Discrimination and classification techniques applied on Mallotus and Phyllanthus high performance liquid chromatography fingerprints

Gas Chromatographic Fingerprint Analysis for the Comparison of Seized Cannabis Samples

Cannabis sativa L. is widely used as recreational illegal drugs. Illicit Cannabis profiling, comparing seized samples, is challenging due to natural Cannabis heterogeneity. The aim of this study was to use GC–FID and GC–MS herbal fingerprints for intra (within)- and inter (between)-location variability evaluation. This study focused on finding an acceptable threshold to link seized samples. Through Pearson correlation-coefficient calculations between intra-location samples, ‘linked’ thresholds were derived using 95% and 99% confidence limits. False negative (FN) and false positive (FP) error rate calculations, aiming at obtaining the lowest possible FP value, were performed for different data pre-treatments. Fingerprint-alignment parameters were optimized using Automated Correlation-Optimized Warping (ACOW) or Design of Experiments (DoE), which presented similar results. Hence, ACOW data, as reference, showed 54% and 65% FP values (95 and 99% confidence, respectively). An additional fourth root normalization pre-treatment provided the best results for both the GC–FID and GC–MS datasets. For GC–FID, which showed the best improved FP error rate, 54 and 65% FP for the reference data decreased to 24 and 32%, respectively, after fourth root transformation. Cross-validation showed FP values similar as the entire calibration set, indicating the representativeness of the thresholds. A noteworthy improvement in discrimination between seized Cannabis samples could be concluded.

Quality Control of Herbal Medicines: From Traditional Techniques to State-of-the-art Approaches

Herbal medicines are important options for the treatment of several illnesses. Although their therapeutic applicability has been demonstrated throughout history, several concerns about their safety and efficacy are raised regularly. Quality control of articles of botanical origin, including plant materials, plant extracts, and herbal medicines, remains a challenge. Traditionally, qualitative (e.g., identification and chromatographic profile) and quantitative (e.g., content analyses) markers are applied for this purpose. The compound-oriented approach may stand alone in some cases (e.g., atropine in Atropa belladonna). However, for most plant materials, plant extracts, and herbal medicines, it is not possible to assure quality based only on the content or presence/absence of one (sometimes randomly selected) compound. In this sense, pattern-oriented approaches have been extensively studied, introducing the use of multivariate data analysis on chromatographic/spectroscopic fingerprints. The use of genetic methods for plant material/plant extract authentication has also been proposed. In this study, traditional approaches are reviewed, although the focus is on the applicability of fingerprints for quality control, highlighting the most used approaches, as well as demonstrating their usefulness. The literature review shows that a pattern-oriented approach may be successfully applied to the quality assessment of articles of botanical origin, while also providing directions for a compound-oriented approach and a rational marker selection. These observations indicate that it may be worth considering to include fingerprints and their data analysis in the regulatory framework for herbal medicines concerning quality control since this is the foundation of the holistic view that these complex products demand.

Feasibility study on exhaled-breath analysis by untargeted Selected-Ion Flow-Tube Mass Spectrometry in children with cystic fibrosis, asthma, and healthy controls: Comparison of data pretreatment and classification techniques

Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) has been applied in a clinical context as diagnostic tool for breath samples using target biomarkers. Exhaled breath sampling is non-invasive and therefore much more patient friendly compared to bronchoscopy, which is the golden standard for evaluating airway inflammation. In the actual pilot study, 55 exhaled breath samples of children with asthma, cystic-fibrosis and healthy individuals were included. Rather than focusing on the analysis of target biomarkers or on the identification of biomarkers, different data analysis strategies, including a variety of pretreatment, classification and discrimination techniques, are evaluated regarding their capacity to distinguish the three classes based on subtle differences in their full scan SIFT-MS spectra. Proper data-analysis strategies are required because these full scan spectra contain much external, i.e. unwanted, variation. Each SIFT-MS analysis generates three spectra resulting from ion-molecule reactions of analyte molecules with H₃O⁺, NO⁺ and O₂⁺. Models were built with Linear Discriminant Analysis, Quadratic Discriminant Analysis, Soft Independent Modelling by Class Analogy, Partial Least Squares - Discriminant Analysis, K-Nearest Neighbours, and Classification and Regression Trees. Perfect models, concerning overall sensitivity and specificity (100% for both) were found using Direct Orthogonal Signal Correction (DOSC) pretreatment. Given the uncertainty related to the classification models associated with DOSC pretreatments (i.e. good classification found also for random classes), other models are built applying other preprocessing approaches. A Partial Least Squares - Discriminant Analysis model with a combined pre-processing method considering single value imputation results in 100% sensitivity and specificity for calibration, but was less good predictive. Pareto scaling prior to Quadratic Discriminant Analysis resulted in 41/55 correctly classified samples for calibration and 34/55 for cross-validation. In future, the uncertainty with DOSC and the applicability of the promising preprocessing methods and models must be further studied applying a larger representative data set with a more extensive number of samples for each class. Nevertheless, this pilot study showed already some potential for the untargeted SIFT-MS application as a rapid pattern-recognition technique, useful in the diagnosis of clinical breath samples.

