Exploring chemical markers and identifying phenolic markers using a metabolomics strategy and chemometrics to study the different origins of defatted Coix seed.

Coix seed, a prevalent medicinal and food-homologous plant, is extensively consumed in Asia. It has various pharmacological properties, such as anti-inflammatory and anticancer effects. Coix seed oil, as its main component, is widely produced. However, during the industrial production process of Coix seed oil, substantial byproducts are produced, namely, defatted Coix seeds, which are also worth researching. Currently, it remains unclear whether there will be differences in defatted Coix seeds obtained from different geographical locations, with previous studies reporting that phenolic compounds in defatted Coix seeds have a significant utilization value. In this study, firstly, the TPC and TFC of samples collected in three temperature zones were detected. Subsequently, UPLC-Q-TOF/MS was used to analyze the samples, and a metabolomics data processing strategy and chemometric analysis method were established. We have confirmed the presence of flavonoids and phenolic compounds in 30 batches of Coix seed from different temperature zones in China, and concluded that the overall quality of Coix seed from different batches is relatively stable. With the established strategy, 12 characteristic chemical markers were identified, and 5 valuable phenolic chemical markers were selected for distinguishing the origin of Coix seed and evaluating the quality of defatted Coix seed. Among them, proanthocyanidin A2 has the highest content in defatted Coix seed in subtropical regions, while the content of caffeic acid, naringin, rutin, and chlorogenic acid decreases from north to south. The strategy proposed in this study may provide some basis for the quality control and rational use of defatted Coix seeds.

Characterization of the key differential aroma compounds in five dark teas from different geographical regions integrating GC-MS, ROAV and chemometrics approaches.

Dark tea (DT) holds a rich cultural history in China and has gained sizeable consumers due to its unique flavor and potential health benefits. In this study, headspace solid-phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS), relative odor activity value (ROAV), and chemometrics approaches were used to detect and analyze aroma compounds differences among five dark teas from different geographical regions. The results revealed that the five DTs from different geographical regions differed in types, quantities, and relative concentrations of volatile compounds. A total of 1372 volatile compounds of were identified in the 56 DT samples by HS-SPME-GC-MS. Using ROAV and chemometrics approaches, based on ROAV>1 and VIP>1. Eighteen key aroma compounds can be used as potential indicators for DT classification, including dihydroactinidiolide, linalool, 1,2,3-trimethoxybenzene, geranyl acetone, 1,2,4-trimethoxybenzene, cedrol, 3,7-dimethyl-1,5,7-octatrien-3-ol, ß-ionone, 4-ethyl-1,2-dimethoxybenzene, methyl salicylate, α-ionone, geraniol, linalool oxide I, linalool oxide II, 6-methyl-5-hepten-2-one, α-terpineol, 1,2,3-trimethoxy-5-methylbenzene, and 1,2-dimethoxybenzene. These compounds provide a certain theoretical basis for distinguishing the differences in five DTs from different geographical regions. This study provides a potential method for identifying the volatile substances in DTs and elucidating the differences in key aroma compounds. Abbreviations: DT, dark tea; FZT, Fuzhuan tea; LPT, Guangxi Liupao tea; QZT, Hubei Qingzhuan tea; TBT, Sichuan Tibetan tea; PET, Yunnan Pu-erh tea; ROAV, Relative odor activity value; OT, Odor threshold; HS-SPME, Headspace solid-phase microextraction; GC-MS, Gas chromatography-mass spectrometry; PCA, Principal components analysis; PLS-DA, Partial least squares-discriminant analysis; HCA, Hierarchical clustering analysis.

Chemometrics of the composition and antioxidant capacity of essential oils obtained from six Cupressaceae taxa.

