The present study aims at developing a simple, sensitive and specific liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for simultaneous quantification of sildenafil and its metabolite N-desmethyl sildenafil in human plasma using sildenafil-d8, N-desmethyl sildenafil-d8 as internal standards (IS). Chromatographic separation was performed on Zorbax SB C18, 4.6 Â 75 mm, 3.5 mm column with an isocratic mobile phase composed of 10 mM ammonium acetate and acetonitrile (5/95 v/v), at a flow-rate of 0.6 ml min À1 . Sildenafil, sildenafil-d8, N-desmethyl sildenafil and N-desmethyl sildenafil-d8 were detected with proton adducts at m/z 475.2 / 283.4, 483.4 / 283.4, 461.3 / 283.4 and 469.4 / 283.4 in multiple reaction monitoring (MRM) positive mode respectively. Both drug, metabolite and internal standards were extracted by liquid-liquid extraction. The method was validated over a linear concentration range of 1.0-1000.0 ng ml À1 for sildenafil and 0.5-500.0 ng ml À1 for N-desmethyl sildenafil with correlation coefficient (r 2 ) $ 0.9998 for sildenafil and (r 2 ) $ 0.9987 for N-desmethyl sildenafil. This method demonstrated intra and inter-day precision within 1.5 to 5.1 and 2.2 to 3.4% for sildenafil and within 1.3 to 3.1 and 2.8 to 4.3% for N-desmethyl sildenafil. This method demonstrated intra and inter-day accuracy for sildenafil within 97.3 to 98.3 and 96.7 to 97.2% and for N-desmethyl sildenafil within 95.3 to 96.3 and 95.0 to 97.2%. Both analytes were found to be stable throughout three freeze/thaw cycles, bench top and postoperative stability studies. This method was used successfully for the analysis of plasma samples following oral administration of 100 mg in 43 healthy Indian male human volunteers under fasting conditions.

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Hyperspectral image (HSI) classification is a phenomenal mechanism to analyze diversified land cover in remotely sensed hyperspectral images. In the field of remote sensing, HSI classification has been an established research topic, and herein, the inherent primary challenges are (i) curse of dimensionality and (ii) insufficient samples pool during training. Given a set of observations with known class labels, the basic goal of hyperspectral image classification is to assign a class label to each pixel. This chapter discusses the recent progress in the classification of HS images in the aspects of Kernel-based methods, supervised and unsupervised classifiers, classification based on sparse representation, and spectral-spatial classification. Further, the classification methods based on machine learning and the future directions are discussed.

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