Variable Importance In Projection Values Research Articles

Characterization of the Key Aroma Volatile Compounds in Nine Different Grape Varieties Wine by Headspace Gas Chromatography-Ion Mobility Spectrometry (HS-GC-IMS), Odor Activity Values (OAV) and Sensory Analysis.

During this study, the physicochemical properties, color, and volatile aroma compounds of the original wines produced from the grape varieties ‘Hassan’, ‘Zuoshaner’, ‘Beibinghong’, ‘Zuoyouhong’, ‘Beta’, ‘Shuanghong’, ‘Zijingganlu’, ‘Cabernet Sauvignon’, and ‘Syrah’ were determined and sensory evaluation was performed. Results indicated that ‘Hassan’ contained the most solids, ‘Zuoshaner’ produced the most total acid, residual sugar, total anthocyanin, and total phenol, and ‘Shuanghong’ produced the most tannin. Calculation of the chroma and hue of the wines according to the CIEL*a*b* parameters revealed that the ‘Cabernet Sauvignon’ wines were the brightest of the nine varieties and that the ‘Zuoshaner’ wines had the greatest red hue and yellow hue and the greatest saturation’. A total of 52 volatile compounds were identified and quantified in nine wine samples by HS-GC-IMS analysis, with the most significant number of species detected being 20 esters, followed by 16 alcohols, 8 aldehydes, four ketones, one terpene, and one furan, with the highest total volatile compound content being ‘Beta’. A total of 14 volatile components with OAV (odor activity value) >1 were calculated using the odor activity value (OAV) of the threshold of the aromatic compound, and the OPLS-DA analysis was performed by orthogonal partial least squares discriminant analysis (OPLS-DA) using the OAV values of the compounds with OAV values >1 as the Y variable. The VIP (Variable Importance in Projection) values of six compounds, ethyl isobutyrate, ethyl hexanoate-D, 2-methylpropanal, ethyl octanoate, ethyl butanoate-D, and Isoamyl acetate-D, were calculated to be higher than one between groups, indicating that these six compounds may influence aroma differences. It is essential to recognize that the results of this study have implications for understanding the quality differences between different varieties of wines and for developing wines that have the characteristics of those varieties.

Comprehensive two-dimensional gas chromatography mass spectrometry-based untargeted metabolomics to clarify the dynamic variations in the volatile composition of Huangjiu of different ages.

Aging plays an important role in the formation of aroma characteristics of Huangjiu, a traditional Chinese alcoholic beverage. Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-qMS)-based untargeted metabolomics combined with a multivariate analysis was used to investigate the dynamic variations in the aroma profile of Huangjiu during aging process and to establish the relationship between the changing volatile metabolite profiles and the age-dependent sensory attributes. A total of 144 volatile metabolites were identified by GC×GC-qMS and 63 were selected as critical metabolites based on variable importance in projection values and p-values. Based on the results of principal component analysis, orthogonal partial least-squares discriminant analysis, and hierarchical clustering analysis, the samples of six different ages were divided into three groups: 1Y and 3Y samples, 5Y and 8Y samples, and 10Y and 15Y samples. The partial least-squares analysis results further revealed the relationship between the aromas attributes and variations of these volatile compounds. The high esters, aldehydes, and lactones contents contributed to the high intensities of the sweet and ester aroma attributes of the aged Huangjiu, while the high alcohols and ethyl esters contents contributed to the alcoholic and fruity aroma attributes of the newly brewed Huangjiu. These results improve our understanding of the chemical nature of the aroma characteristics of aged Huangjiu. PRACTICAL APPLICATION: Huangjiu is often labeled with its age as a measure of quality, which influences consumers' choice. Dynamic variations in volatile compounds of Huangjiu during aging and its contribution to the aroma characteristics of Huangjiu were figured out, which will assist the industry to produce better quality aged Huangjiu for consumers.

Identification of Serum-Predictive Biomarkers for Subclinical Mastitis in Dairy Cows and New Insights into the Pathobiology of the Disease.