Essential oils (EOs) are complex and susceptible to environmental conditions, they have a wide range of biological activities and are often used to differentiate between similar species. In this study, gas chromatography-mass spectrometry (GC-MS) coupled with chemometric analysis was applied to systematically analyse and evaluate EOs constituents and antioxidant activity of six Chinese Cupressaceae taxa (Platycladus orientalis Franco, P. orientalis Franco 'Sieboldii', P. orientalis Franco 'Aurea', Juniperus chinensis Roxb., J. chinensis Roxb. 'Kaizuca', and J. sabina L.) under identical conditions. The antioxidant activity of the EOs was evaluated using 2,2 -diphenyl-1-picrylhydrazyl (DPPH), 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), and ferric reducing power (FRAP), and the total phenolic content (TPC) of the EOs was determined by Folin-Ciocalteau reagent. In total, seventy individual constituents were identified with the main components being α-pinene, sabinene, D-limonene, bornyl acetate, δ-3-carene and ß-myrcene. Principal component analysis (PCA) and hierarchal cluster analysis (HCA) successfully discriminated the six taxa into three chemotypes and the unique chemotype revealed that J. chinensis 'Kaizuca' may be a species rather than a cultivar of J. chinensis. The results of OPLS-DA analysis showed that the three compounds screened, namely, α-pinene, sabinene, and δ-3-carene, can completely distinguish Platycladus spp. from Juniperus spp. The DPPH assay results ranged from 576.14 (J. chinensis 'Kaizuca') to 1146.12 (J. sabina) µmol eq Trolox/mL EO, while the ABTS values ranged from 1579.62 (P. orientalis 'Aurea') to 5071.82 (J. sabina) µmol eq Trolox/mL. In the FRAP assay, the values ranged from 1086.50 (J. chinensis 'Kaizuca') to 1191.18 (J. sabina) µmol eq Trolox/ml and the TPC of the EOs studied ranged from 15.17 (J. chinensis 'Kaizuca') to 39.37 (J. sabina) mg GAE/mL EO. The results consistently showed that J. sabina possessed the strongest antioxidant activity and can be preferentially used as a rich source of potentially natural antioxidants.

Identification and quantitative detection of illegal additives in wheat flour based on near-infrared spectroscopy combined with chemometrics.

As a common food raw material in daily life, the quality and safety of wheat flour are directly related to people's health. In this study, a model was developed for the rapid identification and detection of three illegal additives in flour, namely azodicarbonamide (ADA), talcum powder, and gypsum powder. This model utilized a combination of near-infrared spectroscopy with chemometric methods. A one-dimensional convolutional neural network was used to reduce data dimensionality, while a support vector machine was applied for non-linear classification to identify illegal additives in flour. The model achieved a calibration set F1 score of 99.38% and accuracy of 99.63%, with a validation set F1 score of 98.81% and accuracy of 98.89%. Two cascaded wavelength selection methods were introduced: The first method involved backward interval partial least squares (BiPLS) combined with an improved binary particle swarm optimization algorithm (IBPSO). The second method utilized the CARS-IBPSO algorithm, which integrated competitive adaptive reweighted sampling (CARS) with IBPSO. The two cascade wavelength selection methods were used to select feature wavelengths associated with additives and construct partial least squares quantitative detection models. The models constructed using CARS-IBPSO selected feature wavelengths for detecting ADA, talcum powder, and gypsum powder exhibited the highest overall performance. The model achieved validation set determination coefficients of 0.9786, 0.9102, and 0.9226, with corresponding to root mean square errors of 0.0024%, 1.3693%, and 1.6506% and residual predictive deviations of 6.8368, 3.5852, and 3.9253, respectively. Near-infrared spectroscopy in combination with convolutional neural network dimensionality reduction and support vector machine classification enabled rapid identification of various illegal additives. The combination of CARS-IBPSO feature wavelength selection and partial least squares regression models facilitated rapid quantitative detection of these additives. This study introduces a new approach for rapidly and accurately identifying and detecting illegal additives in flour.

