Summary Biodiesel production is profitable only under special conditions. Technical challenges including methods to make the transesterification reaction more energy efficient and faster by using catalysts, controlling reaction conditions more efficiently in narrow range, or selection of appropriate feedstocks should be properly addressed to make biodiesel economical viable fuel. Cradle to grave assessment of biodiesel is provided in the present review article. Transesterification reaction variables that affect the purity and performance of biodiesel including quality of raw materials, molar ratio of alcohol to oil, type and concentration of used catalysts, concentration of free fatty acids, water content, temperature, and time required for the reaction are critically described to provide complete understanding and obtaining economical and optimal biodiesel yields. This article also provides a critical review of biodiesel properties such as density, viscosity, cetane number, cloud point, pour point, and flash point. The importance of analytical methods including gas chromatography, high‐performance liquid chromatography, nuclear magnetic resonance spectroscopy, infrared spectroscopy, and Raman spectroscopy is presented and highlighted here in a novel way. Finally, this review will provide complete understanding to readers about biodiesel.

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International audienceRaman spectroscopy has been used to identify the biochemical changes associated with the presence of the Hepatitis C virus (HCV) in infected human blood plasma samples as compared with healthy samples, as control. The aim of the study was to establish the Raman spectral markers of hepatitis infection, which could be used for diagnostic purposes. Moreover, multivariate data analysis techniques, including Principal Component Analysis (PCA), coupled with Linear Discriminant Analysis (LDA), and Partial Least Square Regression (PLSR) are employed to further demonstrate the diagnostic capability of the technique. The PLSR model is developed to predict the viral loads of the HCV infected plasma on the basis of the biochemical changes caused by the viral infection.Specific Raman spectral features are observed in the mean spectra of HCV plasma samples which are not observed in the control mean spectra. PCA differentiated the ‘normal’ and ‘HCV’ groups of the Raman spectra and PCA-LDA was employed to increase the efficiency of prediction of the presence of HCV infection, resulting in a sensitivity and specificity 98.8% and 98.6%, with corresponding Positive Predictive Value of 99.2%, and Negative Predictive Value of 98%. PLSR modelling was found to be 99% accurate in predicting the actual viral loads of the HCV samples, as determined clinically using the Polymerase Chain Reaction (PCR) technique, on the basis of the Raman spectral changes caused by the virus during the process of the development of Hepatitis C

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