W. James Harper

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. SIMCAs 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.

Lactose and Lactose Derivatives

Lactose is the major carbohydrate in the milk of most mammals, being synthesized in the mammary gland from galactose and glucose. The occurrence of lactose in milk from different mammals is reviewed by Jenness & Sloan (1970) and Jenness & Holt (1987), who reported on work that showed lactose to be absent or very low in the milk from some animals, including sea lions, some seals and opossums. Lactose varies widely in the milk of various mammals. The variation in the lactose content found by Jenness & Sloan (1970) for milk from 21 different mammals is shown in Table1, arranged in order of increasing lactose content. For these animals the lactose content ranged from 2.0% for mink to 7.3% for baboons. On the basis of water content, lactose ranged from 25.4 g/kg water for mink to 84.3 g/kg water for the baboon. There was no consistent relationship between lactose and total solids or lactose and non-fat solids.

Vegetable organogels incorporation in cream cheese products

Edible oleogels made from rice bran wax (RBW) or ethylcellulose (EC) organogelators in combination with vegetable oils and other non-fat ingredients were used to produce oleogel cream cheese products. Four oleogel cream cheese products, two containing RBW and two with EC, were prepared and compared to control samples including full-fat and fat-free commercial cream cheese samples. Upon compositional analysis, all the oleogel cream cheese (OCC) samples showed approximately a 25% reduction in total fat content in comparison to the full-fat commercial control. More specifically by the replacement of saturated fat with healthier unsaturated fat alternatives, an improved fatty acid profile of cream cheese products was documented. Similar compositional analysis was also performed on a cream cheese sample made with non-gelled vegetable oil. Using a single penetration test and a strain sweep test, oleogel cream cheese samples prepared with RBW displayed comparable hardness, spreadability, and stickiness values to the full-fat commercial control sample. EC OCC samples also showed comparable hardness, spreadability and stickiness values but exhibited reduced adhesiveness values compared to the full-fat control. The successful microstructural incorporation of oleogels into a cream cheese, along with similarities in fat globule size, between OCC samples and commercial controls was confirmed with Confocal Laser Scanning Microscopy. The similarity in microstructure can be accounted for the similarities in textural properties between the OCC samples and the full-fat control. These results provide a thorough characterization of the use of RBW and EC in oleogels and their potential as a healthy alternative to saturated fat in cream cheese applications.

Headspace quantification of pure and aqueous solutions of binary mixtures of key volatile organic compounds in Swiss cheeses using selected ion flow tube mass spectrometry

RATIONALE Twelve volatile organic compounds (VOCs) have recently been identified as key compounds in Swiss cheese with split defects. It is important to know how these VOCs interact in binary mixtures and if their behavior changes with concentration in binary mixtures. METHODS Selected ion flow tube mass spectrometry (SIFT-MS) was used for the headspace analysis of VOCs commonly found in Swiss cheeses. Headspace (H/S) sampling and quantification checks using SIFT-MS and further linear regression analyses were carried out on twelve selected aqueous solutions of VOCs. Five binary mixtures of standard solutions of VOCs were also prepared and the H/S profile of each mixture was analyzed. RESULTS A very good fit of linearity for the twelve VOCs (95% confidence level) confirms direct proportionality between the H/S and the aqueous concentration of the standard solutions. Henrys Law coefficients were calculated with a high degree of confidence. SIFT-MS analysis of five binary mixtures showed that the more polar compounds reduced the H/S concentration of the less polar compounds, while the addition of a less polar compound increased the H/S concentration of the more polar compound. CONCLUSIONS In the binary experiment, it was shown that the behavior of a compound in the headspace can be significantly affected by the presence of another compound. Thus, the matrix effect plays a significant role in the behavior of molecules in a mixed solution.

Analysis of Selected Volatile Organic Compounds in Split and Nonsplit Swiss Cheese Samples Using Selected‐Ion Flow Tube Mass Spectrometry (SIFT‐MS)

Splits/cracks are recurring product defects that negatively affect the Swiss cheese industry. Investigations to understand the biophysicochemical aspects of these defects, and thus determine preventive measures against their occurrence, are underway. In this study, selected-ion, flow tube mass spectrometry was employed to determine the volatile organic compound (VOC) profiles present in the headspace of split compared with nonsplit cheeses. Two sampling methodologies were employed: split compared with nonsplit cheese vat pair blocks; and comparison of blind, eye, and split segments within cheese blocks. The variability in VOC profiles was examined to evaluate the potential biochemical pathway chemistry differences within and between cheese samples. VOC profile inhomogeneity was most evident in cheeses between factories. Evaluation of biochemical pathways leading to the formation of key VOCs differentiating the split from the blind and eye segments within factories indicated release of additional carbon dioxide by-product. These results suggest a factory-dependent cause of split formation that could develop from varied fermentation pathways in the blind, eye, and split areas within a cheese block. The variability of VOC profiles within and between factories exhibit varied biochemical fermentation pathways that could conceivably be traced back in the making process to identify parameters responsible for split defect.

Model food systems and protein functionality

Publisher Summary Model food systems today find utility for investigating the functionality of many other food components, including starches, gums, and emulsifiers, as well as factors affecting areas of continued interest. Model food systems provide a means of determining how the ingredients and the process alter the characteristics of the final product, as well as evaluating the sensitivity of the characteristics of the food to the different ingredients and processing steps. Model food systems are based on the formulation and processing of real foods, using laboratory and pilot plant facilities. Generally, ingredients that do not have a main effect on the final characteristics of the product are eliminated. One potential limitation is the use of processing equipment that does not scale up to commercial equipment. The utilization of carefully selected statistical designs is essential to unravel the multiple interactions that do occur and to optimize food formulation and processing. A major limitation of model food systems is that they do not provide any information as to the mechanisms by which the ingredients and the process control the final characteristics of the product.

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.

Chapter 15 – Model Food Systems and Protein Functionality

Fabricated foods generally comprise a mixture of components made up of lipids, proteins, simple and complex carbohydrates, emulsifiers, and salts, which are capable of interacting with each other and modifying the final characteristics of the food. Often, processing utilized in the manufacture of the food also modifies these interactions. Model food systems were first developed because of the disparity between laboratory functional tests for proteins and the functionality in the food. Model food systems today find utility for the functionality of many other food components, including starches, gums, and emulsifiers, as well as areas of continued interest (lipid oxidation, Maillard reaction, etc.). Therefore, model food systems provide a means of determining how the ingredients and the process alter the characteristics of the final product, as well as evaluating the sensitivity of the characteristics of the food to the different ingredients and processing steps. Model food systems are based on the formulation and processing of real foods, using laboratory and pilot plant facilities. Generally, ingredients that do not have a main effect on the final characteristics of the product are eliminated. One potential limitation is the use of processing equipment that does not scale up to commercial production. The utilization of carefully selected statistical designs is essential to unravel the multiple interactions that do occur and to optimize food formulation and processing. A major limitation of model food systems is that they do not provide any information as to the mechanisms by which the ingredients and the process control the final characteristics of the product. Thus they have application to only the food under investigation. They do have a major role in food product development.