Targeted direct injection/liquid chromatography coupled to tandem mass spectrometry-based metabolomics was employed to identify metabolite alterations that could differentiate subclinical mastitis (SCM) from control (CON) dairy cows at -8, -4, disease diagnosis, +4 and +8 wks relative to parturition. We identified and measured 128 metabolites in the serum. Univariate analysis revealed significant alterations of serum metabolites at all five time points studied. By applying multivariate analyses including principle component analysis and partial least squares-discriminant analysis, some of the metabolites were found to have the strongest power for discriminating the SCM from CON cows. The top five metabolites with the greatest variable importance in projection values were selected as potential biomarkers for SCM. A set of five serum metabolites including lysine, ornithine, isoleucine, LysoPC a C17:0, and leucine at -8 wks and five other metabolites including lysine, leucine, isoleucine, kynurenine, and sphingomyelin (SM) C26:0 at -4 wks prepartum were determined as predictive biomarkers for SCM, which provided highly predictive capabilities with AUC (area under the curve) at 1.00. Five metabolites including lysine, leucine, isoleucine, kynurenine, and SM C26:1 in the serum were identified as diagnostic biomarkers for SCM with the AUC of 1.00. Moreover, we observed that distinct metabolic pathways were affected in SCM cows including lysine degradation, biotin, cysteine, methionine, and glutathione metabolism, valine, leucine, and isoleucine biosynthesis and degradation, and aminoacyl-tRNA biosynthesis prior to and during the occurrence of the disease. Results of this study showed that metabolomics analyses can be used to identify susceptible cows to SCM starting from -8 and -4 wks prepartum and that blood can be used to diagnose cows with SCM.

Metabolomic Profiling of Amino Acids in Human Plasma Distinguishes Diabetic Kidney Disease From Type 2 Diabetes Mellitus.

Background: Diabetic kidney disease (DKD) is a highly prevalent complication in patients with type 2 diabetes mellitus (T2DM). Patients with DKD exhibit changes in plasma levels of amino acids (AAs) due to insulin resistance, reduced protein intake, and impaired renal transport of AAs. The role of AAs in distinguishing DKD from T2DM and healthy controls has yet to be elucidated. This study aimed to investigate the metabolomic profiling of AAs in the plasma of patients with DKD.Methods: We established an ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method to detect the plasma levels of the 20 AAs in healthy controls (n = 112), patients with T2DM (n = 101), and patients with DKD (n = 101). The key AAs associated with DKD were identified by orthogonal partial least-squares discriminant analysis (OPLS-DA) models with loading plots, shared and unique structures (SUS) plots, and variable importance in projection (VIP) values. The discrimination accuracies of these key AAs were then determined by analyses of receiver-operating characteristic (ROC) curves.Results: Metabolomic profiling of plasma revealed significant alterations in levels of the 20 AAs in patients with DKD when compared to those in either patients with T2DM or healthy controls. Metabolomic profiling of the 20 AAs showed a visual separation of patients with DKD from patients with T2DM and healthy controls in OPLS-DA models. Based on loading plots, SUS plots, and VIP values in the OPLS-DA models, we identified valine and cysteine as potential contributors to the progression of DKD from patients with T2DM. Histidine was identified as a key mediator that could distinguish patients with DKD from healthy controls. Plasma levels of histidine and valine were decreased significantly in patients with DKD with a decline in kidney function, and had excellent performance in distinguishing patients with DKD from patients with T2DM and healthy controls according to ROC curves.Conclusion: Plasma levels of histidine and valine were identified as the main AAs that can distinguish patients with DKD. Our findings provide new options for the prevention, treatment, and management of DKD.

Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms

With the availability of high-resolution data due to sensor technology advancement, it is now easier for researchers and scientists to detect or view the spectral variability of different crops. For this study, Leaf chlorophyll content (LCC) and Leaf area index (LAI) of the crops Maize (Zea mays), Mustard (Brassica), and pink Lentils (Lens esculenta) under different irrigation and fertilizer treatments have been analyzed. In total, rigorous assessment of 25-hyperspectral vegetation indices (VIs) at both leaf and canopy level for chlorophyll content, whereas 7- hyperspectral VIs for LAI at canopy level were computed to investigate the robustness of these VIs for LCC and LAI assessment. Variable importance in projection (VIP) using Partial Least Square regression (PLSR) and coefficient of determination (R2) were computed for all the VIs to extract the most sensitive information for the retrieval of LCC and LAI. As a result, the VIs using the red-edge reflectance bands at 705 and 750 nm were found highly responsive to LAI compared to other wavebands. In contrast, the VIs indices made of green (550 nm), red (670, 690, and 700 nm), and red-edge (705, 750 nm) bands were found highly sensitive to the temporal LCC values of lentils and maize crop beds. In addition, the temporal LCC values of Mustard crop beds’ were found sensitive to the VIs made of green (550 nm), red (670, 690, and 700 nm), and NIR (800 nm) wavebands. The three VIs having high VIP and R2 values were selected as optimum sets of input to build support vector regression models using radial (SVR-Rad), linear (SVR-Li), polynomial (SVR-Poly), Random Forrest Regression (RFR), Partial least square regression (PLSR), and Hybrid neural fuzzy inference system (HyFIS). The analysis showed that the SVR-Rad model outperformed the SVR-Li, SVR-Poly, RFR, PLSR, and HyFIS models in terms of robustness for biophysical and biochemical parameters retrieval using hyperspectral data.

Quality and volatile compound analysis of shrimp heads during different temperature storage

This study aimed to investigate volatile compounds and quality traits of shrimp heads stored at 20 °C, 4 °C, −3 °C, and −18 °C. With increased storage time, sensory scores gradually decreased, while pH and TVB-N content showed a gradually increase trend. L* showed a decreasing and then increasing tendency. The radar chart and principal component analysis showed variation changes. Three compounds including 2-decanone, dimethyl disulphide and dimethyl tetrasulphide, four compounds including 2-pentanone, 3-methyl-1-butanol, 2-methylbutyric acid, and 2,3,5-trimethylpyrazine, and 3-methylbutyraldehyde were the characteristic volatiles for the samples stored at 20 °C, 4 °C, and −3 °C, respectively. Twenty-five volatile compounds were key volatile compounds, among which nine were potential classification compounds with high variable importance in projection values. Trimethylamine and 2-nonanol were selected as potential markers of spoilage. The study provides the theoretical basis for quality and volatile compound investigations for shrimp heads with further high-quality utilization.

Neuropsychological Domains Altered at the Early Stage of Cognitive Decline

Objective: A neuropsychological battery is a gold standard for thorough cognitive evaluation. However, it could not be used as a screening test due to its long inspection time and high cost. This study is conducted to identify specific cognitive subdomains that alter even at the early stage of cognitive decline. Methods: Neuropsychological battery results of 575 persons who visited memory clinic were analysed. We classified stages of cognitive decline according to Clinical Dementia Rating (CDR) and CDR-Sum of boxes (CDR-SB). To explore which subdomains can sensitively distinguish normal cognition from the early cognitive impairment, we used Variable Importance in Projection (VIP) in addition to analysis of covariance. Results: We found that scores in subdomain tests such as Rey Complex Figure Test immediate and delayed recall successfully differentiate persons with CDR 0 from those with CDR 0.5 and also persons with CDR-SB 0 from those with CDR-SB 0.5 as indicated by VIP values greater than 1. Conclusion: These results suggest that alterations in visuospatial memory function might be a sensitive sign reflecting the early cognitive decline. The combination of only a few subtests could be also used as a reliable and sensitive screening tool to detect early cognitive impairment.

