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.

HRMAS 1H NMR and CPMAS 13C NMR spectroscopies coupled with chemometrics for the metabolomic investigation of commercial teas.

In this study, the metabolome of different types of tea (i.e., black, green and earl grey) is explored by means of HRMAS 1H (i.e., semisolid state) NMR and CPMAS 13C (i.e., solid state) NMR spectroscopies. By elaborating the metabolomic data with unsupervised and supervised chemometric tools (PCA, PLS-DA), it was possible to set up classification models with the aim to discriminate the different types of tea as based on differences in their chemical composition. Both the applications of the NMR spectroscopies also allowed to obtain information about the metabolic biomarkers leading the differentiation among teas. These were mainly represented by phenolic compounds. Also, some non-phenolic compounds, such as amino acids, carbohydrates, and terpenoids, played important roles in shaping tea quality. The findings of this study provided useful insights into the application of solid and semisolid state NMR spectroscopies, in combination with chemometrics, in the context of food authentication and traceability.

Visible-short wavelength near infrared hyperspectral imaging coupled with multivariate curve resolution-alternating least squares for diagnosis of breast cancer.

This study investigates the application of visible-short wavelength near-infrared hyperspectral imaging (Vis-SWNIR HSI) in the wavelength range of 400-950 nm and advanced chemometric techniques for diagnosing breast cancer (BC). The research involved 56 ex-vivo samples encompassing both cancerous and non-cancerous breast tissue from females. First, HSI images were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to exploit pure spatial and spectral profiles of active components. Then, the MCR-ALS resolved spatial profiles were arranged in a new data matrix for exploration and discrimination between benign and cancerous tissue samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA classification accuracy of 82.1 % showed the potential of HSI and chemometrics for non-invasive detection of BC. Additionally, the resolved spectral profiles by MCR-ALS can be used to track the changes in the breast tissue during cancer and treatment. It is concluded that the proposed strategy in this work can effectively differentiate between cancerous and non-cancerous breast tissue and pave the way for further studies and potential clinical implementation of this innovative approach, offering a promising avenue for improving early detection and treatment outcomes in BC patients.

Revealing alcoholization-related volatile compounds and determining alcoholization indices in tobacco using GC-IMS coupled with chemometrics.

Alcoholization is an integral part of tobacco processing and volatile compounds are key to assessing tobacco alcoholization. In this study, a total of 154 volatiles from nine categories were determined by gas chromatography-ion mobility spectrometry (GC-IMS) from four grades of tobacco, of which 114 were better identified. And then, the dynamic trends of volatile compounds with significant changes in tobacco alcoholization were analyzed. The relevant volatiles with the alcoholization indices (AIs) (R > 0.8) were screened as indicators of tobacco alcoholization. Cinnamyl isobutyrate, linolenic acid alcohol, propanoic acid-M and propanoic acid-D in all tobacco samples were highly correlated with the AIs and tended to increase during the alcoholization process. In addition, linear discriminant analysis (LDA), back-propagation neural network (BPNN) and random forest (RF) classifiers were constructed for discrimination of tobacco AIs. Three classifiers trained with a combination of 20 volatiles achieved satisfactory results with area under the curve (AUC) of 0.95 (LDA), 0.94 (BPNN) and 0.97 (RF), respectively. The RF classifier gained optimal accuracy of 100 % and 96.1 % for the training and test sets, respectively. The study confirmed that GC-IMS can be used to characterize the changes of volatile compounds in tobacco during alcoholization and combined with machine learning to achieve the determination of AIs. The results of the study may provide a new means for the tobacco industry to monitor the alcoholization process and determine the degree of alcoholization.

Evaluation of spectroscopic techniques for on-site drug testing of festival seizures.

