EVALUATION OF ETHANOLIC ROOT EXTRACT OF PARTHENIUM HYSTEROPHORUS LINN FOR ANTIOXIDANT AND ANTI-INFLAMMATORY ACTIVITY

Objective: The work is aimed to draw out the health beneficial properties of a weed (Parthenium hysterophorus Linn). The present work is organized to evaluate the antioxidant and anti-inflammatory activity of the ethanolic root extract of Parthenium hysterophorus Linn.Methods: In the present work the ethanolic extract was determined by using soxhlet apparatus. The antioxidant scavenging activity of this extract was determined by applying three different assay methods: (1) DPPH (1, 1-diphenyl-2-picryl hydrazyl) free radical method. (2) Nitric oxide scavenging assay and (3) Reducing power method. The anti-inflammatory activity was determined by in vivo method i.e. Carrageenan induced rat paw edema.Results: DPPH radical scavenging activities of the standard antioxidant and extracts were found to be increased in dose dependent manner. The percentage inhibition increases from 4.19% to 97.09% within the concentration range of 10µg/ml to 160µg/ml. Parthenium hysterophorus Linn effectively reduced the generation of nitric oxide radicals from sodium nitroprusside solution in a concentration dependent manner and percentage inhibition increases from 3.53% to 55.21% within the concentration range 10µg/ml to 160µg/ml. All the concentrations of extract significantly showed higher absorbance than the absorbance of control reaction (0.9705) in reducing power assay. A Higher absorbance indicates high reducing power due to the formation of reduced intermediates. Parthenium hysterophorus Linn showed highly significant anti-inflammatory activity at a dose of 200 mg/kg and the lesser effect was observed at 100 mg/kg with the percentage change in paw volume at 0 min, 30 min, 60 min, 90 min and 120 min.Conclusion: Thus, from above experimental observations, it can be stated that Parthenium is a natural antioxidant and bearing anti-inflammatory activity.Â

A NOVEL DRUG DELIVERY SYSTEM: NIOSOMES REVIEW

Treatment of infectious diseases and immunisation has undergone a revolutionary shift in recent years. Not only a large number of disease-specific biological have been developed, but also emphasis has been made to effectively deliver these biological. Niosomes represent an emerging class of novel vesicular systems. Niosomes are self assembled vesicles composed primarily of synthetic surfactants and cholesterol. A comprehensive research carried over niosome as a drug carrier. Various drugs are enlisted and tried in niosome surfactant vesicles. Niosomes proved to be a promising drug carrier and has potential to reduce the side effects of drugs and increased therapeutic effectiveness in various diseases. This article presents an overview of the techniques of preparation of niosome, types of niosomes, characterisation and their applications

Close-range hyperspectral imaging of whole plants for digital phenotyping : recent applications and illumination correction approaches

Digital plant phenotyping is emerging as a key research domain at the interface of information technology and plant science. Digital phenotyping aims to deploy high-end non-destructive sensing techniques and information technology infrastructures to automate the extraction of both structural and physiological traits from plants under phenotyping experiments. One of the promising sensor technologies for plant phenotyping is hyperspectral imaging (HSI). The main benefit of utilising HSI compared to other imaging techniques is the possibility to extract simultaneously structural and physiological information on plants. The use of HSI for analysis of parts of plants, e.g. plucked leaves, has already been demonstrated. However, there are several significant challenges associated with the use of HSI for extraction of information from a whole plant, and hence this is an active area of research. These challenges are related to data processing after image acquisition. The hyperspectral data acquired of a plant suffers from variations in illumination owing to light scattering, shadowing of plant parts, multiple scattering and a complex combination of scattering and shadowing. The extent of these effects depends on the type of plants and their complex geometry. A range of approaches has been introduced to deal with these effects, however, no concrete approach is yet ready. In this article, we provide a comprehensive review of recent studies of close-range HSI of whole plants. Several studies have used HSI for plant analysis but were limited to imaging of leaves, which is considerably more straightforward than imaging of the whole plant, and thus do not relate to digital phenotyping. In this article, we discuss and compare the approaches used to deal with the effects of variation in illumination, which are an issue for imaging of whole plants. Furthermore, future possibilities to deal with these effects are also highlighted

Detecting Forged Alcohol Non-invasively Through Vibrational Spectroscopy and Machine Learning

