A non-target screening study of high-density polyethylene pipes revealed rubber compounds as main contaminant in a drinking water distribution system.

Polyethylene (PE) pipes are often the material of choice for water supply systems, thanks to their favorable properties, such as high strength-density ratio and corrosion resistance. However, previous studies have shown that organic compounds can migrate from PE pipes to the water. This study aimed to identify potential organic compounds migrating from high-density PE (HDPE) pipes used to distribute drinking water in Denmark, based on laboratory experiments and sampling in the distribution system using a two-tiered study design. In the first tier, migration of volatile and semi-volatile organic compounds (VOCs and semi-VOCs) from HDPE pipes were investigated over one, three, and nine days in laboratory experiments, performed according to modified standards for migration testing (EN 12,873-1). The analytical workflow consisted of solid-phase extraction (SPE) for 10,000 times enrichment and gas chromatography - mass spectrometry (GC-MS) analysis from the water phase after migration. A total of 133 compounds originating from the PE pipes were detected. Thirty-one compounds were detected by suspect screening (SS), while the remaining 102 compounds were detected by non-target screening (NTS) analysis. Among the detected compounds were also hindered amine stabilizers (HALS), flame retardant, and plasticizer tris(2-chloroethyl) phosphate. In the second tier, drinking water from a water distribution system in Copenhagen, Denmark, with a newly installed HDPE pipe was sampled and analyzed with GC-MS and liquid chromatography high-resolution mass spectrometry (LCHRMS). A total of 51 compounds were detected in the water, 12 of which were assigned to migration from HDPE. Surprisingly, HDPE antioxidants and their degradation products contributed only a relatively small percentage of the total measured compound intensities in the drinking water distribution system. Instead, a larger proportion of the compounds detected were assigned to rubber seals, used upstream in the water system from the abstraction site to delivery at the consumer tap. Seals are considered trifle in the larger picture of materials in contact with drinking water, however these results may cause a reconsideration of this position.

Predicting growth of Listeria monocytogenes in fresh ricotta.

Challenge tests with eight brands of fresh ricotta showed rapid growth of Listeria monocytogenes and significant variability in physical-chemical characteristics. Thus, two cardinal parameters models were developed for the growth of L. monocytogenes in ricotta including, respectively, terms for temperature (Model 1) and temperature and pH (Model 2). Also an extensive, existing growth model including the effect of organic acids (Model 3) was product recalibrated to predict growth of L. monocytogenes in ricotta. Interestingly, a lack of anti-listerial effect of organic acids in ricotta was observed in this study. The range of applicability of Models 1 and 2 in ricotta (characterized by absence of competitive microbiota) included storage at temperatures from 4.1 to 20.6 °C, pH from 5.5 to 6.6, moisture contents from 72% to 82%, NaCl from 0.38% to 0.60%, concentrations of acetic acid from 579 to 1559 ppm in the water phase, citric acid from 14,774 to 46,116 ppm in the water phase, and lactic acid from 0 to 4146 ppm in the water phase. Comparing observed and predicted maximum specific growth rates of L. monocytogenes in ricotta showed a bias-factor significantly above 1, for existing models developed for broth and these models were thus fail-safe with predicted growth being faster than observed, while typically below 1 for models developed for other food types. The models developed in the present study showed bias-factors of 1.10, 1.06 and 0.78, respectively, for Model 1, 2, and 3. In particular, Model 1 and 2 developed and successfully validated could allow an easy determination of safe shelf-life of ricotta and facilitated the reformulation the product with the aim to increase shelf-life or safety.

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.

Modeling the growth of Listeria monocytogenes in soft blue-white cheese.

The aim of this study was to develop a predictive model simulating growth over time of the pathogenic bacterium Listeria monocytogenes in a soft blue-white cheese. The physicochemical properties in a matrix such as cheese are essential controlling factors influencing the growth of L. monocytogenes. We developed a predictive tertiary model of the bacterial growth of L. monocytogenes as a function of temperature, pH, NaCl, and lactic acid. We measured the variations over time of the physicochemical properties in the cheese. Our predictive model was developed based on broth data produced in previous studies. New growth data sets were produced to independently calibrate and validate the developed model. A characteristic of this tertiary model is that it handles dynamic growth conditions described in time series of temperature, pH, NaCl, and lactic acid. Supplying the model with realistic production and retail conditions showed that the number of L. monocytogenes cells increases 3 to 3.5 log within the shelf life of the cheese.