Identifying the effectual-combination ingredients of Zhi-zi-Hou-po decoction based on metabolic difference-oriented network regulation strategy

Zhi-zi-Hou-po decoction (ZZHPD) has been used to treat depression in the clinic for thousand years in China. However, the pharmacodynamic substance of ZZHPD is still not totally clear due to its complex components. The objective of this study was to identify the effectual combination ingredients (ECIs) of ZZHPD, which could represent the antidepressant effect of the original formula. Firstly, differential plasma absorbed components with different variable importance in projection (VIP) value in chronic unpredictable mild stress (CUMS)-induced depression and control rat were revealed by untargeted metabolomics-driven strategy based on HPLC-ESI-TOF/MS, XCMS online and SIMCA-p software. Secondly, network topology scores (NTS) of plasma absorbed components were exposed by protein–protein interaction (PPI) network analysis based on components-related genes and depression-related genes, which were performed by network pharmacology tools. Finally, the ECIs were screened by considered of VIP value and NTS. Then the bioactivity was evaluated by cell viability and expression of glial fibrillary acid protein (GFAP), tumor necrosis factor α (TNFα) and interleukin 1β (IL-1β) of a lipopolysaccharide-induced astrocyte depression model. An effective combination composed with 12 components was filtrated as ECIs of ZZHPD, exposed to which the cell viability effect, the expression of GFAP and IL-1β in astrocytes were essentially equivalent with original ZZHPD (p > 0.05), and that both characteristic constituents and trace compounds of ZZHPD might exert synergistic actions through multi-targets. The result of this study provided useful information for the clinical application and modern development of ZZHPD, and the proposed strategy could be regard as an alternative solution for effective combination screening of herbal medicines.

Application of an innovative metabolomics approach to discriminate geographical origin and processing of black pepper by untargeted UHPLC-Q-Orbitrap-HRMS analysis and mid-level data fusion

An untargeted metabolomics approach based on ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) fingerprinting was applied to investigate the metabolic differences of black pepper among three geographical origins (Sri Lanka, Vietnam, and Brazil) and two post-harvest processing (sterilized and non-sterilized spice). Principal component analysis (PCA) was employed to assess the overall clustering of samples, whereas supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was effectively used for discrimination purposes. OPLS-DA models were fully validated (R2Y and Q2 values > 0.5) and the variable importance in projection (VIP) approach was employed to provide valuable data about differential metabolites with high discrimination potential (8 markers were putatively identified). For origin differentiation, three markers were highlighted with VIP values > 1.5 (i.e. reynosin, artabsinolide D, and tatridin B). Fatty acid derivates were the most frequent markers within the metabolites annotated for processing discrimination (e.g. 10,16-dihydroxyhexadecanoic acid and 9-hydroperoxy-10E-octadecenoic acid). Additionally, different combinations of mid-level data fusion of chromatographic-mass spectrometric techniques (UHPLC and gas chromatography coupled to HRMS) and proton nuclear magnetic resonance spectroscopy (1H NMR) were evaluated for the first time for geographical and processing discrimination of black pepper. The NMR-UHPLC-GC mid-level fused model was preferred among the tested fusion approaches since good sample clustering and no misclassification were achieved. Enhanced correct classification rate was achieved by mid-level data fusion compared with the findings obtained for one of the individual techniques (1H NMR fingerprinting) (from 92% to 100% of samples correctly classified). This study opens the path to new metabolomics approaches for black pepper authentication and quality control.

Non-targeted LC-MS and CE-MS for biomarker discovery in bioreactors: Influence of separation, mass spectrometry and data processing tools

