Insights into the Effects of Crack Abuse on the Human Metabolome Using a NMR Approach.

Approximately 255 million people consume illicit drugs every year, among which 18 million use cocaine. A portion of this drug is represented by crack, but it is difficult to estimate the number of users since most are marginalized. However, there are no recognized efficacious pharmacotherapies for crack-cocaine dependence. Inflammation and infection in cocaine users may be due to behavior adopted in conjunction with drug-related changes in the brain. To understand the metabolic changes associated with the drug abuse disorder and identify biomarkers, we performed a 1H NMR-based metabonomic analysis of 44 crack users' and 44 healthy volunteers' blood serum. The LDA model achieved 98% of accuracy. From the water suppressed 1H NMR spectra analyses, it was observed that the relative concentration of lactate was higher in the crack group, while long chain fatty acid acylated carnitines were decreased, which was associated with their nutritional behavior. Analyses of the aromatic region of CPMG 1H NMR spectra demonstrated histidine and tyrosine levels increased in the blood serum of crack users. The reduction of carnitine and acylcarnitines and the accumulation of histidine in the serum of the crack users suggest that histamine biosynthesis is compromised. The tyrosine level points to altered dopamine concentration.

Fast discrimination of bacteria using a filter paper-based SERS platform and PLS-DA with uncertainty estimation.

Rapid and reliable identification of bacteria is an important issue in food, medical, forensic, and environmental sciences; however, conventional procedures are time-consuming and often require extensive financial and human resources. Herein, we present a label-free method for bacterial discrimination using surface-enhanced Raman spectroscopy (SERS) and partial least squares discriminant analysis (PLS-DA). Filter paper decorated with gold nanoparticles was fabricated by the dip-coating method and it was utilized as a flexible and highly efficient SERS substrate. Suspensions of bacterial samples from three genera and six species were directly deposited on the filter paper-based SERS substrates before measurements. PLS-DA was successfully employed as a multivariate supervised model to classify and identify bacteria with efficiency, sensitivity, and specificity rates of 100% for all test samples. Variable importance in projection was associated with the presence/absence of some purine metabolites, whereas confidence intervals for each sample in the PLS-DA model were calculated using a resampling bootstrap procedure. Additionally, a potential new species of bacteria was analyzed by the proposed method and the result was in agreement with that obtained via 16S rRNA gene sequence analysis, thereby indicating that the SERS/PLS-DA approach has the potential to be a valuable tool for the discovery of novel bacteria. Graphical abstract This paper describes the discrimination of bacteria at the genus and species levels, after minimal sample preparation, using paper-based SERS substrates and PLS-DA with uncertainty estimation.

Rapid Determination of Nutritional Parameters of Pasta/Sauce Blends by Handheld Near-Infrared Spectroscopy.

Nowadays, near infrared (NIR) spectroscopy has experienced a rapid progress in miniaturization (instruments < 100 g are presently available), and the price for handheld systems has reached the < $500 level for high lot sizes. Thus, the stage is set for NIR spectroscopy to become the technique of choice for food and beverage testing, not only in industry but also as a consumer application. However, contrary to the (in our opinion) exaggerated claims of some direct-to-consumer companies regarding the performance of their "food scanners" with "cloud evaluation of big data", the present publication will demonstrate realistic analytical data derived from the development of partial least squares (PLS) calibration models for six different nutritional parameters (energy, protein, fat, carbohydrates, sugar, and fiber) based on the NIR spectra of a broad range of different pasta/sauce blends recorded with a handheld instrument. The prediction performance of the PLS calibration models for the individual parameters was double-checked by cross-validation (CV) and test-set validation. The results obtained suggest that in the near future consumers will be able to predict the nutritional parameters of their meals by using handheld NIR spectroscopy under every-day life conditions.

Digital Protocol for Chemical Analysis at Ultralow Concentrations by Surface-Enhanced Raman Scattering.

