Metabolomic analysis reveals the changing trend and differential markers of volatile and nonvolatile components of Artemisiae argyi with different aging years.

INTRODUCTION: Artemisia argyi Folium (AAF) is a traditional medicinal herb and edible plant. Analyzing the differential metabolites that affect the efficacy of AAF with different aging years is necessary. OBJECTIVE: The aim of the study was to investigate the changing trend and differential markers of volatile and nonvolatile metabolites of AAF from different aging years, which are necessary for application in clinical medicine. METHODOLOGY: Metabolites were analyzed using a widely targeted metabolomic approach based on ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and gas chromatography tandem mass spectrometry (GC-MS). RESULTS: A total of 153 volatile metabolites and 159 nonvolatile metabolites were identified. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) could clearly distinguish AAF aged for 1 year (AF-1), 3 years (AF-3), and 5 years (AF-5). Seven flavonoids and nine terpenoids were identified as biomarkers for tracking the aging years. CONCLUSIONS: The metabolomic method provided an effective strategy for tracking and identifying biomarkers of AAF from different aging years. This study laid the foundation for analysis of the biological activity of Artemisia argyi with different aging years.

Python workflow for the selection and identification of marker peptides-proof-of-principle study with heated milk.

The analysis of almost holistic food profiles has developed considerably over the last years. This has also led to larger amounts of data and the ability to obtain more information about health-beneficial and adverse constituents in food than ever before. Especially in the field of proteomics, software is used for evaluation, and these do not provide specific approaches for unique monitoring questions. An additional and more comprehensive way of evaluation can be done with the programming language Python. It offers broad possibilities by a large ecosystem for mass spectrometric data analysis, but needs to be tailored for specific sets of features, the research questions behind. It also offers the applicability of various machine-learning approaches. The aim of the present study was to develop an algorithm for selecting and identifying potential marker peptides from mass spectrometric data. The workflow is divided into three steps: (I) feature engineering, (II) chemometric data analysis, and (III) feature identification. The first step is the transformation of the mass spectrometric data into a structure, which enables the application of existing data analysis packages in Python. The second step is the data analysis for selecting single features. These features are further processed in the third step, which is the feature identification. The data used exemplarily in this proof-of-principle approach was from a study on the influence of a heat treatment on the milk proteome/peptidome.

Comparison of Approaches for Authentication of Commercial Terpinen-4-ol-type Tea Tree Oils Using Chiral GC/MS.

A global demand for tea tree oil (TTO) has resulted in increased adulteration in commercial products. In this study, we use a novel enantiomeric gas chromatography mass spectrometry method for chiral analysis of key terpenes ((±)-terpinen-4-ol, (±)-α-terpineol, and (±)-limonene) and quantification of components present at >0.01% to test different methods of identifying adulterated TTO. Data from authentic Australian (n = 88) and oxidized (n = 12) TTO samples of known provenance were consistent with recommended ranges in ISO 4730:2017 and previously published enantiomeric ratios, with p-cymene identified as the major marker of TTO oxidation. The 15 ISO 4730:2017 constituents comprised between 84.5 and 89.8% of the total ion chromatogram (TIC) peak area. An additional 53 peaks were detected in all samples (7.3-11.0% of TIC peak area), while an additional 43 peaks were detected in between 0 and 99% (0.15-2.0% of the TIC peak area). Analysis of nine commercial samples demonstrated that comparison to the ISO 4730:2017 standard does not always identify adulterated TTO samples. While statistical analysis of minor components in TTO did identify two commercial samples that differed from authentic TTO, the (+)-enantiomer percentages for limonene, terpinen-4-ol, and α-terpineol provided clearer evidence that these samples were adulterated. Thus, straightforward identification of unadulterated and unoxidized TTO could be based on analysis of appropriate enantiomeric ratios and quantitation of the p-cymene percentage.

Identification of Vernonia patula Merr. and Its Similar Varieties Based on a Combination of HPLC Fingerprinting and Chemical Pattern Recognition.

