Comparison between Variable-Selection Algorithms in PLS Regression with Near-Infrared Spectroscopy to Predict Selected Metals in Soil.

Soil is one of the Earth's most important natural resources. The presence of metals can decrease environmental quality if present in excessive amounts. Analyzing soil metal contents can be costly and time consuming, but near-infrared (NIR) spectroscopy coupled with chemometric tools can offer an alternative. The most important multivariate calibration method to predict concentrations or physical, chemical or physicochemical properties as a chemometric tool is partial least-squares (PLS) regression. However, a large number of irrelevant variables may cause problems of accuracy in the predictive chemometric models. Thus, stochastic variable-selection techniques, such as the Firefly algorithm by intervals in PLS (FFiPLS), can provide better solutions for specific problems. This study aimed to evaluate the performance of FFiPLS against deterministic PLS algorithms for the prediction of metals in river basin soils. The samples had their spectra collected from the region of 1000-2500 nm. Predictive models were then built from the spectral data, including PLS, interval-PLS (iPLS), successive projections algorithm for interval selection in PLS (iSPA-PLS), and FFiPLS. The chemometric models were built with raw data and preprocessed data by using different methods such as multiplicative scatter correction (MSC), standard normal variate (SNV), mean centering, adjustment of baseline and smoothing by the Savitzky-Golay method. The elliptical joint confidence region (EJCR) used in each chemometric model presented adequate fit. FFiPLS models of iron and titanium obtained a relative prediction deviation (RPD) of more than 2. The chemometric models for determination of aluminum obtained an RPD of more than 2 in the preprocessed data with SNV, MSC and baseline (offset + linear) and with raw data. The metals Be, Gd and Y failed to obtain adequate models in terms of residual prediction deviation (RPD). These results are associated with the low values of metals in the samples. Considering the complexity of the samples, the relative error of prediction (REP) obtained between 10 and 25% of the values adequate for this type of sample. Root mean square error of calibration and prediction (RMSEC and RMSEP, respectively) presented the same profile as the other quality parameters. The FFiPLS algorithm outperformed deterministic algorithms in the construction of models estimating the content of Al, Be, Gd and Y. This study produced chemometric models with variable selection able to determine metals in the Ipojuca River watershed soils using reflectance-mode NIR spectrometry.

A green method for the authentication of sugarcane spirit and prediction of density and alcohol content based on near infrared spectroscopy and chemometric tools.

Cachaça is a Brazilian beverage obtained from the fermentation of sugarcane juice (sugarcane spirit) and is considered one of the most consumed alcoholic beverages in the world with a strong economic impact on the northeastern Brazil, more specifically in the Brejo. This microregion produces sugarcane spirits with high quality associated to edaphoclimatic conditions. In this sense, analysis for sample authentication and quality control that uses solvent-free, environmentally friendly, rapid and non-destructive methods is advantageous for cachaça producers and production chain. Thus, in this work commercial cachaça samples using near-infrared spectroscopy (NIRS) were classified based on geographical origin using one-class classification Data-Driven in Soft Independent Modelling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OCPLS) and predicted quality parameters of alcohol content and density based on different chemometric algorithms. A total of 150 sugarcane spirits samples were purchased from the Brazilian retail market being 100 from Brejo and 50 from other regions of Brazil. The one-class chemometric classification model was obtained with DD-SIMCA using the Savitzky-Golay derivative with first derivative, 9-point window and 1st degree polynomial as preprocessing algorithm and sensibility was 96.70 % and specificity 100 % in the spectral range 7,290-11,726 cm-1. Satisfactory results were obtained in the model constructs for density and the chemometric model, iSPA-PLS algorithm with baseline offset as preprocessing, obtained root mean square errors of prediction (RMSEP) of 0.0011 mg/L and Relative Error of Prediction (REP) of 0.12 %. The chemometric model for alcohol content prediction used the iSPA-PLS algorithm with Savitzky-Golay derivative with first derivative, 9-point window and 1st degree polynomial as algorithm as preprocessing obtaining RMSEP and REP of 0.69 and 1.81 % (v/v), respectively. Both models used the spectral range from 7,290-11,726 cm-1. The results reflected the potential of vibrational spectroscopy coupled with chemometrics to build reliable models for identifying the geographical origin of cachaça samples for predicting quality parameters in cachaça samples.

Trends and Application of Chemometric Pattern Recognition Techniques in Medicinal Plants Analysis.

