TEACHING EXPERIMENT IN CHEMOMETRICS USING DIGITAL IMAGES OBTAINED BY MOBILE PHONE TO DETERMINE ADULTERATION OF OLIVE OIL WITH SOYBEAN OIL: A TUTORIAL, PART VI. The aim of this manuscript was to show the concepts involving data treatment of RGB based image analysis by univariate and multivariate approaches – the latter using Partial Least Squares (PLS) - using as a practical example application the determination of extravirgin olive oil adulteration. Digital images were collected using a device made of LEDs, batteries and a smartphone and results are shown in a tutorial format using Matlab computing environment. The experiment can serve as an example for teaching the subject for undergraduate and graduate students.

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.

The aim of this manuscript was to show the concepts involving data treatment of RGB based image analysis by univariate and multivariate approaches - the latter using Partial Least Squares (PLS) -using as a practical example application the determination of extravirgin olive oil adulteration. Digital images were collected using a device made of LEDs, batteries and a smartphone and results are shown in a tutorial format using Matlab computing environment. The experiment can serve as an example for teaching the subject for undergraduate and graduate students.

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.

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.

Due to food adulteration concerns, analytical assays are routinely performed in labs to evaluate and ensure food quality control. However, classical analytical methods used to acquire reliable results are lengthy and costly. Therefore, we aim to propose a new approach to detect adulterants in cassava starch in a clean, green, cheap, and quick way. Raman spectroscopy meets all these requirements and presents great potential to perform such routine analyses. Data treatment is also an important step in authentication problems, and we propose the use of one-class models to do so. One-class support vector machine (OC-SVM) and soft independent modelling by class analogy (SIMCA) were the two approaches to one-class classifiers assessed in this study. Cassava starch samples were modified in the lab with adulteration ranging from 0.5 to 50%, with adulterants such as wheat flour, sodium bicarbonate, and others. The two chemometric models were statistically compared and OC-SVM was found to outperform SIMCA, reaching higher values of sensitivity (87.1%), specificity (86.8%), and accuracy (86.9%) in the prediction of known data samples. This better performance also resulted in the possibility of detecting adulterations over 2% by OC-SVM, compared to only 5% by SIMCA.

This work demonstrated that a Ni-P film deposited on polydimethylsiloxane (PDMS) substrate could influence the magnetic aggregation of gold-modified magnetic nanoparticles (Fe3O4/AuNPs), improving their performance in surface-enhanced Raman scattering (SERS) detection. A simple and low-cost electroless deposition process was used to deposit the Ni-P film on PDMS (PDMS-Ni-P). By combining the PDMS-Ni-P substrate and a permanent NdFeB magnet, the Fe3O4/AuNPs were aggregated, and the SERS signal for crystal violet was three orders of magnitude higher than that obtained by aggregating the nanoparticles only using the magnet. Moreover, SERS signals for adenine and thiabendazole were also improved using the PDMS-Ni-P substrate, providing limits of detection of 1.65 and 0.24 mg L-1, respectively.

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.

In this study, we aimed to discriminate four commercial blends of green tea in bagged (inside its sachet) and nonbagged conditions using near-infrared (NIR) spectroscopy and support vector machines (SVM) for data modelling. To choose optimal parameters for the models, we applied Bayesian optimization, which provided accurate models. Two spectrometers were evaluated: a benchtop and a handheld, both presenting reliable results for nonbagged tea (accuracies of 90% and 93%, respectively). However, for bagged tea models, the classification performance of benchtop was superior to handheld equipment, yielding accuracies of 93% and 82%, respectively. Classification accuracies using SVM outperformed partial least squares discriminant analysis (PLS?DA) for handheld and tea inside teabag models. The results indicated that the proposed methodology has the potential to be applied in automatic quality control coupling NIR sensors and machine learning for data processing.

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.

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.

In this special edition celebrating 10 years of BrJAC, a compilation of the interviews given during this period by renowned researchers is presented. This set of interviews provides the reader with a broad and diversified view of the evolution of analytical chemistry over the decades, both in Brazil and abroad. This compilation is also very enjoyable, as it brings back personal and curious memories of the interviewees.