Liquid separation coupled to mass spectrometry is often used for non-targeted analyses in various fields, such as metabolomics. However, the combination of non-standardized methods, various mass spectrometers (MS) and processing tools for data evaluation affect biomarker discovery potentially. Here, we present a comprehensive study of these factors based on non-targeted liquid chromatography coupled to time-of-flight (TOF) and Orbitrap MS and capillary zone electrophoresis to Orbitrap analyses of the same bioreactor samples, describing the correlation of its gas yield with changing feature signal intensity. The three datasets were processed with MZmine 2 and XCMS online and subsequential Partial Least Square Regression (PLSR) with Variable Importance in Projection (VIP) ranking for feature prioritization. The six feature tables were compared to evaluate their overlap of shared features and the influence of the processing software and MS instrument on the VIP values and fold changes. The overlaps, defined as a fraction of one feature table found in the comparative table, were from 27% to 57% for the comparison of MZmine and XCMS and from 15% to 50% between Orbitrap and TOF data sets, respectively. Considering the most relevant features only (VIP >1.5), the overlaps were increased significantly in all cases from 26% to 95%. For the same data set, both VIP values and fold changes were well correlated, however, varied significantly between TOF and Orbitrap. CE-MS showed higher total feature numbers compared to LC-MS, most likely due to its more appropriate selectivity, different sample preparation, and/or the sensitive nano-ESI interface. Since only less than 10% of MS/MS data overlapped, CE-MS provided complementary information to LC-MS. Overall, our systematic study proves the benefits of using different separation techniques and processing tools but also indicates a significant influence of mass spectrometry on comprehensive biomarker discovery.

Simultaneous quantification of 24 aldehydes and ketones in oysters (Crassostrea gigas) with different thermal processing procedures by HPLC-electrospray tandem mass spectrometry

Aldehydes and ketones are secondary oxidation products resulting from lipid oxidation that occurs during food processing. These small molecule compounds not only have an impact on the quality, odor and flavor of food, but also play a role in the pathogenesis of many human diseases. In this study, a HPLC-MS/MS analytical method was developed and validated for the simultaneous determination of 24 aldehydes and ketones. The coefficients of determination (R2) for all aldehydes and ketones were higher than 0.9975 at the range of 0.2–2000 ng/mL. The recoveries were in the range 71.20–108.13% with RSD < 10%. The method was tested by analyzing lipids from oysters with different thermal processing (boiling, frying, roasting and air frying) procedures; the highest concentration for saturated aldehydes and ketones while the highest content of unsaturated aldehydes in boiling treatment. Meanwhile, fatty acid oxidative decomposition was in agreement with aldehydes and ketones formation. Moreover, principal component analysis, orthogonal partial least-squares discriminant analysis and variable importance in projection value showed that lipid oxidation is positively related to the formation of a variety of aldehydes and ketones.

Untargeted metabolomics analysis by gas chromatography/time-of-flight mass spectrometry of human serum from methamphetamine abusers.

Methamphetamine (METH) abuse has become a global public health problem. However, the potential mechanisms involving METH-induced metabolic disorders have thus far remained poorly understood. Metabolomics can provide a clue for the cause of apparent changes and consequently be used to investigate the METH-induced dysregulation of metabolite expression and the mechanism of metabolic disorder mechanism. This laboratory investigation included 80 METH abusers and 80 healthy people. The serum metabolites were detected and analysed by gas chromatography/time-of-flight mass spectrometry. Raw data were processed with the software MS DIAL, which includes deconvolution, peak alignment and compound identification. The data matrix was processed by univariate and multivariate analyses for significant metabolite screening with the criteria of variable importance in projection values > 1, fold change > 1.5 and the t test (p value < 0.05). Significant differences in 16 metabolites (deoxycholic acid, cholic acid, hydroxylamine, etc.) in serum were found between the METH abuse group and the control group. Energy metabolic pathways and several amino acid metabolic pathways (alanine, aspartic acid and glutamate metabolism and tryptophan metabolism) were primarily involved. Further analysis indicated that the area under the receiver operating characteristic curve (AUC) was 0.998 for these 16 metabolites. Among the metabolites, three carbohydrates (d-ribose, cellobiose and maltotriose) had an AUC of 0.975, which were determined as potential markers of abuse. We observed metabolic disturbances in METH abusers, particularly perturbation in energy metabolism and amino acid metabolism, which can provide new insights into the search for biomarkers and the mechanisms underlying the adverse effects of METH on human health.

