An improved successive projections algorithm version to variable selection in multiple linear regression.

The aim of the successive projections algorithm (SPA) is to enhance the accuracy of multiple linear regressions (MLR) by minimizing the impact of collinearity effects in the calibration data set. Combining SPA with MLR as a variable selection approach has resulted in the SPA-MLR method, which has been reported in literature to produce models with good prediction ability compared to conventional full-spectrum models obtained with partial-least-squares (PLS) in some cases. This paper proposes the addition of a filter step to the current version of the SPA algorithm to reduce the number of uninformative variables before the projection phase and assist the algorithm in selecting the best variables on subsequent steps. The proposed fSPA-MLR algorithm is evaluated in two case studies involving the near-infrared spectrometric analysis of pharmaceutical tablet and diesel/biodiesel mixture samples. Compared to PLS, the fSPA-MLR models demonstrate similar or better performance. Moreover, the fSPA-MLR models outperform the original SPA-MLR in both cross-validation and external prediction. The fSPA-MLR models deliver superior results regardless of the pre-processing algorithm tested, including first-derivative Savitzky-Golay (SG) and Standard Normal Variate (SNV), or even in raw spectra data.

Analytical and chemometric strategies for elucidation of yerba mate composition.

Yerba mate, a popular plant consumed mainly as an infusion, possesses nutritional and medicinal properties attributed to its secondary metabolites. This study aimed to develop strategies to elucidate the phenolic composition of yerba mate samples from Brazil, Argentina, Uruguay, and Paraguay. Optimization of ultrasonic-assisted extraction (UAE) was performed, and the extracted compounds were characterized using ultra-high-pressure liquid chromatography coupled with quadrupole/time-of-flight mass spectrometry (UHPLC-QTOF-MS), molecular fluorescence and high-pressure liquid chromatography with diode-array detection (HPLC-DAD). Chemometric analysis, including parallel factor analysis (PARAFAC) and principal component analysis (PCA) explored metabolite profiles and identify patterns. PARAFAC modelling of the molecular fluorescence results revealed higher pigment content in Brazilian samples, while other countries' samples exhibited higher phenolic content. PCA modeling of HPLC-DAD results indicated that cultivated yerba mate contained higher chlorogenic acids levels, and samples from Argentina, Paraguay, and Uruguay exhibited higher concentrations of chlorogenic acids and flavonoids.

Variable selection in the chemometric treatment of food data: A tutorial review.

Food analysis covers aspects of quality and detection of possible frauds to ensure the integrity of the food. The arsenal of analytical instruments available for food analysis is broad and allows the generation of a large volume of information per sample. But this instrumental information may not yet give the desired answer; it must be processed to provide a final answer for decision making. The possibility of discarding non-informative and/or redundant signals can lead to models of better accuracy, robustness, and chemical interpretability, in line with the principle of parsimony. Thus, in this tutorial review, we cover aspects of variable selection in food analysis, including definitions, theoretical aspects of variable selection, and case studies showing the advantages of variable selection-based models concerning the use of a wide range of non-informative and redundant instrumental information in the analysis of food matrices.

Pharmaceuticals, pesticides and metals/metalloids in Lake Guaíba in Southern Brazil: Spatial and temporal evaluation and a chemometrics approach.

Compiling and reporting data related to the presence of pharmaceuticals and pesticides are crucial means of assessing the risk those chemicals pose to human health and environment. Data sets from different sources were combined using a data fusion approach to produce a spatial and temporal variation of contaminants presents in water from Lake Guaíba (29°55'-30°24' S; 51°01'-51°20' W). Lake Guaíba is a 496 km2 water body situated in the geological depression of Rio Grande do Sul State, Brazil; that is fed by several rivers from the metropolitan area, the 5th largest metro area in Brazil, with approximately 5 million inhabitants. Analytical methodology to quantify pharmaceuticals and pesticides by LC-QTOF-MS and GC-MS/MS was validated for 41 pharmaceutical and 62 pesticides. Furthermore, 27 chemical elements were analyzed by ICP-MS, and physical chemical parameters were determined using established methodologies. All validation parameters were in accordance with the National Institute of Metrology, Standardization, and Industrial Quality. Thirty-five water samples were analyzed from January to August 2019, and 15 pharmaceuticals and 25 pesticides were present in concentrations ranging from 6.00 ng L-1 to 580.00 ng L-1. Twenty-seven elements were analyzed during the same period, and 18 were present in concentrations ranging from 0.2 µg L-1 to 7060 µg L-1. Samples were tagged according to the points and months of collection to identify temporal and spatial patterns. The main findings show that the compounds are distributed throughout the studied area without an apparent regular pattern, suggesting that events in a specific point affect the entire ecosystem. Conversely, temporal variations were well defined, as samples were grouped according to the climatic conditions of the months of collection. Considering the calculated quotient risks, atrazine, cyproconazole, diuron, and simazine showed the highest risk levels for algae; acetaminophen, diclofenac, and ibuprofen showed the highest risk levels for aquatics invertebrates.

