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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Raman spectroscopy was employed for the characterisation of blood plasma samples from patients at different stages of breast cancer. Blood plasma samples taken from clinically diagnosed breast cancer patients were compared with healthy controls using multivariate data analysis techniques (principal components analysis-PCA) to establishRaman spectral features which can be considered spectral markers of breast cancer development.All the stages of the disease can be differentiated from normal samples. It is also found that stage 2 and 3 are biochemically similar, but can be differentiated from each other by PCA. The Raman spectral data of the stage 4 is found to be biochemically distinct, but very variable between patients.Raman spectral features associated with DNA and proteins were identified, which are exclusive to patient plasma samples. Moreover, there are several other spectral features which are strikingly different in the blood plasma samples of different stages of breast cancer. In order to further explore the potential of Raman spectroscopy as the basis of a minimally invasive screening technique for breast cancer diagnosis and staging, PCA-Factorial Discriminant Analysis (FDA) was employed to classify the Raman spectral datasetsof the blood plasma samples of the breast cancer patients, according to different stages of the disease, yielding promisingly high values of sensitivity and specificity for all stages.

In the current study, Raman spectroscopy is employed for the identification of the biochemical changes taking place during the development of Hepatitis C. The Raman spectral data acquired from the human blood plasma samples of infected and healthy individuals is analysed by Principal Components Analysis and the Raman spectral markers of the Hepatitis C Virus (HCV) infection are identified. Spectral changes include those associated with nucleic acidsat720 cm-1 , 1077 cm-1 1678 (C=O stretching mode of dGTP of RNA), 1778 cm-1 (RNA), with proteins at 1641 cm-1 (amide-I), 1721 cm-1 (C=C stretching of proteins) and lipids at 1738 cm-1 (C=O of ester group in lipids). These differences in Raman spectral features of blood plasma samples of the patients and healthy volunteers can be associated with the development of the biochemical changes during HCV infection.

Infection with the dengue virus is currently clinically detected according to different biomarkers in human blood plasma, commonly measured by enzyme linked immunosorbent assays, including non-structural proteins (Ns1), immunoglobulin M (IgM) and immunoglobulin G (IgG). However, there is little or no mutual correlation between the biomarkers, as demonstrated in this study by a comparison of their levels in samples from 17 patients. As an alternative, the label free, rapid screening technique, Raman spectroscopy has been used for the characterisation/diagnosis of healthy and dengue infected human blood plasma samples. In dengue positive samples, changes in specific Raman spectral bands associated with lipidic and amino acid/protein content are observed and assigned based on literature and these features can be considered as markers associated with dengue development. Based on the spectroscopic analysis of the current, albeit limited, cohort of samples, Principal Components Analysis (PCA) coupled Factorial Discriminant Analysis, yielded values of 97.95% sensitivity and 95.40% specificity for identification of dengue infection. Furthermore, in a comparison of the normal samples to the patient samples which scored low for only one of the biomarker tests, but high or medium for either or both of the other two, PCA-FDA demonstrated a sensitivity of 97.38% and specificity of 86.18%, thus providing an unambiguous screening technology.

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