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, SVMRBF 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.

Chrysobalanus icaco L. (Chrysobalanaceae) is a medicinal species widely used in Brazil mainly to treat diabetes. Despite the medicinal importance of C. icaco , genetic information of this genus remains limited. Thus, our aim was to evaluate the influence of the genetic basis of C. icaco by determining its chemotypes. 25 C. icaco genotypes were collected from 15 sites in Belém, Marajó and Northeastern mesoregions of Pará state, Brazil. The genotypes were selected by evaluating the plant morphological characteristics such as fruit color and plant habit. The DNA fingerprinting profile was performed using PCR based RAPD technique and appropriate statistical methods were used. RAPD markers were used for evaluation of genetic diversity and molecular characterization of the C. icaco , using a total of 18 decamer primers. These primers produced 85 amplification products, with an average of 4.7 bands per primer and 99.2% polymorphism. The genotypes are genetically distinct, forming variable clusters in number and constitution by different methods. By the morphological characteristics considered, there is a tendency of clustering based on the color of the ripe fruit. We found the secondary metabolite content depends not on environmental condition, but rather on C. icaco genome. Therefore, it may have implications for ethnopharmacological use of the chemotypes.

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.

IQ-Unicamp) in 1986 with a degree in chemistry, and holds a master's degree (1989) and a doctoral (1993) degree from the same IQ-Unicamp and a postdoctoral degree from the Free University of Brussels, Belgium (1996).He is currently a full professor in the Analytical Chemistry Department at IQ-Unicamp.He works mainly with chemometrics and spectroscopic methods of analysis.In chemometrics, he has worked in multivariate calibration, neural networks, support vector machines, curve resolution and methods for processing multimode data.In spectroscopy, his studies have emphasized near-and medium-infrared, Raman, molecular fluorescence and image spectroscopy. Could you tell us a little about your childhood?I was born in the city of Campinas, SP, Brazil.My father had a barber shop in the 'Bonfim' neighborhood, my mother always worked at home taking care of the family, and I have an older sister.I had no luxury, but I never lacked anything, and my childhood was like that of any other boy growing up in the 1970s in the neighborhood, playing ball on the street all day and dreaming of being a football player. What early influences encouraged you to study science? Did you have any influencers, such as a teacher?In the elementary school, I was always one of the best students in my class at the state public school named 'Dom João Nery'.When I started taking science classes, I was delighted to be able to understand how things worked.I was very curious to know about the cells in the body, how the light bulb lit or the car battery could generate electricity.At this point, I had a science teacher (Dona Terezinha) who, seeing my interest and how well I was doing in the exams, one day called me aside and said: "You have a lot of potential.You must take a technical course and then go to Unicamp".No one had ever spoken to me like that, and it opened my horizon.I ended up taking her advice, and today I am here.

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.

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 São 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.

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 development. Thus, the aim of this work was to demonstrate the feasibility of using Raman and near‐infrared (NIR) mapping associated with multivariate curve resolution–alternating least squares (MCR‐ALS) and common components and specific weights analysis (CCSWA or ComDim) chemometric methods to assess the solid dispersions of atorvastatin calcium in Gelucire® 48/16 under controlled (3.0 ± 1.0°C, sealed flask) and accelerated (33.0 ± 1.0°C, 75% of relative humidity, open flask) aging conditions. MCR‐ALS allowed the identification of the amorphous and crystalline fractions of the drug within the solid dispersion, which is not easily achieved by traditional techniques such as differential scanning calorimetry and X‐ray diffraction, especially for semisolid formulations. ComDim results indicated that Raman seemed more sensitive to the presence of the API, whereas NIR was found to be more sensitive to alterations in the spectra of the excipient. The use of the ComDim method is not yet widespread in the pharmaceutical area; therefore, this paper shows its potential to support pharmaceutical development in preformulation stages for stability studies.