Single molecule surface-enhanced Raman spectroscopy (SM-SERS) has the potential to revolutionize quantitative analysis at ultralow concentrations (less than 1 nM). However, there are no established protocols to generalize the application of this technique in analytical chemistry. Here, a protocol for quantification at ultralow concentrations using SM-SERS is proposed. The approach aims to take advantage of the stochastic nature of the single-molecule regime to achieved lower limits of quantification (LOQ). Two emerging contaminants commonly found in aquatic environments, enrofloxacin (ENRO) and ciprofloxacin (CIPRO), were chosen as nonresonant molecular probes. The methodology involves a multivariate resolution curve fitting known as non-negative matrix factorization with alternating least-squares algorithm (NMF-ALS) to solve spectral overlaps. The key element of the quantification is to realize that, under SM-SERS conditions, the Raman intensity generated by a molecule adsorbed on a "hotspot" can be digitalized. Therefore, the number of SERS event counts (rather than SERS intensities) was shown to be proportional to the solution concentration. This allowed the determination of both ENRO and CIPRO with high accuracy and precision even at ultralow concentrations regime. The LOQ for both ENRO and CIPRO were achieved at 2.8 pM. The digital SERS protocol, suggested here, is a roadmap for the implementation of SM-SERS as a routine tool for quantification at ultralow concentrations.

Characterization of the aroma profile of novel Brazilian wines by solid-phase microextraction using polymeric ionic liquid sorbent coatings.

In this study, a series of polymeric ionic liquid (PIL) sorbent coatings is evaluated for the extraction of polar volatile organic compounds (VOCs) from Brazilian wines using headspace solid-phase microextraction (HS-SPME), including samples from 'Isabella' and 'BRS Magna' cultivars-the latter was recently introduced by the Brazilian Agricultural Research Corporation - National Grape & Wine Research Center. The structurally tuned SPME coatings were compared to the commercial SPME phases, namely poly(acrylate) (PA) and divinylbenzene/carboxen/poly(dimethylsiloxane) (DVB/CAR/PDMS). The separation, detection and identification of the aroma profiles were obtained using comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS). The best performing PIL-based SPME fiber, namely 1-hexadecyl-3-vinylimidazolium bis[(trifluoromethyl)sulfonyl]imide with 1,12-di(3-vinylimidazolium)dodecane dibis[(trifluoromethyl)sulfonyl]imide incorporated cross-linker supported on an elastic nitinol wire, exhibited superior performance to DVB/CAR/PDMS regarding the average number of extracted peaks and extracted more polar analytes providing additional insight into the aroma profile of 'BRS Magna' wines. Four batches of wine were evaluated, namely 'Isabella' and 'BRS Magna' vintages 2015 and 2016, using highly selective PIL-based SPME coatings and enabled the detection of 350+ peaks. Furthermore, this is the first report evaluating the aroma of 'BRS Magna' wines. A hybrid approach that combined pixel-based Fisher ratio and peak table-based data comparison was used for data handling. This proof-of-concept experiment provided reliable and statistically valid distinction of wines that may guide regulation agencies to create high sample throughput protocols to screen wines exported by Brazilian vintners. Graphical abstract Highly selective extraction of wine aroma using polymeric ionic liquid.

Simple, Expendable, 3D-Printed Microfluidic Systems for Sample Preparation of Petroleum.

In this study, we introduce a simple protocol to manufacture disposable, 3D-printed microfluidic systems for sample preparation of petroleum. This platform is produced with a consumer-grade 3D-printer, using fused deposition modeling. Successful incorporation of solid-phase extraction (SPE) to microchip was ensured by facile 3D element integration using proposed approach. This 3D-printed µSPE device was applied to challenging matrices in oil and gas industry, such as crude oil and oil-brine emulsions. Case studies investigated important limitations of nonsilicon and nonglass microchips, namely, resistance to nonpolar solvents and conservation of sample integrity. Microfluidic features remained fully functional even after prolonged exposure to nonpolar solvents (20 min). Also, 3D-printed µSPE devices enabled fast emulsion breaking and solvent deasphalting of petroleum, yielding high recovery values (98%) without compromising maltene integrity. Such finding was ascertained by high-resolution molecular analyses using comprehensive two-dimensional gas chromatography and gas chromatography/mass spectrometry by monitoring important biomarker classes, such as C10 demethylated terpanes, ααα-steranes, and monoaromatic steroids. 3D-Printed chips enabled faster and reliable preparation of maltenes by exhibiting a 10-fold reduction in sample processing time, compared to the reference method. Furthermore, polar (oxygen-, nitrogen-, and sulfur-containing) analytes found in low-concentrations were analyzed by Fourier transform ion cyclotron resonance mass spectrometry. Analysis results demonstrated that accurate characterization may be accomplished for most classes of polar compounds, except for asphaltenes, which exhibited lower recoveries (82%) due to irreversible adsorption to sorbent phase. Therefore, 3D-printing is a compelling alternative to existing microfabrication solutions, as robust devices were easy to prepare and operate.

