Rapid discrimination and characterization of vanilla bean extracts by attenuated total reflection infrared spectroscopy and selected ion flow tube mass spectrometry.

Vanilla beans have been shown to contain over 200 compounds, which can vary in concentration depending on the region where the beans are harvested. Several compounds including vanillin, p-hydroxybenzaldehyde, guaiacol, and anise alcohol have been found to be important for the aroma profile of vanilla. Our objective was to evaluate the performance of selected ion flow tube mass spectrometry (SIFT-MS) and Fourier-transform infrared (FTIR) spectroscopy for rapid discrimination and characterization of vanilla bean extracts. Vanilla extracts were obtained from different countries including Uganda, Indonesia, Papua New Guinea, Madagascar, and India. Multivariate data analysis (soft independent modeling of class analogy, SIMCA) was utilized to determine the clustering patterns between samples. Both methods provided differentiation between samples for all vanilla bean extracts. FTIR differentiated on the basis of functional groups, whereas the SIFT-MS method provided more specific information about the chemical basis of the differentiation. SIMCA's discriminating power showed that the most important compounds responsible for the differentiation between samples by SIFT-MS were vanillin, anise alcohol, 4-methylguaiacol, p-hydroxybenzaldehyde/trimethylpyrazine, p-cresol/anisole, guaiacol, isovaleric acid, and acetic acid. ATR-IR spectroscopy analysis showed that the classification of samples was related to major bands at 1523, 1573, 1516, 1292, 1774, 1670, 1608, and 1431 cm(-1) , associated with vanillin and vanillin derivatives.

Multicomponent cleaning verification of stainless steel surfaces for the removal of dairy residues using infrared microspectroscopy.

UNLABELLED: The application of infrared microspectroscopy (IRMS) technology, combined with multivariate analysis, was evaluated to develop sensitive and robust methods to assess cleanability of stainless steel surfaces for the removal of dairy food residues. UHT milk samples (skim, 1%, 2%, and whole) were analyzed for total nitrogen (Kjeldahl) and fat (Babcock) contents. The coupons were manually soiled with serially diluted milk samples resulting in soils ranging from 0.1 to 428.1 µg/cm(2) for protein and 0.1 to 374.17 µg/cm(2) for fat, and then autoclaved to simulate a heated equipment surface. Reflectance spectra were collected from stainless steel coupons by using IRMS, and multivariate analysis was used to develop calibration models based on cross-validated partial least squares regression (PLSR). Statistical analysis for the prediction of protein and fat showed a standard error of cross-validation (SECV) of 0.5 and 0.4 µg/cm(2) for prediction of protein and fat, respectively, and correlation coefficients (rVal) > 0.99. To improve the sensitivity, swabbing and concentration steps were used prior to IRMS analysis obtaining SECV of 0.04 and 0.01 µg/cm(2) for the prediction of protein and fat, respectively, and rVal > 0.99. The PLSR models accurately predicted the levels of protein and fat on autoclaved stainless steel coupons soiled with milk. A simple, reliable, and robust protocol based on IRMS and multivariate analysis was developed for multicomponent characterization of stainless steel surfaces that can contribute to more efficient cleaning verification with regard to contamination on surfaces of processing equipment. PRACTICAL APPLICATION: We report the application of Fourier transform infrared microspectroscopy (FTIR) for the validation of CIP cleaning efficiency that would provide a basis for better understanding of the mechanisms involved in the removal of physical soil and food residues from different types of equipment surfaces commonly utilized in the biotech, pharmaceutical, and food industries. Reliable calibration models were generated that showed the ability to predict the amounts of dairy soils on the surface of stainless steel coupons. Including a swabbing step of the coupons before infrared spectral acquisition provided improved sensitivity and reproducibility for multicomponent cleaning verification. Results from this research project would allow designing experiments to rapidly evaluate different materials and finishes, the effects of process variables, the influence of food components, and the development of reliable and robust cleaning validation protocols to ensure the safety and quality of the product.