Identification of Fatty Acids, Amides and Cinnamic Acid Derivatives in Supercritical-CO2 Extracts of Cinnamomum tamala Leaves Using UPLC-Q-TOF-MSE Combined with Chemometrics.

Cinnamomum tamala leaf (CTL), also known as Indian bay leaf, is used all over the world for seasoning, flavoring, and medicinal purposes. These characteristics could be explained by the presence of several essential bioactive substances and lipid derivatives. In this work, rapid screening and identification of the chemical compounds in supercritical (SC)-CO2 extracts of CTL by use of UPLC-Q-TOF-MSE with a multivariate statistical analysis approach was established in both negative and positive mode. A total of 166 metabolites, including 66 monocarboxylic fatty acids, 52 dicarboxylic fatty acids, 27 fatty acid amides, and 21 cinnamic acid derivatives, were tentatively identified based on accurate mass and the mass spectrometric fragmentation pattern, out of which 142 compounds were common in all SC-CO2 extracts of CTL. Further, PCA and cluster hierarchical analysis clearly discriminated the chemical profile of analyzed extracts and allowed the selection of SC-CO2 extract rich in fatty acids, fatty acid amides, and other bioactive constituents. The result showed that the higher number of compounds was detected in CTL4 (300 bar/55 °C) extract than the other CTL extracts. The mono- and di-carboxylic fatty acids, fatty acid amides, and cinnamic acid derivatives were identified in CTL for the first time. UPLC-Q-TOF-MSE combined with chemometric analysis is a powerful method to rapidly screen the metabolite profiling to justify the quality of CTL as a flavoring agent and in functional foods.

Differential Chemical Components Analysis of Periplocae Cortex, Lycii Cortex, and Acanthopanacis Cortex Based on Mass Spectrometry Data and Chemometrics.

Background:Periplocae Cortex (PC), Acanthopanacis Cortex (AC), and Lycii Cortex (LC), as traditional Chinese medicines, are all dried root bark, presented in a roll, light and brittle, easy to break, have a fragrant scent, etc. Due to their similar appearances, it is tough to distinguish them, and they are often confused and adulterated in markets and clinical applications. To realize the identification and quality control of three herbs, in this paper, Ultra Performance Liquid Chromatography-Quadrupole Time of Flight Mass Spectrometry Expression (UHPLC-QTOF-MSE) combined with chemometric analysis was used to explore the different chemical compositions. Methods: LC, AC, and PC were analyzed by UHPLC-QTOF-MSE, and the quantized MS data combined with Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were used to explore the different chemical compositions with Variable Importance Projection (VIP) > 1.0. Further, the different chemical compositions were identified according to the chemical standard substances, related literature, and databases. Results: AC, PC, and LC can be obviously distinguished in PCA and PLS-DA analysis with the VIP of 2661 ions > 1.0. We preliminarily identified 17 differential chemical constituents in AC, PC, and LC with significant differences (p < 0.01) and VIP > 1.0; for example, Lycium B and Periploside H2 are LC and PC's proprietary ingredients, respectively, and 2-Hydroxy-4-methoxybenzaldehyde, Periplocoside C, and 3,5-Di-O-caffeoylquinic acid are the shared components of the three herbs. Conclusions: UHPLC-QTOF-MSE combined with chemometric analysis is conducive to exploring the differential chemical compositions of three herbs. Moreover, the proprietary ingredients, Lycium B (LC) and Periploside H2 (PC), are beneficial in strengthening the quality control of AC, PC, and LC. In addition, limits on the content of shared components can be set to enhance the quality control of LC, PC, and AC.

Exploring the feasibility of multi-elements coupled with chemometrics for discriminating the geographical origins of oysters (Crassostrea ariakensis).