Vernonia patula Merr. (VP) is a traditional medicine used by the Zhuang and Yao people, known for its therapeutic properties in treating anemopyretic cold and other diseases. Distinguishing VP from similar varieties such as Praxelis clematidea (PC), Ageratum conyzoides L. (AC) and Ageratum houstonianum Mill (AH) was challenging due to their similar traits and plant morphology. The HPLC fingerprints of 40 batches of VP and three similar varieties were established. SPSS 20.0 and SIMCA-P 13.0 were used to statistically analyze the chromatographic peak areas of 37 components. The results showed that the similarity of the HPLC fingerprints for each of the four varieties was >0.9, while the similarity between the control chromatogram of VP and its similar varieties was <0.678. Cluster analysis and partial least squares discriminant analysis provided consistent results, indicating that all four varieties could be individually clustered together. Through further analysis, we found isochlorogenic acid A and isochlorogenic acid C were present only in the original VP, while preconene II was present in the three similar varieties of VP. These three components are expected to be identification points for accurately distinguishing VP from PC, AC and AH.

The application of laser­induced fluorescence in oil spill detection.

Over the past two decades, oil spills have been one of the most serious ecological disasters, causing massive damage to the aquatic and terrestrial ecosystems as well as the socio-economy. In view of this situation, several methods have been developed and utilized to analyze oil samples. Among these methods, laser-induced fluorescence (LIF) technology has been widely used in oil spill detection due to its classification method, which is based on the fluorescence characteristics of chemical material in oil. This review systematically summarized the LIF technology from the perspective of excitation wavelength selection and the application of traditional and novel machine learning algorithms to fluorescence spectrum processing, both of which are critical for qualitative and quantitative analysis of oil spills. It can be seen that an appropriate excitation wavelength is indispensable for spectral discrimination due to different kinds of polycyclic aromatic hydrocarbons' (PAHs) compounds in petroleum products. By summarizing some articles related to LIF technology, we discuss the influence of the excitation wavelength on the accuracy of the oil spill detection model and proposed several suggestions on the selection of excitation wavelength. In addition, we introduced some traditional and novel machine learning (ML) algorithms and discussed the strengths and weaknesses of these algorithms and their applicable scenarios. With an appropriate excitation wavelength and data processing algorithm, it is believed that laser-induced fluorescence technology will become an efficient technique for real-time detection and analysis of oil spills.

Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents.

Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.

Fast Discrimination and Quantification Analysis of Atractylodis rhizoma Using NIR Spectroscopy Coupled with Chemometrics Tools.

In this study, near-infrared (NIR) spectroscopy and high-performance liquid chromatography (HPLC) combined with chemometrics tools were applied for quick discrimination and quantitative analysis of different varieties and origins of Atractylodis rhizoma samples. Based on NIR data, orthogonal partial least squares discriminant analysis (OPLS-DA) and K-nearest neighbor (KNN) models achieved greater than 90% discriminant accuracy of the three species and two origins of Atractylodis rhizoma. Moreover, the contents of three active ingredients (atractyloxin, atractylone, and ß-eudesmol) in Atractylodis rhizoma were simultaneously determined by HPLC. There are significant differences in the content of the three components in the samples of Atractylodis rhizoma from different varieties and origins. Then, partial least squares regression (PLSR) models for the prediction of atractyloxin, atractylone, and ß-eudesmol content were successfully established. The complete Atractylodis rhizoma spectra gave rise to good predictions of atractyloxin, atractylone, and ß-eudesmol content with R2 values of 0.9642, 0.9588, and 0.9812, respectively. Based on the results of this present research, it can be concluded that NIR is a great nondestructive alternative to be applied as a rapid classification system by the drug industry.

Chemical and biological characterization of Melaleuca subulata (Cheel) Craven leaves' volatile constituents supported by chemometric analysis and molecular docking.

