Quantitative Chemical Composition, Anti-Oxidant Activity, and Inhibition of TNF Release by THP-1 Cells Induced by Extracts of Echinodorus macrophyllus and Echinodorus grandiflorus.

This study investigated the similarities between Echinodorus macrophyllus and Echinodorus grandiflorus, plant species that are traditionally used in Brazil to treat rheumatism and arthritis, whose anti-inflammatory effects are supported by scientific evidence. The contents of cis- and trans-aconitic acid, homoorientin, chicoric acid, swertisin, caffeoyl-feruloyl-tartaric acid, and di-feruloyl-tartaric acid were quantified by UPLC-DAD in various hydroethanolic extracts from the leaves, whereas their anti-oxidant activity and their effect on TNF release by LPS-stimulated THP-1 cells were assessed to evaluate potential anti-inflammatory effects. The 50% and 70% ethanol extracts showed higher concentrations of the analyzed markers in two commercial samples and a cultivated specimen of E. macrophyllus, as well as in a commercial lot of E. grandiflorus. However, distinguishing between the species based on marker concentrations was not feasible. The 50% and 70% ethanol extracts also exhibited higher biological activity, yet they did not allow differentiation between the species, indicating similar chemical composition and biological effects. Principal component analysis highlighted comparable chemical composition and biological activity among the commercial samples of E. macrophyllus, while successfully distinguishing the cultivated specimen from the commercial lots. In summary, no differences were observed between the two species in terms of the evaluated chemical markers and biological activities.

Authenticity study of commercial samples of St. John's wort by paper spray ionization mass spectrometry and chemometric tools.

Hypericum perforatum L. (St. John's wort) is one of the world's most consumed medicinal plants for treating depression and psychiatric disorders. Counterfeiting can occur in the medicinal plant trade, either due to the lack of active ingredients or the addition of substances not mentioned on the labels, often without therapeutic value or even harmful to health. Hence, 43 samples of St. John's wort commercially acquired in different Brazilian regions and other countries were analyzed by paper spray ionization mass spectrometry (PS-MS) and modeled by principal component analysis. Hence, samples (plants, capsules, and tablets) were extracted with ethanol in a solid-liquid extraction. For the first time, PS-MS analysis allowed the detection of counterfeit H. perforatum samples containing active principles typical of other plants, such as Ageratum conyzoides and Senna spectabilis. About 52.3% of the samples were considered adulterated for having at least one of these two species in their composition. Furthermore, out of 35 samples produced in Brazil, only 13 were deemed authentic, having only H. perforatum. Therefore, there is a clear need to improve these drugs' quality control in Brazil.

One-class model with two decision thresholds for the rapid detection of cashew nuts adulteration by other nuts.

A green screening method to determine cashew nut adulteration with Brazilian nut, pecan nut, macadamia nut and peanut was proposed. The method was based on the development of a one-class soft independent modelling of class analogy (SIMCA) model for non-adulterated cashew nuts using near-infrared (NIR) spectra obtained with portable equipment. Once the model is established, the assignment of unknown samples depends on the threshold established for the authentic class, which is a key aspect in any screening approach. The authors propose innovatively to define two thresholds: lower model distance limit and upper model distance limit. Samples with distances below the lower threshold are assigned as non-adulterated with a 100% probability; samples with distance values greater than the upper threshold are assigned as adulterated with a 100% probability; and samples with distances within these two thresholds will be considered uncertain and should be submitted to a confirmatory analysis. Thus, the possibility of error in the sample assignment significantly decreases. In the present study, when just one threshold was defined, values greater than 95% for the optimized threshold were obtained for both selectivity and specificity. When two class thresholds were defined, the percentage of samples with uncertain assignment changes according to the adulterant considered, highlighting the case of peanuts, in which 0% of uncertain samples was obtained. Considering all adulterants, the number of samples that were submitted to a confirmatory analysis was quite low, 5 of 224 adulterated samples and 3 of 56 non-adulterated samples.

