Effects of a lotion containing probiotic ferment lysate as the main functional ingredient on enhancing skin barrier: a randomized, self-control study.

There is an emergent need to develop functional cosmetic ingredients for the topical management of skin barrier function. This study aimed to investigate the efficacy of a lotion containing fermented lysates VHProbi® Mix R for enhancing the skin barrier. In vitro studies demonstrated that fermented cultures of both Lacticaseibacillus rhamnosus VHProbi® E06 (E06) and L. paracasei VHProbi® E12 (E12) had antioxidant capacity, showing promising scavenging capability for 2,2-diphenyl-1-picryl-hydrazyl. The antioxidant capacity of these strains was also demonstrated in the model of Caenorhabditis elegans. In addition, the fermented lysates of both E06 and E12 enhanced the proliferation of HaCaT cells and ameliorated the toxicity induced by Staphylococcus aureus ATCC 25923, hydrogen peroxide, and ultraviolet B radiation in the HaCaT cell models, which simulated the irritants that facial sensitive skin is exposed to. Subsequently, the ingredient VHProbi® Mix R was formulated using four kinds of fermented lysates: E06, E12, Lactiplantibacillus plantarum VHProbi® E15, and Lactobacillus helveticus VHProbi® Y21. A clinical study was conducted to investigate whether a lotion containing VHProbi® Mix R would be beneficial for people to enhance skin barrier. The participants were asked to use the investigational product for 30 days. Several indicators, including transepidermal water loss (TEWL), skin moisturization, and redness were measured at day 0 and day 30 using VISIA®-CR and CK®-MPA systems. Meanwhile, the burden of sensitive skin (BoSS) and self-assessment questionnaires were performed at baseline and endpoint of this study. The study data showed that at day 30, there was a significant decrease in TEWL (P < 0.01), redness measured by CK®-MPA (P < 0.01), and redness profile measured by VISIA®-CR compared with the baseline measurements. Skin moisturization had significantly increased after treatment with the lotion for 30 days. BoSS and self-assessment questionnaires also substantiated that the participants felt a markedly positive change in their sensitive skin. Hence, we hypothesize that applying the topical functional VHProbi® Mix R could confer effective benefits for people with sensitive skin and this represents a promising intervention for enhancing skin barrier.

Antimicrobial effect of nisin in processed cheese - Quantification of residual nisin by LC-MS/MS and development of new growth and growth boundary model for Listeria monocytogenes.

This study tested the hypothesis that growth of Listeria monocytogenes in processed cheese with added nisin can be predicted from residual nisin A concentrations in the final product after processing. A LC-MS/MS method and a bioassay were studied to quantify residual nisin A concentrations and a growth and growth boundary model was developed to predict the antilisterial effect in processed cheese. 278 growth rates were determined in broth for 11 L. monocytogenes isolates and used to determine 13 minimum inhibitory concentration (MIC) values for nisin between pH 5.5 and 6.5. To supplement these data, 67 MIC-values at different pH-values were collected from the scientific literature. A MIC-term was developed to describe the effect of pH on nisin MIC-values. An available growth and growth boundary model (doi: https://doi.org/10.1016/j.fm.2019.103255) was expanded with the new MIC-term for nisin to predict growth in processed cheese. To generate data for model evaluation and further model development, challenge tests with a total of 45 growth curves, were performed using processed cheese. Cheeses were formulated with 11.2 or 12.0 ppm of nisin A and heat treated to obtain residual nisin A concentrations ranging from 0.56 to 5.28 ppm. Below 15 °C, nisin resulted in extended lag times. A global regression approach was used to fit all growth curves determined in challenge tests. This was obtained by combining the secondary growth and growth boundary model including the new term for the inhibiting effect of nisin on µmax with the primary logistic growth model with delay. This model appropriately described the growth inhibiting effect of residual nisin A and showed that relative lag times depended on storage temperatures. With residual nisin A concentrations, other product characteristics and storage temperature as input the new model correctly predicted all observed growth and no-growth responses for L. monocytogenes. This model can support development of nisin A containing recipes for processed cheese that prevent growth of L. monocytogenes. Residual nisin A concentrations in processed cheese were accurately quantified by the developed LC-MS/MS method with recoveries of 83 to 110% and limits of detection and quantification being 0.04 and 0.13 ppm, respectively. The tested bioassay was less precise and nisin A recoveries varied for 53% to 94%.

Predicting growth of Listeria monocytogenes at dynamic conditions during manufacturing, ripening and storage of cheeses - Evaluation and application of models.