BACKGROUND: The genus Melaleuca (Myrtaceae) comprises dozens of essential oil (EO)-rich species that are appreciated worldwide for their various medicinal values. Additionally, they are renowned in traditional medicine for their antimicrobial, antifungal, and other skin-related activities. The current study investigated the chemical profile and skin-related activities of volatile constituents derived from M. subulata (Cheel) Craven (Synonym Callistemon subulatus) leaves cultivated in Egypt for the first time. METHODS: The volatile components were extracted using hydrodistillation (HD), headspace (HS), and supercritical fluid (SF). GC/MS and Kovat's retention indices were implemented to identify the volatile compounds, while the variations among the components were assessed using Principal Component Analysis and Hierarchical Cluster Analysis. The radical scavenging activity was assessed using 2,2-diphenyl-1-picrylhydrazyl (DPPH), oxygen radical absorbance capacity (ORAC) and ß-carotene assays. Moreover, the anti-aging effect was evaluated using anti-elastase, and anti-collagenase, while the antimicrobial potential was deduced from the agar diffusion and broth microdilution assays. Lastly, the molecular docking study was executed using C-docker protocol in Discovery Studio 4.5 to rationalize the binding affinity with targeted enzymes. RESULTS: The SF extraction approach offered the highest EO yield, being 0.75%. According to the GC/MS analysis, monoterpene hydrocarbons were the most abundant volatile class in the HD oil sample (54.95%), with α-pinene being the most copious component (35.17%). On the contrary, the HS and SF volatile constituents were pioneered with oxygenated monoterpenes (72.01 and 36.41%) with eucalyptol and isopulegone being the most recognized components, representing 67.75 and 23.46%, respectively. The chemometric analysis showed segregate clustering of the three extraction methods with α-pinene, eucalyptol, and isopulegone serving as the main discriminating phytomarkers. Concerning the bioactivity context, both SF and HD-EOs exhibited antioxidant effects in terms of ORAC and ß-carotene bleaching. The HD-EO displayed potent anti-tyrosinase activity, whereas the SF-EO exhibited significant anti-elastase properties. Moreover, SF-EO shows selective activity against gram-positive skin pathogens, especially S. aureus. Ultimately, molecular docking revealed binding scores for the volatile constituents; analogous to those of the docked reference drugs. CONCLUSIONS: M. subulata leaves constitute bioactive volatile components that may be indorsed as bioactive hits for managing skin aging and infection, though further in vivo studies are recommended.

[Quality evaluation of salt-fired Eucommiae Cortex based on HPLC fingerprint, multi-component content determination, and chemometrics].

This study established an HPLC fingerprint and multi-component content determination method for salt-fired Eucommiae Cortex, and evaluated the quality of salt-fired Eucommiae Cortex from different sources using fingerprint similarity evaluation, cluster analysis(CA), principal component analysis(PCA), and orthogonal partial least square discriminate analysis(OPLS-DA). HPLC was launched on a Cosmosil 5C_(18)-MS-Ⅱ column(4.6 mm×250 mm, 5 µm) by gradient elution with a mobile phase of methanol-0.2% phosphoric acid aqueous solution at a flow rate of 1.0 mL·min~(-1), detection wavelength of 238 nm, column temperature of 30 ℃, and an injection volume of 10 µL. The results of fingerprint similarity evaluation for 20 batches of salt-fired Eucommiae Cortex indicated that, except for batch S3 with a similarity of 0.893, the similarity of the other 19 batches was of ≥ 0.919, suggesting good similarity. Fourteen common peaks were calibrated and seven common peaks were identified including geniposidic acid. The mass fractions of geniposidic acid, chlorogenic acid, geniposide, genipin, pinoresinol diglucoside, liriodendrin, and pinoresinol-4-O-ß-D-glucopyranoside were 0.062 0%-0.426 9%, 0.024 9%-0.116 5%, 0.009 5%-0.052 9%, 0.005 5%-0.034 8%, 0.115 9%-0.317 8%, 0.016 4%-0.108 8%, and 0.026 4%-0.039 8%, respectively. Using CA, PCA, and OPLS-DA, the 20 batches of salt-fired Eucommiae Cortex were classified into three categories. Additionally, through the analysis of variable importance in projection(VIP) under OPLS-DA, two differential quality markers, geniposidic acid and chlorogenic acid, were identified. The established HPLC fingerprint and multi-component content determination method is stable and reliable, providing a reference for quality control of salt-fired Eucommiae Cortex.

Nontargeted metabolomics and enzyme inhibitory and antioxidant activities for chemical and biological characterization of jujube (Ziziphus jujuba) extracts.

The chemical and biologically active characterization of jujube samples (fruits, cores, and leaves) were carried out by the integrated nontargeted metabolomics and bioassay. Firstly, collision cross-section values of active compounds in jujubes were determined by ultrahigh-performance liquid chromatography coupled with ion mobility quadrupole time-of-flight mass spectrometry. Then, a multidimensional statistical analysis that contained principal component analysis, partial least squares-discriminant analysis and hierarchical clustering analysis was employed to effectively cluster different tissues and types of jujubes, making identification more scientific. Furthermore, angiotensin-converting enzyme (ACE) and 2, 2-diphenyl-1-picrylhydrazyl (DPPH) were used to evaluate the quality of jujubes from a double activity dimension. The analytical results obtained by using ACE and DPPH to evaluate the quality of jujube were different from multivariate statistics, providing a reference for the application of jujube. Therefore, integrating chemical and biological perspectives to evaluate the quality of jujube provided a more comprehensive evaluation and effective reference for clinical needs.

