Comparison of Partial Least Squares and Artificial Neural Network for the prediction of antioxidant activity in extract of Pegaga (Centella) varieties from 1H Nuclear Magnetic Resonance spectroscopy

Metabolite variations and antioxidant activity of Muntingia calabura leaves in response to different drying methods and ethanol ratios elucidated by NMR-based metabolomics

INTRODUCTION

Muntingia calabura from the Muntingiaceae family has been documented for several medicinal uses. The combinations of drying treatment and extracting solvents for a plant species need to be determined and optimised to ensure that the extracts contain adequate amounts of the bioactive metabolites.

OBJECTIVE

Evaluate the metabolite variations and antioxidant activity among M. calabura leaves subjected to different drying methods and extracted with different ethanol ratios using proton nuclear magnetic resonance (¹ H-NMR)-based metabolomics. Methodology The antioxidant activity of M. calabura leaves dried with three different drying methods and extracted with three different ethanol ratios was determined by using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and nitric oxide (NO) scavenging assays. The metabolites variation among the extracts and correlation with antioxidant activity were analysed by ¹ H-NMR-based metabolomics.

RESULTS

Muntingia calabura leaves extracted with 50% and 100% ethanol from air-drying and freeze-drying methods had the highest total phenolic content and the lowest IC₅₀ value for the DPPH scavenging activity. Meanwhile, oven-dried leaves extracted with 100% ethanol had the lowest IC₅₀ value for the NO scavenging activity. A total of 43 metabolites, including sugars, organic acids, amino acids, phytosterols, phenolics and terpene glycoside were tentatively identified. A noticeable discrimination was observed in the different ethanol ratios by the principal component analysis. The partial least-squares analysis suggested that 32 compounds out of 43 compounds identified were the contributors to the bioactivities.

CONCLUSION

The results established set the preliminary steps towards developing this plant into a high value product for phytomedicinal preparations.

Metabolite profiling of Andrographis paniculata (Burm. f.) Nees. young and mature leaves at different harvest ages using 1H NMR-based metabolomics approach

Andrographis paniculata (Burm. F.) Nees. is considered as the herb of the future due to its precious chemical compounds, andrographolide (ANDRO), neoandrographolide (NAG) and 14-deoxyandrographolide (DAG). This study aims to profile the metabolites in young and mature leaf at six different harvest ages using ¹HNMR-based metabolomics combined with multivariate data analysis. Principal component analysis (PCA) indicated noticeable and clear discrimination between young and mature leaves. A comparison of the leaves stage indicated that young leaves were separated from mature leaves due to its larger quantity of ANDRO, NAG, DAG, glucose and sucrose. These similar metabolites are also responsible for the PCA separation into five clusters representing the harvest age at 14, 16, 18, 20, 22 weeks of leaves extract. Loading plots revealed that most of the ANDRO and NAG signals were present when the plant reached at the pre-flowering stage or 18 weeks after sowing (WAS). As a conclusion, A. paniculata young leaves at pre-flowering harvest age were found to be richer in ANDRO, NAG and DAG compared to mature leaves while glucose and choline increased with harvest age. Therefore, young leaves of A. paniculata should be harvested at 18 WAS in order to produce superior quality plant extracts for further applications by the herbal, nutraceutical and pharmaceutical industries.

Rapid assessment of total MCPD esters in palm-based cooking oil using ATR-FTIR application and chemometric analysis

The technique of Fourier transform infrared spectroscopy is widely used to generate spectral data for use in the detection of food contaminants. Monochloropropanediol (MCPD) is a refining process-induced contaminant that is found in palm-based fats and oils. In this study, a chemometric approach was used to evaluate the relationship between the FTIR spectra and the total MCPD content of a palm-based cooking oil. A total of 156 samples were used to develop partial least squares regression (PLSR), artificial neural network (nnet), average artificial neural network (avNNET), random forest (RF) and cubist models. In addition, a consensus approach was used to generate fusion result consisted from all the model mentioned above. All the models were evaluated based on validation performed using training and testing datasets. In addition, the box plot of coefficient of determination (R²), root mean square error (RMSE), slopes and intercepts by 100 times randomization was also compared. Evaluation of performance based on the testing R² and RMSE suggested that the cubist model predicted total MCPD content with the highest accuracy, followed by the RF, avNNET, nnet and PLSR models. The overfitting tendency was assessed based on differences in R² and RMSE in the training and testing calibrations. The observations showed that the cubist and avNNET models possessed a certain degree of overfitting. However, the accuracy of these models in predicting the total MCPD content was high. Results of the consensus model showed that it slightly improved the accuracy of prediction as well as significantly reduced its uncertainty. The important variables derived from the cubist and RF models suggested that the wavenumbers corresponding to the MCPDs originated from the -CH=CH₂ or CH=CH (990-900 cm^-1) and C-Cl stretch (800-700 cm^-1) regions of the FTIR spectrum data. In short, chemometrics in combination with FTIR analysis especially for the consensus model represent a potential and flexible technique for estimating the total MCPD content of refined vegetable oils.