Trapping of Au nanoparticles in a microfluidic device using dielectrophoresis for surface enhanced Raman spectroscopy.

Dielectrophoresis (DEP) is the electrokinetic movement of non-charged particles when they are subjected to a non-uniform electric field. This is a growing area of research, which can be used for trapping, concentrating and separating different particles. Some work has been reported with the intention of trapping metal particles to optimize the surface enhanced Raman spectroscopy (SERS) effect. In this paper, we use DEP with insulating structures (iDEP) to generate a non-uniform electric field for trapping gold nanoparticles (AuNP). The system was coupled to a Raman spectrometer for the detection of Crystal Violet by utilizing the SERS effect.

A portable SERS method for the determination of uric acid using a paper-based substrate and multivariate curve resolution.

This paper presents a portable quantitative method for the on-site determination of uric acid in urine using surface-enhanced Raman spectroscopy (SERS) and gold nanoparticle-coated paper as a substrate. A procedure was developed for the rapid preparation of cost-effective SERS substrates that enabled the adequate control of a homogeneous active area and the use of small quantities of gold nanoparticles per substrate. The standard addition method and multivariate curve resolution-alternating least squares (MCR-ALS) were applied to compensate for the matrix effect and to address overlapping bands between uric acid and interference SERS spectra. The proposed methodology demonstrated better performance than conventional univariate methods (in terms of linearity, accuracy and precision), a wide linear range (0-3.5 mmol L(-1)) and an adequate limit of detection (0.11 mmol L(-1)). For the first time, a portable SERS method coupled with chemometrics was developed for the routine analysis of uric acid at clinically relevant concentrations with minimal sample preparation and easy extension for the on-site determination of other biomarkers in complex sample matrices.

A survey of adulterants used to cut cocaine in samples seized in the Espírito Santo State by GC-MS allied to chemometric tools.

Cocaine is a stimulant drug of the central nervous system (CNS) extracted from the leaves of Erytroxylum coca. It is defined as a tropane alkaloid containing 1R-(exo,exo)-3-(benzoyloxy)-8-methyl-8-azabicyclo[3.2.1]octane-2-carboxylic acid methyl esther. However, despite its defined composition, a wide variety of chemical additives are present in cocaine found in the illicit market, such as benzocaine, lidocaine, caffeine, procaine and phenacetin. In this work, 512 cocaine samples seized by the Civil Police of Espirito Santo state (PC-ES, Brazil) were analyzed by gas chromatography mass spectrometry (GC-MS) allied to principal component analysis (PCA) in order to classify the samples as a function of seizure year (2008, 2009, 2010, 2011 and 2012) and location (metropolitan, north, south and central). The cocaine content (wt.%) and its adulterants were also estimated. Analyzing the samples seized between 2008 and 2011, three sample sets are clearly grouped according to the degree of adulteration with caffeine and lidocaine: 100-50 wt.% of cocaine; 50-20 wt.% of cocaine; and 20-80 wt.% of lidocaine and 60-80 wt.% of caffeine, simultaneously. The last group is formed by samples seized between 2008 and 2009, which proves the higher degree of adulteration during this period. In 2012, higher cocaine content was observed for the 191 analyzed samples than in samples from previous years. The PCA data also suggests that the metropolitan region samples had a higher degree of adulteration than the state countryside samples.

Petroleomics by electrospray ionization FT-ICR mass spectrometry coupled to partial least squares with variable selection methods: prediction of the total acid number of crude oils.