Inhibition of pancreatic lipase by coffee leaves-derived polyphenols: A mechanistic study.

The suppression of pancreatic lipase has been employed to mitigate obesity. This study explored the mechanism of coffee leaf extracts to inhibit pancreatic lipase. The ethyl acetate fraction derived from coffee leaves (EAC) exhibited the highest inhibitory capacity with a half-maximal inhibitory concentration (IC50) of 0.469 mg/mL and an inhibitor constant (Ki) of 0.185 mg/mL. This fraction was enriched with 3,5-dicaffeoylquinic acid (3,5-diCQA, 146.50 mg/g), epicatechin (87.51 mg/g), and isoquercetin (48.29 mg/g). EAC inhibited lipase in a reversible and competitive manner, and quenched its intrinsic fluorescence through a static mechanism. Molecular docking revealed that bioactive compounds in EAC bind to key amino acid residues (HIS-263, PHE-77, and SER-152) located within the active cavity of lipase. Catechin derivatives play a key role in the lipase inhibitory activity within EAC. Overall, our findings highlight the promising potential of coffee leaf extract as a functional ingredient for alleviating obesity through inhibition of lipase.

Three-dimensional fluorescence combined with alternating trilinear decomposition and random forest algorithm for the rapid prediction of species, geographical origin and main components of Glycyrrhizae Radix et Rhizoma (Gancao).

Glycyrrhizae Radix et Rhizoma (Gancao) is a functional food whose quality varies significantly between distinct geographical sources owing to the influence of genetics and the geographical environment. This study employed three-dimensional fluorescence coupled with alternating trilinear decomposition (ATLD) and random forest (RF) algorithms to rapidly predict Gancao species, geographical origins, and primary constituents. Seven fluorescent components were resolved from the three-dimensional fluorescence of the ATLD for subsequent analysis. Results indicated that the RF model distinguished Gancao from various species and origins better than other algorithms, achieving an accuracy of 94.4 % and 88.9 %, respectively. Furthermore, the RF regressor algorithm was used to predict the concentrations of liquiritin and glycyrrhizic acid in Gancao, with 96.4 % and 95.6 % prediction accuracies compared to HPLC, respectively. This approach offers a novel means of objectively evaluating the origin of food and holds substantial promise for food quality assessment.

Gain deeper insights into traditional Chinese medicines using multidimensional chromatography combined with chemometric approaches.

Traditional Chinese medicines (TCMs) possess a rich historical background, unique theoretical framework, remarkable therapeutic efficacy, and abundant resources. However, the modernization and internationalization of TCMs have faced significant obstacles due to their diverse ingredients and unknown mechanisms. To gain deeper insights into the phytochemicals and ensure the quality control of TCMs, there is an urgent need to enhance analytical techniques. Currently, two-dimensional (2D) chromatography, which incorporates two independent separation mechanisms, demonstrates superior separation capabilities compared to the traditional one-dimensional (1D) separation system when analyzing TCMs samples. Over the past decade, new techniques have been continuously developed to gain actionable insights from complex samples. This review presents the recent advancements in the application of multidimensional chromatography for the quality evaluation of TCMs, encompassing 2D-gas chromatography (GC), 2D-liquid chromatography (LC), as well as emerging three-dimensional (3D)-GC, 3D-LC, and their associated data-processing approaches. These studies highlight the promising potential of multidimensional chromatographic separation for future phytochemical analysis. Nevertheless, the increased separation capability has resulted in higher-order data sets and greater demands for data-processing tools. Considering that multidimensional chromatography is still a relatively nascent research field, further hardware enhancements and the implementation of chemometric methods are necessary to foster its robust development.

The "crosstalk" between microbiota and metabolomic profile in high-fat-diet-induced obese mice supplemented with Bletilla striata polysaccharides and composite polysaccharides.

