Study on the buffering capacity of wort

Li, Hong; Liu, Fang; Kang, Lidong; Zheng, Mingjie

doi:10.1002/jib.286

Cited by 18 publications

(15 citation statements)

References 15 publications

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“…For relatively simple defined solutions of weak acids or bases, buffering may be modeled analytically to determine pH, the concentrations of all the ions in solution, as well as uncharged acid or base species. For undefined systems with multiple unknown buffers, the BC can be estimated using the derivative of titration curves for environmental applications such as landfill leachate (Gibs, Shoenberger, & Suffet, 1982), water quality (Van Vooren, Van De Steene, Ottoy, & Vanrolleghem, 2001), and beverages including beer and wine (Dartiguenave, Jeandet, & Maujean, 2000; Li, Liu, Kang, & Zheng, 2015). Estimating the p K and concentration of buffers in solution requires numerical methods for nonlinear curve fitting.…”

Section: Introductionmentioning

confidence: 99%

Modeling buffer capacity and pH in acid and acidified foods

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Conley‐Payton

et al. 2020

Journal of Food Science

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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.

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Section: Introductionmentioning

confidence: 99%

Modeling buffer capacity and pH in acid and acidified foods

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Conley‐Payton

et al. 2020

Journal of Food Science

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“…Previously, BC has been investigated as an important factor influencing beer fermentations (Li, Liu, Kang, & Zheng, ), fermentation disorders in ruminant animals (Hille et al., ) water quality (Van Vooren, Van De Steene, Ottoy, & Vanrolleghem, ), and the BC of whey in cheese‐making (Hill, Irvine, & Bullock, ). However, the use of BC to estimate the pH of food ingredient mixtures or determine the influence food ingredients on equilibrium pH of acid or acidified foods has not been previously reported.…”

Section: Resultsmentioning

confidence: 99%

Modeling the buffer capacity of ingredients in salad dressing products

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Mishra

et al. 2020

Journal of Food Science

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The pH of most acid food products depends on undefined and complex buffering of ingredients but is critically important for regulatory purposes and food safety. Our objective was to define the buffer capacity (BC) of ingredients in salad dressing products. Ingredients of salad dressings were titrated individually and in combination using concentrations typical of dressing products. Titration curves from pH 2 to 12 were generated with sodium hydroxide and hydrochloric acid, which were then used to generate BC curves. A matrix of concentration and pK values for a series of monoprotic buffers approximated the pH of each ingredient. Some buffer series required anion or cation corrections for accurate pH prediction, possibly due to the presence of salts of acid or bases. Most buffers had BC values less than 10-fold the BC of acetic acid (0.25 β) typically in dressing formulations and had little influence on the final product pH of the dressings tested. Unexpectedly, we found that sugars in dressing formulations, including sucrose or corn syrup, exhibited buffering at pH values greater than 11 (0.035 β and 0.059 β, respectively), which was likely due to weakly acidic hydroxyl groups on the sugar molecules. However, the concentration and pK for buffers above pH 11 or below pH 2 were difficult to quantify due to the BC of water. The BC data may help to quantify the effects of salad dressing ingredients on the final product pH and benefit regulatory agencies and manufacturers in assessing product pH and safety.

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“…Both nitrogenous compounds (amino acids and peptides) and phosphates have long been considered as the most important buffering substances in wort . However, a recent study by Li et al questioned the relevant contribution of both free amino acids and phosphates to the overall BC. The authors concluded that, because of the very low or very high p K a values of the α ‐carboxylic acid group (range 1.7–2.2) or α ‐amino group (range 8.8–10.6), respectively, most amino acids contribute only poorly to the BC in the relevant pH range for lactic fermentation .…”

Section: Discussionmentioning

confidence: 99%

Impact of buffering capacity on the acidification of wort by brewing-relevant lactic acid bacteria

et al. 2017

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Acidified wort produced biologically using lactic acid bacteria (LAB) has application during sour beer production and in breweries adhering to the German purity law (Reinheitsgebot). LAB cultures, however, suffer from end product inhibition and low pH, leading to inefficient lactic acid (LA) yields. Three brewing‐relevant LAB (Pediococcus acidilactici AB39, Lactobacillus amylovorus FST2.11 and Lactobacillus plantarum FST1.7) were examined during batch fermentation of wort possessing increasing buffering capacities (BC). Bacterial growth was progressively impaired when exposed to higher LA concentrations, ceasing in the pH range of 2.9–3.4. The proteolytic rest (50°C) during mashing was found to be a major factor improving the BC of wort. Both a longer mashing profile and the addition of an external protease increased the BC (1.21 and 1.24, respectively) compared with a control wort (1.18), and a positive, linear correlation (R2 = 0.957) between free amino nitrogen and BC was established. Higher levels of BC led to significant greater LA concentration (up to +24%) after 48 h of fermentation, reaching a maximal value of 11.3 g/L. Even higher LA (maximum 12.8 g/L) could be obtained when external buffers were added to wort, while depletion of micronutrient(s) (monosaccharides, amino acids and/or other unidentified compounds) was suggested as the cause of LAB growth cessation. Overall, a significant improvement in LA production during batch fermentation of wort is possible when BC is improved through mashing and/or inclusion of additives (protease and/or external buffers), with further potential for optimization when strain‐dependent nutritional requirements, e.g. sugar and amino acids, are considered. Copyright © 2017 The Institute of Brewing & Distilling

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Study on the buffering capacity of wort

Cited by 18 publications

References 15 publications

Modeling buffer capacity and pH in acid and acidified foods

Modeling buffer capacity and pH in acid and acidified foods

Modeling the buffer capacity of ingredients in salad dressing products

Impact of buffering capacity on the acidification of wort by brewing-relevant lactic acid bacteria

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