Medicinal plants have been used and studied for ages, from very old registers to modern ethnopharmacology, which encompasses analytical chemistry, foods, and pharmacy. Based on international norms and governmental organizations of health, phytomedicine-for example, herbal drugs-needs to guarantee the quality control of products and identify contaminants, biomarkers, and chemical profiles, among other issues. In this sense, is necessary to develop advanced analytical methods that show interesting possibilities and obtain a great amount of data. In order to treat the data, a set of mathematical and statistical procedures named chemometrics is necessary. In terms of herbal drugs, chemometric tools may be used to identify the following in plants: parts, development stages, processing, geographic origin, authentication, and chemical markers. This review describes applications of chemometric pattern recognition tools to analyze herbal drugs in different conditions associated with analytical methods in the last six years (2015-2020).

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.

Feasibility study on quantification and authentication of the cassava starch content in wheat flour for bread-making using NIR spectroscopy and digital images.

This works proposed a feasibility study on NIR spectroscopy and chemometrics-assisted color histogram-based analytical systems (CACHAS) to determine and authenticate the cassava starch content in wheat flour. Prediction results of partial least squares (PLS) achieved coefficient of correlation (rpred) of 0.977 and root mean square error of prediction (RMSEP) of 1.826 mg kg-1 for the certified additive-free wheat flour, while rpred of 0.995 and RMSEP of 1.004 mg kg-1 were obtained for the commercial wheat flour containing chemical additives. Additionally, Data-Driven Soft Independent Modelling of Class Analogy (dd-SIMCA) presented similar predictive ability using NIR and CACHAS for the certified wheat flour, authenticating all target samples, besides correctly recognizing samples that could represent a fraud. No satisfactory results were obtained for the commercial wheat flour. Therefore, NIR spectroscopy is more useful to offer definitive quantitative and qualitative analysis, while CACHAS can only provide an alternative preliminary analysis.

Applications of NIR spectroscopy and chemometrics to illicit drug analysis: An example from inhalant drug screening tests.

The "loló" stands out among the most used inhalant drugs in Brazil. This drug is a non-specific blend of organic solvents, traditionally composed of ether and chloroform. Reports in the literature and forensic practice have revealed changes in the composition profile of this drug, based on availability of acquisition. This diversity has an effect on the efficiency of the preliminary tests used to detect illicit substances in situations that require rapid response time from the criminal investigations, such as arrests in the act. Considering the diversity of volatile substances with potential use as inhalant drugs and the limited detection abilities of preliminary exams routinely used by forensic laboratories, this present work applied NIR spectroscopy associated with chemometric models to detect the presence of organic solvents in samples of "loló". Initially, the chemical profile of the seized samples was surveyed in the geographic region of study (Paraiba State, Brazilian northeast), and from the observation of the prevalent substances, classification models were produced using samples made in the laboratory and samples from real apprehensions. Then, an analysis protocol was developed, based on SIMCA models, to detect the predominant solvents in the regional composition profile (dichloromethane, trichloroethene and chloroform). The proposed analysis protocol obtained an overall accuracy of 94.7% in detecting halogenated hydrocarbons in suspect samples and 100% accuracy in characterizing the composition of samples composed exclusively of the studied halogenated hydrocarbons and their binary mixtures. Considering that the colorimetric tests used in the routine of forensic laboratories do not detect many components, the proposed method was technically and economically viable in preliminary tests for samples seized as suspicious of being "loló".

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.

Near-infrared spectroscopy for the prediction of rare earth elements in soils from the largest uranium-phosphate deposit in Brazil using PLS, iPLS, and iSPA-PLS models.

The largest uranium-phosphate deposit in Brazil also contains considerable levels of rare earth elements (REEs), which allows for the co-mining of these three ores. The most common methods for REE determination are time-consuming and demand complex sample preparation and use of hazardous reagents. Thus, the development of a safer and faster method to predict REEs in soil could aid in the assessment of these elements. We investigated the efficiency of near-infrared (NIR) spectroscopy to predict REEs in the soil of the uranium-phosphate deposit of Itataia, Brazil. We collected 50 composite topsoil samples in a well-distributed sampling grid along the deposit. The NIR measures in the soils ranged from 750 to 2500 nm. Three partial least squares regressions (PLSR) were selected to calibrate the spectra: full-spectrum partial least squares (PLS), interval partial least squares (iPLS), and successive projections algorithms for interval selection in partial least squares (iSPA-PLS). The concentrations of REEs were measured by inductively coupled plasma optical emission spectroscopy (ICP-OES). In addition to raw spectral data, we also used spectral pretreatments to investigate the effects on prediction results: multiplicative scatter correction (MSC), Savitzky-Golay derivatives (SG), and standard normal variate transformation (SNV). Positive results were obtained in PLS for La and ΣLREE using MSC pretreatment and in iSPA-PLS for Nd and Ce using raw data. The accuracy of the measurements was related to the REE concentration in soil; i.e., elements with higher concentrations tended to present more accurate results. The results obtained here aim to contribute to the development of NIR spectroscopy techniques as a tool for mapping the concentrations of REEs in topsoil.

