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This study aimed at developing control charts and classification models to investigate sugar and water addition in guava pulp applying near- and mid-infrared (NIR and MIR) spectroscopies and low-level data fusion to compare performance of them. The pulp was produced in a pilot plant (authentic samples) during the harvest season in Sao Paulo (Brazil), and part of samples was adulterated with sugar or water. Authentic and adulterated samples were analyzed by NIR and MIR. The spectra data obtained were preprocessed, and the principal component analysis was applied. MIR spectra data presents a fingerprint region, which is an important tool to differ authentic and adulterated samples. Control charts and classification models (SIMCA, k-NN, and PLS-DA), which were authenticated by external validation, were used to discriminate authentic from adulterated samples (sugar or water in different concentrations). It was possible to differentiate adulterated from authentic samples through control charts, except for water-adulterated samples using NIR spectral. The models presented excellent values of sensitivity, specificity, accuracy, and efficiency. However, k-NN presented better performance. The results obtained by data fusion presented worse performance than the models based in only one of the techniques. Therefore, these results suggested that NIR and MIR techniques can be used for adulteration detection; however, MIR control charts and k-NN models are more effective to detect sugar or water adulteration in guava pulp.

Evaluation of electrochemical behavior of 5-type phosphodiesterase inhibitory drugs is of major concern since they have been added illegally as adulterants in dietary supplements. Then, for the first time, the solid-sate electrochemistry of sildenafil and tadalafil is described using voltammetry of immobilized microparticles (VIMP). An electrochemical cell was designed and printed in a fused deposition modeling (FDM)-based 3D printer for VIMP measurements. The 3D-printed cell designed with a distance of 5 mm between the working and reference electrode presented better voltammetric response and decrease in the uncompensated resistance of the electrochemical system. Cyclic and square wave voltammograms of the individual compounds were recorded in a paraffin-impregnated graphite electrode in contact with 0.01 mol L−1 H2SO4. The electrochemical oxidation/reduction products were characterized by Raman spectroscopy and scanning electron microscopy (SEM). Different voltammetric responses were detected for the solid compounds depending on the solubility of the parent compound and their products of oxidation/reduction. Moreover, significant crystal re-structuring was also observed after voltammetric scan of the solid compounds. Under optimized conditions, sildenafil and tadalafil could be electrochemical identified and distinguished in dietary supplements.

A new consumable-free three-dimensional comprehensive gas chromatography (GC3-FID) was designed with modulators already known to comprehensive two-dimensional gas chromatography (GC × GC) users to showcase the opportunity of implementing GC3 systems. A hybrid interface composed of a thermal desorption modulator and a fast forward-flush differential flow modulator was used with modulation periods of 6 s and 300 ms, respectively. The determination of allergens in perfumes associated with chemometric tools, namely, parallel factor analysis (PARAFAC) was accomplished to demonstrate the validity of the proposed system. Evaluated allergens included already regulated ones in Brazil and Europe, such as geraniol and menthol, as well as potential allergens recommended in a 2011 opinion reported by the European Scientific Committee on Consumer Safety. Limits of detection were found to be suitable for current regulations, under 0.001% or 10 µL L−1 for leave-on products. Considerations on the required acquisition rates, the limitations imposed by current modulator technology, the modulation periods used, and how they affect all three dimensions are thoroughly discussed. A GC3 coupled with mass spectrometry (GC3-MS) approach is also covered, as well as a comparison of the signal resolution/deconvolution with the increase of data dimensionality. Lastly, the union of GC3 and chemometric tools allowed for a solution that may be used for both qualitative and quantitative analyses.