Gas chromatographic fingerprint analysis of secondary metabolites of Stachys lanata (Stachys byzantine C. Koch) combined with antioxidant activity modelling using multivariate chemometric methods

S. lanata has been traditionally used as a medicinal plant due to its various biological activities such as antioxidant activity. Therefore, identification and quality control studies of this plant are of great importance. To this end, gas chromatography (GC) combined with chemometrics was proposed for fingerprint analysis of S. lanata samples. This study sought to classify GC fingerprints of twenty-eight S. lanata samples from eight different regions of Iran and more importantly, to correlate fingerprints to the antioxidant activity to select S. lanata volatile antioxidant markers. S. lanata samples were classified into five and three classes using partial least squares-discriminant analysis (PLS-DA) according to their GC fingerprints and antioxidant peaks, respectively. The results of PLS regression (PLS-R) and variable importance in projection (VIP) showed that phenol, 2,4-bis (1,1-dimethylethyl)-, hexadecanoic acid- ethyl ester, vitamin E, Beta- sitosterol, and 1- monolinoleoylglycerol trimethylsily ether are volatile antioxidant markers of S. lanata samples.

Chemometric Assessment of Chromatographic Methods for Herbal Medicines Authentication and Fingerprinting

Nowadays, increasingly more individuals turn to supplementation of the diet with herbal medicines and many such products are marketed lately. Thus the problem that this article focuses on is that these products are not subjected to rigorous quality control like synthetic drugs are, which rises a constant debate whether the supplements actually contain the herb or mixture of herbs that the manufacturer claims they do. As a solution, micellar electrokinetic chromatography and high performance liquid chromatography were investigated in order to fingerprint and authenticate herbal medicines. For this purpose, minimal sample pre-treatment was applied to several fruit based herbal medicines, which were compared with the ethanolic extract of the respective fruit. The holistic evaluation of the electropherograms and chromatograms was made by using appropriate chemometric tools, such as principal component analysis (PCA), cluster analysis and a combination of PCA and linear discriminant analysis (PCA-LDA). The results suggest that the developed method was able to successfully discriminate between different herbal medicines, based on their raw material content. Moreover, this simple and efficient methodology might also be used for routine screening and authenticity control of different products and could be implemented in any quality control laboratory.

Development of a UHPLC-MS/MS method for the quantitation of bioactive compounds in Phyllanthus species and its herbal formulations

Phyllanthus species are extensively used in traditional medicines for the treatment of hepatic diseases due to their bioactive hypophyllanthin and phyllanthin. This work describes the development and validation of an ultra high performance liquid chromatography with tandem mass spectrometry method in polarity switching multiple reaction monitoring mode for the simultaneous detection and quantitation of 23 compounds using ultra-performance liquid chromatography coupled with electrospray-hybrid triple quadrupole-linear ion trap mass spectrometer. The validated parameters showed good linearity (R²  ≥ 0.996), limit of detection (0.05-1.62 ng/mL), limit of quantitation (0.15-4.95 ng/mL), precisions (intra-day: RSD ≤ 2.11%), (inter-day: RSD ≤ 2.91%), stability (RSD ≤ 2.56%) and overall recovery (98.22-104.48%; RSD ≤ 2.93%). The validated method was successfully applied in ethanolic extracts of P. amarus, P. niruri, P. emblica, P. fraternus, fractions of P. amarus and their herbal formulations for quantitation. The maximum content of hypophyllanthin (29.40 mg/g) and phyllanthin (56.60 mg/g) was detected in ethyl acetate fraction of P. amarus. The total content of 23 compounds was abundant in the ethanolic extract of P. emblica fruit. Principal component analysis was used to differentiate the selected Phyllanthus species and their herbal formulations. The results indicated that the present method could be used for quality control of Phyllanthus species and its herbal formulations.

