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.

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.

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.

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.

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.

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.

Comparison of different chemometric methods to extract chemical and physical information from Raman images of homogeneous and heterogeneous semi-solid pharmaceutical formulations

In formulations of nanostructured lipid carriers, lipid solid dispersions and self-emulsifying drug delivery systems, it is common that a solid or semi-solid lipid excipient is mixed with a liquid solvent or liquid lipid. Even when the excipients are visually miscible upon melting, they might have microscopic non-homogeneities which could lead to instability over time and future phase separation. Raman mapping associated with chemometric methods can be useful to evaluate spatial distribution of compounds, however it has not been extensively applied to the formulations mentioned above. The aim of this work was to compare the outcomes of three different chemometric methods - principal components analysis (PCA), multivariate curve resolution with alternating least squares (MCR-ALS) and independent components analysis (ICA) - to study two systems of very different degrees of microscopic miscibility: cetyl palmitate + Transcutol^© (heterogeneous) and polyethylene glycol 6000 (PEG 6000) + Tween 80^© (homogeneous). These two samples were chosen due to large differences in spatial distribution of the compounds over the pixels which could require different approaches for data treatment. The three methods were compared regarding recovered concentrations (or scores), signals (or loadings) and the need for matrix augmentation to obtain reliable results. Results showed that PCA loadings were the mathematical differences of the spectra of pure compounds for both samples, and therefore only 'contrast images' could be generated. MCR and ICA provided signals that could be related to the chemical components, however MCR presented rotational ambiguities even for the very heterogeneous sample, a situation in which ICA performed better as a blind search method. For the homogeneous sample, both methods showed rank deficiency and therefore the use of a matrix augmentation was necessary. ICA and PCA allowed identifying physical modifications in the homogeneous semi-solid PEG 6000/Tween 80^® sample over the time, probably due to the folding/unfolding of the crystalline chains of PEG 6000. Therefore, this work discusses the ability of the three chemometrics methods to extract information from Raman spectra in order to characterize the chemical, spatial and even physical aspects of semi-solid pharmaceutical formulations, which could be of much use for stability studies of different drug delivery systems.

Surface-enhanced Raman spectroscopy and MCR-ALS for the selective sensing of urinary adenosine on filter paper

Adenosine is a purine nucleoside that is present in all human cells and is essential for regulating certain physiological activities in tissues and organs. Since adenosine is considered to be a potential cancer biomarker in urine, its determination may be crucial for the early diagnosis and non-invasive monitoring of cancer. Herein, we present a label-free method to quantify urinary adenosine using surface-enhanced Raman spectroscopy (SERS) and multivariate curve resolution-alternating least squares (MCR-ALS). Ring-oven preconcentration and direct deposition of monodisperse gold nanoparticles on filter paper were employed to improve the sampling efficiency. Further, MCR-ALS (assessed with and without a correlation constraint), the standard addition method and pH controls were combined to compensate for the matrix effect and to address overlapping bands in the analysis of human urine samples. As a result, the proposed method showed to be sensitive (LOD varying between 3.8 and 4.9 µmol L^-1, S/R = 3), reproducible (RSD less than ± 15%), and selective over other nucleosides (guanosine, cytidine, thymidine and uridine) and unknown interferences (second-order advantage). This is the first report of a SERS-chemometric method applied to urinary adenosine sensing at physiologically relevant concentrations, with minimal sample preparation, and has strong potential to be a valuable tool in cancer research.

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.

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.

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.

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

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.

Integrative analysis to select cancer candidate biomarkers to targeted validation

Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS.

Temporal drift in Raman signal intensity during SERS measurements performed on analytes in liquid solutions

In this communication, we report one factor that could limit the quantitative analysis by SERS, which has not yet been discussed in the literature. Our results show that SERS experiments performed with the substrate immersed in liquid solutions are subjected to a temporal drift in the Raman signal intensity. Measurements were performed using gold nanoparticle suspensions and gold-covered nanostructured ITO surfaces as SERS substrates, immersed in analyte solutions of crystal violet and 4-mercaptobenzoic acid. Depending on the substrate and the conditions used for measurements, the Raman signal can take between 30 min and several hours to stabilize. This effect, if not taken into account, could have a negative impact on the results of the quantitative chemical analysis by SERS performed in situ in liquid solutions.

Quality evaluation of frozen guava and yellow passion fruit pulps by NIR spectroscopy and chemometrics

The present study investigated the application of near infrared spectroscopy as a green, quick, and efficient alternative to analytical methods currently used to evaluate the quality (moisture, total sugars, acidity, soluble solids, pH and ascorbic acid) of frozen guava and passion fruit pulps. Fifty samples were analyzed by near infrared spectroscopy (NIR) and reference methods. Partial least square regression (PLSR) was used to develop calibration models to relate the NIR spectra and the reference values. Reference methods indicated adulteration by water addition in 58% of guava pulp samples and 44% of yellow passion fruit pulp samples. The PLS models produced lower values of root mean squares error of calibration (RMSEC), root mean squares error of prediction (RMSEP), and coefficient of determination above 0.7. Moisture and total sugars presented the best calibration models (RMSEP of 0.240 and 0.269, respectively, for guava pulp; RMSEP of 0.401 and 0.413, respectively, for passion fruit pulp) which enables the application of these models to determine adulteration in guava and yellow passion fruit pulp by water or sugar addition. The models constructed for calibration of quality parameters of frozen fruit pulps in this study indicate that NIR spectroscopy coupled with the multivariate calibration technique could be applied to determine the quality of guava and yellow passion fruit pulp.