Metabolic analysis of early nonalcoholic fatty liver disease in humans using liquid chromatography-mass spectrometry

BackgroundNonalcoholic fatty liver disease (NAFLD) is a common metabolic disease that affects 20–30% of individuals worldwide. Liver puncture remains the gold standard for the diagnosis of liver diseases despite limitations regarding invasive nature and sample variability. It is of great clinical significance to find noninvasive biomarkers to detect and predict NAFLD.ObjectiveThe aims of this study were to identify potential serum markers in individuals with early-stage NAFLD and to advance the mechanistic understanding of this disease using a high-throughput mass spectrometry-based untargeted metabolomics approach.MethodsOne hundred and twelve patients with early-stage NAFLD aged 18–55 were recruited according to the guidelines. The control group included 112 healthy participants. The demographic, anthropometric, clinical and laboratory data of all participants were systematically collected. Serum samples were obtained after an overnight fast. The comprehensive serum metabolomic analysis was performed by ultra-performance liquid chromatography-Orbitrap mass spectrometry. The resultant data was processed by Compound Discover and SIMCA-P software to validate the potential biomarkers. Significantly altered metabolites were evaluated by variable importance in projection value (VIP > 1) and ANOVA (p < 0.01). Pathway analysis was performed using MetaboAnalyst 4.0.ResultsThe liver function test of early NAFLD patients showed no statistical differences to control group (p > 0.05). However, obvious differences in blood lipids were observed between subjects with NAFLD and controls (p < 0.001). In total, 55 metabolites showed significant changes in experimental group were identified. The area under curve (AUC) values deduced by receiver operating curve (ROC) analysis indicated that these newly identified biomarkers have high predictability and reliability. Of these, 15 metabolites with AUC greater than 0.9 were of great diagnostic value in early NAFLD patients.ConclusionIn this study, a total of 15 serum metabolites were found to strongly associate with early NAFLD. These biomarkers may have great clinical significance in the early diagnosis of NAFLD, as well as to follow response to therapeutic interventions.

Volatilomics for halal and non-halal meatball authentication using solid-phase microextraction–gas chromatography–mass spectrometry

The adulteration of beef meatballs with wild boar (Sus scrova) meat or chicken may be undertaken for economic reasons. This adulteration is a very sensitive issue, particularly for Muslim consumers, as the consumption of wild boar is strictly prohibited by Islamic law. This study aimed to discriminate volatile compounds in meatballs made from beef, chicken, and wild boar and mixtures thereof using solid-phase microextraction–gas chromatography–mass spectrometry (SPME/GC–MS) and multivariate data analysis. SPME is a non-destructive method for the extraction of volatile compounds and does not alter the original chemical composition of the volatiles. A validated partial least squares discriminant analysis (PLS-DA) model with three classes was used to uncover the discriminating volatiles of each type of meatball. The results indicated that β-cymene, 3-methyl-butanal, and 2-pentanol were among the positive discriminating volatiles with the highest variable importance in projection (VIP) values among the chicken meatballs. The highest VIP positive discriminating volatiles in the beef meatballs were 5-ethyl-m-xylene, benzaldehyde, and 3-ethyl-2-methyl-1,3-hexadiene. The mixed meatballs exhibited an interesting profile, with all appearing in the same group as the pure wild boar meatballs. However, the discriminating volatiles derived from a separate PLS-DA model indicated that they contained different compounds. In the pure wild boar meatballs, six compounds (pentanal, 2,6-dimethylcyclohexanone, 1-undecanol, cyclobutanol, 2,4,5-trimethyl-thiazole, and 5-ethyl-3-(3-methyl-5-phenyl pyrazol-1-yl)-1,2,4-triazol-4-amine) were identified as discriminating volatile compounds with the highest VIP values. These compounds were consistently found as significant discriminating volatile compounds in mixture meatballs group although with different VIP value. This research demonstrated that SPME-GC/MS combined with multivariate data analysis was a fast and reliable method for differentiating meatballs made from beef, chicken, and wild boar meat based on their volatile compound contents.

