A one-class classification approach for authentication of specialty coffees by inductively coupled plasma mass spectroscopy (ICP-MS)

Due to the lucrative nature of specialty coffees, there have been instances of adulteration where low-cost materials are mixed in to increase the overall volume, resulting in illegal profit. A widely used and recommended approach to detect possible adulteration is the application of one-class classifiers (OCC), which only require information about the target class to build the models. Thus, this work aimed to identify adulterations in specialty coffees with low-quality coffee using multielement analysis determined by ICP-MS and to evaluate the performance of one-class classifiers (dd-SIMCA, OCRF, and OCPLS). Therefore, authentic specialty coffee samples were adulterated with low-quality coffee in 25 % to 75 % (w/w) proportions. Samples were subjected to acid decomposition for analysis by ICP-MS. OCPLS method presented the best performance to detect adulterations with low-quality coffee in specialty coffees, showing higher specificity (SPE = 100 %) and reliability rate (RLR = 94.3 %).

The terroir of Brazilian Coffea canephora: Characterization of the chemical composition

FTIR spectroscopy and multivariate analysis were used in the chemical study of the terroirs of Coffea canephora. Conilon coffees from Espírito Santo and Amazon robusta from Matas of Rondônia, were separated by PCA, with lipids and caffeine being the markers responsible for the separation. Coffees from Bahia, Minas Gerais, and São Paulo did not exhibit separation, indicating that the botanical variety had a greater effect on the terroir than geographic origin. Thus, the genetic factor was investigated considering the conilon and robusta botanical varieties. This last group was composed of hybrid robusta and apoatã. The DD-SIMCA favored the identification of the genetic predominance of the samples. PLS-DA had a high classification performance regarding the conilon, hybrid robusta, and apoatã genetic nature. Lipids, caffeine, chlorogenic acids, quinic acid, trigonelline, proteins, amino acids, and carbohydrates were identified as chemical markers that discriminated the genetic groups.

Chemical profile and cytotoxic evaluation of aerial parts of Euphorbia tirucalli L. on gastric adenocarcinoma (AGS cells)

Ethanol extract from the aerial parts of Euphorbia tirucalli L. as well as the latex of the plant suspended in water are used by the Brazilian population for the treatment of various diseases, including cancer. The purposes of this study were to determine if the ethanol extract is effective as cytotoxic agent against gastric adenocarcinoma cells (AGS) and its chemical composition by GC-MS, ESI-(-)-FT-ICR MS and (-)-ESI-LTQ-MS/MS. The results were compared with that of latex previously described by us. Hexane and aqueous fractions showed higher cytotoxicity on AGS cells. Nine triterpene compounds were detected by GC-MS in hexane fraction, including euphol and friedelin, while ellagic acid was identified as main phenolic compound in aqueous extract. Therefore, the greater cytotoxic activity of the ethanol extract of the aerial parts of Euphorbia tirucalli for gastric cancer, when compared to latex, seems to originate from the antiproliferative effects of ellagic acid and triterpenes.

SHS-GC-MS applied in Coffea arabica and Coffea canephora blend assessment

Considering the great economic significance of Coffea arabica (arabica) associated with the lower production cost of C. canephora (conilon), blends of these coffees are commercially available to reduce costs and combine sensory attributes. Thus, analytical tools are required to ensure consistency between real and labeled compositions. In this sense, chromatographic methods based on volatile analysis using static headspace-gas chromatography-mass spectrometry (SHS-GC-MS) and Fourier transform infrared (FTIR) spectroscopy associated with chemometric tools were proposed for the identification and quantification of arabica and conilon blends. The peak integration from the total ion chromatogram (TIC) and extracted ion chromatogram (EIC) was compared in multivariate and univariate scenarios. The optimized partial least squares (PLS) models with uninformative variable elimination (UVE) and chromatographic data (TIC and EIC) have similar accuracy according to a randomized test, with prediction errors between 3.3% and 4.7% and R_p² > 0.98. There was no difference between the univariate models for the TIC and EIC, but the FTIR model presented a lower performance than GC-MS. The multivariate and univariate models based on chromatographic data had similar accuracy. For the classification models, the FTIR, TIC, and EIC data presented accuracies from 96% to 100% and error rates from 0% to 5%. Multivariate and univariate analyses combined with chromatographic and spectroscopic data allow the investigation of coffee blends.