Negative-ion mode electrospray ionization, ESI(-), with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was coupled to a Partial Least Squares (PLS) regression and variable selection methods to estimate the total acid number (TAN) of Brazilian crude oil samples. Generally, ESI(-)-FT-ICR mass spectra present a power of resolution of ca. 500,000 and a mass accuracy less than 1 ppm, producing a data matrix containing over 5700 variables per sample. These variables correspond to heteroatom-containing species detected as deprotonated molecules, [M - H](-) ions, which are identified primarily as naphthenic acids, phenols and carbazole analog species. The TAN values for all samples ranged from 0.06 to 3.61 mg of KOH g(-1). To facilitate the spectral interpretation, three methods of variable selection were studied: variable importance in the projection (VIP), interval partial least squares (iPLS) and elimination of uninformative variables (UVE). The UVE method seems to be more appropriate for selecting important variables, reducing the dimension of the variables to 183 and producing a root mean square error of prediction of 0.32 mg of KOH g(-1). By reducing the size of the data, it was possible to relate the selected variables with their corresponding molecular formulas, thus identifying the main chemical species responsible for the TAN values.

Quantification of moxifloxacin in urine using surface-enhanced Raman spectroscopy (SERS) and multivariate curve resolution on a nanostructured gold surface.

A simple procedure is proposed for the determination of the antibiotic moxifloxacin in urine using nanostructured gold as surface-enhanced Raman scattering signal enhancer. The standard addition method in conjunction to multivariate curve resolution-alternating least squares was applied to eliminate the matrix effect and to isolate the spectral contribution of the analyte. Even in the presence of unexpected interferences in the urinary media, it was possible to extract and quantify the analyte response, reaching, in this way, the so-called second-order advantage from first-order data. Moreover, although a saturation phenomenon of the metallic surface was observed, the results of the proposed methodology presented important advantages such as high sensitivity and simpler experimental procedures. The moxifloxacin was determined at levels of 0.70 and 1.50 µg mL(-1) in urine diluted to 1.0% (corresponding to 70.0 and 150 µg mL(-1) in the original samples) with relative errors of 4.23 and 8.70%, respectively. The limit of detection (0.085 µg mL(-1)) and limit of quantification (0.26 µg mL(-1)) values indicated that the quantification can be accomplished in urine up to 24 h after the administration of a single 400-mg dose.

Use of color based chromatographic images obtained from comprehensive two-dimensional gas chromatography in authentication analyses.

Comprehensive two-dimensional gas chromatography (GC×GC) has been an important technique used to acquire as much information as possible from a wide variety of samples. Qualitative contour plots analysis provides useful information and in daily use it ends up being handled as images of the volatile organic compounds by analysts. Cachaça samples are used in this paper to showcase the use of two-dimensional chromatographic images as the main source for authentication purposes through one-class classifiers, such as data-driven soft independent modeling of class analogy (DD-SIMCA). The proposed workflow summarizes this fast and easy process, which can be used to certify a specific brand in comparison to other brands, as well as to authenticate if samples have been adulterated. Lower quality cachaças, non-aged cachaças and cachaças aged in different wooden barrels were tested as adulterants. Chromatographic images allowed for the distinction of all brands and nearly every adulteration tested. Sensitivity was estimated at 100% for all models and specificity ranged from 96% to 100%. Different approaches were used, alternating from working with whole-sized images to working with smaller resized versions of those images. Resized chromatographic images could be potentially useful to easily compensate for slight chromatographic misalignments, allowing for faster calculations and the use of simpler software. Reductions to 50% and 25% of the original size were tested and the results did not greatly differ from whole images model. As such, 2D chromatographic images have been found to be an interesting form of evaluating a product's authenticity.

Portable NIR spectrometer for quick identification of fat bloom in chocolates.

Cocoa butter provides desirable sensory properties to chocolates; however, the exposure of chocolate to temperature variations during transportation and/or storage can lead to changes in the polymorphic form of butter, with the appearance of a dull-white film on the chocolate surface, known as fat bloom. This study investigated the use of a portable NIR spectrometer combined with chemometric tools to discriminate milk chocolate, white chocolate, 40% cocoa chocolate, and 70% cocoa chocolate samples, which were subjected to temperature abuse for 6 hours. The PCA allowed separating the samples into three classes: control at 20 °C, chocolate subjected to 35 °C, and chocolate subjected to 40 °C, for each type of chocolate studied. The PLS-DA models provided sensibility, specificity, and accuracy values in the range of 80 to 100%, and allowed identifying the wavelengths associated with the different chocolates that most impacted the construction of the models.

Determination of amylose content in starch using Raman spectroscopy and multivariate calibration analysis.

