Modeling the buffer capacity of ingredients in salad dressing products

Comparison of commercial allergen ELISA kits for egg detection in food matrices

Consumption of low levels of egg already can evoke harmful physiological responses in humans in those allergic to eggs. By detection of egg in food products, using Egg ELISA kits to determine its unintended presence, food producers can respond to avoid potential safety or quality risks of their products. Selection of an ELISA kit fit for the issue at hand is challenging due to, amongst others, lack of information on assay performances with specified matrices. In this study, performances of seven commercial egg ELISA kits are compared for nine different relevant matrices: cookie, chocolate, pasta, dressing, stock cube, wine, vegetable drink and milk, ice cream and meat/meat replacers. The presence of egg was unified for all ELISA kits to mg total egg protein kg-1 food product. In every matrix, kit performances for recovery, intra- and interassay were compared, and also processing is accounted for by determination of egg in incurred samples. All seven kits were able to detect egg qualitatively at the VITAL3 ED01 level of 0.2 mg total egg protein and the corresponding relevant portion size for each matrix. For quantitative results, each ELISA kit showed an increase in detected egg concentration with increased egg levels and performed within the set criteria for recovery for the cookie, chocolate, stock cube and wine. For pasta, vegetable drink and milk, ice cream, and salad dressing, recovery of egg was within the set criteria for at least 4 ELISA kits. Most challenging matrices were meat/meat replacers, showing high matrix effects which could not be explained by the possible egg presence in the cognate blank. Only one ELISA kit was able to recover egg within the set criteria for the meat/meat replacer matrix. Results enable food industry to choose for ELISA kits suitable for egg detection in the matrix of interest.

Buffer Models for pH and Acid Changes Occurring in Cucumber Juice Fermented with Lactiplantibacillus pentosus and Leuconostoc mesenteroides

ABSTRACT

The pH changes that occur during the fermentation of vegetables by lactic acid bacteria depend on the production of weak acids and on the buffering of the fermentation medium. Undefined buffering components of fermentation media make estimates of pH from acid production difficult. The objective of this research was to develop buffer models for a model cucumber fermentation brine system linking pH changes to acid concentrations. A novel titration method was used to measure buffer capacity in cucumber juice medium made from three grades of pickling cucumbers based on diameter. Fermentation of juice made from cucumbers of different sizes resulted in differences in fermentation biochemistry. The results of modeling indicated that the pH of the medium after 24 and 48 h of fermentation by heterolactic Leuconostoc mesenteroides and homolactic Lactiplantibacillus pentosus could be predicted from acid concentrations based on the measured buffer capacity of the corresponding unfermented medium. The differences for all observed and predicted pH values of the fermentation samples, based on measured acid concentrations, had a root mean square error of 0.064 pH units. The buffer models included a quantitative measure of the effect on pH of the malolactic reaction caused by the lactic acid bacteria. These models may have application for assessing the influence of a variety of lactic acid bacteria buffering reactions on pH and fermentation ecology by linking pH to fermentation acid concentrations.

HIGHLIGHTS

Modeling buffer capacity and pH in acid and acidified foods

Standard ionic equilibria equations may be used for calculating pH of weak acid and base solutions. These calculations are difficult or impossible to solve analytically for foods that include many unknown buffering components, making pH prediction in these systems impractical. We combined buffer capacity (BC) models with a pH prediction algorithm to allow pH prediction in complex food matrices from BC data. Numerical models were developed using Matlab software to estimate the pH and buffering components for mixtures of weak acid and base solutions. The pH model was validated with laboratory solutions of acetic or citric acids with ammonia, in combinations with varying salts using Latin hypercube designs. Linear regressions of observed versus predicted pH values based on the concentration and pK values of the solution components resulted in estimated slopes between 0.96 and 1.01 with and without added salts. BC models were generated from titration curves for 0.6 M acetic acid or 12.4 mM citric acid resulting in acid concentration and pK estimates. Predicted pH values from these estimates were within 0.11 pH units of the measured pH. Acetic acid concentration measurements based on the model were within 6% accuracy compared to high-performance liquid chromatography measurements for concentrations less than 400 mM, although they were underestimated above that. The models may have application for use in determining the BC of food ingredients with unknown buffering components. Predicting pH changes for food ingredients using these models may be useful for regulatory purposes with acid or acidified foods and for product development. PRACTICAL APPLICATION: Buffer capacity models may benefit regulatory agencies and manufacturers of acid and acidified foods to determine pH stability (below pH 4.6) and how low-acid food ingredients may affect the safety of these foods. Predicting pH for solutions with known or unknown buffering components was based on titration data and models that use only monoprotic weak acids and bases. These models may be useful for product development and food safety by estimating pH and buffering capacity.