Antioxidants and α-glucosidase inhibitors from Neptunia oleracea fractions using 1H NMR-based metabolomics approach and UHPLC-MS/MS analysis

Background

Neptunia oleracea is a plant cultivated as vegetable in Southeast Asia. Previous works have revealed the potential of this plant as a source of natural antioxidants and α-glucosidase inhibitors. Continuing our interest on this plant, the present work is focused in identification of the bioactive compounds from different polarity fractions of N. oleracea, namely hexane (HF), chloroform (CF), ethyl acetate (EF) and methanol (MF).

Methods

The N. oleracea fractions were obtained using solid phase extraction (SPE). A metabolomics approach that coupled the use of proton nuclear magnetic resonance (¹H NMR) with multivariate data analysis (MVDA) was applied to distinguish the metabolite variations among the N. oleracea fractions, as well as to assess the correlation between metabolite variation and the studied bioactivities (DPPH free radical scavenging and α-glucosidase inhibitory activities). The bioactive fractions were then subjected to ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC–MS/MS) analysis to profile and identify the potential bioactive constituents.

Results

The principal component analysis (PCA) discriminated EF and MF from the other fractions with the higher distributions of phenolics. Partial least squares (PLS) analysis revealed a strong correlation between the phenolics and the studied bioactivities in the EF and the MF. The UHPLC-MS/MS profiling of EF and MF had tentatively identified the phenolics present. Together with some non-phenolic metabolites, a total of 37 metabolites were tentatively assigned.

Conclusions

The findings of this work supported that N. oleracea is a rich source of phenolics that can be potential antioxidants and α-glucosidase inhibitors for the management of diabetes. To our knowledge, this study is the first report on the metabolite-bioactivity correlation and UHPLC–MS/MS analysis of N. oleracea fractions.

Solvent Extraction and Identification of Active Anticariogenic Metabolites in Piper cubeba L. through ¹H-NMR-Based Metabolomics Approach

The aim of this study was to determine the effects of different solvents for extraction, liquid⁻liquid partition, and concentrations of extracts and fractions of Piper cubeba L. on anticariogenic; antibacterial and anti-inflammatory activity against oral bacteria. Furthermore, ¹H-Nuclear Magnetic Resonance (NMR) coupled with multivariate data analysis (MVDA) was applied to discriminate between the extracts and fractions and examine the metabolites that correlate to the bioactivities. All tested bacteria were susceptible to Piper cubeba L. extracts and fractions. Different solvents extraction, liquid⁻liquid partition and concentrations of extracts and fractions have partially influenced the antibacterial activity. MTT assay showed that P. cubeba L. extracts and fractions were not toxic to RAW 264.7 cells at selected concentrations. Anti-inflammatory activity evaluated by nitric oxide (NO) production in lipopolysaccharide (LPS) stimulated cells showed a reduction in NO production in cells treated with P. cubeba L. extracts and fractions, compared to those without treatment. Twelve putative metabolites have been identified, which are (1) cubebin, (2) yatein, (3) hinokinin, (4) dihydrocubebin, (5) dihydroclusin, (6) cubebinin, (7) magnosalin, (8) p-cymene, (9) piperidine, (10) cubebol, (11) d-germacrene and (12) ledol. Different extraction and liquid⁻liquid partition solvents caused separation in principal component analysis (PCA) models. The partial least squares (PLS) models showed that higher anticariogenic activity was related more to the polar solvents, despite some of the active metabolites also present in the non-polar solvents. Hence, P. cubeba L. extracts and fractions exhibited antibacterial and anti-inflammatory activity and have potential to be developed as the anticariogenic agent.

Comparison of partial least squares and random forests for evaluating relationship between phenolics and bioactivities of Neptunia oleracea

BACKGROUND

Neptunia oleracea is a plant consumed as a vegetable and which has been used as a folk remedy for several diseases. Herein, two regression models (partial least squares, PLS; and random forest, RF) in a metabolomics approach were compared and applied to the evaluation of the relationship between phenolics and bioactivities of N. oleracea. In addition, the effects of different extraction conditions on the phenolic constituents were assessed by pattern recognition analysis.

RESULTS

Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities.

CONCLUSION

Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry.