This study explored the efficacy of multi-elements combined with chemometrics to discriminate the geographical origins of oysters (Crassostrea ariakensi). We determined 52 elements in 166 samples from four regions along the southeast coast of China. Significant regional variations of 51 elements were revealed (P < 0.05), while the principal component analysis (PCA) provided no clear regional delineations. The training models (n = 117) established on linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), and random forest (RF) uniformly achieved 100% predictive accuracy. The cross-validation accuracies of the final models (n = 166) derived from LDA, PLS-DA, and RF were 100%, 100%, and 99.4%, respectively. Even with the models simplified to 8 elements (Zn, Al, K, Cd, Cu, Rb, B, and Ag), high predictive and cross-validation accuracies were maintained, underscoring the robustness and algorithm flexibility of elemental profiling for accurately identifying the geographical origins of oysters.

Structure-based chemical ontology improves chemometric prediction of antibacterial essential oils.

Plants are valuable resources for drug discovery as they produce diverse bioactive compounds. However, the chemical diversity makes it difficult to predict the biological activity of plant extracts via conventional chemometric methods. In this research, we propose a new computational model that integrates chemical composition data with structure-based chemical ontology. For a model validation, two training datasets were prepared from literature on antibacterial essential oils to classify active/inactive oils. Random forest classifiers constructed from the data showed improved prediction performance in both test datasets. Prior feature selection using hierarchical information criterion further improved the performance. Furthermore, an antibacterial assay using a standard strain of Staphylococcus aureus revealed that the classifier correctly predicted the activity of commercially available oils with an accuracy of 83% (= 10/12). The results of this study indicate that machine learning of chemical composition data integrated with chemical ontology can be a highly efficient approach for exploring bioactive plant extracts.

A Comprehensive Analysis of Fel Ursi and Its Common Adulterants Based on UHPLC-QTOF-MSE and Chemometrics.

BACKGROUND: As one of the four most valuable animal medicines, Fel Ursi, named Xiong Dan (XD) in China, has the effect of clearing heat, calming the liver, and brightening the eyes. However, due to the special source of XD and its high price, other animals' bile is often sold as XD or mixed with XD on the market, seriously affecting its clinical efficacy and consumers' rights and interests. In order to realize identification and adulteration analysis of XD, UHPLC-QTOF-MSE and multivariate statistical analysis were used to explore the differences in XD and six other animals' bile. METHODS: XD, pig gall (Zhu Dan, ZD), cow gall (Niu Dan, ND), rabbit gallbladder (Tu Dan, TD), duck gall (Yan Dan, YD), sheep gall (Yang Dan, YND), and chicken gall (Ji Dan, JD) were analyzed by UHPLC-QTOF-MSE, and the MS data, combined with multivariate analysis methods, were used to distinguish between them. Meanwhile, the potential chemical composition markers that contribute to their differences were further explored. RESULTS: The results showed that XD and six other animals' bile can be distinguished from each other obviously, with 27 ions with VIP > 1.0. We preliminarily identified 10 different bile acid-like components in XD and the other animals' bile with significant differences (p < 0.01) and VIP > 1.0, such as tauroursodeoxycholic acid, Glycohyodeoxycholic acid, and Glycodeoxycholic acid. CONCLUSIONS: The developed method was efficient and rapid in accurately distinguishing between XD and six other animals' bile. Based on the obtained chemical composition markers, it is beneficial to strengthen quality control for bile medicines.

Geographical origin determination of the PDO hazelnut (cv. Giresun Tombul) by chemometric analysis of FT-NIR and Raman spectra acquired from shell and kernel.