Despite the fact that drugs of abuse are illegal, a drug-free festival still remains an utopia in most settings. For law enforcement purposes, it is necessary to rapidly determine whether controlled substances are involved. On-site testing is a challenging task because drugs appear in different physical forms and concentrations. The aim of this study was to compare the performance of two spectroscopic techniques, Raman and Fourier transform-infrared (FT-IR), for the testing of drug seizures at a dance festival. First, samples were measured through packaging with Raman. Subsequently, homogenized samples were analysed with FT-IR. For MDMA tablets, a chemometric model was applied on the FT-IR spectra for the dose estimation. After the festival, results were confirmed in the forensic laboratory with gas chromatography coupled with mass spectrometry (GC-MS) and gas chromatography with flame ionization detection (GC-FID). In total, 166 samples of which 90 tablets, 53 powders, 16 crystals and 7 liquids were analysed. MDMA, cocaine and ketamine were the top three drugs seized. The Raman technique was suitable for powders and crystals (sensitivity of 100% and 81%, respectively). However, in comparison with FT-IR, Raman performance was lower for the analysis of liquids (sensitivity of 67%) and 'ecstasy'-like tablets (sensitivity of 41%). Overall, sensitivities above 95% were obtained with FT-IR. The MDMA doses of the tablets, determined on-site, ranged between 52 mg and 336 mg MDMA hydrochloride. For a quick identification of a variety of drugs on-site, the combination of Raman and FT-IR is recommended. It should be emphasized that optimized settings, in-house libraries and analysis by trained operators are essential to obtain correct results.

Evidence on C-C Coupling to Acetate as Key Reaction Intermediate in Photocatalytic Reduction of CO2 over Pt/TiO2.

Photocatalytic conversion of CO2 with H2O is an attractive application that has the potential to mitigate environmental and energy challenges through the conversion of CO2 to hydrocarbon products such as methane. However, the underlying reaction mechanisms remain poorly understood, limiting real progress in this field. In this work, a mechanistic investigation of the CO2 photocatalytic reduction on Pt/TiO2 is carried out using an operando FTIR approach, combined with chemometric data processing and isotope exchange of (12CO2 + H2O) toward (13CO2 + H2O). Multivariate curve resolution analysis applied to operando spectra across numerous cycles of photoactivation and the CO2 reaction facilitates the identification of principal chemical species involved in the reaction pathways. Moreover, specific probe-molecule-assisted reactions, including CO and CH3COOH, elucidate the capacity of selected molecules to undergo methane production under irradiation conditions. Finally, isotopic exchange reveals conclusive evidence regarding the nature of the identified species during CO2 conversion and points to the significant role of acetates resulting from the C-C coupling reaction as key intermediates in methane production from the CO2 photocatalytic reduction reaction.

Chemical profiling and quality evaluation of raw and vinegar-processing frankincense by multiple UPLC-MS/MS techniques.

INTRODUCTION: Frankincense is used for analgesic, tumor-suppressive, and anti-inflammatory treatments in Traditional Chinese Medicine but poses toxicological concerns. Vinegar processing is a common technique used to reduce the toxicity of frankincense. OBJECTIVE: This study aimed to investigate the chemical composition and quality evaluation of raw and vinegar-processing frankincense by multiple UPLC-MS/MS techniques. Additionally, we purposed refining the vinegar processing technique and identifying potentially harmful ingredients in the raw frankincense. METHODOLOGY: Sub-chronic oral toxicity studies were conducted on raw and vinegar-processing frankincense in rats. The composition of frankincense was identified by UPLC-Q-TOF-MS/MS. Chemometrics were used to differentiate between raw and vinegar-processing frankincense. Potential chemical markers were identified by selecting differential components, which were further exactly determined by UPLC-QQQ-MS/MS. Moreover, the viability of the HepG2 cells of those components with reduced contents after vinegar processing was assessed. RESULTS: The toxicity of raw frankincense is attenuated by vinegar processing, among which vinegar-processing frankincense (R40) (herb weight: rice vinegar weight = 40:1) exhibited the lowest toxicity. A total of 83 components were identified from frankincense, including 40 triterpenoids, 37 diterpenoids, and 6 other types. The contents of six components decreased after vinegar-processing, with the lowest levels in R40. Three components, specifically 3α-acetoxy-11-keto-ß-boswellic acid (AKBA), 3α-acetoxy-α-boswellic acid (α-ABA), and 3α-acetoxy-ß-boswellic acid (ß-ABA), inhibited the viability of HepG2 cells. The processing of frankincense with vinegar at a ratio of 40:1 could be an effective method of reducing the toxicity in raw frankincense. CONCLUSION: Our research improves understanding of the toxic substance basis and facilitates future assessments of frankincense quality.

