Surface-enhanced Raman spectroscopy for identification of food processing bacteria.

Food processing bacteria play important role in providing flavors, ingredients and other beneficial characteristics to the food but at the same time some bacteria are responsible for food spoilage. Therefore, quick and reliable identification of these food processing bacteria is very necessary for the differentiation between different species which may help in the development of more useful food processing methodologies. In this study, analysis of different bacterial species (Lactobacillus fermentum, Fructobacillus fructosus, Pediococcus pentosaceus and Halalkalicoccus jeotgali) was performed with our in-house developed Ag NPs-based surface-enhanced Raman spectroscopy (SERS) method. The SERS spectral data was analyzed by multivariate data analysis techniques including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). Bacterial species were differentiated on the basis of SERS spectral features and potential of SERS was compared with the Raman spectroscopy (RS). SERS along with PCA and PLS-DA was found to be an efficient technique for identification and differentiation of food processing bacterial species. Differentiation with accuracy of 99.5% and sensitivity of 99.7% was depicted by PLS-DA model using leave one out cross validation.

Surface-enhanced Raman spectroscopy analysis of serum samples of typhoid patients of different stages.

BACKGROUND: Surface-enhanced Raman spectroscopy (SERS) of body fluids is considered a quick, simple and easy to use method for the diagnosis of disease. OBJECTIVES: To evaluate rapid, reliable, and non-destructive SERS-based diagnostic tool with multivariate data analysis including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) for classification of different stages of typhoid on the basis of characteristic SERS spectral features. METHODS: SERS has been used for analysis of serum samples of different stages of typhoid including early acute stage and late acute stage in comparison with healthy samples, in order to investigate capability of this technique for diagnosis of typhoid. SERS spectral features associated with the biochemical changes taking place during the development of the typhoid fever were analyzed and identified. RESULTS: The value of area under the receiver operating characteristics (AUROC) for early acute stage versus healthy is 0.87 and that for healthy versus late acute stage is 0.52. PLS-DA classifier model gives values of 100 % for accuracy, sensitivity and specificity, respectively for the SERS spectral data sets of healthy versus early acute stage. Moreover, this classifier model gives values of 91 %, 89 % and 97 % for accuracy, sensitivity and specificity, respectively for the SERS spectral data sets of healthy versus late acute stage. CONCLUSIONS: Based on preliminary work it is concluded that SERS has potential to diagnose various stages of typhoid fever including early acute and late acute stage in comparison with healthy samples.

Raman spectroscopy for the qualitative and quantitative analysis of solid dosage forms of Sitagliptin.

To demonstrate the potential of Raman spectroscopy for the qualitative and quantitative analysis of solid dosage pharmacological formulations, different concentrations of Sitagliptin, an Active Pharmaceutical Ingredient (API) currently prescribed as an anti-diabetic drug, are characterised. Increase of the API concentrations induces changes in the Raman spectral features specifically associated with the drug and excipients. Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR), were used for the qualitative and quantitative analysis of the spectral responses. A PLSR model is constructed which enables the prediction of different concentrations of drug in the complex excipient matrices. During the development of the prediction model, the Root Mean Square Error of Cross Validation (RMSECV) was found to be 0.36 mg and the variability explained by the model, according to the (R2) value, was found to be 0.99. Moreover, the concentration of the API in the unknown sample was determined. This concentration was predicted to be 64.28/180 mg (w/w), compared to the 65/180 mg (w/w). These findings demonstrate Raman spectroscopy coupled to PLSR analysis to be a reliable tool to verify Sitagliptin contents in the pharmaceutical samples based on calibration models prepared under laboratory conditions.

Quantitative analysis of solid dosage forms of cefixime using Raman spectroscopy.

Quantification of antibiotics is of significant importance because of their use in the prevention and treatment of different diseases. Cefixime (CEF) is a cephalosporin antibiotic that is used against bacterial infections. In the present study, Raman spectroscopy has been applied for the identification and quantification of Raman spectral features of cefixime with different concentrations of Active Pharmaceutical Ingredient (API) and excipients in solid dosage forms. The changes in Raman spectral features of API and excipients in the solid dosage forms of cefixime were studied and Raman peaks were assigned based on the literature. Multivariate data analysis techniques including the Principal Component Analysis (PCA) and Partial Least Squares Regression analysis (PLSR) have been performed for the qualitative and quantitative analysis of solid dosage forms of cefixime. PCA was found helpful in differentiating all the Raman spectral data associated with the different solid dosage forms of cefixime. The coefficient of determination (R2), mean absolute error (MAE), and mean relative error (MRE) for the calibration data-set were 0.99, 0.72, and 0.01 respectively and for the validation data-set were 0.99, 3.15, and 0.02 respectively, that shows the performance of the model. The root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were found to be 0.56 mg and 3.13 mg respectively.