Turkey is the leading producer of hazelnuts, contributing to 62% of the total global production. Among 18 distinct local hazelnut cultivars, Giresun Tombul is the only cultivar that has received Protected Designation of Origin denomination from the European Comission (EC). However, there is currently no practical objective method to ensure its geographic origin. Therefore, in this study NIR and Raman spectroscopy, along with chemometric methods, such as principal component analysis, PLS-DA (partial least squares-discriminant analysis), and SVM-C (support vector machine-classification), were used to determine the geographical origin of the Giresun Tombul hazelnut cultivar. For this purpose, samples from unique 118 orchards were collected from eight different regions in Turkey during the 2021 and 2022 growing seasons. NIR and Raman spectra were obtained from both the shell and kernel of each sample. The results indicated that hazelnut samples exhibited distinct grouping tendencies based on growing season regardless of the spectroscopic technique and sample type (shell or kernel). Spectral information obtained from hazelnut shells demonstrated higher discriminative power concerning geographical origin compared to that obtained from hazelnut kernels. The PLS-DA models utilizing FT-NIR (Fourier transform near-infrared) and Raman spectra for hazelnut shells achieved validation accuracies of 81.7% and 88.3%, respectively, while SVM-C models yielded accuracies of 90.9% and 86.3%. It was concluded that the lignocellulosic composition of hazelnut shells, indicative of their geographic origin, can be accurately assessed using FT-NIR and Raman spectroscopy, providing a nondestructive, rapid, and user-friendly method for identifying the geographical origin of Giresun Tombul hazelnuts. PRACTICAL APPLICATION: The proposed spectroscopic methods offer a rapid and nondestructive means for hazelnut value chain actors to verify the geographic origin of Giresun Tombul hazelnuts. This could definitely enhance consumer trust by ensuring product authenticity and potentially help in preventing fraud within the hazelnut market. In addition, these methods can also be used as a reference for future studies targeting the authentication of other shelled nuts.

Quantitative determination of zearalenone in wheat by the CSA-NIR technique combined with chemometrics algorithms.

In the current study, a colorimetric sensor array combined with near-infrared (NIR) spectroscopy was used to quantitatively analyze zearalenone in wheat. The portable NIR spectrometer was used to scan the porphyrin reaction points of the wheat colorimetric sensor and collect spectral data. Subsequently, based on all the NIR spectral data, the two models and three feature selection algorithms are compared, and the best performance model and the best feature variable input are selected. Concurrently, the Kernel-based Extreme Learning Machine (KELM) model optimized by the two parameter optimization algorithms was compared, and the best parameter optimization algorithm was selected. Among all evaluation models, the KELM model optimized by the Competitive Adaptive Reweighted Sampling algorithm combined with the rime optimization algorithm has the best prediction effect. The predicted RP2 is 0.9900, and the root mean square error of prediction (RMSEP) is 18.4610 µg∙kg-1.

Characterization of the flavor profile of UHT milk during shelf-life via volatile metabolomics fingerprinting combined with chemometrics.

Ultra-high temperature (UHT) milk is popular among consumers. However, its flavor and texture change in its shelf life. Flavor is highly determinative for the success of dairy products and for consumers' willingness to buy. It is important to milk producers to ensure the optimal flavor of their products in the shelf life. In order to be able to control and predict the flavor quality of UHT milk during the shelf life, this study compared the variations in sensory quality, volatile aroma release and backbone flavor factors and developed a discriminant model to assess flavor quality based on flavouromics data of five competing milk sample during storage. Using partial least squares discriminant analysis (PLS-DA) with Electronic-nose (E-nose) data excellent classification sensitivity and specificity were achieved compared to models based on gas chromatography-mass spectrometry (GC-MS) data. The PLS-DA model using E-nose data exhibited a 100% correct classification rate for the storage period, and a 92% correct rate based on the eight variable importance in the projection (VIP) elements screened for volatile components from different groups. The discriminative model developed herein based on E-nose combined with chemometrics demonstrated advantages such as speed, efficiency, and environmental friendliness. This method shows promise as a precise tool for analyzing aroma changes in UHT milk during its shelf life, and provide support for controlling the flavor substances and milk product development.

A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis-NIR-SWIR hyperspectral imaging.