The identification of key components relevant to sensory perception of quality from commercial chocolate samples was accomplished after chemometric processing of GC×GC-MS (Comprehensive Two-dimensional Gas Chromatography with Mass Spectrometric Detection) profiles corresponding to HS-SPME (Headspace Solid Phase Microextraction) extracts of the samples. Descriptive sensory evaluation of samples was carried out using Optimized Descriptive Profile (ODP) procedures, where sensory attributes of 24 commercial chocolate samples were used to classify them in two classes (low and high chocolate flavor). 2D Fisher Ratio analysis was applied to four-way chromatographic data tensors (1st dimension retention time 1tR × 2nd dimension retention time 2tR × m/z × sample), to identify the crucial areas on the chromatograms that resulted on ODP class separation on Principal Component Analysis (PCA) scores plot. Comparing the relevant sections of the chromatograms to the analysis of the corresponding mass spectra, it was possible to assess that most of the information regarding the sample main sensory attributes can be related to only 14 compounds (2,5-dimethylpyrazine, 2,6-dimethyl-4-heptanol, 1-octen-3-ol, trimethylpyrazine, β-pinene, o-cimene, 2-ethyl-3,5-dimethylpyrazine, tetramethylpyrazine, benzaldehyde, 1,3,5-trimethylbenzene, 6-methyl-5-hepten-2-one, limonene, benzeneethanol and 1,1-dimethylbutylbenzene) among the complex blend of volatiles found on these extremely complex samples.

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.

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.

In this work, a methodology was proposed for detection and quantification of the four main roasted and ground coffee adulterants: coffee husks, roasted and powdered corn kernels, wood sticks, and soil, based on the combination of near-infrared hyperspectral imaging and multivariate curve resolution (MCR). By using this procedure, it was possible to detect the adulterants and their confirmation was accomplished comparing the reference spectra of each adulterant with the spectra recovered by the MCR model. The methodology was suitable for the quantification of adulterants in the mixtures which ranged from 1 to 40% (w/w), with errors lower than 4%. Also, a chart control card based on the scores of the MCR was developed, in order to detect the adulterated samples in a qualitative way, without the need of concentration values.

Near-infrared spectroscopy (NIR) is one of the most promising alternative technique for soil analysis in routine laboratories, which can provide faster and cheaper determinations of some soil parameters, including soil organic matter (SOM) content. However, NIR spectroscopy is quite sensitive to external environmental conditions, such as soil moisture, drastically reducing the accuracy of the methodology. In this study we used the external parameter orthogonalization (EPO) to minimize the moisture effect on soil spectra, making possible to apply the model developed with dry samples in moist ones. It was used 163 soil samples collected from several regions of Brazil, of these 103 dry samples were used to build the calibration model and the remaining 60 samples were moisturized at five different levels (5–35% w/w) to build the EPO model and to validate the methodology. The results obtained by EPO model (1.85 g/dm3, 0.86 and 2.02 for RMSEP, R2val, and RPDval, respectively) showed that the method was able to remove the effect of moisture in the NIR spectra, with accuracy values statistically equivalent for dry and moist soil samples.

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.

The antibiotic moxifloxacin had a recent surge in its use due to its broad spectrum of activity. However, due to the low metabolization inside the organism, it became an environmental concern. Here, the photolytic degradation of moxifloxacin antibiotic in alkaline medium was carried out and monitored through SERS spectroscopy. Multivariate curve resolution method was applied to extract quantitative and kinetic information about the whole process, using correlation constraint to simultaneously quantify the variation of moxifloxacin concentration. The results showed that the photolysis follows an apparent first order kinetics with half-life of 47.5 min. Also, SERS spectrum along with the calculated Raman spectra suggest that cleavage of the diazabicyclonyl substituent is the preferred photodegradation pathway, in agreement with previous reports.