Chemodiversity and Antibacterial Activity of the Essential Oil of Leaves of Croton argyrophyllus.

The Croton argyrophyllus leaf essential oil has anti-inflammatory, antioxidant, cytotoxic among other activities. However, there are chemical composition variations in the literature. This work reports the first study of the intraspecific chemical variation of C. argyrophyllus population from Pernambuco state, Brazil. The essential oils of nine specimens (OCA1 to OCA9) were analyzed by GC/MS and NIR to identify the chemical compositions and to verify the similarities between the analyzed samples. These analyses resulted in the identification of bicyclogermacrene (mean 38.42 %), (Z)-caryophyllene (mean of 14.06 %), epi-longipinanol (mean of 9.78 %) and germacrene B (mean of 9.1 %) as the major constituents, as well as the same chemical markers for all oil samples. However, these are different to those that were previously registered in the literature for C. argyrophyllus essential oil. The data obtained from the analysis by NIR spectroscopy were treated by PCA and HCA and showed similarities in the chemical samples' profile. By statistical analyses three clusters were obtained: OCA1-6, OCA7-8 and OCA9. All these groups were potentially active against Staphylococcus aureus. However, the OCA7-8 group was the most active.

Antimicrobial Activity of Schinopsis brasiliensis Engler Extract-Loaded Chitosan Microparticles in Oral Infectious Disease.

Enterococcus faecalis infections represent a health concern, mainly in oral diseases, in which treatments with chlorhexidine solution (0.2%) are often used; however, it presents high toxicity degree and several side effects. Based on this, the use of natural products as an alternative to treatment has been explored. Nonetheless, plant extracts have poor organoleptic characteristics that impair theirs in natura use. Therefore, this work aimed to evaluate the analytical profile, biological activity, and cytotoxicity in vitro of S. brasiliensis-loaded chitosan microparticles (CMSb) produced using different aspersion flow rates. The analytical fingerprint was obtained by FTIR and NIR spectra. Principal components analysis (PCA) was used to verify the similarity between the samples. The crystallinity degree was evaluated by X-ray diffraction (XRD). Phytochemical screening (PS) was performed to quantify phytocompounds. Antimicrobial activity was evaluated by minimum inhibitory concentration (MIC). Antibiofilm activity and bactericidal kinetics against E. faecalis (ATCC 29212 and MB 146-clinical isolated) were also assessed. The hemolytic potential was performed to evaluate the cytotoxicity. Data provided by FTIR, NIR, and PCA analyses revealed chemical similarity between all CMSb. Furthermore, the results from XRD analysis showed that the obtained CMSb present amorphous characteristic. Tannins and polyphenols were accurately quantified by the PS, but methodology limitations did not allow the flavonoid quantification. The low hemolytic potential assay indicates that all samples are safe. Antimicrobial assays revealed that CMSb were able to inhibit not only the E. faecalis ATCC growth but also the biofilm formation. Only one CMSb sample was able to inhibit the clinical strain. These results highlighted the CMSb antimicrobial potential and revealed this system as a promising product to treat infections caused by E. faecalis.

Proximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS).

BACKGROUND: In Brazil, over the last few years there has been an increase in the production and consumption of goat cheeses. In addition, there was also a demand to create options to use the whey extracted during the production of cheeses. Whey can be used as an ingredient in the development of many products. Therefore, knowing its composition is a matter of utmost importance, considering that the reference methods of food analysis require time, trained labor and expensive reagents for its execution. METHODS: Goat whey samples produced in winter and summer were submitted to proximate composition analysis (moisture, total solids, ashes, proteins, fat and carbohydrates by difference) using reference methods and near infrared spectroscopy (NIRS). The spectral data was preprocessed by baseline correction and the Savitzky-Golay derivative. The models were built using Partial Least Square Regression (PLSR) with raw and preprocessed data for each dependent variable (proximate composition parameter). RESULTS: The average whey composition values obtained using the referenced methods were in accordance with the consulted literature. The composition did not differ significantly (p > 0.05) between the summer and winter whey samples. The PLSR models were made available using the following figures of merit: coefficients of determination of the calibration and prediction models (R 2cal and R 2pred, respectively) and the Root Mean Squared Error Calibration and Prediction (RMSEC and RMSEP, respectively). The best models used raw data for fat and protein determinations and the values obtained by NIRS for both parameters were consistent with their referenced methods. Consequently, NIRS can be used to determine fat and protein in goat whey.