Identification of Euphorbiae pekinensis Radix and its counterfeit and adulterated products based on DNA barcode, UPLC-Q-TOF-MS, UPLC fingerprint, and chemometrics.

Euphorbiae pekinensis Radix (EPR) is a traditional Chinese herb commonly used to treat edema, pleural effusion, and ascites. However, counterfeit and adulterated products often appear in the market because of the homonym phenomenon, similar appearance, and artificial forgery of Chinese herbs. This study comprehensively evaluated the quality of EPR using multiple methods. The DNA barcode technique was used to identify EPR, while the UPLC-Q-TOF-MS technique was utilized to analyze the chemical composition of EPR. A total of 15 tannin and phenolic acid components were identified. Furthermore, UPLC fingerprints of EPR and its common counterfeit products were established, and unsupervised and supervised pattern recognition models were developed using these fingerprints. The backpropagation artificial neural network and counter-propagation artificial neural network models accurately identified counterfeit and adulterated products, with a counterfeit ratio of more than 25%. Finally, the contents of the chemical markers 3,3'-di-O-methyl ellagic acid-4'-O-ß-D-glucopyranoside, ellagic acid, 3,3'-di-O-methyl ellagic acid-4'-O-ß-d-xylopyranoside, and 3,3'-di-O-methyl ellagic acid were determined to range from 0.05% to 0.11%, 1.95% to 8.52%, 0.27% to 0.86%, and 0.10% to 0.42%, respectively. This proposed strategy offers a general procedure for identifying Chinese herbs and distinguishing between counterfeit and adulterated products.

Machine learning approach for high-throughput phenolic antioxidant screening in black Rice germplasm collection based on surface FTIR.

Pigmented rice contains beneficial phenolic antioxidants but analysing them across germplasm collections is laborious and time-consuming. Here we utilised rapid surface Fourier transform infrared (FTIR) spectroscopy and machine learning algorithms (ML) to predict and classify polyphenolic antioxidants. Total phenolics, flavonoids, anthocyanins, and proanthocyanidins were quantified biochemically from 270 diverse global coloured rice collection and attenuated total reflectance (ATR) FTIR spectra were obtained by scanning whole grain surfaces at 800-4000 cm-1. Five ML classification models were optimised using the biochemical and spectral data which performed predictions with 93.5%-100% accuracy. Random Forest and Support Vector Machine models identified key FTIR peaks linked to flavonols, flavones and anthocyanins as important model predictors. This research successfully established direct and non-destructive surface chemistry spectroscopy of the aleurone layer of pigmented rice integrated with ML models as a viable high-throughput platform to accelerate the analysis and profiling of nutritionally valuable coloured rice varieties.

PROSAC as a selection tool for SO-PLS regression: A strategy for multi-block data fusion.

BACKGROUND: Spectral data from multiple sources can be integrated into multi-block fusion chemometric models, such as sequentially orthogonalized partial-least squares (SO-PLS), to improve the prediction of sample quality features. Pre-processing techniques are often applied to mitigate extraneous variability, unrelated to the response variables. However, the selection of suitable pre-processing methods and identification of informative data blocks becomes increasingly complex and time-consuming when dealing with a large number of blocks. The problem addressed in this work is the efficient pre-processing, selection, and ordering of data blocks for targeted applications in SO-PLS. RESULTS: We introduce the PROSAC-SO-PLS methodology, which employs pre-processing ensembles with response-oriented sequential alternation calibration (PROSAC). This approach identifies the best pre-processed data blocks and their sequential order for specific SO-PLS applications. The method uses a stepwise forward selection strategy, facilitated by the rapid Gram-Schmidt process, to prioritize blocks based on their effectiveness in minimizing prediction error, as indicated by the lowest prediction residuals. To validate the efficacy of our approach, we showcase the outcomes of three empirical near-infrared (NIR) datasets. Comparative analyses were performed against partial-least-squares (PLS) regressions on single-block pre-processed datasets and a methodology relying solely on PROSAC. The PROSAC-SO-PLS approach consistently outperformed these methods, yielding significantly lower prediction errors. This has been evidenced by a reduction in the root-mean-squared error of prediction (RMSEP) ranging from 5 to 25 % across seven out of the eight response variables analyzed. SIGNIFICANCE: The PROSAC-SO-PLS methodology offers a versatile and efficient technique for ensemble pre-processing in NIR data modeling. It enables the use of SO-PLS minimizing concerns about pre-processing sequence or block order and effectively manages a large number of data blocks. This innovation significantly streamlines the data pre-processing and model-building processes, enhancing the accuracy and efficiency of chemometric models.