The potential prebiotic feature of Bletilla striata polysaccharides (BSP) has been widely accepted, while the beneficial effect of BSP on high-fat-diet-induced obesity is unclear. Moreover, the "crosstalk" between microbiota and metabolomic profile in high-fat-diet-induced obese mice supplemented with BSP still need to be further explored. The present study attempted to illustrate the effect of BSP and/or composite polysaccharides on high-fat-diet-induced obese mice by combining multi-matrix (feces, urine, liver) metabolomics and gut microbiome. The results showed that BSP and/or composite polysaccharides were able to reduce the abnormal weight gain induced by high-fat diet. A total of 175 molecules were characterized by proton nuclear magnetic resonance (1H NMR) in feces, urine and liver, suggesting that multi-matrix metabolomics could provide a comprehensive view of metabolic regulatory mechanism of BSP in high-fat-diet-induced obese mice. Several pathways were altered in response to BSP supplementation, mainly pertaining to amino acid, purine, pyrimidine, ascorbate and aldarate metabolisms. In addition, BSP ameliorated high-fat-diet-induced imbalanced gut microbiome, by lowering the ratio of Firmicutes/Bacteroidetes. Significant correlations were illustrated between particular microbiota's features and specific metabolites. Overall, the anti-obesity effect of BSP could be attributed to the amelioration of the disorders of gut microbiota and to the regulation of the "gut-liver axis" metabolism.

Rapid identification of Radix Astragali origin by using fluorescence probe combined with chemometrics.

Fluorescent probes for metal ion recognition can be divided into selective probes, weakly selective probes, and non-selective probes roughly. Weakly selective probes are not often used for quantitative analysis of metal ions due to their overlapping spectra resulting from simultaneous interactions with multiple metal ions. Conversely, the different metal ions contained in herbal medicine extracts from different geographical origins will produce corresponding fluorescence fingerprint profiles after interaction with weakly selective fluorescence probes. The performance can be used in the study of origin tracing of food or Chinese herbal medicine. Weakly selective fluorescent probes of benzimidazole derivatives have been synthesized and attempted to be used in the origin tracing of Radix Astragali in this work. Radix Astragali from different origins will produce different fluorescence fingerprint spectra due to the difference of metal ions and content in combination with the probe. Excitation-emission matrix (EEM) fluorescence spectroscopy in conjunction with N-way partial least squares discriminant analysis (N-PLS-DA), and unfolded partial least squares discriminant analysis (U-PLS-DA) were used to identify the origin of 150 Radix Astragali samples from five geographical origins. The prediction results showed that the correct recognition rates of the U-PLS-DA model and N-PLS-DA model are 95.92% and 93.88%, respectively. In comparison, the results of U-PLS-DA are slightly better than those of N-PLS-DA. These findings indicate that EEM fluorescence spectroscopy based on weakly selective fluorescent probes combined with multi-way chemometrics provides a good idea for the origin tracing of traditional Chinese medicine.

The terroir of Brazilian Coffea canephora: Characterization of the chemical composition.

FTIR spectroscopy and multivariate analysis were used in the chemical study of the terroirs of Coffea canephora. Conilon coffees from Espírito Santo and Amazon robusta from Matas of Rondônia, were separated by PCA, with lipids and caffeine being the markers responsible for the separation. Coffees from Bahia, Minas Gerais, and São Paulo did not exhibit separation, indicating that the botanical variety had a greater effect on the terroir than geographic origin. Thus, the genetic factor was investigated considering the conilon and robusta botanical varieties. This last group was composed of hybrid robusta and apoatã. The DD-SIMCA favored the identification of the genetic predominance of the samples. PLS-DA had a high classification performance regarding the conilon, hybrid robusta, and apoatã genetic nature. Lipids, caffeine, chlorogenic acids, quinic acid, trigonelline, proteins, amino acids, and carbohydrates were identified as chemical markers that discriminated the genetic groups.

Gas chromatography ion mobility spectroscopy: A rapid and effective tool for monitoring oil oxidation.

Oil autoxidation is an early process of food deterioration, monitoring oil oxidation is therefore of great significance to ensure food quality and safety. In this study, a detection method of the primary and secondary oxidative products was developed by gas chromatography ion mobility spectrometry (GC-IMS).The secondary oxidative products was analyzed by GC-IMS. Then, the relationships between peroxide values and the contents of secondary oxidative products were investigated by constructing a prediction model of peroxide value of rapeseed oil with the help of secondary oxidative products and chemometrics. The coefficient of determination Q2 of the model validation set is 0.96, and the RMSECV is 0.1570 g/100 g. These validation results indicated that secondary oxidative products could also reflect the content of the primary oxidative products. Moreover, 10 characteristic markers related to oxidative rancidity were identified for monitoring edible oil rancidity and oxidative stability.