The wide gap between the demand and supply of edible mustard oil can be overcome to a certain extent by enhancing the oil-recovery during mechanical oil expression. It has been reported that microwave (MW) pre-treatment of mustard seeds can have a positive effect on the availability of mechanically expressible oil. Hyperspectral imaging (HSI) was used to understand the change in spatial spread of oil in the microwave (MW) treated seeds with bed thickness and time of exposure as variables, using visible near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-1700 nm) systems. The spectral data was analysed using chemometric techniques such as partial least square discriminant analysis (PLS-DA) and regression (PLSR) to develop prediction models. The PLS-DA model demonstrated a strong capability to classify the mustard seeds subjected to different MW pre-treatments from control samples with a high accuracy level of 96.6 and 99.5% for Vis-NIR and SWIR-HSI, respectively. PLSR model developed with SWIR-HSI spectral data predicted (R2 > 0.90) the oil content and fatty acid components such as oleic acid, erucic acid, saturated fatty acids, and PUFAs closest to the results obtained by analytical techniques. However, these predictions (R2 > 0.70) were less accurate while using the Vis-NIR spectral data.

Optimal harvest period and quality control markers of cultivated Flos Chrysanthemi Indici using untargeted/targeted metabolomics, chemometric analysis and in vivo study.

ETHNOPHARMACOLOGICAL RELEVANCE: Flos Chrysanthemi Indici (FCI), the flower of Chrysanthemum Indicum L., is a popular traditional Chinese medicine (TCM) for treatment of inflammatory diseases in China. FCI is also a functional food, and is widely used as herbal tea for clearing heat and detoxicating. AIM OF THE STUDY: To explore quality control markers of FCI based on the optimal harvest period. MATERIALS AND METHODS: First, UPLC-Q-TOF/MS based untargeted metabolomics was applied to explore the chemical profiles of FCIs collected at bud stages (BS), initial stages (IS), full bloom stages (FS) and eventual stages (ES) from eight cultivated regions in China. Subsequently, lipopolysaccharide (LPS)-induced RAW264.7 cell inflammatory model and carrageenan-induced rat paw edema model were used to confirm the anti-inflammatory effect of FCIs collected at IS/FS. Then, UPLC-PDA targeted metabolomics was used to quantitatively analyze 9 constituents with anti-inflammatory activity (7 flavonoids and 2 phenolic acids) changed significantly (VIP > 4) during flowering stages. Finally, ROC curves combined with PCA analysis based on the variation of 9 active constituents in FCIs from different flowering stages were applied to screen the quality markers of FCI. RESULTS: FCIs at IS/FS had almost same chemical characteristics, but quite different from those at BS and ES. A total of 32 constituents in FCIs including flavonoids and phenolic acids were changed during flowering development. Most of the varied constituents had the highest or higher contents at IS/FS compared with those at ES, indicating that the optimal harvest period of FCI should be at IS/FS. FCI extract could effectively suppress nitric oxide (NO) production in LPS-induced RAW264.7 cells and regulate the abnormal levels of cytokines and PGE2 in carrageenan-induced paw edema model rat. The results of quantitatively analysis revealed that the variation trends of phenolic acids and flavonoids in FCIs were different during flowering development, but most of them had higher contents at IS/FS than those at ES in all FCIs collected from eight cultivated regions, except one sample from Anhui. Finally, linarin, luteolin, apigenin and 3,5-dicaffeoylquinic acid were selected as the Q-markers based on the contribution of their AUC values in ROC and clustering of PCA analysis. CONCLUSIONS: Our study demonstrates the optimal harvest period of FCI and specifies the multi-constituents Q-markers of FCI based on the influence of growth progression on the active constituents using untargeted/targeted metabolomics. The findings not only greatly increase the utilization rate of FCI resources and improve quality control of FCI products, but also offer new strategy to identify the Q-markers of FCI.

Exploring accurate mass measurements in pixel-based chemometrics: Advancing coffee classification with GC-HRMS-A proof of concept study.