Alcoholic spirits are a common target for counterfeiting and adulteration, with potential costs to public health, the taxpayer and brand integrity. Current methods to authenticate spirits include examinations of superficial appearance and consistency, or require the tester to open the bottle and remove a sample. The former is inexact, while the latter is not suitable for widespread screening or for high-value spirits, which lose value once opened. We study whether non-invasive near infrared spectroscopy, in combination with traditional and time series classification methods, can correctly classify the alcohol content (a key factor in determining authenticity) of synthesised spirits sealed in real bottles. Such an experimental setup could allow for a portable, cheap to operate, and fast authentication device. We find that ethanol content can be classified with high accuracy, however methanol content proved difficult with the algorithms evaluated

Screening of antioxidant properties of the apple juice using the front-face synchronous fluorescence and chemometrics

Fluorescence spectroscopy is gaining increasing attention in food analysis due to its higher sensitivity and selectivity as compared to other spectroscopic techniques. Synchronous scanning fluorescence technique is particularly useful in studies of multi-fluorophoric food samples, providing a further improvement of selectivity by reduction in the spectral overlapping and suppressing light-scattering interferences. Presently, we study the feasibility of the prediction of the total phenolics, flavonoids, and antioxidant capacity using front-face synchronous fluorescence spectra of apple juices. Commercial apple juices from different product ranges were studied. Principal component analysis (PCA) applied to the unfolded synchronous fluorescence spectra was used to compare the fluorescence of the entire sample set. The regression analysis was performed using partial least squares (PLS1 and PLS2) methods on the unfolded total synchronous and on the single-offset synchronous fluorescence spectra. The best calibration models for all of the studied parameters were obtained using the PLS1 method for the single-offset synchronous spectra. The models for the prediction of the total flavonoid content had the best performance; the optimal model was obtained for the analysis of the synchronous fluorescence spectra at Delta lambda = 110 nm (R (2) = 0.870, residual predictive deviation (RPD) = 2.7). The optimal calibration models for the prediction of the total phenolic content (Delta lambda = 80 nm, R (2) = 0.766, RPD = 2.0) and the total antioxidant capacity (Delta lambda = 70 nm, R (2) = 0.787, RPD = 2.1) had only an approximate predictive ability. These results demonstrate that synchronous fluorescence could be a useful tool in fast semi-quantitative screening for the antioxidant properties of the apple juices.info:eu-repo/semantics/publishedVersio

A three-dimensional discriminant analysis approach for hyperspectral images

Raman hyperspectral imaging is a powerful technique that provides both chemical and spatial information of a sample matrix being studied. The generated data are composed of three-dimensional (3D) arrays containing the spatial information across the x- and y-axis, and the spectral information in the z-axis. Unfolding procedures are commonly employed to analyze this type of data in a multivariate fashion, where the spatial dimension is reshaped and the spectral data fits into a two-dimensional (2D) structure and, thereafter, common first-order chemometric algorithms are applied to process the data. There are only a few algorithms capable of working with the full 3D array. Herein, we propose new algorithms for 3D discriminant analysis of hyperspectral images based on a three-dimensional principal component analysis linear discriminant analysis (3D-PCA-LDA) and a three-dimensional discriminant analysis quadratic discriminant analysis (3D-PCA-QDA) approach. The analysis was performed in order to discriminate simulated and real-world data, comprising benign controls and ovarian cancer samples based on Raman hyperspectral imaging, in which 3D-PCA-LDA and 3D-PCA-QDA achieved far superior performance than classical algorithms using unfolding procedures (PCA-LDA, PCA-QDA, partial lest squares discriminant analysis [PLS-DA], and support vector machines [SVM]), where the classification accuracies improved from 66% to 83% (simulated data) and from 50% to 100% (real-world dataset) after employing the 3D techniques. 3D-PCA-LDA and 3D-PCA-QDA are new approaches for discriminant analysis of hyperspectral images multisets to provide faster and superior classification performance than traditional techniques