Beeswaxes are interesting solid lipids for the development of nanostructured lipid carriers (NLC), and their origin can be either natural or synthetic. Due to this difference, their performance should be distinct and unstable formulations can be generated. The objective of this work was to investigate miscibility and structural changes (polymorphism) in pre-formulations (blends of solid and liquid lipids) using synthetic and natural beeswaxes in combination with copaiba oil (a natural liquid lipid), in the concentration range of 5.0 to 50.0% (w/w). Raman spectra were acquired over a region of 4 mm2 (mapping mode), dead pixels were removed using Independent Components Analysis (ICA) and Multivariate Curve Resolution – Alternating Least Squares (MCR-ALS) was then used to generate the images. Samples were analyzed at the initial time and after 3 months, using the Distributional Homogeneity Index (DHI) and standard deviation of the histograms. The pre-formulation containing synthetic beeswax showed different structural forms before and after melting, and structural changes over time, depending on the amount of the liquid lipid incorporated. These results demonstrate how spectroscopic imaging techniques can be valuable in pharmaceutical development, as well as the importance of choosing the type and proportion of solid lipid to achieve stable NLC formulations.

In order to confirm that the mitigation of greenhouse gas emissions could indeed be achieved by farmers, determinations of soil organic carbon (SOC) in total and stabilized fractions are essential, proving the effectiveness of the sustainable practices adopted by the farmer. In this sense, this study proposes an analytical methodology based on near infrared spectroscopy (NIRS) and partial least squares regression (PLSR) as an alternative for the measurement of stabilized and total soil organic carbon (SOC) in agricultural production systems. A set of 122 samples of four different land uses were sampled and the stabilized and labile SOC content were determined by the dry combustion method. In order to eliminate the extra step regarding soil fractionation to determine the stabilized SOC content, this study investigated different strategies to build the regression model based on partial least squares regression for the determination of the stabilized SOC from the total soil fraction. Two strategies presented the same accuracy as the reference method used to determine the stabilized SOC content in stabilized fraction, with root mean square error in validation of 1.47 g/dm3. These results indicate that both strategies proposed can determine simultaneously the total and stabilized SOC from the total soil fraction, thus eliminating the extra sample preparation.

International audience; Solid dispersions are an interesting option to improve the solubility of Class II (high permeability, low water solubility) drugs of the Biopharmaceutical Classification System without the use of organic solvents. However, structural changes (polymorphism) may be present in both active pharmaceutical ingredients (API) and excipients, which require evaluation during pharmaceutical

In the present work, Raman spectroscopy and chemometrics tools were explored as an analytical method to discriminate blue pen strokes on common office paper and bank check paper. The sample set was constituted of blue ballpoint pen inks available on local market and promotional pens as well. The samples were prepared to simulate an adulterated document using different pens. The proposal methodology was based on Raman imaging spectroscopy and Independent Components Analysis algorithm, which were able to recover the original spectrum of each pen, as well as images containing special information about each of the pens used. All recovered spectra could be match with references spectra at least with 0.8572 of correlation coefficient, while recovered images left no doubt about whether a forgery occurred. At the end of analysis, all the examined cases could be solved based on the discrimination between the pens inks, without any prior information.

A teaching experiment on supervised pattern recognition in chemometrics was proposed in this tutorial to introduce partial least squares discriminant analysis (PLS-DA). A new approach of the experiment published in the first tutorial of this series was revisited and employed to the classification of edible vegetable oils. The spectra of olive, canola, soybean and corn oils were obtained using an attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectrometer in the range of 600 to 4000 cm-1. The combination of ATR-FTIR and PLS-DA classification method was able to correctly classify 100% of the validation samples. The Matlab commands, routines and functions were presented, and a didactic explanation of the concepts and interpretation of the data was provided. Made available in DSpace on 2020-06-23T11:13:01Z (GMT). No. of bitstreams: 1 Experimento-didatico-de-quimiometria-para-classificacao-de-oleos-vegetais-comestiveis-2020.pdf: 3697819 bytes, checksum: 05d368657c156ed3e2b9190d911f5515 (MD5) Previous issue date: 2020