This paper presents a study that assesses the application of chemometrics for classifying coffee samples in a quality control context. High-resolution and accurate mass measurements were utilized as input for pixel-based orthogonal partial least squares discriminant analysis (OPLS-DA) models. The compositional data were acquired through a fully automated workflow combining headspace solid-phase microextraction and gas chromatography-high-resolution mass spectrometry (GC-HRMS) using an FT-Orbitrap® mass analyzer. A workflow centered on accurate mass measurements was successfully utilized for group-type analysis, offering an alternative to methods relying solely on MS similarity searches. The predictive models underwent thorough evaluation, demonstrating robust multivariate classification performance. Five key coffee attributes, bitterness, acidity, body, intensity, and roasting level were successfully predicted using GC-HRMS data. The results revealed strong predictive accuracy across all models, ranging from 88.9 % (bitterness) to 94.4 % (roasting level). This study represents a significant advancement in automating methods for coffee quality control, notably increasing the predictive ability of the models compared to existing literature.

Rapid and ultrasensitive detection of thiram and carbaryl pesticide residues in fruit juices using SERS coupled with the chemometrics technique.

A gold nanogap substrate was used to measure the thiram and carbaryl residues in various fruit juices using surface-enhanced Raman scattering (SERS). The gold nanogap substrates can detect carbaryl and thiram with limits of detection of 0.13 ppb (0.13 µgkg-1) and 0.22 ppb (0.22 µgkg-1). Raw SERS data were first preprocessed to reduce noise and undesirable effects and, were later used for model creation, implementing classification, and regression analysis techniques. The partial least-squares regression models achieved the highest prediction correlation coefficient (R2) of 0.99 and the lowest root mean square of prediction value below 0.62 ppb for both pesticide-infected juice samples. Furthermore, to differentiate between juice samples contaminated by both pesticides and control (pesticide-free), logistic-regression classification models were produced and achieved the highest classification accuracies of 100% and 99% for contaminated juice containing thiram and 100% accurate results for contaminated juice containing carbaryl. This indicates that the gold nanogap surface has significant potential for achieving high sensitivity in detecting trace contaminants in food samples.

Chemometrics-based analysis of the phytochemical profile and antioxidant activity of Salvia species from Iran.

In recent years, the exploration of the therapeutic potential of Salvia has gained considerable attention, leading to a growing number of scientific studies emphasizing its pharmacological properties. Despite this, therapeutic applications of Salvia remain underexploited, requiring further investigation. Iran is a major center for sage diversity in Asia, boasting 60 Salvia species, 17 of which are unique to the area. This study aimed to comprehensively explore and compare the extracts of 102 Salvia samples belonging to 20 distinct Salvia species from Iran, providing a deeper understanding of their specific polyphenol content and, consequently, their antioxidant capabilities and potential therapeutic uses. All samples were analyzed to determine the contents of total phenolics, total flavonoids, total tannin, photosynthetic pigments, and ascorbic acid, along with their antioxidant activity. These data were then combined with the forty distinct chemical fingerprints identified by ultrafast high-pressure liquid chromatography coupled with high-resolution mass spectrometry. Multivariate data analysis was employed to find correlations and differences among the huge number of data obtained and to identify Salvia species with similar phytochemical and/or antioxidant properties. The results show that each Salvia species is characterized by a distinct class of polyphenols recognized for their antidiabetic, anti-inflammatory, cardioprotective and neuroprotective properties. Overall, our findings reveal the potential of some Salvia species for targeted therapeutic applications and provide a rational basis for the development of Salvia-derived nutraceuticals, ultimately improving the prospects for the use of Salvia in medicine.

Holistic quality assessment and monitoring of YiXinShu capsule based on three-dimensional fingerprints combined with quantitative analysis, antioxidant activity and chemometrics.