Fourier transform Raman spectroscopy and chemometric tools have been used for exploratory analysis of pure corn and cassava starch samples and mixtures of both starches, as well as for the quantification of amylose content in corn and cassava starch samples. The exploratory analysis using principal component analysis shows that two natural groups of similar samples can be obtained, according to the amylose content, and consequently the botanical origins. The Raman band at 480 cm(-1), assigned to the ring vibration of starches, has the major contribution to the separation of the corn and cassava starch samples. This region was used as a marker to identify the presence of starch in different samples, as well as to characterize amylose and amylopectin. Two calibration models were developed based on partial least squares regression involving pure corn and cassava, and a third model with both starch samples was also built; the results were compared with the results of the standard colorimetric method. The samples were separated into two groups of calibration and validation by employing the Kennard-Stone algorithm and the optimum number of latent variables was chosen by the root mean square error of cross-validation obtained from the calibration set by internal validation (leave one out). The performance of each model was evaluated by the root mean square errors of calibration and prediction, and the results obtained indicate that Fourier transform Raman spectroscopy can be used for rapid determination of apparent amylose in starch samples with prediction errors similar to those of the standard method.

Paper-based SERS substrate and one-class classifier to monitor thiabendazole residual levels in extracts of mango peels.

The assessment of pesticide residue levels demands fast, low cost and easy-to-use procedures which are not found in conventional methods. In this work, SERS substrates based on the deposition of gold nanoparticles (GNPs) on common office paper were prepared using a wax printer. These substrates combined with Data Driven Soft Independent Modelling of Class Analogies (DD-SIMCA), a one-class classifier algorithm, were used for detection of pesticide residues in water extracts of mango peels. Paper-based substrates made sample collection easier compared with conventional SERS methods, since few microliters of the pesticide aqueous extract from fruit peels needed to be deposited onto the substrate. Moreover, one-class classifiers dismiss the need for quantification or calibration curves. Classification of a fruit with residue levels in accordance to regulatory bodies' limits is based on a mathematical threshold. Just as in an authentication problem, all the possibilities for a given analysed fruit are now restricted to agreeing or not agreeing with current regulations. The performance of the one-class model was demonstrated by detecting thiabendazole (TBZ) residues at various mango samples, with all results being confirmed by HPLC-DAD analysis. The final model could distinguish samples with TBZ levels above the ones allowed by the Brazilian Health Regulatory Agency with 94% of selectivity and 92% of sensitivity, even in the presence of other pesticides.

Colloidal gold clusters formation and chemometrics for direct SERS determination of bioanalytes in complex media.

In this work, we report the sensitive and selective sensing of the purine bases adenine and guanine in urine matrix by using surface-enhanced Raman spectroscopy (SERS) and a colloidal SERS substrate. To identify suitable conditions for quantitative analysis, the pH dependence of spectra of adenine, guanine, urine simulant and their mixtures was studied on gold nanoparticles suspension. Interestingly, although the urine matrix promotes the analytes signal suppression and overlapping bands, it can also cause an improvement in repeatability of the SERS measurements. This effect was associated to the relatively controlled formation of small-sized gold clusters and it was investigated both experimentally and theoretically. Furthermore, a correlation constrained multivariate curve resolution-alternating least squares (MCR-ALS) method was developed to resolve overlapping SERS bands and to quantify physiologically relevant (micromolar) concentrations of the bioanalytes. The performance of the proposed MCR-ALS approach (assessed in terms of figures of merit) was similar to that obtained by using partial least squares regression, but with the additional advantage of retrieving valuable spectral information. Therefore, this method can be used for improving selectivity of colloidal clusters in qualitative and quantitative SERS analysis of complex media, avoiding the need for tedious nanoparticle-surface modification or preliminary chromatographic separation.

Microstructures formation in a seemingly ideal homogeneous mixture of ethanol and methanol: An experimental evidence and two-dimensional correlation spectroscopy approach.

An anomalous solution behavior at the molecular scale was observed for macroscopically homogeneous mixtures of methanol and ethanol. Two-dimensional Raman correlation spectroscopy was used to elucidate the possible existence of microstructures formed in the mixture. The result suggests that separate methanol and ethanol clusters are formed without heterohydrogen bonding between different alcohol species. Supramolecular structures seem to be formed by the interaction of such clusters with each other through cohesion and dispersion forces, but not through direct hydrogen bonding connections.