A t-test ranking-based discriminant analysis for classification of free-range and barn-raised broiler chickens by 1H NMR spectroscopy.

Intensive systems of raising chickens in barns prevail worldwide for financial reasons. In contrast, free-range chickens are raised in better welfare conditions, and preferred by consumers due to their distinctive taste/flavor, having higher market prices. Thus, free-range chickens have been the target of frauds. In this study, 1H NMR metabolic profiles of breasts of free-range and barn-raised broilers (108 individuals) were compared by two discriminant models, based on t-test ranking and partial least squares (PLS-DA). Both models provided 100 % of correct classification in both training and test sets, being the univariate model based on t-test screening simpler and more robust. Among other differences, barn-raised broilers presented lower carnosine and anserine concentrations, and higher free amino acids contents. Univariate discrimination was based on the ratio of two NMR signals assigned to ß-alanine and carnosine + anserine, respectively. As an additional advantage, this profiling method could be adapted to other measurement platforms.

Screening method for the characterization of anabolic steroids seized in Brazil using paper spray mass spectrometry and chemometric tools.

This paper reports the use of paper spray mass spectrometry (PS-MS) combined with chemometric models to analyze seized samples of anabolic steroids. Because many forensic laboratories typically demand high-throughput analysis for this type of sample, we developed a quicker and simpler alternative analytical method for routine analysis with minimal sample preparation. Oily samples (n = 39) resulting from seizures carried out by Brazilian Federal and State Police units were selected for this study. These samples were analyzed by PS-MS in the positive ion mode and full scan (50-1000 m/z), providing spectra containing patterns of the respective active ingredients present in each product. A principal component analysis (PCA) model was built, which discriminated samples mainly according to their active ingredients and allowed to detect and characterize some cases of product counterfeiting. The variable selection method ordered predictors selection was employed jointly with PCA to improve sample cluster separation and to provide model simplification. The final PCA model was built with three principal components and using only 28 spectral variables. This model accounted for 69.82% of the variance and discriminated samples according to their specific active ingredients.

Definition of chemical markers for Hancornia speciosa Gomes by chemometric analysis based on the chemical composition of extracts, their vasorelaxant effect and α-glucosidase inhibition.

ETHNOPHARMACOLOGICAL RELEVANCE: Hancornia speciosa Gomes (Apocynaceae) is a tree found in the Brazilian savannah, traditionally used to treat several diseases, including diabetes and hypertension. The anti-hypertensive activity of H. speciosa leaves (HSL) has been demonstrated in different models and is credited to the vasodilator effect and ACE (angiotensin-converting enzyme) inhibition. The hypoglycemic effect of HSL has been also reported. AIM OF THE STUDY: To establish correlations between the biological activities elicited by H. speciosa extracts and the contents of their major compounds, aiming to define chemical markers related to the potential antihypertensive and antidiabetic effects of the species. Additionally, it aimed to isolate and characterize the chemical structure of a marker related to the α-glucosidase inhibitory effect. MATERIALS AND METHODS: Extracts of a single batch of H. speciosa leaves were prepared by extraction with distinct solvents (ethanol/water in different proportions; methanol/ethyl acetate), employing percolation or static maceration as extraction techniques, at different time intervals. The contents of chlorogenic acid, rutin and FlavHS (a tri-O-glycoside of quercetin) were quantified by a developed and validated HPLC-PDA method. Bornesitol was determined by HPLC-PDA after derivatization with tosyl chloride, whereas total flavonoids were measured spectrophotometrically. Identification of other constituents in the extracts was performed by UPLC-DAD-ESI-MS/MS analysis. The vasorelaxant activity was assayed in rat aortic rings precontracted with phenylephrine, and α-glucosidase inhibition was tested in vitro. Principal component analysis (PCA) was employed to evaluate the contribution of each marker to the biological responses. Isolation of compound 1 was carried out by column chromatography and structure characterization was accomplished by NMR and UPLC-DAD-ESI-MS/MS analyses. RESULTS: The contents of the chemical markers (mean ± s.d. % w/w) varied significantly among the extracts, including total flavonoids (2.68 ± 0.14 to 5.28 ± 0.29), bornesitol (5.11 ± 0.26 to 7.75 ± 0.78), rutin (1.46 ± 0.06 to 1.97 ± 0.02), FlavHS (0.72 ± 0.05 to 0.94 ± 0.14) and chlorogenic acid (0.67 ± 0.09 to 0.91 ± 0.02). All extracts elicited vasorelaxant effect (pIC50 between 4.97 ± 0.22 to 6.48 ± 0.10) and α-glucosidase inhibition (pIC50 between 3.49 ± 0.21 to 4.03 ± 0.10). PCA disclosed positive correlations between the vasorelaxant effect and the contents of chlorogenic acid, rutin, total flavonoids, and FlavHS, whereas a negative correlation was found with bornesitol concentration. No significant correlation between α-glucosidase inhibition and the contents of the above-mentioned compounds was found. On the other hand, PCA carried out with the areas of the ten major peaks from the chromatograms disclosed positive correlations between a peak ascribed to co-eluted triterpenes and α-glucosidase inhibition. A triterpene was isolated and identified as 3-O-ß-(3'-R-hydroxy)-hexadecanoil-lupeol. CONCLUSION: According to PCA results, the vasorelaxant activity of H. speciosa extracts is related to flavonoids and chlorogenic acid, whereas the α-glucosidase inhibition is associated with lipophilic compounds, including esters of lupeol like 3-O-ß-(3'-R-hydroxy)-hexadecanoil-lupeol, described for the first time for the species. These compounds can be selected as chemical markers for the quality control of H. speciosa plant drug and derived extracts.