The Use of Factorial Planning in the Investigation of Structural Electronics Property for the Rational Design of Benzophenone Derivatives

Human exposure to ultraviolet (UV) radiation leads photochemical excitation processes in the skin, causing problems to human health. The use of photoprotectors helps to minimize these intrinsic hazards. Benzophenone molecules stand out for the absorption of energy in the UVA and UVB range and structural changes in these, it is an area of ​​interest to obtain safer and more effective molecules. This work applied the full factorial design methodology 24 in an investigation by molecular modeling using semi-empirical method PM7, in order to evaluate the impact generated on the decrease of the energy GAP with the insertion of OH (level +) and H (level –-). The results showed unfavorable and favorable contributions between interactions and in the main effect, and the compounds disubstituted in ortho and para position for the same aromatic ring showed a better percentage of contribution, indicating that these conditions are relevant for greater reactivity compared to the others. Thus, we conclude that the use of the experimental planning methodology is an ally in obtaining information for planning new protective filtersmore stable and safe.

Multivariate calibration applied to study of volatile predictors of arabica coffee quality

The chemical complexity of coffee influences the sensory evaluation of the beverage, the main method used to define the quality of the coffee. In view of the subjectivity that method offers, we propose the association of an instrumental method with multivariate calibration (PLS and GA-SVR) to predict the quality of arabica coffee as support for sensory analysis. Arabica coffee samples were submitted to sensory evaluation using the Specialty Coffee Association (SCA) protocol and HS-SPME-GC/MS analysis. The models presented RMSEp results from 0.20 to 0.25, within the evaluation range the quality levels of sensory attributes (0.25). For the fragrance/aroma attribute, a value of R²p equal to 0.8503 was reached. 15 volatile compounds were identified as responsible for predicting the quality of arabica coffee, among which, 1-nonadecene was first reported as an impact compound in the prediction of important sensory attributes.

Lactose quantification in bovine milk by nuclear magnetic resonance without deuterated solvent (No-D qNMR)

Milk is a homogeneous mixture of substances such as lactose, proteins, and glycerides. Among carbohydrates, lactose is a disaccharide composed of glucose and galactose, and it is present in bovine milk at a level of 4.6%. According to resolution no. 135 of the National Health Surveillance Agency (ANVISA) from Brazil, dairy products labeled "lactose-free" must contain 1.0 mg mL-1 or less of this disaccharide. Thus, this work aims to develop and validate a method for quantifying the lactose content by quantitative nuclear magnetic resonance without the use of deuterated solvent (No-D qMNR). The validation of the developed method followed the norms provided by ANVISA resolution RDC no. 166, based on the figures of merit such as selectivity, linearity, the limit of detection (LOD) and quantification (LOQ), accuracy, precision, and robustness. The obtained results validated the method due to excellent linearity, demonstrated by the value of R > 0.990 and the homoscedasticity of the results, as well as precision, accuracy, and robustness values lower than 5%. Furthermore, LOD and LOQ values around 0.1345 mg mL-1 and 0.4076 mg mL-1, respectively, were obtained, which are lower than those required by legislation. The No-D qNMR technique was also able to quantify lactose content in commercial lactose-free milk.

Analytical methods to access the chemical composition of an Euphorbia tirucalli anticancer latex from traditional Brazilian medicine

ETHNOPHARMACOLOGICAL RELEVANCE

Euphorbia tirucalli L. is widely used by Brazilian folk medicine, mainly for its anticancer activity. However, its commercialization was banned by The Brazilian National Sanitary Surveillance Agency (ANVISA) due to the presence of some compounds considered toxic, such as the diterpene esters.

