Determination of terpenoid contents of aromatic plants using NIRS.

The method was developed in order to provide a fast, simple and non-destructive analysis of terpenoid compounds of aromatic plants. For this purpose, spectroscopic data were collected on the surface of dried plant samples by using near infrared spectroscopic (NIRS) analysis. Volatile substances were extracted from aromatic plants using hydro-distillation method to determine terpenoid composition by using gas chromatography-mass spectrometry (GC-MS). Multivariate calibration methods namely, partial least squares (PLS) regression, were used for data analysis. The correlation between NIRS spectral data and the concentrations of terpenoid contents were established with coefficient of determination (R2) values in the range of 0.953-0.997. The model validation results showed that the contents of 24 terpenoids were predicted accurately with a satisfactory limit of detection (LOD) values. In this study, 24 terpenoids, the major constituents of volatile compounds, from nine aromatic plants were investigated.

Rapid discrimination between buffalo and cow milk and detection of adulteration of buffalo milk with cow milk using synchronous fluorescence spectroscopy in combination with multivariate methods.

This research paper describes the potential of synchronous fluorescence (SF) spectroscopy for authentication of buffalo milk, a favourable raw material in the production of some premium dairy products. Buffalo milk is subjected to fraudulent activities like many other high priced foodstuffs. The current methods widely used for the detection of adulteration of buffalo milk have various disadvantages making them unattractive for routine analysis. Thus, the aim of the present study was to assess the potential of SF spectroscopy in combination with multivariate methods for rapid discrimination between buffalo and cow milk and detection of the adulteration of buffalo milk with cow milk. SF spectra of cow and buffalo milk samples were recorded between 400-550 nm excitation range with Δλ of 10-100 nm, in steps of 10 nm. The data obtained for ∆λ = 10 nm were utilised to classify the samples using principal component analysis (PCA), and detect the adulteration level of buffalo milk with cow milk using partial least square (PLS) methods. Successful discrimination of samples and detection of adulteration of buffalo milk with limit of detection value (LOD) of 6% are achieved with the models having root mean square error of calibration (RMSEC) and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) values of 2, 7, and 4%, respectively. The results reveal the potential of SF spectroscopy for rapid authentication of buffalo milk.

Use of Raman spectroscopy for determining erucic acid content in canola oil.

This study presents a novel method to determine erucic acid in canola oil samples by using Raman spectroscopy and chemometric analysis. The oil mixtures were prepared at various concentrations of erucic acid ranging from 0% to 33.56% (w/w) through binary combinations of different oils. In order to predict erucic acid content, Raman spectroscopy and GC results were correlated by means of partial least squares analysis. High coefficient of determination values was obtained for both calibration and validation data sets, which are 0.990 and 0.982, respectively. The results of the present study reveal the potential of Raman spectroscopy for rapid determination (45s) of erucic acid in canola oil. Further research would be useful to improve the method to put it forward as an alternative to GC in the erucic acid analysis.