Variable Importance In Projection Research Articles

Application of molecular networking to improve the compound annotation in liquid chromatography-mass spectrometry-based metabolomics analysis: A case study of Bupleuri radix.

Compound annotation is always a challenging step in metabolomics studies. The molecular networking strategy has been developed recently to organize the relationship between compounds as a network based on their tandem mass (MS2) spectra similarity, which can be used to improve compound annotation in metabolomics analysis. This study used Bupleuri Radix from different geographic areas to evaluate the performance of molecular networking strategy for compound annotation in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. The Bupleuri Radix extract was analyzed by LC-quadrupole time-of-flight MS under MSe acquisition mode. After raw data preprocessing, the resulting dataset was used for statistical analysis, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The chemical makers related to the sample growth place were selected using variable importance in projection (VIP) > 2, fold change (FC) > 2, and p < 0.05. The molecular networking analysis was applied to conduct the compound annotation. The score plots of PCA showed that the samples were classified into two clusters depending on their growth place. Then, the PLS-DA model was constructed to explore the chemical changes of the samples further. Sixteen compounds were selected as chemical makers and tentatively annotated by the feature-based molecular networking (FBMN) analysis. The results showed that the molecular networking method fully exploits the MS information and is a promising tool for facilitating compound annotation in metabolomics studies. However, the software used for feature extraction influenced the results of library searching and molecular network construction, which need to be taken into account in future studies.

Volume of detonation determination based on gaseous products of energetic materials by time resolved LIPS combined with schlieren image

The safe and fast measurement of volume of detonation (VoD) is always a hard task for macroscale explosion even though it is one of the important parameters to evaluate the explosion performance. Therefore, a promising technology to determine the VoD is highly desirable for evaluation of energetic materials. Herein, a new method of VoD determination based on gaseous products via small dose energetic materials by time correlated laser induced plasma spectroscopy (LIPS) combined with schlieren image was proposed. Hydrodynamics of products after laser ablation on a time scale ranging from microsecond to millisecond was investigated. Based on the analysis of hydrodynamics of products after laser ablation, the effective spectra of gaseous products of each energetic material are obtained. Subsequently, a high-accuracy quantitative analysis model of VoD based on gaseous products using the method of principal component analysis - partial least squares (PCA-PLS) with small sample modeling algorithm has been developed( R2&gt;0.96). The VOD model accurately predicts the detonation parameters with the average relative error of test set (ARET) &lt; 3% and the maximum relative error of test set (MRET) &lt; 5%. Moreover, the results without spectra selection of the relative error of blind data show the max relative error is less than 7%. The results of variable importance in projection (VIP) identification indicate a robust association between the spectral signatures of carbon (C), nitrogen (N), hydrogen (H), oxygen (O) and VoD. Furthermore, the N lines exert the most substantial influence on the VoD model. This method provides a new safe and fast determination technology for the evaluation of VoD and clarification of the related mechanism.

Chemometrics analysis of camellia oil authenticity using LF NMR and fatty acid GC fingerprints

Low-field nuclear magnetic resonance (LF NMR) parameters and fatty acid contents, combined with chemometrics, were employed to detect the adulteration of camellia oil (CAO) with various oils, including rapeseed oil (RO), sunflower oil (SSO), sesame oil (SEO), corn oil (COO), rice bran oil (RBO), peanut oil (PEO), palm superolein (PAO), or high oleic-peanut oil (HO-PEO). In principal component analysis (PCA), CAO was characterized by higher levels of C18:1n9 and S22, which differentiates it from other vegetable oils. Partial least squares-discriminant analysis (PLS-DA) revealed robust clustering among CAO and other samples. Adulterated CAO samples, with an adulteration level exceeding 10 %, could be accurately classified, achieving a total discrimination accuracy of over 97.15 %. Notably, when the adulteration rate surpassed 40 %, the recognition accuracy for all samples reached 100 %. The optimized partial least squares (PLS) models, relying on fewer than 18 potential key markers identified by variable importance in projection, demonstrated efficiency and precision in predicting the adulteration levels in CAO.