Determination of fragrance content in perfume by Raman spectroscopy and multivariate calibration

An alternative methodology is herein proposed for determination of fragrance content in perfumes and their classification according to the guidelines established by fine perfume manufacturers. The methodology is based on Raman spectroscopy associated with multivariate calibration, allowing the determination of fragrance content in a fast, nondestructive, and sustainable manner. The results were considered consistent with the conventional method, whose standard error of prediction values was lower than the 1.0%. This result indicates that the proposed technology is a feasible analytical tool for determination of the fragrance content in a hydro-alcoholic solution for use in manufacturing, quality control and regulatory agencies.

Study of the homogeneity of drug loaded in polymeric films using near-infrared chemical imaging and split-plot design

Split-plot design (SPD) and near-infrared chemical imaging were used to study the homogeneity of the drug paracetamol loaded in films and prepared from mixtures of the biocompatible polymers hydroxypropyl methylcellulose, polyvinylpyrrolidone, and polyethyleneglycol. The study was split into two parts: a partial least-squares (PLS) model was developed for a pixel-to-pixel quantification of the drug loaded into films. Afterwards, a SPD was developed to study the influence of the polymeric composition of films and the two process conditions related to their preparation (percentage of the drug in the formulations and curing temperature) on the homogeneity of the drug dispersed in the polymeric matrix. Chemical images of each formulation of the SPD were obtained by pixel-to-pixel predictions of the drug using the PLS model of the first part, and macropixel analyses were performed for each image to obtain the y-responses (homogeneity parameter). The design was modeled using PLS regression, allowing only the most relevant factors to remain in the final model. The interpretation of the SPD was enhanced by utilizing the orthogonal PLS algorithm, where the y-orthogonal variations in the design were separated from the y-correlated variation.

Classification of diesel pool refinery streams through near infrared spectroscopy and support vector machines using C-SVC and ν-SVC

The use of near infrared (NIR) spectroscopy combined with chemometric methods have been widely used in petroleum and petrochemical industry and provides suitable methods for process control and quality control. The algorithm support vector machines (SVM) has demonstrated to be a powerful chemometric tool for development of classification models due to its ability to nonlinear modeling and with high generalization capability and these characteristics can be especially important for treating near infrared (NIR) spectroscopy data of complex mixtures such as petroleum refinery streams. In this work, a study on the performance of the support vector machines algorithm for classification was carried out, using C-SVC and ν-SVC, applied to near infrared (NIR) spectroscopy data of different types of streams that make up the diesel pool in a petroleum refinery: light gas oil, heavy gas oil, hydrotreated diesel, kerosene, heavy naphtha and external diesel. In addition to these six streams, the diesel final blend produced in the refinery was added to complete the data set. C-SVC and ν-SVC classification models with 2, 4, 6 and 7 classes were developed for comparison between its results and also for comparison with the soft independent modeling of class analogy (SIMCA) models results. It is demonstrated the superior performance of SVC models especially using ν-SVC for development of classification models for 6 and 7 classes leading to an improvement of sensitivity on validation sample sets of 24% and 15%, respectively, when compared to SIMCA models, providing better identification of chemical compositions of different diesel pool refinery streams.

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

FT-ICR MS coupled to PLS regression in petroleomics.

Application of Kohonen neural network for evaluation of the contamination of Brazilian breast milk with polychlorinated biphenyls

Due to the tendency of polychlorinated biphenyls (PCB) to accumulate in matrixes with high lipid content, the contamination of the breast milk with these compounds is a serious issue, mainly to the newborn. In this study, milk samples were collected from breastfeeding mothers belonging to 4 Brazilian regions (south, southeast, northeast and north). Twelve PCB were analyzed by HS-SPME-GC-ECD and the corresponding peak areas were correlated to the answers to a questionnaire of general habits, breastfeeding and characteristics of the living places. To realize this exploratory analyze, self-organizing maps generated applying Kohonen neural network were applied. It was possible to verify the occurrence of different PCB congeners in the breast milk relating to the region of the Brazil that the breastfeeding lives, the proximity to an industry, the proximity to a contaminated river or sea, the type of milk (colostrum, foremilk and hindmilk) and the number of past pregnancies.

Pharmaceutical analysis in solids using front face fluorescence spectroscopy and multivariate calibration with matrix correction by piecewise direct standardization

This paper reports the application of piecewise direct standardization (PDS) for matrix correction in front face fluorescence spectroscopy of solids when different excipients are used in a pharmaceutical preparation based on a mixture of acetylsalicylic acid (ASA), paracetamol (acetaminophen) and caffeine. As verified in earlier studies, the use of different excipients and their ratio can cause a displacement, change in fluorescence intensity or band profile. To overcome this important drawback, a standardization strategy was adopted to convert all the excitation-emission fluorescence spectra into those used for model development. An excitation-emission matrix (EEM) for which excitation and emission wavelengths ranging from 265 to 405 nm and 300 to 480 nm, respectively, was used. Excellent results were obtained using unfolded partial least squares (U-PLS), with RMSEP values of 8.2 mg/g, 10.9 mg/g and 2.7 mg/g for ASA, paracetamol and caffeine, respectively, and with relative errors lesser than 5% for the three analytes.