AIM OF THE STUDY

Chemical and biological analyses were performed with the Brazilian Euphorbia tirucalli latex to support its wide traditional use in Brazil.

MATERIAL AND METHODS

Latex was collected by using two procedures, in a solution of dichloromethane: methanol (3:1, 100 mL) and in 100 mL of distilled water. The first procedure was concentrated as a crude extract and the second one was partitioned with hexane and dichloromethane. The partitions and crude extract were subjected to phytochemical analyses using three different methods: Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) with electrospray ionization sources in negative mode (ESI(-)) as well as in tandem mass spectrometry ESI(-) MS/MS and Atmospheric Pressure Chemical Ionization in positive mode (APCI(+)), Gas Chromatography coupled Mass Spectrometry (GC-MS) and Nuclear Magnetic Resonance (NMR) (¹H-NMR and ¹³C-NMR). The cytotoxic potential was evaluated using the crude extract in macrophages RAW 264.7 and Gastric Adenocarcinoma (AGS) cancer cells. The evaluation of immunomodulatory activity was made through the detection of Nitric Oxide (NO) and cytokines as Tumor necrosis factor α (TNF-α) and Interleukin-6 (IL-6).

RESULTS

GC-MS showed the presence of some esters of fatty acids, for instance myristic, palmitic, stearic, oleic and linoleic acid and, mainly, triterpenes such as euphol and tirucallol. With NMR, most of the signals were related to triterpenoids euphol and tirucallol. However, when the latex was analyzed with ESI(-) FT-ICR MS, a wide variety of molecules from different classes of natural products (fatty acids, diterpenes, triterpenes, steroids) were found. On the other hand, when APCI(+)FT-ICR MS was used, the ion M^+. At ratio mass-charge (m/z) 426.38567, related to triterpenes euphol and tirucallol masses, presented the most intense peak, with a mass error of -0.11, indicating high accuracy. Diterpene esters from 4-deoxyphorbol and ingenol were identified only by ESI(-)FT-ICR MS and ESI(-)FT-ICR MS/MS. When evaluated biologically, the crude latex showed immunomodulatory activity, as it reduced the production of the pro-inflammatory cytokines TNF-α, IL-6 and NO, and the effect on NO reduction was more significant, obtaining in a similar result to the N(ω)-Nitro-L-Arginine Methyl Ester (L-NAME) standards, as well as significant cytotoxic activity with half inhibitory concentration (IC₅₀) values of 69.43 ± 1.29 μg/mL against AGS without damaging healthy ones.

CONCLUSION

It was verified that the Brazilian Euphorbia tirucalli latex consists mainly of the triterpenes euphol and tirucallol, which may be the main cause of the anticancer activity attributed to the plant, but many other minor compounds could have been determined by the FT-ICR MS method, such as the diterpene esters. It has antitumor potential because it acts selectively against cancer cells and it also prevents the progression of tumors, because it carries an important immunomodulatory effect.

Quantification of animal fat biodiesel in soybean biodiesel and B20 diesel blends using near infrared spectroscopy and synergy interval support vector regression

In this work, multivariate calibration based on partial least squares (PLS) and support vector regression (SVR) using the whole spectrum and variable selection by synergy interval (siPLS and siSVR) were applied to NIR spectra for the determination of animal fat biodiesel content in soybean biodiesel and B20 diesel blends. For all models, prediction errors, bias test for systematic errors and permutation test for trends in the residuals were calculated. The siSVR produced significantly lower prediction errors compared to the full spectrum methods and siPLS, with a root mean squares error (RMSEP) of 0.18%(w/w) (concentration range: 0.00%-69.00%(w/w)) in the soybean biodiesel blend and 0.10%(w/w) in the B20 diesel (concentration range: 0.00%-13.80%(w/w)). Additionally, in the models for the determination of animal fat biodiesel in blends with soybean diesel, PLS and SVR showed evidence of systematic errors, and PLS/siPLS presented trends in residuals based on the permutation test. For the B20 diesel, PLS presented evidence of systematic errors, and siPLS presented trends in the residuals.