Screening the capability of vibrational spectroscopic techniques for simultaneous quantification of vitamins B1, B6, and B12 in a powder blend

The rising possibilities of vibrational spectroscopy coupled with multivariate modeling, enables many applications of these well-established analytical techniques, easy-to-use for rapid and non-destructive analysis. Owing to the lack of need for hazardous solvents or reagents in sample preparation, FTIR, NIR and Raman spectroscopy belong to the group of environmentally friendly techniques, known as “green analytical techniques”. Such characteristics re-introduce them into the pharmaceutical industry, especially as part of process-analytical-technology (PAT) tools for both qualitative and quantitative analysis.This research aimed to assess the potential of ATR-FTIR, NIR and Raman as a PAT for the quantification of Vitamins B1 (thiamine), B6 (pyridoxine) and B12 (cyanocobalamin) in a powder blend, using a partial least square (PLS) regression model, as well as to monitor the powder blend homogeneity.Results from each of the vibrational spectroscopy techniques were compared with those obtained by HPLC, which served as the reference analytical technique for quantifying the active substances in complex matrices using multivariate analysis. The developed PLS models were evaluated for their ability to quantify each of the active substances in the powder blend. The ATR-FTIR, NIR and Raman spectrum segments, revealed by the Variable Importance for Projection (VIP) plot for each model, respectively, were linked to the band assignment of each active ingredient.The statistical indicators of the models (interpretation rate (R2X and R2Y), predictive ability (Q2 and RMSEP) and accuracy (RMSEE)) demonstrated their suitability for in-process estimation of vitamin B1, B6 and B12 contents in powder blends. Estimating B12 content proved more challenging, likely due to sampling issues related to its low content in the powder blend. Nevertheless, the observed variability in the models aligns with the variability in results obtained by HPLC, indicating the lack of blend homogeneity. Therefore, the quantification models could be considered for a further upgrade as PAT for monitoring the powder blend homogeneity during the manufacturing of vitamin blends.

Sunflower Origin Identification Based on Multi-Source Information Fusion Technique of Kernel Extreme Learning Machine

This study constructs a model for the rapid identification of the origins of edible sunflower (Helianthus) using Kernel Extreme Learning Machine (KELM) with multi-source information fusion technology. Near-infrared spectroscopy (NIRS) and nuclear magnetic resonance spectroscopy (NMRS) were utilized to analyze 180 sunflower samples from the Xinjiang, Heilongjiang, and Inner Mongolia regions. Initially, the identification models for the origin of sunflowers using NIR and NMR data were compared between two algorithms: the Extreme Learning Machine (ELM) and KELM, combined with various spectral preprocessing methods. The experiment found that the NIR spectral model preprocessed with standard normal variate (SNV) using the KELM algorithm was the most accurate, achieving accuracies of 98.7% in the training set and 97.2% in the test set. The spin-echo NMR spectral model preprocessed with non-local means (NLMs) using the KELM algorithm was the second best, with accuracies of 98.4% in the training set and 96.4% in the test set. To further improve the accuracy of the identification models, innovative sunflower origin identification models were developed based on data layer fusion and feature layer fusion using NIRS and NMRS. In the data layer fusion model, the KELM algorithm model was optimal, achieving a test set accuracy and F1 score of 98.2% and 98.18%, respectively, an improvement of 1.0% over the best single data source model. In the feature layer fusion model, four types of feature-layer information-fusion identification models were established using two feature extraction algorithms, Competitive Adaptive Reweighted Sampling (CARS) and Variable Importance Projection (VIP), combined with joint feature and simple merging feature strategies. The CARS-KELM algorithm combined with the joint feature method was found to be the best, achieving 100% accuracy in both the training and test sets, an improvement of 2.8% over the best single data source model. Identifying the origin of edible sunflower using NIRS and NMRS is demonstrated as feasible by the results. The best single-spectrum sunflower origin identification model was achieved using the KELM algorithm with SNV preprocessing. The feature layer fusion method combining NIRS and NMRS data is suitable for handling the task of sunflower origin identification. This method significantly improves the recognition accuracy of the model compared to a single model, achieving fast and accurate origin identification of edible sunflowers. The research results provide a new method for rapid identification of sunflower origin.