Tablet of Ximenia Americana L. Developed from Mucoadhesive Polymers for Future Use in Oral Treatment of Fungal Infections.

The use of biocompatible polymers such as Hydroxypropylmethylcellulose (HPMC), Hydroxyethylcellulose (HEC), Carboxymethylcellulose (CMC), and Carbopol in solid formulations results in mucoadhesive systems capable of promoting the prolonged and localized release of Active Pharmaceutical Ingredients (APIs). This strategy represents a technological innovation that can be applied to improving the treatment of oral infections, such as oral candidiasis. Therefore, the aim of this study was to develop a tablet of Ximenia americana L. from mucoadhesive polymers for use in the treatment of oral candidiasis. An X. americana extract (MIC of 125 µg·mL-1) was obtained by turbolysis at 50% of ethanol, a level that demonstrated activity against Candida albicans. Differential Thermal Analysis and Fourier Transform Infrared Spectroscopy techniques allowed the choice of HPMC as a mucoadhesive agent, besides polyvinylpyrrolidone, magnesium stearate, and mannitol to integrate the formulation of X. americana. These excipients were granulated with an ethanolic solution 70% v/v at PVP 5%, and a mucoadhesive tablet was obtained by compression. Finally, mucoadhesive strength was evaluated, and the results demonstrated good mucoadhesive forces in mucin disk and pig buccal mucosa. Therefore, the study allowed a new alternative to be developed for the treatment of buccal candidiasis, one which overcomes the inconveniences of common treatments, costs little, and facilitates patients' adhesion.

Overview of Analytical Techniques Associated with Pattern Recognition Methods in Sugarcane Spirits Samples.

Sugarcane spirits and cachaça are distilled beverages derived from the fermentation of sugarcane juice. The production of these spirits has significant influence on the economy of several regions in Brazil, being the third most consumed distilled beverage in the world. To ensure the safety for human consumption and also to add value to these products, it is imperative to apply quality control techniques. The complexity of food matrices along with the fact that the currently used instrumental techniques have several disadvantages, such as being expensive, laborious and time-consuming, have turned research attention to multivariate analysis techniques. In consequence, chemometric techniques have been applied in laboratories around the world aiming at data reduction, pattern recognition, cluster analysis, classification and quantification of data. This article provides an overview of the application of analytical techniques with nonsupervised and supervised pattern recognition methods for the analysis of sugarcane spirits samples. Assessments discussed include promising results for the discrimination among samples, verification of adulteration, tendencies in sensorial characteristics as well as relationships between chemical information and the geographical origins.

Simultaneous identification of the wood types in aged cachaças and their adulterations with wood extracts using digital images and SPA-LDA.

Cachaça is a sugarcane-derived alcoholic spirit exclusively produced in Brazil. It can be aged in barrels made from different types of wood, similar to other distilled beverages. The choice of wood type promotes different effects on color, flavor, aroma and consequently the price of cachaça, favoring fraudulent activities. This paper proposes the simultaneous identification of different wood types in aged cachaças and their adulterations with wood extracts using a digital-image based methodology employing color histograms obtained from digital images associated with pattern recognition methods, without any sample preparation step. Linear Discriminant Analysis, coupled with Successive Projections Algorithm for variable selection (SPA-LDA), obtained the best results, reaching accuracy, sensitivity, and specificity rates higher than 90.0% in the test set. This can be a rapid and reliable tool to prevent fraudulent labeling; ensuring that what is on the label reflects the quality of aged cachaças, affording security to consumers and regulatory agencies.

Dissolution and uniformity of content of tablets developed with extract of Ximenia americana L.