Scores selection via Fisher's discriminant power in PCA-LDA to improve the classification of food data.

This paper proposes an adaptation of the Fisher's discriminability criterion (named here as discriminant power, DP) for choosing principal components (obtained from Principal Component Analysis, PCA), which will be used to construct supervised Linear Discriminant Analysis (LDA) models for solving classification problems of food data. The proposed PCA-DP-LDA algorithm was then applied to (i) simulated data, (ii) classify soybean oils with respect to expiration date, and (iii) identify cachaça adulteration with wood extracts that simulated aging. For comparison, PCA-DP-LDA was evaluated against conventional PCA-LDA (based on explained variance) and Partial Least Squares-Discriminant Analysis (PLS-DA). Among them, PCA-DP-LDA achieved the most parsimonious and interpretable results, with similar or better classification performance. Therefore, the new algorithm can be considered a good alternative to the already well-established discriminant methods, being potentially applied where the discriminability of the principal components may not follow the same behavior of the explained variance.

Geographical origin authentication of southern Brazilian red wines by means of EEM-pH four-way data modelling coupled with one class classification approach.

EEM data recorded at different pH values was exploited by MCR-ALS in order to determine qualitative information about Brazilian red wines. In addition, the geographical traceability of wines produced in the Serra Gaúcha (Rio Grande do Sul) was carried out by DD-SIMCA considering 53 samples from the target class and 20 from other producing regions. The fluorescence signal corresponds to 9 EEMs recorded at different pH (3-11), generating four-way data. By MCR-ALS decomposition, eight factors were retrieved and related to typical chemical compounds found in red wine. In addition, the EEM pH data was used to build a one-class classification model, considering that MCR scores and all samples of the target class were properly recognised as belonging to the target class, with maximal sensitivity equal to 1. Samples of the non-target class were also adequately rejected by the model, and the specificity was found to be 0.97.

Presence of antibiotic resistance genes and its association with antibiotic occurrence in Dilúvio River in southern Brazil.

It is known that antibiotics are widely used in human and veterinary medicine. In some countries the use is controlled, however few restrictions to their use are enforced in many countries. Antibiotics and their metabolites can reach the water bodies through sewage systems, especially in those countries with partial or absent wastewater treatment systems. The overuse and misuse of antibiotics has been linked with the increase of antibiotic resistant bacteria. The relation between the occurrence of antibiotics and resistance genes in surface waters has been widely studied worldwide evincing the great importance of this subject. In this work, a methodology for quantification of 40 antibiotics of 5 different classes, in river water, by SPE-LC-MS/MS was validated. Samples were taken during a two-year period from Dilúvio River, a stream that crosses the city of Porto Alegre (RS - Brazil) and receives in nature domestic effluent. The methodology met the requirements of validation, with Limit of Quantification varying from 20 ng L-1 to 100 ng L-1. A total of 48 samples was analyzed for the presence of antibiotics for two years. From the 40 antibiotics analyzed, 8 of them (Azithromycin, Cephalexin, ciprofloxacin, clindamycin, norfloxacin, sulfadiazine, sulfamethoxazole and trimethoprim) were present in all sampling points in the range of

Mineral Composition Evaluation in Energy Drinks Using ICP OES and Chemometric Tools.