Comparative investigation on the aroma profiles of edible citrus flowers in the main organs and different developmental stages

Pomelo flowers emit a strong fragrance and give aromatic odors. Volatile compounds from pomelo flowers were analyzed at three developmental stages and in the main organs by molecular sensory science. A total of 134 volatiles including 25 odorants, were analyzed by gas chromatography–mass spectrometry/pulsed flame photometric detector (GC–MS/PFPD) and multidimensional GC–MS/olfactory (MDGC–MS/O). The total volatile content varied among pomelo flowers at different developmental stages (stage-III > stage-II > stage-I) and among different organs of pomelo flowers (petal > pistil > stamen). Linalool was an important odorant with a high OAV, and floral/fruity comprised the predominant aroma profile. Four odorants, ethyl 2-methylbutanoate, linalool, β-myrcene, and 2-butenal, were selected based on variable importance in projection (VIP) values and contributed mainly to the discrimination of pomelo flowers at three different developmental stages. Linalool, β-myrcene, d-limonene, and ethyl hexanoate were potential markers for evaluating flavor differences in pomelo floral organs.

Use of GC-IMS and Stoichiometry to Characterize Flavor Volatiles in Different Parts of Lueyang Black Chicken during Slaughtering and Cutting.

Chilled and cut chicken is preferred by consumers for its safeness and readiness to cook. To evaluate the quality characteristics of various chilled chicken products, differences in volatile organic components (VOCs) of six different cut parts (breast, back, leg, heart, liver, and gizzard) of Lueyang black chicken were characterized through gas chromatography-ion mobility spectroscopy (GC-IMS) combined with stoichiometry. A total of 54 peaks in the signal of VOCs were detected by GC-IMS, and 43 VOCs were identified by qualitative analysis. There were 22 aldehydes (20.66-54.07%), 8 ketones (25.74-62.87%), 9 alcohols (4.17-14.69%), 1 ether (0.18-2.22%), 2 esters (0.43-1.54%), and 1 furan (0.13-0.52%), in which aldehydes, ketones, and alcohols were the main categories. Among the six cut parts, the relative content of aldehydes (54.07%) was the highest in the gizzard, and the relative content of ketones (62.87%) was the highest in the heart. Meanwhile, the relative content of alcohols (14.69%) was the highest in the liver. Based on a stable and reliable predictive model established by orthogonal partial least squares-discriminant analysis (OPLS-DA), 3-hydroxy-2-butanone (monomer and dimer), acetone, 2-butanone monomer, hexanal (monomer and dimer), isopentyl alcohol monomer, and n-hexanol monomer were picked out as characteristic VOCs based on variable importance in projection (VIP value > 1.0, p < 0.05). Principal component analysis (PCA) and the clustering heatmap indicated that the characteristic VOCs could effectively distinguish the six cut parts of Lueyang black chicken. The specific VOCs responsible for flavor differences among six different cut parts of Lueyang black chicken were hexanal (monomer and dimer) for the gizzard, 2-butanone monomer and hexanal dimer for the breast, hexanal monomer for the back, 3-hydroxy-2-butanone monomer for the leg, 3-hydroxy-2-butanone (monomer and dimer) for the heart, and acetone and isopentyl alcohol monomer for the liver. These findings could reveal references for quality assessment and development of chilled products related to different cut parts of Lueyang black chicken in the future.

Being Stung Once or Twice by Bees (Apis mellifera L.) Slightly Disturbed the Serum Metabolome of SD Rats to a Similar Extent.

In most cases, the number of honeybee stings received by the body is generally small, but honeybee stings can still cause serious allergic reactions. This study fully simulated bee stings under natural conditions and used 1H Nuclear Magnetic Resonance (1H NMR) to analyze the changes in the serum metabolome of Sprague-Dawley (SD) rats stung once or twice by honeybees to verify the impact of this mild sting on the body and its underlying mechanism. The differentially abundant metabolites between the blank control rats and the rats stung by honeybees included four amino acids (aspartate, glutamate, glutamine, and valine) and four organic acids (ascorbic acid, lactate, malate, and pyruvate). There was no separation between the sting groups, indicating that the impact of stinging once or twice on the serum metabolome was similar. Using the Principal Component Discriminant Analysis ( PCA-DA) and Variable Importance in Projection (VIP) methods, glucose, lactate, and pyruvate were identified to help distinguish between sting groups and non-sting groups. Metabolic pathway analysis revealed that four metabolic pathways, namely, the tricarboxylic acid cycle, pyruvate metabolism, glutamate metabolism, and alanine, aspartate, and glutamate metabolism, were significantly affected by bee stings. The above results can provide a theoretical basis for future epidemiological studies of bee stings and medical treatment of patients stung by honeybees.