ETHNOPHARMACOLOGICAL RELEVANCE: YiXinShu capsule (YXSC), originally from the classical TCM formula named "Sheng-Mai-San", has been extensively utilized in clinic for the treatment of cardiovascular diseases. However, there were few reports about the quality assessment of YXSCs both internationally and domestically. AIM OF THE STUDY: The objective was to develop a multi-strategy platform incorporating systematic quantitative fingerprint analysis and antioxidant activity determination, with chemometric analysis and bivariate correlation analysis as the auxiliary approaches, to assess and monitor the quality of YXSCs. MATERIALS AND METHODS: Firstly, according to the Chinese Pharmacopoeia (2020 edition), 12 key indicator components from seven herb medicines were quantified by HPLC method. Then, three-dimensional fingerprints comprising five-wavelength fusion fingerprint (FWF-FP), electrochemical fingerprint (EC-FP) and Differential Scanning Calorimetry fingerprint (DSC-FP) were established to assess and monitor YXSCs using systematically quantified fingerprint method (SQFM) and principal component analysis (PCA). Moreover, by integrating the analysis of the three-dimensional fingerprints, the quality of YXSCs from different batches was effectively screened. Finally, the antioxidant activity of this TCM was assessed through DPPH and ABTS methods, and the L-ascorbic acid equivalent antioxidant capacity (AEAC) values were compared to evaluate the antioxidant activities of the two methods. A Partial Least Squares (PLS) model was used to develop the spectrum-activity relationship between FWF-FP and AEAC, and a bivariate correlation analysis (BCA) was used to assess the correlation between FWF-FP and EC-FP. RESULTS: The key indexes including tanshinone I, tol, toe, Atp, first exothermic peak, and second exothermic peak can differentiate between various batches of YXSCs based on their three-dimensional fingerprint profiles. The integration evaluation results from 42 batches of YXSCs were categorized into 2-5 grades, indicating good quality consistency across different batches. In vitro studies have indicated a significant antioxidant activity capacity of YXSCs. The PLS model revealed that 37 out of the 41 fingerprint peaks exhibited antioxidant activity. The overall trend of BCA was consistent with PLS model results. CONCLUSION: This research presents a scientific and holistic strategy for the quality consistency evaluation of YXSCs, thereby offering an effective approach for the thorough evaluation of TCMs.

Characterization of volatile profile from different coriander (Coriandrum sativum L.) varieties via HS-SPME/GC-MS combined with E-nose analyzed by chemometrics.

Headspace-solid phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) and electronic nose (E-nose) technologies were implemented to characterize the volatile profile of aerial part from 40 coriander varieties. A total of 207 volatile compounds were identified and quantified, including aldehydes, alcohols, terpenes, hydrocarbons, esters, ketones, acids, furans, phenols and others. E-nose results showed that W5S and W2W were representative sensors responding to coriander odor. Among all varieties, the number (21-30 species) and content (449.94-1050.55 µg/g) of aldehydes were the highest, and the most abundant analytes were (Z)-9-hexadecenal or (E)-2-tetratecenal, which accounted for approximately one-third of the total content. In addition, 37 components were determined the characteristic constituents with odor activity values (OAVs) ≥ 1, mainly presenting citrusy, fatty, soapy and floral smells. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) could effectively distinguish different varieties. This study provided a crucial theoretical basis for flavor evaluation and quality improvement of coriander germplasm resources.

Traceability Research on Geographic Erigeron breviscapus Based on High-Resolution Mass Spectrometry and Chemometric Analysis.

A method was developed to identify and trace the geographic sources of Erigeron breviscapus using high-resolution mass spectrometry and chemometrics. The representative samples were collected from the geographic area of Honghe Dengzhanhua and other areas in Yunnan province and Guizhou province. The data points could be determined well using the PCA and PLS-DA diagram. A total of 46 characteristic compounds were identified from Honghe Dengzhanhua and within Guizhou province, but 37 compounds were different from Honghe Dengzhanhua and other counties in Yunnan province. Two biomarkers were found from three regions. Their structures were inferred as 8-amino-7-oxononanoic acid and 8-hydroxyquinoline, and they had the same molecular composition. This may suggest that a possible synthesis pathway can be proven in the future.