This study reports the simultaneous determination of the total concentrations of Al, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, and Zn in 17  samples of commercial energy drinks through inductively coupled plasma optical emission spectrometry and multivariate methods, such as Pearson correlation and principal component analysis (PCA), in order to conduct a more thorough evaluation of the original data. The samples studied were stored in two types of containers (polyethylene terephthalate bottles and aluminum cans) and purchased in the city of Belém (State of Pará, Brazil). The results showed high Na content in energy drinks, followed by K, Ca, and Mg. The accuracy of the optimized method was evaluated with the certified reference materials to assess trace elements in water (NIST 1643e); the resultant recoveries varied from 83 to 105%. Energy drinks stored in cans presented higher levels of aluminum and magnesium, while those bottled in polyethylene terephthalate bottles had a higher K content. There were significant differences between the observed Na concentrations and the values dictated on the drink package. Furthermore, PCA explained 70.38% of the total variance, allowing for an evaluation of the degree of similarity between the energy drinks studied and showing that the main contributions to the formation of groups are related to Fe, Na, Mg, and Zn contents. These results will be used to better understand the distribution of inorganic elements contained in energy drinks.

Comparison between counterfeit and authentic medicines: A novel approach using differential scanning calorimetry and hierarchical cluster analysis.

Erectile dysfunction medicines such as Cialis and Viagra are very popular worldwide and are between the most prevalent counterfeit medicines in Brazil. A range of analytical methods has been used to analyze Cialis and Viagra, such as ATR-FTIR, GCMS and UPLC-MS. Until now, there are no data available of DSC methods for analysis of counterfeit medicines of Cialis and Viagra. DSC is a thermal analysis that provides useful information of physico-chemical events, and however is almost not used for forensic purposes. In this study, thermal analysis of 25 counterfeit Viagra and Cialis seized by Brazilian Federal Police were performed by DSC and compared to their authentic medicines and analytical standards, along with chemometric tools. Authentic samples of Viagra displayed a similar thermal profile with the API, while Cialis were different with additional endothermic peaks, that could be related to excipients interference. Thermograms of Viagra counterfeit samples were similar to authentic samples, while Cialis showed an enlargement and displacement of endothermic peaks. Also, some Cialis counterfeit samples showed melting peaks attributed to sildenafil, the API of Viagra, instead tadalafil, confirming previous results obtained by UPLC-MS. Multivariate analysis with application of Hierarchical Cluster Analysis classified different groups of samples, including a cluster with counterfeit Cialis and Viagra, indicating the use of same API for both counterfeit medicines and possibly the same illicit production; and a cluster with authentic Viagra and counterfeit Cialis, confirming the addition of sildenafil instead tadalafil to Cialis counterfeit samples. Here for the first time we described the use of DSC for chemical profiling of Cialis and Viagra and showed that even when applied to a small group of samples, DSC along with chemometric tools can be considered as a good auxiliary method in forensic casework samples. DSC provided useful data to perform the identification of counterfeit and authentic medicines, with low cost and a simple method.

Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm.

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions.

Fluorescent fingerprints of edible oils and biodiesel by means total synchronous fluorescence and Tucker3 modeling.

The present work proposes the use of total synchronous fluorescence spectroscopy (TSFS) as a discrimination methodology for fluorescent compounds in edible oils, which are preserved after the transesterification processes in the biodiesel production. In the same way, a similar study is presented to identify fluorophores that do not change in expired vegetal oils, to associate physicochemical parameters to fluorescent measures, as contribution to a fingerprint for increasing the chemical knowledge of these products. The fluorescent fingerprints were obtained by Tucker3 decomposition of a three-way array of the total synchronous fluorescence matrices. This chemometric method presents the ability for modeling non-bilinear data, as Total Synchronous Fluorescence Spectra data, and consists in the decomposition of the three way data arrays (samples×Δλ×λ excitation), into four new data matrices: A (scores), B (profile in Δλ mode), C (profile in spectra mode) and G (relationships between A, B and C). In this study, 50 samples of oil from soybean, corn and sunflower seeds before and after its expiration time, as well as 50 biodiesel samples obtained by transesterification of the same oils were measured by TSFS. This study represents an immediate application of chemical fingerprint for the discrimination of non-expired and expired edible oils and biodiesel. This method does not require the use of reagents or laborious procedures for the chemical characterization of samples.

Highly sensitive quantitation of pesticides in fruit juice samples by modeling four-way data gathered with high-performance liquid chromatography with fluorescence excitation-emission detection.