Using Spectroradiometry to Measure Organic Carbon in Carbonate-Containing Soils.

This study explores the feasibility of analyzing soil organic carbon (SOC) in carbonate-rich soils using visible near-infrared spectroscopy (VIS-NIR). Employing a combination of datasets, feature groups, variable selection methods, and regression models, 22 modeling pipelines were developed. Spectral data and spectral data combined with carbonate contents were used as datasets, while raw reflectance, first-derivative (FD) reflectance, and second-derivative (SD) reflectance constituted the feature groups. The variable selection methods included Spearman correlation, Variable Importance in Projection (VIP), and Random Frog (Rfrog), while Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and Support Vector Regression (SVR) were the regression models. The obtained results indicated that the FD preprocessing method combined with RF, results in the model that is sufficiently robust and stable to be applied to soils rich in calcium carbonate.

Hybrid wavelength selection strategy combined with ATR-FTIR spectroscopy for preliminary exploration of vintage labeling traceability of sauce-flavor baijiu

The allure of substantial profits has perpetuated the illicit trade of counterfeit vintage labels for baijiu. While various approaches have been employed to intelligently ascertain the vintage of baijiu, many of them are both cost-intensive and time-consuming. This work pioneered the use of Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric analysis, offering a non-destructive and economically viable method for discriminating sauce-flavor baijiu across different aging periods (1-, 2-, and 3-year). In this research, principal component analysis (PCA) was first conducted to explore clustering trends among distinct vintage groups. Subsequently, the effect of spectral pre-processing on modeling performance was explored. For wavelength selection, four wavelength selection methods (ReliefF, random forest variable importance (RFVI), variable importance in projection (VIP), and Venn) were first used to identify the subset of candidate features that potentially best mapped the vintage labels. Immediately following this, to explore the possibility of further improving the identification capabilities of the model as well as to reduce the redundant data that may still be present, sequential backward selection (SBS) was utilized for secondary feature reduction within the subset of candidates. The amalgamation of these two techniques is termed a “hybrid wavelength selection strategy.” Additionally, the dimensionality reduction effects of PCA and kernel principal component analysis (KPCA) were compared to demonstrate the robustness of the proposed method. Finally, classification models such as partial least squares discriminant analysis (PLS-DA), random forest (RF), and grasshopper optimization algorithm-based support vector machine (GOA-SVM) were developed. The results show that the spectral data need not be pre-processed, and the proposed hybrid wavelength selection strategy can further improve the identification ability of the model. Among the many models developed, ReliefF-SBS-GOA-SVM emerged as the most proficient classification model, yielding accuracy, sensitivity, and specificity rates of 94.44%, 95.23%, and 94.44%, respectively. This method not only holds promise for the discrimination of baijiu class attributes such as brand, origin, flavor, and vintage but also exhibits potential applicability in other non-targeted identification studies involving spectroscopy methodologies.

Comparative study on chemical constituents of different medicinal parts of Lonicera japonica Thunb. Based on LC-MS combined with multivariate statistical analysis

