FT-MIR Spectroscopy Research Articles

FT-MIR and Raman spectroscopy coupled to multivariate analysis for the detection of clenbuterol in murine model

A fast and simple screening method for the determination of clenbuterol at the ppb level in a murine model was demonstrated by Mid Infrared (MIR) and Raman spectroscopy in conjunction with multivariate analysis. In order to build the calibration models to quantify clenbuterol in rat meat, mixtures of rat meat and clenbuterol were prepared in a range of 5-10,000 ppb. Partial Least Square (PLS) analysis was used to build the calibration model. The results shown that Mid Infrared and Raman spectroscopy were efficient, but Mid Infrared (R(2) = 0.966 and SEC = 0.27) were superior to Raman (R(2) = 0.914 and SEC = 1.167). The SIMCA model developed showed 100% classification rate of rat meat samples with or without clenbuterol. The results were confirmed with contaminated meat samples from animals treated with clenbuterol. Chemometric models represent an attractive option for meat quality screening without sample pretreatments which can identify veterinary medicinal products at the ppb level.

Fourier transform near- and mid-infrared spectroscopy can distinguish between the commercially important Pelargonium sidoides and its close taxonomic ally P. reniforme

Pelargonium sidoides is indigenous to South Africa and abundant in the Eastern Cape Province. Several herbal products have been formulated using P. sidoides of which Umckaloabo® is probably the most popular and successfully marketed in Germany. The objective of this study was to discriminate between P. sidoides and Pelargonium reniforme by FT-IR spectroscopy. Absorbance spectra were collected for P. sidoides (n=96) and its close taxonomic ally P. reniforme (n=57) in the near infrared (NIR) and mid infrared (MIR) regions. The spectroscopic data were analysed using chemometric computations including principal component analysis and orthogonal projections to latent structures discriminant analysis. Phytochemical variation of 5.79% in the NIR dataset (R2X(cum)=0.962; Q2(cum)=0.918) and 9.22% variation in the MIR dataset (R2X(cum)=0.497; Q2(cum)=0.658) was responsible for the separation of the two species. Seven absorption areas were identified as putative biomarkers responsible for the differences between the two species. These results indicate that FT-NIR and FT-MIR spectroscopy can be used to discriminate between these two closely related species which occupy a sympatric distribution in South Africa.

Characterization of Selected South African Young Cultivar Wines Using FTMIR Spectroscopy, Gas Chromatography, and Multivariate Data Analysis

The powerful combination of analytical chemistry and chemometrics and its application to wine analysis provide a way to gain knowledge and insight into the inherent chemical composition of wine and to objectively distinguish between wines. Extensive research programs are focused on the chemical characterization of wine to establish industry benchmarks and authentication systems. The aim of this study was to investigate the volatile composition and mid-infrared spectroscopic profiles of South African young cultivar wines with chemometrics to identify compositional trends and to distinguish between the different cultivars. Data were generated by gas chromatography and FTMIR spectroscopy and investigated by using analysis of variance (ANOVA), principal component analysis (PCA), and linear discriminant analysis (LDA). Significant differences were found in the volatile composition of the cultivar wines, with marked similarities in the composition of Pinotage wines and white wines, specifically for 2-phenylethanol, butyric acid, ethyl acetate, isoamyl acetate, isoamyl alcohol, and isobutyric acid. Of the 26 compounds that were analyzed, 14 had odor activity values of >1. The volatile composition and FTMIR spectra both contributed to the differentiation between the cultivar wines. The best discrimination model between the white wines was based on FTMIR spectra (98.3% correct classification), whereas a combination of spectra and volatile compounds (86.8% correct classification) was best to discriminate between the red wine cultivars.

Application of two-dimensional cross-correlation spectroscopy to analyse infrared (MIR and NIR) spectra recorded during bread dough mixing

During dough mixing chemical, biochemical and physical transformations occur that allow dough formation to be characterized by common chemical and biochemical methods. Recently, spectrometric methods were used to characterize the dough mixing. The Mid-infrared (MIR) and the Near-infrared (NIR) spectroscopy allow information concerning chemical content and composition of food products to be obtained. The aim of this study is to apply FT-NIR and FT-MIR spectroscopy to monitor dough chemical changes, and to correlate those signals by the 2D Cross-Correlation (2D CORR) method. The 2D CORR was used to emphasize chemical assignment of the NIR band modifications (particularly for protein) during dough mixing. The 2D CORR analysis of the raw NIR and MIR spectra demonstrated that five NIR regions are highly correlated to protein vibrations. The 2D CORR analysis of the NIR and MIR spectra after second derivative demonstrated that the amide bands present high R 2 for the NIR bands at (1189–1216), (1351–1474) and (1873) nm. A low R 2 is obtained between the amide I and amide II bands and the (2026–2123) and (2280–2325) nm regions. The amide III band presents a slightly higher R 2 for those NIR regions. The 2D CORR analysis of NIR and MIR spectra allow more specific NIR regions associated to chemical modifications of protein structure to be identified. The 2D CORR analysis of the second derivative spectra is more precise for the identification of the NIR regions implied in dough mixing compared to the 2D CORR analysis of raw NIR and MIR spectra.

Detection of the Presence of Hazelnut Oil in Olive Oil by FT-Raman and FT-MIR Spectroscopy

The detection of the presence of refined hazelnut oil in refined olive oil at low percentages is still a challenge with the current official standards. FT-Raman and FT-MIR spectroscopies have been used to determine the level of detection of the presence of hazelnut oil in olive oil. Spectroscopic analysis has been made not only with the entire oil but also with its unsaponifiable matter. Univariate and multivariate statistical models have been designed with this objective. This study shows that a complete discrimination between olive and hazelnut oils is possible and that adulteration can be detected if the presence of hazelnut oil in olive oil is >8% and if the blends are of Turkish olive and hazelnut oils. The limit of detection is higher when the blends are of edible oils from diverse geographical origins.