Screening method for the rapid detection of diethylene glycol in beer based on chemometrics and portable near-infrared spectroscopy.

A recent case of contamination of some batches of a Brazilian beer brand with diethylene glycol (DEG) had great repercussion, resulting in at least seven deaths. In this article, a direct method was developed for the rapid detection of DEG in beer samples based on portable near-infrared spectroscopy combined with partial least squares discriminant analysis (PLS-DA). The discriminant model was built with 100 uncontaminated beer samples and 100 samples containing DEG in a concentration range between 10 and 1000 mg L-1, totalizing 200 samples of different brands and styles. The method was validated by estimating figures of merit, such as false positive and false negative rates, sensitivity, specificity, accuracy, accordance, and concordance. The decision limit (CCα) of the method was 52 mg L-1 and the detection capability (CCß) was 106 mg L-1. This method does not consume reagents/solvents and can be suitable for the beer industry quality control or forensic investigations.

Detection of adulterations in a valuable Brazilian honey by using spectrofluorimetry and multiway classification.

Spectrofluorimetry combined with multiway chemometric tools were applied to discriminate pure Aroeira honey samples from samples adulterated with corn syrup, sugar cane molasses and polyfloral honey. Excitation emission spectra were acquired for 232 honey samples by recording excitation from 250 to 500 nm and emission from 270 to 640 nm. Parallel factor analysis (PARAFAC), partial least squares discriminant analysis (PLS-DA), unfolded PLS-DA (UPLS-DA) and multilinear PLS-DA (NPLS-DA) methods were used to decompose the spectral data and build classification models. PLS-DA models presented poor classification rates, demonstrating the limitation of the traditional two-way methods for this dataset, and leading to the development of three-way classification models. Overall, UPLS-DA provided the best classification results with misclassification rates of 4% and 8% for the training and test sets, respectively. These results showed the potential of the proposed method for routine laboratory analysis as a simple, reliable, and affordable tool.

A data fusion model merging information from near infrared spectroscopy and X-ray fluorescence. Searching for atomic-molecular correlations to predict and characterize the composition of coffee blends.