Lonicerae japonicae flos (LJF), Lonicerae japonicae caulis (LJC), Lonicerae folium (LF) and Lonicerae fructus (LFR) are derived from Lonicera japonica Thunb., which are formed due to different medicinal parts. The efficacy of the 4 medicinal materials has similarities and differences. However, little attention has been paid to illustrate the differences in efficacy from the perspective of phytochemistry. In this study, ultra-high performance liquid chromatography coupled with hybrid quadrupole-orbitrap mass spectrometry (UPLC-Q-Exactive-Orbitrap-MS) was used to qualitatively analyze the ingredients in 4 herbs. A total of 86 compounds were plausibly or unambiguously identified, there were 54 common components among the 4 medicinal materials, and each kind of medicinal materials had its own unique components. On the basis of qualitative analysis, ultra-performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry (UPLC-QQQ-MS/MS) was used to quantitatively analyze 31 components contained in 4 medicinal materials, and principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and other multivariate statistical analysis were furtherly performed for comparing the component contents. The results showed that the samples from the same parts were clustered into one group, and the samples from different medicinal parts were significantly different. The analysis of variable importance projection (VIP) value of the OPLS-DA model showed that 10 components including chlorogenic acid, secologanic acid, isochlorogenic acid A, loganin, lonicerin, loganic acid, secoxyloganin, sweroside, luteolin and rhoifolin were the main difference components among the 4 medicinal materials. The study not only lays a solid foundation for the intrinsic quality control of 4 medicinal materials and the study of different effects of the 4 medicinal materials at the phytochemical level, but also provides a basis for more rational utilization of various parts of L. japonica and expansion of medicinal resources.

Opportunities offered by latent-based multiblock strategies to integrate biomarkers of chemical exposure and biomarkers of effect in environmental health studies

Modern environmental epidemiology benefits from a new generation of technologies that enable comprehensive profiling of biomarkers, including environmental chemical exposure and omic datasets. The integration and analysis of large and structured datasets to identify functional associations is constrained by computational challenges that cannot be overcome using conventional regression methods. Some extensions of Partial Least Squares (PLS) regression have been developed to efficently integrate multiple datasets, including Multiblock PLS (MB-PLS) and Sequential and Orthogonalized PLS; however, these approaches remain seldom applied in environmental epidemiology. To address that research gap, this study aimed to assess and compare the applicability of PLS-based multiblock models in an observational case study, where biomarkers of exposure to environmental chemicals and endogenous biomarkers of effect were simultaneously integrated to highlight biological links related to a health outcome. The methods were compared with and without sparsity coupling two metrics to support the variable selection: Variable Importance in Projection (VIP) and Selectivity Ratio (SR). The framework was applied to a case-study dataset mimicking the structure of 36 environmental exposure biomarkers (E-block), 61 inflammation biomarkers (M-block), and their relationships with the gestational age at delivery of 161 mother-infant pairs. The results showed an overall consistency in the selected variables across models, although some specific selection patterns were identified. The block-scaled concatenation-based approaches (e.g. MB-PLS) tended to select more variables from the E-block, while these methods were unable to identify certain variables in the M-block. Overall, the number of variables selected using the SR criterion was higher than using the VIP criterion, with lower predictive performances. The multiblock models coupled to VIP, appeared to be the methods of choice for identifying relevant variables with similar statistical performances. Overall, the use of multiblock PLS-based methods appears to be a good strategy to efficiently support the variable selection process in modern environmental epidemiology.

Quantitative analysis of four PAHs in oily sludge by surface-enhanced Raman spectroscopy (SERS) combined with partial least squares regression (PLS) based on a novel nano-silver-silicon coupling substrate

Polycyclic aromatic hydrocarbons (PAHs) present in oily sludge generated by the petroleum and petrochemical industries have emerged as a prominent concern within the realm of environmental conservation. The precise determination of PAHs holds immense significance in both petroleum geochemistry and environmental protection. In this study, a combination of surface-enhanced Raman spectroscopy (SERS) and solid–liquid extraction was employed for the screening of PAHs in oily sludge. Methanol was utilized as the extraction solvent for PAHs, while nanosilver-silicon coupling substrates were employed for their detection. The SERS spectrum was acquired using a portable Raman spectrometer. The nano silver-silicon coupling substrate exhibits excellent uniformity, with relative standard deviations (RSDs) of Phenanthrene, Fluoranthrene, Fluorene and Naphthalene (Phe, Flt, Flu and Nap) being 2.8%, 1.08%, 1.41%, and 5.44% respectively. Moreover, the limits of detection (LODs) achieved remarkable values of 0.542 μg/g, 0.342 μg/g, 0.541 μg/g, and 5.132 μg/g. The quantitative analysis of PAHs in oily sludge was investigated using SERS technology combined with partial least squares (PLS). The optimal PLS calibration model was optimized by combining spectral preprocessing methods and using the SiPLS (Synergy interval partial least squares)-VIP (Variable Importance in Projection) hybrid variable selection strategy. The prediction performance of the D1st (First derivative)-WT (Wavelet transform)-SiPLS-VIP-PLS model was deemed satisfactory, as evidenced by high R2P values of 0.9851, 0.9917, and 0.9925 for Phe, Flt, and Flu respectively; additionally, the corresponding MREP values were found to be 0.0580, 0.0668, and 0.0669 respectively. However, for Nap analysis, the D1st-WT-PLS model proved to be a better calibration model with an R2P value of 0.9864 and an MREP (Mean relative error of prediction) value of 0.0713. In summary, SERS technology combined with PLS based on different spectral pretreatment methods and mixed variable selection strategies is a promising method for quantitative analysis of PAHs in oily sludge, which will provide new ideas and methods for the quantitative analysis of PAHs in oily sludge.