Biospectroscopy for Plant and Crop Science

Plants as our most renewable natural resource are indispensable within earth's biosphere, especially for food security. Providing food security in a modern world requires an ever-increasing understanding of how plants, and their products, respond to changes in the environment. In this respect, a combination of physical and chemical analytical methods can be used to study the structure and function of plants at the whole-plant, organ, tissue, cellular, and biochemical levels. Vibrational spectroscopy in biology, sometimes known as biospectroscopy, encompasses a number of techniques, among them mid-infrared and Raman spectroscopy. These techniques are well-established label-free, nondestructive, and environmentally friendly analytical methods that generate a spectral “signature” of samples using mid-infrared radiation. The resultant wavenumber spectrum containing hundreds of variables as unique as a biochemical “fingerprint” represents the biomolecules (proteins, lipids, carbohydrates, nucleic acids) present within a sample, which may serve as spectral “biomarkers” for the discrimination of distinct as well as closely related biomaterials, for various applications. In plants, biospectroscopy has been used to characterize surface structures in intact plant tissues such as leaves and fruit, plant cuticles, and cell walls, as well as to study the effects of stress on plant species. Not only does this allow the effective discrimination and “chemoidentification” of different plant structures, varieties, and cultivars, it also permits chemical profiling of plant tissues for physiological applications such as plant health monitoring and disease detection. Technical advancements are starting to overcome the major limitations of biospectroscopy such as detection sensitivity, penetration/imaging depth, and computational analysis speed, expanding the application of biospectroscopy in the plant and crop sciences. Vibrational spectra thereby serve as a basis for localization, identification, quantification of key compounds within plants, as well as to track dynamic processes for molecular-level analytics and diagnostics. This provides development potential as sensors in automatic decision-making platforms for areas including precision farming and the food production/supply chain. In this chapter we will discuss the application of biospectroscopy to study plant and crop biology and consider the potential for advancements to make biospectroscopy a more prominent technology for fundamental plant research and applied crop science as part of solutions to agricultural challenges both now and in the future

Improved prediction of protein content in wheat kernels with a fusion of scatter correction methods in NIR data modelling

The study aims to test the hypothesis that modelling of near-infrared (NIR) spectroscopic data based on a single scatter correction technique is sub-optimal. Better predictive performance of the multivariate analysis method can be obtained when the information from differently scatter corrected data is jointly used. To demonstrate it, an open-source NIR spectroscopy data set related to protein prediction in wheat kernels was used. Two different pre-processing fusion approaches i.e., sequential and parallel fusion, were used for fusing the complementary information from four different scatter correction techniques, namely standard normal variate (SNV), variable sorting for normalisation (VSN), 2nd derivative, and multiplicative scatter correction (MSC). As a comparison, partial least-squares regression (PLSR) was performed on the SNV pre-processed data. The results showed that fusion of scatter correction can improve the predictive performance of NIR spectroscopic models. The results revealed that both sequential and parallel fusion approaches improved the predictive performance compared to the PLSR performed using a single scatter correction technique. The R2p was improved by up to 3% and the RMSEP was reduced by up to 13% compared to the results obtained with conventional PLSR model developed with a single scatter correction technique.</p

EVALUATION OF SALVIA LANATA LEAF EXTRACT FOR ANTI-INFLAMMATORY AND ANTIOXIDANT ACTIVITY

Objective: The work is aimed to evaluate the anti-inflammatory and antioxidant activity of the ethanolic leaf extract of Salvia lanata. Methods: Anti-inflammatory activity of the leaf extract of S. lanata at a dose of 100 mg/kg and 200 mg/kg against the standard drug indomethacin at a dose of 10 mg/kg i.p. was evaluated by carrageenan-induced rat paw edema and protein denaturation method. Antioxidant activity was determined by 1, 1 diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging method, reducing power method, and nitric oxide scavenging assay. Results: S. lanata leaf extract showed highly significant dose-dependent efficacy against carrageenan-induced paw edema at a dose of 200 mg/kg and lesser effect at 100 mg/kg. It inhibited heat-induced albumin denaturation with a maximum inhibition of 79.26% at 160 μg/ml. DPPH free radical scavenging activity of extract exhibited inhibition of 25.96%–87.74% within the concentration range of 10 μg/ml–160 μg/ml, nitric oxide assay from 12.26% to 79.22% in the same concentration range. In reducing power assay with an increase in concentrations, an increase in the absorbance of the reaction mixture was observed. Antioxidant activity was compared to standard drug ascorbic acid. Conclusion: The leaf extract of S. lanata has potent anti-inflammatory and antioxidant activity