and validation of a method for detection and quantification of ochratoxin A in green coffee using liquid chromatography coupled to mass spectrometry

A method using Liquid Chromatography Tanden Mass Spectrometry (LC-MS/MS) with matrix-matched calibration curve was developed and validated for determining ochratoxin A (OTA) in green coffee. Linearity was found between 3.0 and 23.0 ng.g-1. Mean recoveries ranged between 90.45% and 108.81%; the relative standard deviation under repeatability and intermediate precision conditions ranged from 5.39% to 9.94% and from 2.20% to 14.34%, respectively. The limits of detection and quantification were 1.2 ng.g-1 and 3.0 ng.g-¹, respectively. The method developed was suitable and contributed to the field of mycotoxin analysis, and it will be used for future production of the Certified Reference Material (CRM) for OTA in coffee.

Characterization and classification of pequi trees (Caryocar brasiliense Camb.) based on the profile of volatile constituents using headspace solid-phase microextraction - gas chromatography - mass spectrometry and multivariate analysis

In order to determine the variability of pequi tree (Caryocar brasiliense Camb.) populations, volatile compounds from fruits of eighteen trees representing five populations were extracted by headspace solid-phase microextraction and analyzed by gas chromatography-mass spectrometry. Seventy-seven compounds were identified, including esters, hydrocarbons, terpenoids, ketones, lactones, and alcohols. Several compounds had not been previously reported in the pequi fruit. The amount of total volatile compounds and the individual compound contents varied between plants. The volatile profile enabled the differentiation of all of the eighteen plants, indicating that there is a characteristic profile in terms of their origin. The use of Principal Component Analysis and Cluster Analysis enabled the establishment of markers (dendrolasin, ethyl octanoate, ethyl 2-octenoate and β-cis-ocimene) that discriminated among the pequi trees. According to the Cluster Analysis, the plants were classified into three main clusters, and four other plants showed a tendency to isolation. The results from multivariate analysis did not always group plants from the same population together, indicating that there is greater variability within the populations than between pequi tree populations.