Effect of unsaturated fatty acids on glycation product formation pathways

Glycation and lipid oxidation in high-nutrient foods are closely related and exhibit complex interactions. To evaluate the effect of unsaturated fatty acids (UFAs) on glycation pathways, glycation products in glucose-lysine-UFA models were detected by ultra performance liquid chromatography-tandem mass spectrometry and electron spin resonance spectroscopy, together with multivariate data analysis. Results indicated that UFAs inhibited glucose oxidation by decreasing the contents of carbonyl compounds about 73.85–86.19%. UFAs promoted the formation of glycation products mainly via production of active radical. In three models, linoleic acid (LA) exhibits stronger glycation activity than oleic acid and eicosapentaenoic acid. LA significantly promoted radical formation, as well as the formation and degradation of fructosyllysine (FL), the signal intensity of active radical increased 647.45% and FL increased 78.73%. The comparison of E(k3), E(k7) and variable importance in projection values of orthogonal projections to latent structures discriminant analysis in three models also proved these conclusions. By studying the characteristics of LA on glycation in three UFA, we hypothesized that unsaturation is not the key factor in evaluating their effects on glycation, the radical activity, UFA solubility, spatial structure and interaction should be considered as potentially important factors.

The Tapio Decoupling Principle and Key Strategies for Changing Factors of Chinese Urban Carbon Footprint Based on Cloud Computing.

The expansion of Xi’an City has caused the consumption of energy and land resources, leading to serious environmental pollution problems. For this purpose, this study was carried out to measure the carbon carrying capacity, net carbon footprint and net carbon footprint pressure index of Xi’an City, and to characterize the carbon sequestration capacity of Xi’an ecosystem, thereby laying a foundation for developing comprehensive and reasonable low-carbon development measures. This study expects to provide a reference for China to develop a low-carbon economy through Tapio decoupling principle. The decoupling relationship between CO2 and driving factors was explored through Tapio decoupling model. The time-series data was used to calculate the carbon footprint. The auto-encoder in deep learning technology was combined with the parallel algorithm in cloud computing. A general multilayer perceptron neural network realized by a parallel BP learning algorithm was proposed based on Map-Reduce on a cloud computing cluster. A partial least squares (PLS) regression model was constructed to analyze driving factors. The results show that in terms of city size, the variable importance in projection (VIP) output of the urbanization rate has a strong inhibitory effect on carbon footprint growth, and the VIP value of permanent population ranks the last; in terms of economic development, the impact of fixed asset investment and added value of the secondary industry on carbon footprint ranks third and fourth. As a result, the marginal effect of carbon footprint is greater than that of economic growth after economic growth reaches a certain stage, revealing that the driving forces and mechanisms can promote the growth of urban space.

Controlling gully- and revegetation-induced dried soil layers across a slope–gully system

The introduction of exotic plants and improper management strategies with regard to plant species can change the soil-water balance of deep soils, which in turn results in the formation of a dried soil layer (DSL) within the soil profile. The Loess Plateau (LP) of China has a complex terrain; however, only a few studies have evaluated the effects of the gully-induced DSL patterns, especially in hilly and gully regions of the northern LP. In this study, we collected soil-water content data to a depth of 5 m at 40 sampling sites in a slope–gully system to investigate and characterize DSLs and their spatial patterns. Results show that the DSL indices vary greatly in different slope positions. The thickness of DSLs (DSLT) and quantitative index (QI) in the gully were significantly (p < 0.05) higher than those in the non-gully areas. The relative contribution of soil properties was higher than those of terrain factors in the gully, whereas the contribution of terrain factors was higher than those of soil properties under shrubland. Gullies contributed to the complex spatial DSL patterns in the slope–gully system. Partial least squares regression (PLSR) was used to detect the relative significance of 10 selected environmental factors that affect spatial DSL patterns. Variable importance in projection (VIP) demonstrated that soil properties, especially Clay and Silt content, significantly influenced the DSL formation depth (DSLFD), DSLT, and QI. Land-use and slope position were the most important factors that influenced the mean soil-water content (SWC) within DSLs (DSL-SWC), which exhibited the highest VIP values. PLSR models simulated DSL indices accurately in DSL-SWC; the values for variation in response (R2) and goodness of prediction (Q2) were 0.94 and 0.92, respectively. Therefore, our findings provide a helpful base reference for DSL management and reclamation of hill and gully regions of the LP.