NMR and multivariate methods: Identification of chemical markers in extracts of pedra-ume-caá and their antiglycation, antioxidant, and enzymatic inhibition activities.

INTRODUCTION: In Brazil, the plant group popularly known as "pedra-ume-caá" is used in folk medicine for the treatment of diabetes, and its raw material is commonly sold. OBJECTIVE: The aim of the study was to apply a method for chemical identification of extracts of dry pedra-ume-caá leaves using HPLC-high-resolution mass spectrometry (HRMS) and NMR and develop a multivariate model with NMR data to authenticate commercial samples. In addition, to evaluate the biological activities of the extracts. MATERIALS AND METHODS: Dry extracts of Myrcia multiflora, Myrcia amazonica, Myrcia guianensis, Myrcia sylvatica, Eugenia punicifolia leaves, and 15 commercial samples (sold in Manaus and Belém, Brazil) were prepared by infusion. All the extracts were analysed using HPLC-high-resolution mass spectrometry (HRMS), NMR, principal component analysis (PCA), and hierarchical cluster analysis (HCA). The antidiabetic effect of extracts was evaluated according to enzymatic inhibition. Their content of total phenols, cell viability, and antioxidant and antiglycation activities were also determined. RESULTS: HPLC-HRMS and NMR analysis of these extracts permitted the identification of 17 compounds. 1H NMR data combined with multivariate analyses allowed us to conclude that catechin, myricitrin, quercitrin, and gallic and quinic acids are the main chemical markers of pedra-ume-caá species. These markers were identified in 15 commercial samples of pedra-ume-caá. Additionally, only the extracts of M. multiflora and E. punicifolia inhibited α-glucosidase. All the extracts inhibited the formation of advanced glycation end products (AGEs) and showed free-radical-scavenging activity. These extracts did not present cytotoxicity. CONCLUSION: This study revealed the chemical markers of matrices, and it was possible to differentiate the materials marketed as pedra-ume-caá. Moreover, this study corroborates the potential of these species for treating diabetes.

Chemometrics-based Chemical Analysis of Myrrh and Its Vinegar-processed Products by UPLC-MS/MS.

Myrrh is widely used in clinical practice but accompanied by obvious toxicity. According to traditional Chinese medicines theory, processing with vinegar can effectively reduce its toxicity. However, the detoxification processing technology of Myrrh and the corresponding mechanism have been unclear. The objective of this study is to systematically analyze the variation in chemical composition of raw Myrrh and its processed products using UPLC-Q-TOF-MS/MS coupled with chemometrics. A total of 75 compounds including 56 sesquiterpenoids, 2 diterpenoids, 15 triterpenoids and 2 other types were identified. Raw Myrrh and its processed products were divided into two major groups, and 14 chemical markers were selected out by principal component analysis and partial least square discriminant analysis. Additionally, the exact content of 5 representative chemical markers was determined to be significantly reduced after vinegar-processing by UPLC-QQQ-MS/MS. Moreover, multivariate statistical analysis and the quantitative results comprehensively indicated that the optimized processing method was processing at a ratio of 200 : 5 (Myrrh:vinegar). This research provides not only a reliable foundation for the study of Myrrh, but also a scientific reference for clinical use of this herb.

Evaluation of different regression models for detection of adulteration of mustard and canola oil with argemone oil using fluorescence spectroscopy coupled with chemometrics.

Mustard and canola oils are commonly used cooking oils in Asian countries such as India, Nepal, and Bangladesh, making them prone to adulteration. Argemone is a well-known adulterant of mustard oil, and its alkaloid sanguinarine has been linked with health conditions such as glaucoma and dropsy. Utilising a non-destructive spectroscopic method coupled with a chemometric approach can serve better for the detection of adulterants. This work aimed to evaluate the performance of various regression algorithms for the detection of argemone in mustard and canola oils. The spectral dataset was acquired from fluorescence spectrometer analysis of pure as well as adulterated mustard and canola oils with some local and commercial samples also. The prediction performance of the eight regression algorithms for the detection of adulterants was evaluated. Extreme gradient boosting regressor (XGBR), Category gradient boosting regressor (CBR), and Random Forest (RF) demonstrate potential for predicting adulteration levels in both oils with high R2 values.