This study aimed at developing a non-invasive and rapid method to determine the authenticity of plant-based protein powders (free of soy, lactose, and gluten), and classify possible adulterations in the powders using near-infrared spectroscopy (NIR) and chemometric tools. Three potential powder adulterants were investigated: soy protein, whey (lactose source), and wheat (gluten source). The goal was to achieve untargeted and targeted detection to solve problems related to the authentication of the protein powders and the classification of the adulterants. For this purpose, the OC-PLS (one-class partial least squares) model was used for authentication and the PLS2-DA (partial least squares discriminant analysis) model was used to classify the adulterants. VIP (variable importance in projection) scores were used to confirm the main relevant variables and spectral ranges were responsible for each class in PLS2-DA. Laboratory samples were prepared by adding 10, 15, 20, 25, 30, 35 and 40% (w/w) of each adulterant into pure plant-based protein powder samples. In total, 47 pure plant-based protein powder samples and 144 adulterated samples were analyzed. The analysis results indicate a promising way of combining one-class (OC-PLS) with multiclass (PLS-DA) methods, in tandem with NIR to investigate plant-based protein powders. Due to the speed, high sensitivity, and specificity of the methodology, and no requirement of sample preparation, the proposed methodology could be successfully used in a range of 10–40% of adulteration, to verify the authenticity of the plant-based protein powders and to classify adulterants into soy, whey, and wheat.

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.

Near-field plasmonic properties are fundamental for the various applications of metal nanoparticles. In this sense, a characterization of such properties is essential for a better understanding of the electromagnetic interactions probed by the materials. In this manuscript we discuss the possibility of using surface-enhanced Raman scattering (SERS) as a possible experimental probe of near-field resonance properties of metal nanoparticles aggregates. In this specific case, we investigate both, theoretically and experimentally, the behavior of broad and narrow near-field enhancement resonances in the SERS spectra of adsorbed molecules. We believe the results presented here may open new possibilities and insights in plasmonic nanoparticle properties characterization for different applications.

Chocolate quality depends on the type of cocoa and how its manufacture was conducted. In this work, the analysis of the volatile fraction of the principal steps - from cocoa to chocolate - was performed. For this purpose, unroasted nibs, liquor and chocolate samples from five different places were used, and the isolation was conducted by HS-SPME (headspace solid phase microextraction) using a DVB/CAR/PDMS (divinylbenzene/carboxen/polydimethylsiloxane) fiber, lately desorbed in a homemade GC × GC chromatograph and detected by mass spectrometry. All the compounds with appropriate chromatographic resolution in the contour plots were identified by mass spectrum and retention index. After that, the compounds areas with match greater than 90% were used on two principal component analyzes to determine the volatile compounds differences in some steps of chocolate production. In the unroasted nibs-liquor step, pyrazines were found as the main difference between the composition of the sample, while in the liquor-chocolate step several chemical classes compounds were found. It was also possible identify the main compounds responsible for the different steps in chocolate production using HS-SPME and GC × GC-QMS.

This work reports on the use of micro- and macro-Raman measurements for quantification of mebendazole (MBZ) polymorphs A, B, and C in mixtures. Three Raman spectrophotometers were studied with a laser spot size of 3, 80 and 100 μm and spectral resolutions of 3.9, 9 and 4 cm−1, respectively. The samples studied were ternary mixtures varying the MBZ polymorphs A and C from 0 to 100% and polymorph B from 0 to 30%. Partial Least Squares (PLS) regression models were developed using the pre-processing spectra (2nd derivative) of the ternary mixtures. The best performance was obtained when the macro-Raman configuration was applied, obtaining RMSEP values of 1.68%, 1.24% and 2.03% w/w for polymorphs A, B, and C, respectively. In general, micro-Raman presented worst results for MBZ polymorphs prediction because the spectra obtained with this configuration does not represent the bulk proportion of mixtures, which have different particle morphologies and sizes. In addition, the influence of these particle features on micro-Raman measurements was also studied. Finally, the results demonstrated that reliable analytical quantifying of MBZ polymorphs can be reached using a laser with wider area illuminated, thus enabling acquisition of more reproductive and representative spectra of the mixtures.