A study regarding the acquisition and analytical utilization of four-way data acquired by monitoring excitation-emission fluorescence matrices at different elution time points in a fast HPLC procedure is presented. The data were modeled with three well-known algorithms: PARAFAC, U-PLS/RTL and MCR-ALS, the latter conveniently adapted to model third-order data. The second-order advantage was exploited when analyzing samples containing uncalibrated components. The best results were furnished with the algorithm U-PLS/RTL. This fact is indicative of both no peak time shifts occurrence among samples and high colinearity among spectra. Besides, this latent-variable structured algorithm is capable of better handle the need of achieving high sensitivity for the analysis of one of the analytes. In addition, a significant enhancement in both predictions and analytical figures of merit was observed for carbendazim, thiabendazole, fuberidazole, carbofuran, carbaryl and 1-naphtol, when going from second- to third-order data. LODs obtained were ranged between 0.02 and 2.4µgL(-1).

Second-order capillary electrophoresis diode array detector data modeled with the Tucker3 algorithm: A novel strategy for Argentinean white wine discrimination respect to grape variety.

Data obtained by capillary electrophoresis with diode array detection (CE-DAD) were modeled with the purpose to discriminate Argentinean white wines samples produced from three grape varieties (Torrontés, Chardonnay, and Sauvignon blanc). Thirty-eight samples of commercial white wine from four wine-producing provinces of Argentina (Mendoza, San Juan, Salta, and Rio Negro) were analyzed. CE-DAD matrices with dimensions of 421 elution times (from 1.17 to 7.39 minutes) × 71 wavelengths (from 227 to 367 nm) were joined in a three way data array and decomposed by Tucker3 method under non-negativity constraint, employing 18, 18 and six factors in the modes 1, 2 and 3, respectively. Using the scores of Tucker model, it was possible to discriminate samples of Argentinean white wine by linear discriminant analysis and Kernel linear discriminant analysis. Core element analysis of the Tucker3 model allows identifying the loading profiles in spectral mode related to Argentinean white wine samples.

Modeling excitation-emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety.

This paper reports the modeling of excitation-emission matrices for classification of Argentinean white wines according to the grape variety employing chemometric tools for pattern recognition. The discriminative power of the data was first investigated using Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC). The score plots showed strong overlapping between classes. A forty-one samples set was partitioned into training and test sets by the Kennard-Stone algorithm. The algorithms evaluated were SIMCA, N- and U-PLS-DA and SPA-LDA. The fit of the implemented models was assessed by mean of accuracy, sensitivity and specificity. These models were then used to assign the type of grape of the wines corresponding to the twenty samples test set. The best results were obtained for U-PLS-DA and SPA-LDA with 76% and 80% accuracy.

Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method.

A new residual modeling algorithm for nonbilinear data is presented, namely unfolded partial least squares with interference modeling of non bilinear data by multivariate curve resolution by alternating least squares (U-PLS/IMNB/MCR-ALS). Nonbilinearity represents a challenging data structure problem to achieve analyte quantitation from second-order data in the presence of uncalibrated components. Total synchronous fluorescence spectroscopy (TSFS) generates matrices which constitute a typical example of this kind of data. Although the nonbilinear profile of the interferent can be achieved by modeling TSFS data with unfolded partial least squares with residual bilinearization (U-PLS/RBL), an extremely large number of RBL factors has to be considered. Simulated data show that the new model can conveniently handle the studied analytical problem with better performance than PARAFAC, U-PLS/RBL and MCR-ALS, the latter modeling the unfolded data. Besides, one example involving TSFS real matrices illustrates the ability of the new method to handle experimental data, which consists in the determination of ciprofloxacin in the presence of norfloxacin as interferent in water samples.

Flow injection photometric determination of NaCl, KCl and glucose in injectable drugs exploiting Schlieren signals.

A flow injection photometric system that exploits Schlieren signals for analytical measurement is described. The system was designed to be used as a new strategy for determining the contents of sodium chloride, potassium chloride and glucose, each respectively in injectable drugs. The proposed methodology was based on the difference between the refractive indices of the sample zone and of the carrier stream. With this perspective, a lab-made photometer based on LED-phototransistor technology was employed as a detection system to investigate the different analytical profiles related to the Schlieren effect in low flow rate conditions. The parameters of the flow system, such as flow-rate, optical path length, and sampling loop, were adjusted in order to obtain suitable Schlieren profiles for the measurements. Data evaluation was performed with the application of partial least squares regression (PLS-1). The obtained results demonstrated the predictive ability of the constructed PLS models, and the predicted concentration values were in agreement with the reference values, with a 95% confidence level.