Mahesh Chandra

β-Selinene-Rich Essential Oils from the Parts of Callicarpa macrophylla and Their Antioxidant and Pharmacological Activities

Background: Callicarpa macrophylla (Varbenaceae) is a medicinal shrub and is traditionally used in India, China, and South Asia. Methods: The plant material was collected from lower Himalayan region of Uttarakhand in India. The essential oils from three different aerial parts were analyzed by GC-MS. Antioxidant activity, phenolic assay, and various pharmacological activities were determined by using existing methods which are generally practiced widely. Results: Over 51, 53, and 40 compounds were identified in C. macrophylla leaves essential oil (CMLEO), C. macrophylla pre mature seeds and fruits essential oil (CMEO-I) and C.macrophylla mature seeds and fruits essential oil (CMEO-II), respectively. These oils differ in relative contents of major compounds viz; β-selinene (37.51% in CMLEO, 44.66% in CMEO-I and 57.01% in CMEO-II), phyllocladene (9.76% in CMLEO, 5.80% in CMEO-I and 12.38% in CMEO-II), caryophelline oxide (7.34% in CMLEO, 8.74% in CMEO-I and 5.0% in CMEO-II), 9E-epi-caryophelline (6.23% in CMLEO, 1.27% in CMEO-I and 3.43% in CMEO-II), longipinocarvone (4.96% in CMLEO, 1.17% in CMEO-I and 2.0% in CMEO-II), and 1,8-cineole (2.23% in CMLEO, 3.10% in CMEO-I and 1.62% in CMEO-II). The oils exhibited good in vitro antioxidant activity. The maximum activity was found in CMEO-II with IC50 values 7.37 ± 0.11, 11.49 ± 0.87, 14.59 ± 0.18, 15.66 ± 0.03, and 17.49 ± 0.13 µL/mL. The essential oils showed qualitative and quantitative diversity in the makeup of essential oils constituents. The oils were found to exhibit anti-inflammatory, analgesic, and antipyretic activity on swiss albino mice compared to the standard drugs, viz; ibuprofen, paracetamol and indomethacin. Conclusion: It is inferred from the study that the plant parts can be used scientifically in traditional systems as folk herbal medicine. Furthermore, we have generated a database for future reference and judicious exploitation of these oils from their natural setting.

Multi scale feature extraction and enhancement using SVD towards secure Face Recognition system

Biometric devices provide secure mechanism towards gaining access. One of the Biometric features is Face and the system implemented is Face Recognition system. The Classical Face Recognition System is implemented with Principal Component Analysis and is successful. PCA is a linear method of extracting the features in a lower dimension space and is severely affected by the Pose and surrounding illumination variation. To implement effective face recognition system, pose variation is to be considered and the problem is well addressed with Kernel PCA (nonlinear PCA). KPCA extracts features in a higher dimension space, there by the system is rugged to pose variation. The illumination variation is accounted for capture range of the front end device and its surrounding and is not dealt in KPCA. In this work Singular Value Decomposition is used to deal with surrounding illumination and wavelets are employed to aid the KPCA in capturing the Multi Scale Features there by making the System robust to pose and illumination variation. To show the performance, the proposed method is tested on YaleB, ORL Databases. The results obtained show the impact of the method and is compared with PCA, KPCA.

Preprocessing using SVD towards illumination invariant face recognition

Uncontrolled lighting Conditions poses obstacle to face recognition. To deal with this problem, this paper proposes a preprocessing scheme using Singular Value Decomposition and Histogram Equalization to enhance and facilitate illumination invariant face recognition. The proposed method first generates synthetic image using Histogram equalization. Original and synthetic images are singular value decomposed; from the estimates of singular values enhanced image is reconstructed. Enhanced image is discrete wavelet decomposed (Haar & Db4) in to different frequency sub bands (LL, LH, HL, HH). The LL sub band is the best approximation of original image with lower-dimensional space and is used as biometric template. Pose Invariant Feature vectors are extracted from this template using Kernel Principal Component Analysis (KPCA). To show the performance, the proposed method is tested on YaleB, ORL benchmarking Databases. The results obtained show the impact of the method and is compared with PCA, KPCA without any preprocessing.

Segmentation of blurred images using improved Chan-Vese snake model

Accurately extracting the features of interest from a blurred image is one of the difficult tasks in image segmentation. This paper uses the blind deconvolution, deblurring algorithm to find original features of interest, and then uses the improved Chan - Vese snake model to get the accurate features. The presented algorithm is tested on the MRI images of brain and results are found to be satisfactorily.