Comparative study on the quality of wild and ecologically farmed large yellow croaker through on-site synchronous sampling from the Nanji Archipelago in the East China Sea

With consumers demanding higher quality in fish products, ecologically farmed fish are increasingly favored due to their quality being close to that of wild fish. However, the differences between the ecologically farmed and wild fish remain largely unclear. In the present study, the wild and ecologically farmed large yellow croakers of the same size were captured simultaneously from the same marine area around Nanji islands (27.479°E, 121.052°N). Then, synchronous tests were conducted to evaluate quality to clarify the key quality parameters that differentiate between the wild and farmed fish. We obtained 49 quality parameters after orthogonal partial least squares discriminant analysis (OPLS-DA) with variable importance in projection (VIP) scores >1 and p < 0.05 using 307 indicators related to 121 length ratios, 4 body color indicators, 5 muscle fiber characteristic indicators, 87 nutrition quality parameters, 40 taste parameters, and 50 volatile compounds. Compared to the farmed fish, the wild fish exhibited a more yellow skin color, higher levels of carotenoids, and a more streamlined conical shape. Changes in histology and ultrastructure of muscle fibers were noted, including increased muscle fiber density and the ratio of fiber area to gap, as well as reduced I band width, observed in the wild fish. The wild fish had high nutritional value of fatty acids and minerals and low nutritional value of amino acids, as reflected by higher levels of C22: 6n3 (DHA), Σn-3 PUFA (polyunsaturated fatty acids), Σn-3/Σn-6 PUFA, Na (sodium), K (potassium), Fe (iron), and Se (selenium), and lower values of ΣMUFA (monosaturated fatty acids), Σn-6 PUFA, TI (thrombogenic index), amino acid and chemistry scores of isoleucine, ΣEAA/ΣTAA (essential amino acid/ total amino acid), and ΣTAA/ΣNEAA (total amino acid/non-essential amino acid) in the muscles of the wild fish. The wild fish muscle showed high levels of hydrolyzed glycine and proline and free methionine, and low levels of free glycine and proline, indicating a propensity for collagen synthesis. Despite significant differences in the contents of fourteen flavor substances of free amino acids, 5′-nucleotides, and volatile compounds, their concentrations were notably below the corresponding threshold and contributed minimally to flavor, suggesting that the wild and farmed fish shared similar flavor characteristics. In summary, this study reveals the quality differences between ecologically farmed and wild large yellow croaker based on on-site synchronous sampling, providing a theoretical foundation for further enhancing the quality of this species.

Unravelling the metabolomic diversity of pigmented and non-pigmented traditional rice from Tamil Nadu, India

Rice metabolomics is widely used for biomarker research in the fields of pharmacology. As a consequence, characterization of the variations of the pigmented and non-pigmented traditional rice varieties of Tamil Nadu is crucial. These varieties possess fatty acids, sugars, terpenoids, plant sterols, phenols, carotenoids and other compounds that plays a major role in achieving sustainable development goal 2 (SDG 2). Gas-chromatography coupled with mass spectrometry was used to profile complete untargeted metabolomics of Kullkar (red colour) and Milagu Samba (white colour) for the first time and a total of 168 metabolites were identified. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), Orthogonal Partial Least Square Discrimination Analysis (OPLS-DA) and Heat map analysis. OPLS-DA identified 144 differential metabolites between the 2 rice groups, variable importance in projection (VIP) ≥ 1 and fold change (FC) ≥ 2 or FC ≤ 0.5. Volcano plot (64 down regulated, 80 up regulated) was used to illustrate the differential metabolites. OPLS-DA predictive model showed good fit (R2X = 0.687) and predictability (Q2 = 0.977). The pathway enrichment analysis revealed the presence of three distinct pathways that were enriched. These findings serve as a foundation for further investigation into the function and nutritional significance of both pigmented and non-pigmented rice grains thereby can achieve the SDG 2.