In this study a systematic comparison was carried out to assess differences on the accuracy between partial least squares (PLS) and support vector machine (SVM) regression algorithms in soil organic matter and particle size determinations using vis-NIR spectroscopy. The comparison consisted in investigating the influence on the size of calibration set on the external validation set accuracy. For this purpose, three vis-NIR soil libraries containing 14,212, 15,330 and 42,471 soil samples were used to determine sand, clay, and SOM content, respectively. To increase the variability of the results obtained, each calibration subset was randomly generated 49 times and for each iteration a PLS, SVM-Linear and SVM-RBF (radial basis function) regression models were built. These calibration subsets were composed by 250, 1000, 2000, 5000 and 8000 or 10,000 samples. In all situations the SVM-Linear obtained the worst accuracy results. For sand and clay determinations, SVM-RBF models shows a significant improvement on the accuracy, compared to PLS, when the calibration model was built using at least 1000 samples, resulting in a reduction of ~14–29% on the RMSEP. For SOM determinations the difference in RMSEP values of SVM-RBF and PLS starts to be significant when 2000 or more samples were used in calibration set, presenting a reduction of ~8–22% on the RMSEP values. In addition, for all soil attributes investigated between 20 and 27% of the external validation set (1173–2241 samples) were considered outliers and excluded by the PLS regression models. This loss of PLS performance for large calibration sets, indicates the correlation between the vis-NIR spectra and clay, sand and SOM contents tends to be more complex by increasing the variability/number of samples. Requiring the use of machine learnings models with high generalization capacity, such as the SVM-RBF, which increased the performance as the number of samples that compose the calibration set increased.

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.

Andiroba oil (Carapa guianensis Aubl) from the Amazonian region is one of the most important oils in popular medicine and cosmetic industry, has a high added value and is liable to adulteration with other cheaper or lower quality oils. Based on these facts, this work proposes a simple, fast, and accurate methodology to discriminate authentic andiroba oil from adulterated andiroba oil-containing soybean and corn oils in different proportions. The approach is based on the applicability of FTIR-HATR spectroscopy combined with chemometrics tools for classification such as random forest and partial least squares for discriminant analysis (PLS-DA). The random forest model showed 100% of correct classification and, consequently, an accuracy of 100% in training and test sets, while PLS-DA presented an accuracy of approximately 100 and 94% in training and test sets, respectively. Therefore, the developed methodology can be helpful in routine laboratories, regulatory agencies, and industry for quality analysis of andiroba oil.

This work presents the use of near-infrared (NIR) spectroscopy as a fast and eco-friendly alternative to the conventional methods currently used for the quality control of cocoa beans. A key feature of this study was that the multivariate calibration models were built from the spectra of 81 samples of commercial cocoa beans from different producing regions and sampled over the period of 1 year. This aspect is crucial to demonstrate the feasibility of NIR for predicting chemical parameters of cocoa beans, since it provides a realistic variability in the calibration models. PLS regression models were constructed from the near-infrared diffuse reflectance spectra, allowing the prediction of moisture, pH, acidity, fat, shell content, protein, total phenolic compounds, caffeine, and theobromine through direct analysis of samples milled and sieved without any additional preparation. All of these parameters were predicted with adequate coefficient of determination values (R2), which ranged from 0.67 to 0.89 and relative errors smaller than 10.2%, which is quite suitable when compared to the coefficients of variation of the reference methods. Thus, the results demonstrated that the use of NIR combined with chemometrics is effective and recommended for the quality control of commercial cocoa beans.

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.