Mathematical models were evaluated to predict growth of L. monocytogenes in mould/smear-ripened cheeses with measured dynamic changes in product characteristics and storage conditions. To generate data for model evaluation three challenge tests were performed with mould-ripened cheeses produced by using milk inoculated with L. monocytogenes. Growth of L. monocytogenes and lactic acid bacteria (LAB) in the rind and in the core of cheeses were quantified together with changes in product characteristics over time (temperature, pH, NaCl/aw, lactic- and acetic acid concentrations). The performance of nine available L. monocytogenes growth models was evaluated using growth responses from the present study and from literature together with the determined or reported dynamic product characteristics and storage conditions (46 kinetics). The acceptable simulation zone (ASZ) method was used to assess model performance. A reduced version of the Martinez-Rios et al. (2019) model (https://doi.org/10.3389/fmicb.2019.01510) and the model of Østergaard et al. (2014) (https://doi.org/10.1016/j.ijfoodmicro.2014.07.012) had acceptable performance with a ASZ-score of 71-70% for L. monocytogenes growth in mould/smear-ripened cheeses. Models from Coroller et al. (2012) (https://doi.org/10.1016/j.ijfoodmicro.2011.09.023) had close to acceptable performance with ASZ-scores of 67-69%. The validated models (Martinez-Rios et al., 2019; Østergaard et al., 2014) can be used to facilitate the evaluation of time to critical L. monocytogenes growth for mould/smear-ripened cheeses including modification of recipes with for example reduced salt/sodium or to support exposure assessment studies for these cheeses.

Growth and growth boundary model with terms for melting salts to predict growth responses of Listeria monocytogenes in spreadable processed cheese.

The aim of this study was to develop and validate a growth and growth boundary model with terms for melting salts to predict growth of Listeria monocytogenes in spreadable processed cheese. Cardinal parameter terms for phosphate salts and citric acid were developed in broth studies and used to expand an available growth and growth boundary model. The expanded model includes the effect of nine environmental factors (temperature, pH, aw, lactic acid, acetic acid, citric acid, orthophosphate, di-phosphate and tri-phosphate). To generate growth data for model evaluation challenge tests with inoculated commercial (n = 10) and customized (n = 10) spreadable processed cheeses were performed. Evaluation of the new model by comparison of observed and predicted µmax-values resulted in a bias factor of 1.12 and an accuracy factor of 1.33 (n = 42). Prediction of growth and no-growth responses in processed cheese (n = 60) were 89% correct with 11% fail-safe and 0% fail-dangerous predictions. The developed model can be used to support product development, reformulation or risk assessment for spreadable processed cheese.

New Term to Quantify the Effect of Temperature on pH min -Values Used in Cardinal Parameter Growth Models for Listeria monocytogenes.

The aim of this study was to quantify the influence of temperature on pH min -values of Listeria monocytogenes as used in cardinal parameter growth models and thereby improve the prediction of growth for this pathogen in food with low pH. Experimental data for L. monocytogenes growth in broth at different pH-values and at different constant temperatures were generated and used to determined pH min -values. Additionally, pH min -values for L. monocytogenes available from literature were collected. A new pH min -function was developed to describe the effect of temperatures on pH min -values obtained experimentally and from literature data. A growth and growth boundary model was developed by substituting the constant pH min -value present in the Mejlholm and Dalgaard (2009) model (J. Food. Prot. 72, 2132-2143) by the new pH min -function. To obtain data for low pH food, challenge tests were performed with L. monocytogenes in commercial and laboratory-produced chemically acidified cheese including glucono-delta-lactone (GDL) and in commercial cream cheese. Furthermore, literature data for growth of L. monocytogenes in products with or without GDL were collected. Evaluation of the new and expanded model by comparison of observed and predicted µ max -values resulted in a bias factor of 1.01 and an accuracy factor of 1.48 for a total of 1,129 growth responses from challenge tests and literature data. Growth and no-growth responses of L. monocytogenes in seafood, meat, non-fermented dairy products, and fermented cream cheese were 90.3% correctly predicted with incorrect predictions being 5.3% fail-safe and 4.4% fail-dangerous. The new pH min -function markedly extended the range of applicability of the Mejlholm and Dalgaard (2009) model from pH 5.4 to pH 4.6 and therefore the model can now support product development, reformulation or risk assessment of food with low pH including chemically acidified cheese and cream cheese.

Modelling and predicting growth of psychrotolerant pseudomonads in milk and cottage cheese.

Mathematical models were developed and evaluated for growth of psychrotolerant pseudomonads in chilled milk and in cottage cheese with cultured cream dressing. The mathematical models include the effect of temperature, pH, NaCl, lactic acid and sorbic acid. A simplified cardinal parameter growth rate model was developed based on growth in broth. Subsequently, the reference growth rate parameter µref25°C-broth of 1.031/h was calibrated by fitting the model to a total of 35 growth rates from cottage cheese with cultured cream dressing. This resulted in a µref25°C-cottage cheese value of 0.621/h. Predictions from both growth rate models were evaluated by comparison with literature and experimental data. Growth of psychrotolerant pseudomonads in heat-treated milk (n=33) resulted in a bias factor (Bf) of 1.08 and an accuracy factor (Af) of 1.32 (µref25°C-broth), whereas growth in cottage cheese with cultured cream dressing and in non-heated milk (n=26) resulted in Bf of 1.08 and Af of 1.43 (µref25°C-cottage cheese). Lag phase models were developed by using relative lag times and data from both the present study and from literature. The acceptable simulation zone method showed the developed models to successfully predict growth of psychrotolerant pseudomonads in milk and cottage cheese at both constant and dynamic temperature storage conditions. The developed models can be used to predict growth of psychrotolerant pseudomonads and shelf life of chilled cottage cheese and milk at constant and dynamic storage temperatures. The applied methodology and the developed models seem likely to be applicable for shelf life assessment of other types of products where psychrotolerant pseudomonads are important for spoilage.