Metabolite profiling, antioxidant, and α-glucosidase inhibitory activities of germinated rice: nuclear-magnetic-resonance-based metabolomics study

In an attempt to profile the metabolites of three different varieties of germinated rice, specifically black (GBR), red, and white rice, a ¹H-nuclear-magnetic-resonance-based metabolomics approach was conducted. Multivariate data analysis was applied to discriminate between the three different varieties using a partial least squares discriminant analysis (PLS-DA) model. The PLS model was used to evaluate the relationship between chemicals and biological activities of germinated rice. The PLS-DA score plot exhibited a noticeable separation between the three rice varieties into three clusters by PC1 and PC2. The PLS model indicated that α-linolenic acid, γ-oryzanol, α-tocopherol, γ-aminobutyric acid, 3-hydroxybutyric acid, fumaric acid, fatty acids, threonine, tryptophan, and vanillic acid were significantly correlated with the higher bioactivities demonstrated by GBR that was extracted in 100% ethanol. Subsequently, the proposed biosynthetic pathway analysis revealed that the increased quantities of secondary metabolites found in GBR may contribute to its nutritional value and health benefits.

Discrimination and Nitric Oxide Inhibitory Activity Correlation of Ajwa Dates from Different Grades and Origin

This study was aimed at examining the variations in the metabolite constituents of the different Ajwa grades and farm origins. It is also targeted at establishing the correlations between the metabolite contents and the grades and further to the nitric oxide (NO) inhibitory activity. Identification of the metabolites was generated using ¹H-NMR spectroscopy metabolomics analyses utilizing multivariate methods. The NO inhibitory activity was determined using a Griess assay. Multivariate data analysis, for both supervised and unsupervised approaches, showed clusters among different grades of Ajwa dates obtained from different farms. The compounds that contribute towards the observed separation between Ajwa samples were suggested to be phenolic compounds, ascorbic acid and phenylalanine. Ajwa dates were shown to have different metabolite compositions and exhibited a wide range of NO inhibitory activity. It is also revealed that Ajwa Grade 1 from the al-Aliah farm exhibited more than 90% NO inhibitory activity compared to the other grades and origins. Phenolic compounds were among the compounds that played a role towards the greater capacity of NO inhibitory activity shown by Ajwa Grade 1 from the al-Aliah farm.

Metabolic alteration in obese diabetes rats upon treatment with Centella asiatica extract

ETHNOPHARMACOLOGICAL RELEVANCE

'Pegaga' is a traditional Malay remedy for a wide range of complaints. Among the 'pegaga', Centella asiatica has been used as a remedy for diabetes mellitus. Thus, we decided to validate this claim by evaluating the in vivo antidiabetic property of C. asiatica (CA) on T2DM rat model using the holistic (1)H NMR-based metabolomics approach.

METHOD

In this study, an obese diabetic (mimic of T2DM condition) animal model was developed using Sprague-Dawley rats fed with a high-fat diet and induced into diabetic condition by the treatment of a low dose of streptozotocin (STZ). The effect of C. asiatica extract on the experimental animals was followed based on the changes observed in the urinary and serum metabolites, measured by (1)H NMR of urine and blood samples collected over the test period.

RESULTS

A long-term treatment of obese diabetic rats with CA extract could reverse the glucose and lipid levels, as well as the tricarboxylic acid cycle and amino acid metabolic disorders, back towards normal states. Biochemical analysis also showed an increase of insulin production in diabetic rats upon treatment of CA extract.

CONCLUSION

This study has provided evidence that clearly supported the traditional use of CA as a remedy for diabetes. NMR-based metabolomics was successfully applied to show that CA produced both anti-hyperglycemic and anti-hyperlipidemic effects on a rat model. In addition to increasing the insulin secretion, the CA extract also ameliorates the metabolic pathways affected in the induced diabetic rats. This study further revealed the potential usage of CA extract in managing diabetes mellitus and the results of this work may contribute towards the further understanding of the underlying molecular mechanism of this herbal remedy.

Uncovering the antifungal components from turmeric (Curcuma longa L.) essential oil as Aspergillus flavus fumigants by partial least squares

Three volatile compounds in turmeric essential oil that work as fumigants againstAspergillus flavushave been uncovered based on PLS modelling.

The identification of the relationship between chemical and electrical parameters of honeys using artificial neural networks

A number of significant scientific studies have confirmed the health benefits of honey. Due to the high price of natural honey, it is a common target for adulteration which reduces its medicinal value. Adulteration detection methods require specific laboratory equipment and are very expensive. The development of measurement techniques enables the measurement of electrical characteristics of strained honey. Honey electrical parameters can possibly be used for its quality assessment. The identification of the relationship between chemical and electrical parameters of honeys and analysis to determine if there are frequency-dependent changes, can help in developing of that group of methods. The aim of this research was to determine how the chemical parameters of certain honeys influence the dielectric loss factor and the permittivity of strained honey measured in various frequencies. Another aim was to determine whether the percentage influence of certain chemical parameters of honeys on electrical characteristics significantly depends on frequency value. The research was based on neural network models and sensitivity analysis. The percentage influence of certain chemical parameters on electrical characteristics significantly depends on frequency value.