Identification of synthetic cathinone positional isomers using electron activated dissociation mass spectrometry.

BACKGROUND: Synthetic cathinones (SCs) are a large category of new psychoactive substances (NPS), which pose a serious threat to public health due to limited information about their toxicology and pharmacology. Many SCs are closely related in their chemical structures, with some substances being positional isomers. In this study, we propose a new workflow for the identification of SC isomers using liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS2) combined with electron activated dissociation (EAD) and chemometrics. Differentiation between isomeric SCs is essential for both legislative and public safety reasons, since minor differences in their molecular structures may change their legal status and pharmacological profiles. RESULTS: The workflow was optimized using ring-substituted isomers of methylmethcathinones, methylethcathinones, and chloromethcathinones. The kinetic energy in the EAD cell was investigated at three levels (i.e., 15, 18, and 20 eV) for each group. Two data analysis methods (i.e., t-distributed stochastic neighbor embedding [t-SNE] and a Random Forest [RF] algorithm) were applied using the obtained EAD mass spectral data. The three sets of ring-substituted SCs were clearly distinguished using t-SNE and an RF algorithm. Moreover, the RF approach resulted in a 97 % classification accuracy for isomer identification using various combinations of compounds, isomers, and electron kinetic energies. This workflow was subsequentially applied to the analysis of 26 blind street samples, resulting in a 92 % classification accuracy for isomer identification. However, the accuracy varied based on the kinetic electron energy. A subset of the original data set, focusing on 15-eV data only, was used, resulting in a classification accuracy of 100 %. SIGNIFICANCE: This study presents the first LC-HRMS2 workflow based on EAD and chemometrics, which resulted in a classification accuracy of 100 % of authentic street samples. The developed LC-HRMS2 workflow demonstrates that EAD product ions and their characteristic ion ratios can be successfully used to identify ring-substituted positional isomers of SCs.

Decoding of the isotopic fingerprint of tequila 100% agave silver class and image analysis to evaluate differences between spirits.

This communication shows the decoding of Isotopic Fingerprint of Tequila 100% agave silver class (IFTequila100% agave) in three areas corresponding to isotopic variations due to: plant used as raw material, fermentation and distillation process, and hydrolysis process. Isotopic tracers that make them up correspond to the δ13CVPDB ethanol-δ13CVPDB ethyl acetate-δ13CVPDB isoamyl alcohol, δ13CVPDB ethyl acetate-δ13CVPDB isoamyl alcohol-δ13CVPDB n-propanol and δ13CVPDB ethyl acetate-δ13CVPDB n-propanol-δ13CVPDB methanol, respectively. Once the IFTequila100%agave has been decoded, an image comparison was performed against isotopic fingerprints of spirits (Tequila, Bacanora, Raicilla, Sotol, and Mezcal). Results show that it is possible classifies 100% of samples analyzed. Likewise, from decoding it is possible to determine the critical process stage to determine variations with respect to the IFTequila100%agave. The chemometric analysis developed corresponds to an auxiliary analytical tool useful for the inspection processes currently carried out by the authorities to determine the authenticity of the beverage.

Probing Biological Nitrogen Fixation in Legumes Using Raman Spectroscopy.