Random forest as one-class classifier and infrared spectroscopy for food adulteration detection.

This paper proposes the use of random forest for adulteration detection purposes, combining the random forest algorithm with the artificial generation of outliers from the authentic samples. This proposal was applied in two food adulteration studies: evening primrose oils using ATR-FTIR spectroscopy and ground nutmeg using NIR diffuse reflectance spectroscopy. The primrose oil was adulterated with soybean, corn and sunflower oils, and the model was validated using these adulterated oils and other different oils, such as rosehip and andiroba, in pure and adulterated forms. The ground nutmeg was adulterated with cumin, commercial monosodium glutamate, soil, roasted coffee husks and wood sawdust. For the primrose oil, the proposed method presented superior performance than PLS-DA and similar performance to SIMCA and for the ground nutmeg, the random forest was superior to PLS-DA and SIMCA. Also, in both applications using the random forest, no sample was excluded from the external validation set.

Green methodology for soil organic matter analysis using a national near infrared spectral library in tandem with learning machine.

Precision agriculture requires faster and automatic responses for fertility parameters, especially regarding soil organic matter (SOM). In Brazil, the standard methodology for SOM determination is a wet procedure based on the oxidation of the sample by an excess of potassium dichromate based on Walkley-Black method. This methodology has serious drawbacks, since, at a national level, generates approximately 600,000 L/year of toxic acid waste containing Cr3+ and possibly Cr6+, besides time consuming and expensive. Herein, we present a faster green methodology that can eliminate the generation of these hazardous wastes and reduces the costs of analysis by approximately 80%, democratizing the soil fertility information and increasing the productivity. The methodology is based on the use of a national near infrared spectral library with approximately 43,000 samples and learning machine data analysis based on a random forest algorithm. The methodology was validated by submitting the prediction results of 12 blind soil samples to a proficiency assay used for fertility soil laboratories qualification, receiving the maximum quality excellence index, indicating that it is suitable for use in routine analysis.

Evaluation of miscibility and polymorphism of synthetic and natural lipids for nanostructured lipid carrier (NLC) formulations by Raman mapping and multivariate curve resolution (MCR).

Nanostructured lipid carriers (NLC) belong to youngest lipid-based nanocarrier class and they have gained increasing attention over the last ten years. NLCs are composed of a mixture of solid and liquid lipids, which solubilizes the active pharmaceutical ingredient, stabilized by a surfactant. The miscibility of the lipid excipients and structural changes (polymorphism) play an important role in the stability of the formulation and are not easily predicted in the early pharmaceutical development. Even when the excipients are macroscopically miscible, microscopic heterogeneities can result in phase separation during storage, which is only detected after several months of stability studies. In this sense, this work aimed to evaluate the miscibility and the presence of polymorphism in lipid mixtures containing synthetic (cetyl palmitate, Capryol 90®, Dhaykol 6040 LW®, Precirol ATO5® and myristyl myristate) and natural (beeswax, cocoa and shea butters, copaiba, sweet almond, sesame and coconut oils) excipients using Raman mapping and multivariate curve resolution - alternating least squares (MCR-ALS) method. The results were correlated to the macroscopic stability of the formulations. Chemical maps constructed for each excipient allowed the direct comparison among formulations, using standard deviation of the histograms and the Distributional Homogeneity Index (DHI). Lipid mixtures of cetyl palmitate/Capryol®; cetyl palmitate/Dhaykol®; myristyl myristate/Dhaykol® and myristyl myristate/coconut oil presented a single histogram distribution and were stable. The sample with Precirol®/Capryol® was not stable, although the histogram distribution was narrower than the samples with cetyl palmitate, indicating that miscibility was not the factor responsible for the instability. Structural changes before and after melting were identified for cocoa butter and shea butter, but not in the beeswax. Beeswax + copaiba oil sample was very homogenous, without polymorphism and stable over 6 months. Shea butter was also homogeneous and, in spite of the polymorphism, was stable. Formulations with cocoa butter presented a wider histogram distribution and were unstable. This paper showed that, besides the miscibility evaluation, Raman imaging could also identify the polymorphism of the lipids, two major issues in lipid-based formulation development that could help guide the developer understand the stability of the NLC formulations.