This article aims to develop and validate a multivariate model for quantifying Robusta-Arabica coffee blends by combining near infrared spectroscopy (NIRS) and total reflection X-ray fluorescence (TXRF). For this aim, 80 coffee blends (0.0-33.0%) were formulated. NIR spectra were obtained in the wavenumber range 11100-4950 cm-1 and 14 elements were determined by TXRF. Partial least squares models were built using data fusion at low and medium levels. In addition, selection of predictive variables based on their importance indices (SVPII) improved results. The best model reduced the number of variables from 1114 to 75 and root mean square error of prediction from 4.1% to 1.7%. SVPII selected NIR regions correlated with coffee components, and the following elements were chosen: Ti, Mn, Fe, Cu, Zn, Br, Rb, Sr. The model interpretation took advantage of the data fusion between atomic and molecular spectra in order to characterize the differences between these coffee varieties.

Combining mid infrared spectroscopy and paper spray mass spectrometry in a data fusion model to predict the composition of coffee blends.

This paper describes a robust multivariate model for quantifying and characterizing blends of Robusta and Arabica coffees. At different degrees of roasting, 120 ground coffee blends (0.0-33.0%) were formulated. Spectra were obtained by two different techniques, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and paper spray mass spectrometry (PS-MS). Partial least squares (PLS) models were built individually with the two types of spectra. Nevertheless, better predictions were obtained by low and medium-level data fusion, taking advantage from the synergy between these two data sets. Data fusion models were improved by variable selection, using genetic algorithms (GA) and ordered predictors selection (OPS). The smallest prediction errors were provided by OPS low-level data fusion model. The number of variables used for regression was reduced from 2145 (full spectra) to 230. Model interpretation was performed by assigning some of the selected variables to specific coffee components, such as trigonelline and chlorogenic acids.

Discrimination between conventional and omega-3 fatty acids enriched eggs by FT-Raman spectroscopy and chemometric tools.

This work developed an analytical method to differentiate conventional and omega-3 fat acids enriched eggs by Raman spectroscopy and multivariate supervised classification with Partial Least Squares Discriminant Analysis (PLS-DA). Forty samples of enriched eggs and forty samples of different types of common eggs from different batches were used to build the model. Firstly, gas chromatography was employed to analyze fatty acid profiles in egg samples. Raman spectra of the yolk extracts were recorded in the range from 3100 to 990 cm-1. PLS-DA model was able to correctly classify samples with nearly 100% success rate. This model was validated estimating appropriate figures of merit. Predictions uncertainties were also estimated by bootstrap resampling. The most discriminant Raman modes were identified based on VIP (variables importance in projection) scores. This method has potential to assist food industries and regulatory agencies for food quality control, allowing detecting frauds and enabling faster and reliable analyzes.

Detection of adulterants in grape nectars by attenuated total reflectance Fourier-transform mid-infrared spectroscopy and multivariate classification strategies.

There is no any doubt about the importance of food fraud control, as it has implications in food safety and in consumer health. Focusing on fruit beverages, some types of adulterations have been detected more frequently, such as substitution with less expensive fruits. A methodology based on attenuated total reflectance Fourier-transform mid-infrared spectroscopy (ATR-FTIR) and multivariate classification was applied to detect whether grape nectars were adulterated by substitution with apple juice or cashew juice. A total of 126 samples were obtained and analyzed. Two strategies were proposed: one-class and multiclass approaches. Soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and partial least squares density modeling (PLS-DM) were used to build the models. Among them, PLS-DA presented the best performance with a sensitivity and specificity of nearly 100%. The multiclass strategy was preferred if the adulterants to be studied are known because it provides additional information.

Variable selection for multivariate classification aiming to detect individual adulterants and their blends in grape nectars.

During the quality inspection control of fruit beverages, some types of adulterations can be detected, such as the addition or substitution with less expensive fruits. To determine whether grape nectars were adulterated by substitution with apple or cashew juice or by a mixture of both, a methodology based on attenuated total reflectance Fourier transform mid infrared spectroscopy (ATR-FTIR) and multivariate classification methods was proposed. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) models were developed as multi-class methods (classes unadulterated, adulterated with cashew and adulterated with apple) with the full-spectra. PLS-DA presented better performance parameters than SIMCA in the classification of samples with just one adulterant, while poor results were achieved for samples with blends of two adulterants when using both classification methods. Three variable selection methods were tested in order to improve the effectiveness of the classification models: interval partial least squares (iPLS), variable importance in projection scores (VIP scores) and a genetic algorithm (GA). Variable selection methods improved the performance parameters for the SIMCA and PLS-DA methods when they were used to predict samples with only one adulterant. Only PLS-DA coupled with iPLS was able to classify samples with blends of two adulterants, providing sensitivity values between 100% and 83% at 100% specificity for the three studied classes.

Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods.

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated.

Paper spray mass spectrometry and chemometric tools for a fast and reliable identification of counterfeit blended Scottish whiskies.

A direct method based on the application of paper spray mass spectrometry (PS-MS) combined with a chemometric supervised method (partial least square discriminant analysis, PLS-DA) was developed and applied to the discrimination of authentic and counterfeit samples of blended Scottish whiskies. The developed methodology employed the negative ion mode MS, included 44 authentic whiskies from diverse brands and batches and 44 counterfeit samples of the same brands seized during operations of the Brazilian Federal Police, totalizing 88 samples. An exploratory principal component analysis (PCA) model showed a reasonable discrimination of the counterfeit whiskies in PC2. In spite of the samples heterogeneity, a robust, reliable and accurate PLS-DA model was generated and validated, which was able to correctly classify the samples with nearly 100% success rate. The use of PS-MS also allowed the identification of the main marker compounds associated with each type of sample analyzed: authentic or counterfeit.

Paper Spray Mass Spectrometry for the Forensic Analysis of Black Ballpoint Pen Inks.

This article describes the use of paper spray mass spectrometry (PS-MS) for the direct analysis of black ink writings made with ballpoint pens. The novel approach was developed in a forensic context by first performing the classification of commercially available ballpoint pens according to their brands. Six of the most commonly worldwide utilized brands (Bic, Paper Mate, Faber Castell, Pentel, Compactor, and Pilot) were differentiated according to their characteristic chemical patterns obtained by PS-MS. MS on the negative ion mode at a mass range of m/z 100-1000 allowed prompt discrimination just by visual inspection. On the other hand, the concept of relative ion intensity (RII) and the analysis at other mass ranges were necessary for the differentiation using the positive ion mode. PS-MS combined with partial least squares (PLS) was utilized to monitor changes on the ink chemical composition after light exposure (artificial aging studies). The PLS model was optimized by variable selection, which allowed the identification of the most influencing ions on the degradation process. The feasibility of the method on forensic investigations was also demonstrated in three different applications: (1) analysis of overlapped fresh ink lines, (2) analysis of old inks from archived documents, and (3) detection of alterations (simulated forgeries) performed on archived documents. Graphical Abstract ᅟ.

Paper spray mass spectrometry and PLS-DA improved by variable selection for the forensic discrimination of beers.

Paper spray mass spectrometry (PS-MS) combined with partial least squares discriminant analysis (PLS-DA) was applied for the first time in a forensic context to a fast and effective differentiation of beers. Eight different brands of American standard lager beers produced by four different breweries (141 samples from 55 batches) were studied with the aim at performing a differentiation according to their market prices. The three leader brands in the Brazilian beer market, which have been subject to fraud, were modeled as the higher-price class, while the five brands most used for counterfeiting were modeled as the lower-price class. Parameters affecting the paper spray ionization were examined and optimized. The best MS signal stability and intensity was obtained while using the positive ion mode, with PS(+) mass spectra characterized by intense pairs of signals corresponding to sodium and potassium adducts of malto-oligosaccharides. Discrimination was not apparent neither by using visual inspection nor principal component analysis (PCA). However, supervised classification models provided high rates of sensitivity and specificity. A PLS-DA model using full scan mass spectra were improved by variable selection with ordered predictors selection (OPS), providing 100% of reliability rate and reducing the number of variables from 1701 to 60. This model was interpreted by detecting fifteen variables as the most significant VIP (variable importance in projection) scores, which were therefore considered diagnostic ions for this type of beer counterfeit.