Key Aroma Differences in Volatile Compounds of Aged Feng-Flavored Baijiu Determined Using Sensory Descriptive Analysis and GC×GC-TOFMS.

Sensory descriptive analysis of aged feng-flavored Baijiu liquor indicated notable differences in samples of different ages. The samples of freshly distilled Baijiu and those with shorter storage times exhibit bran and fresh green flavors, whereas, with increasing storage time, honey, sweet, and floral fragrances are gradually enhanced. Samples of feng-flavored Baijiu were prepared using headspace solid-phase microextraction, followed by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry. A total of 496 compounds were identified in all samples, mainly categorized as 14 groups of substances, including esters and aldehydes. Interestingly, 42 of these substances were found in Feng-flavored Baijiu for the first time. Chemometrics was used to analyze the key differential compounds. First, 143 differential compounds closely related to aging were preliminarily screened, and principal component analysis revealed that these compounds were separated by baijiu age. Then, 65 differential compounds were selected by partial least squares discriminant analysis. Furthermore, 43 key differential compounds were selected by combined analysis with variable importance in projection and Pearson correlation coefficients. Partial least squares regression was used to study the correlation between the sensory properties and key differential compounds, and the results indicated that most compounds were closely related to the aging period of the Baijiu. The results of this study provide a theoretical basis and reference for flavor research on feng-flavored Baijiu.

An efficient method for rapid screening of triterpenoid saponins in three Glycyrrhiza species using rapid resolution liquid chromatography quadrupole time-of-flight mass spectrometry combined with mass defect filtering

Triterpenoid saponins, a major bioactive component of liquorice, possess high hydrophilicity and often co-occur with other impurities of similar polarity. Additionally, subtle structural differences of some triterpenoid saponins bring challenges to comprehensive characterisation. In this study, triterpenoid saponins of three Glycyrrhiza species were systematically analysed using rapid resolution liquid chromatography quadrupole time-of-flight mass spectrometry (RRLC-Q-TOF-MS) coupled with mass defect filtering (MDF). Firstly, comprehensive date acquisition was achieved using RRLC-Q-TOF-MS. Secondly, a polygonal MDF method was established by summarizing known and speculated substituents and modifications based on the core structure to rapidly screen potential triterpenoid saponins. Thirdly, based on the fragmentation patterns of reference compounds, an identification strategy for characterisation of triterpenoid saponins was proposed. The strategy divided triterpenoid saponins into three distinct classes. By this strategy, 98 triterpenoid saponins including 10 potential new ones were tentatively characterised. Finally, triterpenoid saponins of three Glycyrrhiza species were further analysed using principle component analysis (PCA) and orthogonality partial least squares discriminant analysis (OPLS-DA). Among these, 18 compounds with variable importance in projections (VIP) > 1.0 and P values < 0.05 were selected to distinguish three Glycyrrhiza species. Overall, our study provided a reference for quality control and rational use of the three species.

Advances in the individual authentication of cocoa beans: Vis/NIR spectroscopy as a tool to distinguish fermented from unfermented beans and classify genotypes in the eastern Amazonia