Biological nitrogen fixation (BNF) by symbiotic bacteria plays a vital role in sustainable agriculture. However, current quantification methods are often expensive and impractical. This study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans. Raman spectra were obtained from soybean plants grown with and without rhizobia bacteria to identify spectral signatures associated with BNF. δN15 isotope ratio mass spectrometry (IRMS) was used to determine actual BNF percentages. Partial least squares regression (PLSR) was employed to develop a model for BNF quantification based on Raman spectra. The model explained 80% of the variation in BNF activity. To enhance the model's specificity for BNF detection regardless of nitrogen availability, a subsequent elastic net (Enet) regularisation strategy was implemented. This approach provided insights into key wavenumbers and biochemicals associated with BNF in soybeans.

QCL Infrared Spectroscopy Combined with Machine Learning as a Useful Tool for Classifying Acetaminophen Tablets by Brand.

The development of new methods of identification of active pharmaceutical ingredients (API) is a subject of paramount importance for research centers, the pharmaceutical industry, and law enforcement agencies. Here, a system for identifying and classifying pharmaceutical tablets containing acetaminophen (AAP) by brand has been developed. In total, 15 tablets of 11 brands for a total of 165 samples were analyzed. Mid-infrared vibrational spectroscopy with multivariate analysis was employed. Quantum cascade lasers (QCLs) were used as mid-infrared sources. IR spectra in the spectral range 980-1600 cm-1 were recorded. Five different classification methods were used. First, a spectral search through correlation indices. Second, machine learning algorithms such as principal component analysis (PCA), support vector classification (SVC), decision tree classifier (DTC), and artificial neural network (ANN) were employed to classify tablets by brands. SNV and first derivative were used as preprocessing to improve the spectral information. Precision, recall, specificity, F1-score, and accuracy were used as criteria to evaluate the best SVC, DEE, and ANN classification models obtained. The IR spectra of the tablets show characteristic vibrational signals of AAP and other APIs present. Spectral classification by spectral search and PCA showed limitations in differentiating between brands, particularly for tablets containing AAP as the only API. Machine learning models, specifically SVC, achieved high accuracy in classifying AAP tablets according to their brand, even for brands containing only AAP.

Discrimination and Quantification of Cotton and Polyester Textile Samples Using Near-Infrared and Mid-Infrared Spectroscopies.

In the textile industry, cotton and polyester (PES) are among the most used fibres to produce clothes. The correct identification and accurate composition estimate of fibres are mandatory, and environmentally friendly and precise techniques are welcome. In this context, the use of near-infrared (NIR) and mid-infrared (MIR) spectroscopies to distinguish between cotton and PES samples and further estimate the cotton content of blended samples were evaluated. Infrared spectra were acquired and modelled through diverse chemometric models: principal component analysis; partial least squares discriminant analysis; and partial least squares (PLS) regression. Both techniques (NIR and MIR) presented good potential for cotton and PES sample discrimination, although the results obtained with NIR spectroscopy were slightly better. Regarding cotton content estimates, the calibration errors of the PLS models were 3.3% and 6.5% for NIR and MIR spectroscopy, respectively. The PLS models were validated with two different sets of samples: prediction set 1, containing blended cotton + PES samples (like those used in the calibration step), and prediction set 2, containing cotton + PES + distinct fibre samples. Prediction set 2 was included to address one of the biggest known drawbacks of such chemometric models, which is the prediction of sample types that are not used in the calibration. Despite the poorer results obtained for prediction set 2, all the errors were lower than 8%, proving the suitability of the techniques for cotton content estimation. It should be stressed that the textile samples used in this work came from different geographic origins (cotton) and were of distinct presentations (raw, yarn, knitted/woven fabric), which strengthens our findings.

Characterization of semi-volatile compounds in 56 Italian ciders using GC×GC-TOF-MS and multivariate analysis.