Two methods of asphaltenes fractionation have been employed to facilitate the characterization of their respective subfractions. The methods are based on step-wise precipitation with different n-hexane/crude oil ratios, and on adsorption onto CaCO3. Three subfractions were produced for each method, being named of 3.5 V, 3.5–6 V, and 6–40 V (for the first method); and non-adsorbed (bulk), adsorbed, and irreversibly adsorbed (for the second method). The fractions were characterized by elementary analysis, nuclear magnetic resonance of proton (1H NMR) and by positive ion-mode electrospray Fourier transform ion cyclotron resonance mass spectrometry (ESI(+)FT-ICR MS). The elemental analysis, described in previous work, revealed that the C/H ratio for whole asphaltene and its sub-fractions varied between a narrow range (0.83–0.88) which means they present similar aromaticity or unsaturation. Furthermore, the elemental analysis corroborates with the 1H NMR analysis suggesting that subfraction 6–40 V presented a more aromatic profile than of remaining subfractions, while for the fractionation using CaCO3, this behavior was observed for the adsorbed subfraction. However, a more detailed molecular information was obtained from ESI(+)-FT-ICR MS data, showing that polar compounds species with lower carbon numbers were mainly found for the irreversibly adsorbed subfraction. Besides, the double bond equivalent (DBE) distribution is an important tool to associate the chemical information with solubility parameters, in which, a narrower DBE distribution was observed for irreversibly adsorbed (for fractionation onto CaCO3) and subfraction 3.5 V (fractionation in n-hexane) samples, indicating that they are less soluble in hydrocarbons. Also, solubility parameters (δ) were calculated from ESI(+)FT-ICR MS data, where the results indicate that subfractions produced in n-hexane have a lower tendency to precipitate in hydrocarbons in relation to subfractions produced onto CaCO3.

This study evaluates the use of Raman spectroscopy with a multivariate curve resolution–alternating least squares (MCR-ALS) analysis to monitor the adulteration and purity of coconut oil. Sunflower, soybean, canola, sesame, corn, castor bean, peanut, palm kernel, babassu, mineral, and Vaseline oils have been used as adulterants in this work. Control charts were developed to evaluate the purity of an oil sample using the scores from the MCR-ALS analysis of a data set containing pure and adulterated oils. These control charts were able to detect the adulteration of coconut oil in a range of 2–30% with all the oils tested. Additionally, quantification models were developed using MCR-ALS with correlation constraints for coconut oil adulterated with sunflower, canola, Vaseline, babassu, and palm kernel oils. The models presented satisfactory results, which had absolute errors below 5%, for samples adulterated with sunflower, canola, and Vaseline oils. The babassu and palm kernel adulterants could also be quantified with a superior margin of error. The results indicated that using Raman spectroscopy with MCR is a clean and non-destructive method for assessing coconut oil purity that can be used without removing a sample from its bottle.

Raman spectroscopy and multivariate curve resolution with correlation constraint were used for the identification and quantification of biofuels and adulterants in petroleum diesel. Firstly, quantification of two biofuels present in petroleum diesel (biodiesel and hydrotreated esters and fatty acids (HEFA)) was performed. The developed models for biodiesel and HEFA presented RMSEPs equal to 0.70%, indicating the practical application of the proposed methodology. Additionally, the identification and quantification of six adulterants (ethanol, cyclohexane, gasoline, hexane, kerosene and an ethanol/gasoline blend) in petroleum diesel with 10% of biofuels (B-10) were performed. In this case, it was possible to identify the adulterant with high confidence based on the recovered spectra and to estimate its concentration with absolute errors below to 8%.

The short time of action and systemic toxicity of local anaesthetics limit their clinical application. Bupivacaine is the most frequently used local anaesthetic in surgical procedures worldwide. The discovery that its S(-) enantiomeric form is less toxic than the R(+) form led to the introduction of products with enantiomeric excess (S75:R25 bupivacaine) in the market. Nevertheless, the time of action of bupivacaine is still short; to overcome that, bupivacaine S75:R25 (BVCS75) was encapsulated in nanostructured lipid carriers (NLC). In this work, we present the development of the formulation using chemometric tools of experimental design to study the formulation factors and Raman mapping associated with Classical Least Squares (CLS) to study the miscibility of the solid and the liquid lipids. The selected formulation of the nanostructured lipid carrier containing bupivacaine S75:R25 (NLCBVC) was observed to be stable for 12 months under room conditions regarding particle size, polydispersion, Zeta potential and encapsulation efficiency. The characterisation by DSC, XDR and TEM confirmed the encapsulation of BVCS75 in the lipid matrix, with no changes in the structure of the nanoparticles. The in vivo analgesic effect elicited by NLCBVC was twice that of free BVCS75. Besides improving the time of action, no statistical difference in the blockage of the sciatic nerve of rats was found between 0.125% NLCBVC and 0.5% free BVCS75. Therefore, the formulation allows a reduction in the required anaesthesia dose, decreasing the systemic toxicity of bupivacaine, and opening up new possibilities for different clinical applications.