Supercritical Fluid Chromatography of Drugs: Parallel Factor Analysis for Column Testing in a Wide Range of Operational Conditions

Retention mechanisms involved in supercritical fluid chromatography (SFC) are influenced by interdependent parameters (temperature, pressure, chemistry of the mobile phase, and nature of the stationary phase), a complexity which makes the selection of a proper stationary phase for a given separation a challenging step. For the first time in SFC studies, Parallel Factor Analysis (PARAFAC) was employed to evaluate the chromatographic behavior of eight different stationary phases in a wide range of chromatographic conditions (temperature, pressure, and gradient elution composition). Design of Experiment was used to optimize experiments involving 14 pharmaceutical compounds present in biological and/or environmental samples and with dissimilar physicochemical properties. The results showed the superiority of PARAFAC for the analysis of the three-way (column × drug × condition) data array over unfolding the multiway array to matrices and performing several classical principal component analyses. Thanks to the PARAFAC components, similarity in columns' function, chromatographic trend of drugs, and correlation between separation conditions could be simply depicted: columns were grouped according to their H-bonding forces, while gradient composition was dominating for condition classification. Also, the number of drugs could be efficiently reduced for columns classification as some of them exhibited a similar behavior, as shown by hierarchical clustering based on PARAFAC components.

Identification and characterization of phenolics and terpenoids from ethanolic extracts of Phyllanthus species by HPLC-ESI-QTOF-MS/MS

Phyllanthus species plants are a rich source of phenolics and widely used due to their medicinal properties. A liquid chromatography–tandem mass spectrometry (LC–MS/MS) method was developed using high-pressure liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (HPLC-ESI-QTOF-MS/MS) for the identification and characterization of quercetin, kaempferol, ellagic acid and their derivatives in ethanolic extracts of Phyllanthus species. The chromatographic separation was carried out on Thermo Betasil C₈ column (250 mm×4.5 mm, 5 µm) using 0.1% formic acid in water and 0.1% formic acid in methanol as the mobile phase. The identification of diagnostic fragment ions and optimization of collision energies were carried out using 21 reference standards. Totally 51 compounds were identified which include 21 compounds identified and characterized unambiguously by comparison with their authentic standards and the remaining 30 were tentatively identified and characterized in ethanolic extracts of P. emblica, P. fraternus, P. amarus and P. niruri.

Comparative analysis of volatiles difference of Yunnan sun-dried Pu-erh green tea from different tea mountains: Jingmai and Wuliang mountain by chemical fingerprint similarity combined with principal component analysis and cluster analysis

BACKGROUND

Modern instrumental analysis technology can provide various chemical data and information on tea samples. Unfortunately, it remains difficult to extract the useful information. We describe the use of chemical fingerprint similarities, combined with principal component and cluster analyses, to distinguish and recognize Pu-erh green teas, which from two tea mountains, Wuliang and Jingmai, in the Pu-erh district of Yunnan province. The volatile components of all 20 Pu-erh green teas (10 Wuliang and 10 Jingmai teas) were extracted and identified by headspace solid-phase micro extraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS).

RESULTS

Sixty-three volatiles (including alcohols, hydrocarbons, ketones, and aldehydes) were identified in the 20 Pu-erh green teas, and differences in compound compositions between them were also observed. Through fingerprint similarity, combined with principal component and cluster analyses, the 20 Pu-erh green teas were differentiated successfully based on their volatile characteristics.

CONCLUSIONS

This study demonstrates that the GC-MS combined with chemical fingerprint and unsupervised pattern recognition method is suitable for the investigation of the volatile profiling and evaluating the quality and authenticity of teas related to the different origins.Graphical abstractDifferentiate Pu-erh green teas from different tea mountains by using chemical fingerprint similarity and multivariate statistical methods.