Reliable analytical methods to authenticate high-quality and economically valuable cocoa beans are highly desirable, and NIR spectroscopy stands out as a rapid and non-destructive alternative. This study employs NIR for the authentication and differentiation of 19 whole Forastero cocoa beans from Eastern Amazonia (Pará, Brazil), based on their fermentation status and genetic profiles. Partial Least Squares Discriminant Analysis (PLS-DA) models in wavelength ranges of 400–700 nm, 1400–1600 nm, and 1900–2500 nm, demonstrated high sensitivity and specificity in discriminating fermented from unfermented beans, regardless of genotype. Various compounds, including proteins, lipids, carbohydrates, anthocyanins, and theobromine, provide crucial insights into the spectral regions essential for distinction. The variable importance in projection (VIP) score value greater than 1 was used to select relevant variables, and Linear Discriminant Analysis (LDA) was performed in both the visible (472 nm and 636 nm) and infrared ranges (2096 nm and 2278 nm), demonstrating that absorbances at two specific wavelengths are sufficient for discrimination. The t-distributed stochastic neighbor embedding (t-SNE) indicated a segregation trend of the genotypes based on classification by major genetic groups in unfermented beans, suggesting that the biochemical characteristics shared by them are more prominent before fermentation. The PLS-DA models based on complete vis/NIR spectra showed comparable results in discriminating the 19 cocoa genotypes in both fermented (0.14% prediction error) and unfermented beans (0.16% prediction error). The model's classification errors can be attributed to shared genetic ancestry among the samples, primarily in unfermented beans. This research corroborates the effectiveness of vis/NIR spectroscopy as a straightforward tool for whole cocoa bean authentication, providing rapid insights into genetic diversity regardless of their fermentation state.

Analysis of differential metabolites of spikes of Prunella vulgaris at different stages based on UPLC-MS/MS

Prunella vulgaris, aptly named for its withering at the summer solstice, displays significant variation in quality arising from differing harvest time. However, research on the chemical composition changes of its spikes at various stages is limited, and the specific metabolites remain unclear. In order to elucidate the metabolites and metabolic pathways of the spikes of P. vulgaris, the current study deployed ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS) and targeted metabolomics to characterize the compound variability in the spikes of P. vulgaris across different periods. Multivariate statistical techniques such as principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) were used to identify the differences in metabolites, and relevant metabolic pathways were analyzed. A total of 602 metabolites were identified by metabolomics, of which organic acids and their derivatives were the most abundant, followed by flavonoids. Multiple differential metabolites, including p-hydroxybenzoic acids and gallic acids were identified based on variable importance in projection(VIP)&gt;1 and P&lt;0.05. The results of enrichment analysis suggested that isoflavonoids biosynthesis, aminobenzoate degradation, benzoate degradation, anthocyanins biosynthesis, metabolic pathways, microbial metabolism in different environments, secondary plant metabolite biosynthesis, tryptophan metabolism, and phenylpropanoid synthesis were the main metabolic pathways. These results intend to elucidate the dynamic changes of differential metabolites of P. vulgaris and provide a theoretical basis for further study of the harvesting mechanism of spikes of P. vulgaris.

Untargeted metabolomics using liquid chromatography-high resolution mass spectrometry and chemometrics for analysis of non-halal meats adulteration in beef meat.

The adulteration of raw beef (BMr) with dog meat (DMr) and pork (PMr) becomes a serious problem because it is associated with halal status, quality, and safety of meats. This research aimed to develop an effective authentication method to detect non-halal meats (dog meat and pork) in beef using metabolomics approach. Liquid chromatography-high resolution mass spectrometry (LC-HRMS) using untargeted approach combined with chemometrics was applied for analysis non-halal meats in BMr. The untargeted metabolomics approach successfully identified various metabolites in BMr DMr, PMr, and their mixtures. The discrimination and classification between authentic BMr and those adulterated with DMr and PMr were successfully determined using partial least square-discriminant analysis (PLS-DA) with high accuracy. All BMr samples containing non-halal meats could be differentiated from authentic BMr. A number of discriminating metabolites with potential as biomarkers to discriminate BMr in the mixtures with DMr and PMr could be identified from the analysis of variable importance for projection value. Partial least square (PLS) and orthogonal PLS (OPLS) regression using discriminating metabolites showed high accuracy (R2>0.990) and high precision (both RMSEC and RMSEE <5%) in predicting the concentration of DMr and PMr present in beef indicating that the discriminating metabolites were good predictors. The developed untargeted LC-HRMS metabolomics and chemometrics successfully identified non-halal meats adulteration (DMr and PMr) in beef with high sensitivity up to 0.1% (w/w). A combination of LC-HRMS untargeted metabolomic and chemometrics promises to be an effective analytical technique for halal authenticity testing of meats. This method could be further standardized and proposed as a method for halal authentication of meats.