Fifty-six samples of differently produced commercial Italian ciders were analysed for semi-volatile organic compounds (SVOCs) profiling, using comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-TOF-MS) technique for the very first time. To properly support the compositional investigation of this emerging beverage, a chemometric approach through Principal Component Analysis (PCA) was employed. This revealed a sample distribution in agreement with results of the sensory tasting panel performed on such ciders, highlighting an excellent correlation between variables and perceived odorants. In particular, the positions of peculiar and anomalous objects in the Principal Components (PCs) space are explicitly evaluated, exploring the associated loadings (i.e., the importance of the identified chemical compounds), paying attention to their biochemical origin along the cider-making process and their impact on the sample olfactory analysis. Besides this, the t-distributed Stochastic Neighbor Embedding (t-SNE) method was shown to be an efficient tool for gathering pear ciders from the other samples (apple ciders), better than PCA. This study stands for the first survey on Italian commercial craft cider, and its results are aimed to be a milestone for its characterization and to start and promote cider culture in this country.

Novel authentication of African geographical coffee types (bean, roasted, powdered) by handheld NIR spectroscopic method.

African coffee is among the best traded coffee types worldwide, and rapid identification of its geographical origin is very important when trading the commodity. The study was important because it used NIR techniques to geographically differentiate between various types of coffee and provide a supply chain traceability method to avoid fraud. In this study, geographic differentiation of African coffee types (bean, roasted, and powder) was achieved using handheld near-infrared spectroscopy and multivariant data processing. Five African countries were used as the origins for the collection of Robusta coffee. The samples were individually scanned at a wavelength of 740-1070 nm, and their spectra profiles were preprocessed with mean centering (MC), multiplicative scatter correction (MSC), and standard normal variate (SNV). Support vector machines (SVM), linear discriminant analysis (LDA), neural networks (NN), random forests (RF), and partial least square discriminate analysis (PLS-DA) were then used to develop a prediction model for African coffee types. The performance of the model was assessed using accuracy and F1-score. Proximate chemical composition was also conducted on the raw and roasted coffee types. The best classification algorithms were developed for the following coffee types: raw bean coffee, SD-PLSDA, and MC + SD-PLSDA. These models had an accuracy of 0.87 and an F1-score of 0.88. SNV + SD-SVM and MSC + SD-NN both had accuracy and F1 scores of 0.97 for roasted coffee beans and 0.96 for roasted coffee powder, respectively. The results revealed that efficient quality assurance may be achieved by using handheld NIR spectroscopy combined with chemometrics to differentiate between different African coffee types according to their geographical origins.

Spatial distribution of polycyclic aromatic hydrocarbons in sediment deposits in a Seine estuary tributary by hyperspectral imaging.

Water bodies allow the storage of sediments from their catchment areas, including sediments containing persistent contaminants. This study used visible and near-infrared hyperspectral imaging to characterize the composition of sediment deposits collected in Martot Pond (France) and to reconstruct the volume of polycyclic aromatic hydrocarbon (PAH) contaminated sediments in the pond. Additionally, combining this method with polychlorinated biphenyl (PCB) analysis enhanced the age model associated with these sediments. To achieve this, indicators of oxides and chlorophyll a (and its derivatives) were employed to correlate various sediment cores, and to propose a sedimentary filling mode for the pond. Furthermore, one sedimentary unit, which appears homogeneous but of variable size within the pond, exhibited repetitive alternations associated with tidal cycles due to a defect in the Martot dam, corresponding to 34 +/- 3 days. A chemometric approach was used to model PAHs with near-infrared hyperspectral imaging data (validation determination coefficient of 0.85, Root Mean Squared Error of Prediction of 1.64 mg/kg). This model was then applied to other cores, coupled with the sedimentary filling mode in the pond, allowing the reconstruction of the volume of PAH contamination. Thus, this study demonstrates that hyperspectral imaging is a powerful tool for estimating various contaminants in sediments: not only is it much faster than conventional chromatographic methods, it also provides a more detailed understanding of a sample, and even of a site through the correlation of multiple core samples.

Integrative metabolomics and chemometrics depict the metabolic alterations of differently processed red kidney beans (Phaseolus vulgaris L.) and in relation to in-vitro anti-diabetic efficacy.