High-performance liquid chromatography-based fingerprint analysis with chemical pattern recognition for evaluation of Mahonia bealei (Fort.) Carr.

A simple and efficient high-performance liquid chromatography method combined with chemical pattern recognition was established for quality evaluation of Mahonia bealei (Fort.) Carr. A common pattern of 30 characteristic peaks was applied for similarity analysis, hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis in the 37 batches of M. bealei (Fort.) Carr. to discriminate wild M. bealei (Fort.) Carr., cultivated M. bealei (Fort.) Carr., and its substitutes. The results showed that partial least squares discriminant analysis was the most effective method for discrimination. Eight characteristics peaks with higher variable importance in projection values were selected for pattern recognition model. A permutation test and 26 batches of testing set samples were performed to validate the model that was successfully established. All of the training and testing set samples were correctly classified into three clusters (wild M. bealei (Fort.) Carr., cultivated M. bealei (Fort.) Carr., and its substitutes) based on the selected chemical markers. Moreover, 26 batches of unknown samples were used to predict the accuracy of the established model with a discrimination accuracy of 100%. The obtained results indicated that the method showed great potential application for accurate evaluation and prediction of the quality of M. bealei (Fort.) Carr.

Quality evaluation of Panax notoginseng using high‐performance liquid chromatography with chemical pattern recognition

AbstractPanax notoginseng (Burk.) F.H. Chen, also known as Sanqi, is a high‐value Chinese medicine with a hemostatic effect. It was mainly used to treat traumatic and ischemic cardiovascular diseases. According to different harvest times, Panax notoginseng is divided into “Chunqi” and “Dongqi.” To effectively classify them, a high‐performance liquid chromatography coupled with a chemical pattern recognition method was established. Similarity analysis, principal component analysis, and orthogonal partial least squares discriminant analysis were applied to classify and evaluate 35 batches of Panax notoginseng. The results showed that similarity analysis based on the peak area of 20 components could not achieve accurate classification. Principal component analysis and orthogonal partial least squares discriminant analysis methods could clearly classify Chunqi and Dongqi. In order to find definite chemical markers and simplify the established model of orthogonal partial least squares discriminant analysis, seven characteristic peaks with higher variable importance in projection values were selected. As a result, this method could be used for the classification of Chunqi and Dongqi, which showed an excellent potential application for quality evaluation of Panax notoginseng.

Assessment of the molecular composition of humic acid as an indicator of soil carbon levels by ultra-high-resolution mass spectrometric analysis

Long-term stabilization of soil organic matter (SOM) plays an important role in the carbon cycle. Hence, understanding biogeochemical mechanisms of soil C sequestration is crucial to control its release to the atmosphere. This study aims at investigating the biogeochemical mechanisms of soil C sequestration. An exploratory assessment was carried out on the information about the soil C levels provided by the molecular composition of humic acids (HAs) analyzed by electrospray ionization (ESI) Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). Significant PLS forecasting model for total soil C was obtained using as descriptors the 131 compounds in common in all the HAs detected by FTICR-MS, and its variable importance for projection (VIP) was plotted in the space defined by their atomic ratios using van Krevelen diagrams. The results indicated that significant relationship exists between the HAs molecular composition and the soil organic C levels. The VIP values for the different groups of compounds illustrate how HA contains information about the amounts of C stored in the soil: the HAs in the soils with high levels of organic C have significantly (P < 0.1) higher proportions of unsaturated lipid and lignin-derived compounds; on the other side, low soil organic C levels are associated to HAs with comparatively high proportions of saturated lipid compounds.