Herbal medicines currently represent an important part of the world pharmaceutical market, which shows growing interest in the use of herbal medicines. However, the production of such medicines involves a complex series of steps, which determine the production viability and the quality of the final product. Ximenia americana L. is a plant occurring in several regions of the world, with well-known and applied medicinal properties. Thus, the aim of this work was to develop and evaluate the physical and physical-chemical quality of tablets produced with X. americana L. extract. The extract was spray-dried from a hydroethanolic extractive solution and characterized as to its phytochemical composition. The chemical marker was determined and quantified using validated chromatographic methods. These methods indicated the presence of gallic acid at a concentration of 1.61 mg g(-1). Formulations were proposed and analyzed for their flow and compaction properties. The best formulation was used to obtain a batch of tablets, which was evaluated for its quality characteristics and showed to be within the pharmacopoeial specifications for average weight, hardness, friability, and disintegration time. The dissolution profile of the tablets produced was obtained, showing the release of about 70% of the vegetable extract content within 30 minutes. Results showed that it was possible to obtain herbal tablets containing a high content of vegetal extract by direct compression, developing a rapid process of formulation and production and guaranteeing the quality characteristics of the final product.

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).

Using near infrared spectroscopy to classify soybean oil according to expiration date.

A rapid and non-destructive methodology is proposed for the screening of edible vegetable oils according to conservation state expiration date employing near infrared (NIR) spectroscopy and chemometric tools. A total of fifty samples of soybean vegetable oil, of different brands andlots, were used in this study; these included thirty expired and twenty non-expired samples. The oil oxidation was measured by peroxide index. NIR spectra were employed in raw form and preprocessed by offset baseline correction and Savitzky-Golay derivative procedure, followed by PCA exploratory analysis, which showed that NIR spectra would be suitable for the classification task of soybean oil samples. The classification models were based in SPA-LDA (Linear Discriminant Analysis coupled with Successive Projection Algorithm) and PLS-DA (Discriminant Analysis by Partial Least Squares). The set of samples (50) was partitioned into two groups of training (35 samples: 15 non-expired and 20 expired) and test samples (15 samples 5 non-expired and 10 expired) using sample-selection approaches: (i) Kennard-Stone, (ii) Duplex, and (iii) Random, in order to evaluate the robustness of the models. The obtained results for the independent test set (in terms of correct classification rate) were 96% and 98% for SPA-LDA and PLS-DA, respectively, indicating that the NIR spectra can be used as an alternative to evaluate the degree of oxidation of soybean oil samples.

Digital image-based classification of biodiesel.

This work proposes a simple, rapid, inexpensive, and non-destructive methodology based on digital images and pattern recognition techniques for classification of biodiesel according to oil type (cottonseed, sunflower, corn, or soybean). For this, differing color histograms in RGB (extracted from digital images), HSI, Grayscale channels, and their combinations were used as analytical information, which was then statistically evaluated using Soft Independent Modeling by Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and variable selection using the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). Despite good performances by the SIMCA and PLS-DA classification models, SPA-LDA provided better results (up to 95% for all approaches) in terms of accuracy, sensitivity, and specificity for both the training and test sets. The variables selected Successive Projections Algorithm clearly contained the information necessary for biodiesel type classification. This is important since a product may exhibit different properties, depending on the feedstock used. Such variations directly influence the quality, and consequently the price. Moreover, intrinsic advantages such as quick analysis, requiring no reagents, and a noteworthy reduction (the avoidance of chemical characterization) of waste generation, all contribute towards the primary objective of green chemistry.

Using color histograms and SPA-LDA to classify bacteria.

In this work, a new approach is proposed to verify the differentiating characteristics of five bacteria (Escherichia coli, Enterococcus faecalis, Streptococcus salivarius, Streptococcus oralis, and Staphylococcus aureus) by using digital images obtained with a simple webcam and variable selection by the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). In this sense, color histograms in the red-green-blue (RGB), hue-saturation-value (HSV), and grayscale channels and their combinations were used as input data, and statistically evaluated by using different multivariate classifiers (Soft Independent Modeling by Class Analogy (SIMCA), Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Partial Least Squares Discriminant Analysis (PLS-DA) and Successive Projections Algorithm-Linear Discriminant Analysis (SPA-LDA)). The bacteria strains were cultivated in a nutritive blood agar base layer for 24 h by following the Brazilian Pharmacopoeia, maintaining the status of cell growth and the nature of nutrient solutions under the same conditions. The best result in classification was obtained by using RGB and SPA-LDA, which reached 94 and 100 % of classification accuracy in the training and test sets, respectively. This result is extremely positive from the viewpoint of routine clinical analyses, because it avoids bacterial identification based on phenotypic identification of the causative organism using Gram staining, culture, and biochemical proofs. Therefore, the proposed method presents inherent advantages, promoting a simpler, faster, and low-cost alternative for bacterial identification.