Red kidney beans (RKB) serve as a powerhouse packed with a plethora of largely unexplored extraordinary chemical entities with potential significance. However, their nutraceutical applications as a functional hypoglycemic food still lag behind and warrant further investigation. With a scope to optimize chemical and biological traits of RKB, green modification approaches (processing methods) seem inevitable. Accordingly, the current study offered the first integrative workflow to scrutinize dynamic changes in chemical profiles of differently processed RKB and their potential entanglements on diabetes mitigation using Ultra Performance Liquid Chromatography-mass spectrometry (UPLC-MS/MS) coupled with chemometrics. Different physical and biological processing treatments namely germination, fermentation, cooking and dehulling were preliminarily implemented on RKB. Complementarily, the concomitant metabolite alterations among differently processed RKB were monitored and interpreted. Next, an in-vitro α-amylase and α-glycosidase inhibitory testing of the differently processed samples was conducted and integrated with orthogonal projection to latent structures (OPLS) analysis to pinpoint the possible efficacy compounds. A total of 72 compounds spanning fatty acids and their glycerides, flavonoids, phenolic acids, amino acids, dipeptides, phytosterols and betaxanthins were profiled. Given this analysis and compared with raw unprocessed samples, it was found that flavonoids experienced notable accumulation during germination while both fermentation and dehulling approaches sharply intensified the content of amino acids and dipeptides. Comparably, Fatty acids, phytosterols and betaxanthins were unevenly distributed among the comparable samples. Admittedly, OPLS-DA revealed an evident discrimination among the processed samples assuring their quite compositional discrepancies. In a more targeted approach, kaempferol-O-sophoroside, quercetin, carlinoside and betavulgarin emerged as focal discriminators of sprouted samples while citrulline, linoleic acid, linolenoyl-glycerol and stigmasterol were the determining metabolites in cooked samples. Our efficacy experimental findings emphasized that the different RKB samples exerted profound inhibitory actions against both α-amylase and α-glycosidase enzymes with the most promising observations in the case of sprouted and cooked samples. Coincidently, OPLS analysis revealed selective enhancement of possible efficacy constituents primarily citrulline, formononetin, gamabufotalin, kaempferol-O-sophoroside, carlinoside, oleic acid and ergosterol in sprouted and cooked samples rationalizing their noteworthy α-amylase and α-glucosidase inhibitory activities. Taken together, this integrated work provides insightful perspectives beyond the positive impact of different processing protocols on bioactives accumulation and pharmacological traits of RKB expanding their utilization as functional hypoglycemic food to rectify diabetes.

High-coverage characterization and discovery of molecular markers for quality control of natural fragrant plant extracts using UPLC-HRMS-based untargeted metabolomics.

The chemical components of natural fragrant plant extracts are of high complexity, and the strategies for quality control (QC) and further discovery of fragrance mechanisms still need to be further investigated. This study integrated the strategies and methods of untargeted metabolomics and chemometrics and statistical modeling to attain the goal. The techniques of reversed-phase and HILIC analysis of ultra-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS) were simultaneously used to collect data in both positive and negative ion modes. The pattern analysis of fingerprints and discovery of characteristic molecular markers for QC analysis were comprehensively employed to reach in-depth analysis of the quality variation and discovery of differential molecules among natural fragrant plant extracts. The former uses fingerprint technique to analyze their overall similarities and differences, and the latter comprehensively discovers molecular substances characterizing the chemical characteristics of fragrant extracts with the help of metabolomics and univariate and multivariate methods. The findings are expected to be used as the molecular markers in product manufacturing, sales, and consumption to achieve accurate quality control and recognition of targeted molecules for potential quality monitoring using spectroscopy techniques. In this work, 27 natural fragrant extracts were applied as examples, and their chemical components were comprehensively analyzed with discovery of markers for quality control. After data integration, 1178 molecules were annotated, and 267 differential metabolite molecules with the values of variable importance in the projection (VIP) larger than 1.0 were found. The results show that the method proposed in this work is of great significance for high-coverage analysis, QC marker discovery, and aroma mechanism elucidation, which has potential applications in the areas of food, cosmetics, pharmaceuticals, tobacco, and others.