The emergence of antibiotics and their active metabolites in aquatic ecosystem has motivated the development of sensitive and reliable sensors to monitor traces of antibiotics and metabolites in drinking water sources (i.e. surface water). The surface enhanced Raman scattering (SERS) technique, which is widely recognized as a high sensitivity method for molecular vibrational detection, is potentially a powerful tool for trace environmental contamination analysis. The main goal of this work is to demonstrate pharmaceutical and metabolite multiplexing detection using the SERS approach. Periodic metallic nanostructures were fabricated using laser interference lithography (LIL) and used as SERS substrates (platform that supports the SERS effect). The LIL method allowed excellent substrate-to-substrate geometric parameters variations; for instance, the variations in periodicity were determined to be less than 1%. A common fluoroquinolone (FQ) parent-and-metabolite pair, enrofloxacin (ENRO) and ciprofloxacin (CIPRO), was targeted for multiplexing detection on the relative uniform substrates fabricated by LIL. The quantifications of the analytes mixtures were achieved by chemometric analysis (i.e. non-negative matrix factorization with alternating least square algorithm (NMF-ALS)). The limit of the quantification (LOQ) of the present method is in the ppm-level with less than 10% spatial variation in the SERS signal.

Using 1H NMR-based metabolomics in association to chemometrics analysis, we analyzed here the metabolic differences between schizophrenia patients (SCZ) compared to healthy controls (HCs). HCs and SCZ patients underwent clinical interview using the Structured Clinical Interview for DSM Disorders (SCID). SCZ patients were further assessed by Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale, Global Assessment of Functioning Scale (GAF), and Clinical Global Impressions Scale (CGI). Using the principal component analysis (PCA) and supervised partial least-squares discriminate analysis (PLS-DA) in obtained NMR data, a clear group separation between HCs and SCZ patients was achieved. Interestingly, all metabolite compounds identified as exclusively present in the SCZ group, except for the gamma-aminobutyric acid (GABA), were never previously associated with mental disorders. Although the initial perception of an absence of obvious biological link among the different key molecules exclusively observed in each group, and no identification of any specific pathway yet, the present work represents an important contribution for the identification of potential biomarkers to inform diagnosis, as it was possible to completely separate the affected SCZ patients from HCs, with no outliers or exceptions. In addition, the data presented here reinforced the role of the modulation of glycolysis pathway and the loss of GABA interneuron/hyperglutamate hypothesis in SCZ.

Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq, Brasilia, Brazil) FAPESP Background: The objective of this study was to identify molecular alterations in the human blood serum related to bipolar disorder, using nuclear magnetic resonance (NMR) spectroscopy and chemometrics. Methods: Metabolomic profiling, employing H-1-NMR, H-1-NMR -T-2-edited, and 2D-NMR spectroscopy and chemometrics of human blood serum samples from patients with bipolar disorder (n = 26) compared with healthy volunteers (n = 50) was performed. Results: The investigated groups presented distinct metabolic profiles, in which the main differential metabolites found in the serum sample of bipolar disorder patients compared with those from controls were lipids, lipid metabolism-related molecules (choline, myo-inositol), and some amino acids (N-acetyl-L-phenyl alanine, N-acetyl-L-aspartyl-L-glutamic acid, L-glutamine). In addition, amygdalin, alpha-ketoglutaric acid, and lipoamide, among other compounds, were also present or were significantly altered in the serum of bipolar disorder patients. The data presented herein suggest that some of these metabolites differentially distributed between the groups studied may be directly related to the bipolar disorder pathophysiology. Conclusions: The strategy employed here showed significant potential for exploring pathophysiological features and molecular pathways involved in bipolar disorder. Thus, our findings may contribute to pave the way for future studies aiming at identifying important potential biomarkers for bipolar disorder diagnosis or progression follow-up. Univ Fed Sao Paulo UNIFESP, Dept Psychiat, Rua Borges Lagoa 570, BR-04038020 Sao Paulo, Brazil Univ Fed Sao Paulo UNIFESP, Dept Pharmacol, Rua Tres Maio 100, BR-04044020 Sao Paulo, Brazil ISCMSP, Dept Psychiat, Rua Major Maragliano 287, BR-04017030 Sao Paulo, Brazil Univ Estadual Campinas UNICAMP, Lab Quim Biol, Dept Organ Chem, Inst Chem, Caixa Postal 6154, BR-13083970 Sao Paulo, Brazil Univ Estadual Campinas UNICAMP, Dept Analyt Chem, Inst Chem, Caixa Postal 6154, BR-13083970 Sao Paulo, Brazil Univ Fed Sao Paulo UNIFESP, Dept Psychiat, Rua Borges Lagoa 570, BR-04038020 Sao Paulo, Brazil Univ Fed Sao Paulo UNIFESP, Dept Pharmacol, Rua Tres Maio 100, BR-04044020 Sao Paulo, Brazil CNPq FAPESP: 2014/18938-8 Web of Science

We investigated a strategy for the chemotaxonomy study of Chrysobalanus icaco Linnaeus (Chrysobalanaceae) based on ultra-high performance liquid chromatography coupled with diode array detection fingerprint in combination with multivariate analysis. Two models using principal component analysis and partial least squares discriminant analysis were developed, and the samples could be successfully classified into two classes: Class 1 (red morphotype) and Class 2 (white and black morphotypes). Furthermore, ultra-high performance liquid chromatography coupled with diode array and electrospray ionization tandem mass spectrometry was used to identify the main compounds responsible for class separation. The partial least squares discriminant analysis model accurately classified the C. icaco samples using an external validation subset with prediction ability of 100% and revealed the existence of two chemotypes. The most important finding obtained in this study is that the three morphotypes distinguished by the mature fruit color (white, red, and black) are not all phytoequivalent to each other.

Cancer is responsible for millions of deaths worldwide, but most base diseases may be cured if detected early. Screening tests may be used to identify early-stage malignant neoplasms. However, the major screening tool for prostate cancer, the prostate-specific antigen test, has unsuitable sensitivity. Since cancer cells may affect the pattern of consumption and excretion of nucleosides, such biomolecules are putative biomarkers that can be used for diagnosis and treatment evaluation. Using a previously validated method for the analysis of nucleosides in blood serum by capillary electrophoresis with UV-vis spectroscopy detection, we investigated 60 samples from healthy individuals and 42 samples from prostate cancer patients. The concentrations of nucleosides in both groups were compared and a multivariate partial least squares-discriminant analysis classification model was optimized for prediction of prostate cancer. The validation of the model with an independent sample set resulted in the correct classification of 82.4% of the samples, with sensitivity of 90.5% and specificity of 76.7%. A significant downregulation of 5-methyluridine and inosine was observed, which can be indicative of the carcinogenic process. Therefore, such analytes are potential candidates for prostate cancer screening. Graphical Abstract Separation of the studied nucleosides and the internal standard 8-Bromoguanosine by CE-UV (a); classification of the external validation samples (30 from healthy volunteers and 21 from prostate cancer patients) by the developed Partial Least Square - Discriminant Analysis (PLS-DA) model with accuracy of 82.4% (b); Receiver Operating Characteristics (ROC) curve (c); and Variable Importance in the Projection (VIP) values for the studied nucleosides (d). A significant down-regulation of 5- methyluridine (5mU) and inosine (I) was observed, which can be indicative of the presence of prostate tumors.