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).

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.

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.

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.

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.

Classification of biodiesel using NIR spectrometry and multivariate techniques.

This article describes the classification of biodiesel samples using NIR spectroscopy and chemometric techniques. A total of 108 spectra of biodiesel samples were taken (being three samples each of four types of oil, cottonseed, sunflower, soybean and canola), from nine manufacturers. The measurements for each of the three samples were in the spectral region between 12,500 and 4000 cm(-1). The data were preprocessed by selecting a spectral range of 5000-4500 cm(-1), and then a Savitzky-Golay second-order polynomial was used with 21 data points to obtain second derivative spectra. Characterization of the biodiesel was done using chemometric models based on hierarchical cluster analysis (HCA), principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) elaborated for each group of biodiesel samples (cotton, sunflower, soybean and canola). For the HCA and PCA, the formation of clusters for each group of biodiesel was observed, and SIMCA models were built using 18 spectral measurements for each type of biodiesel (training set), and nine spectral measurements to construct a classification set (except for the canola oil which used eight spectra). The SIMCA classifications obtained 100% accurate identifications. Using this strategy, it was feasible to classify biodiesel quickly and nondestructively without the need for various analytical determinations.