Survival and thermal resistance of Salmonella in chocolate products with different water activities.

Contamination of Salmonella in chocolate products has caused worldwide outbreaks and recalls. There is a lack of information on the impact of water activity (aw) on the stability of Salmonella in chocolate products during storage and thermal treatments. In this research, the survival and thermal resistance of a Salmonella cocktail (S. Enteritidis PT30, S. Tennessee K4643, S. Typhimurium S544) was examined in different chocolate products (dark chocolate, white chocolate, milk chocolate) at two aw levels (0.25, 0.50) over 12 months at 22 °C. A reduction of 4.19 log10 CFU/gof Salmonella was obtained in dark chocolate after 12 months (aw = 0.50, at 22 °C); less reductions were observed in white and milk chocolates. In all three products, more reductions were observed ataw = 0.50 than at aw = 0.25 over the 12-months storage. When treated at 80 °C, the D-values (time required to cause 1 log reduction) of the Salmonella cocktail in the chocolate samples with initial aw of 0.25 were 35.7, 25.2 and 11.6 min in dark, white and milk chocolate, respectively, before the storage. The D80°C -values of Salmonella cocktail in the samples with initial aw of 0.50 were 6.45, 7.46, and 3.98 min in dark, white and milk chocolate, respectively. After 12 months of storage at 22 °C, the D80°C-value of Salmonella cocktail decreased to 9.43 min (p < 0.05) in milk chocolate but remained 22.7 min in white chocolate with an aw of 0.25 at 22 °C. The data suggests that Salmonella can survive in chocolate products for up to 12 months, and its thermal resistance remained relatively stable. Thus, Salmonella is resistant to desiccation in chocolates, particularly in milk and white chocolates, and its thermal resistance remains during one-year storage, which could pose a potential threat for future outbreaks.

Time-interleaved system mismatch estimation based on correlation function and particle swarm optimization algorithm.

The time-interleaved analog-to-digital converters (TIADCs) technique is an efficient solution to improve the sampling rate of the acquisition system with low-speed ADCs. However, channel mismatches such as gain mismatch, time skew mismatch, and offset mismatch may seriously degrade the performance of TIADC. Furthermore, for high-speed signal acquisition, the gain and time skew mismatches would vary with the signal frequency, and the traditional fixed model does not work any longer. In this paper, a series of sinusoidal signals are adopted to estimate the variable mismatches. First, an autocorrelation-based approach is presented to estimate the gain mismatch. The information about the gain mismatch is extracted from the autocorrelation function of sub-ADC output samples. Then, the time skew mismatch is estimated by utilizing the particle swarm optimization algorithm. The reported simulation results show that the mismatches can be accurately estimated. Finally, a commercial 12.5 GSPS four-channel TIADC system is utilized to verify the performance of the proposed method. The spurious free dynamic range of the system can be improved by about 20 dB, and the effectiveness of the proposed estimation method is demonstrated.

Understanding water activity change in oil with temperature.

Our recent studies and several publications suggest that the low water activity (aw) of oil in thermal processing might be a major contributing factor towards the increased thermal resistance of bacteria in oils. In this study, we developed a reliable method to measure the water activity of oil by measuring the equilibrium relative humidity in a small headspace. Using this method, water activity of peanut oil was found to decrease exponentially with increasing temperature. A model derived from excess Gibbs free energy was fitted to the observations with an R2 = 99.6% and RMSE = 0.01 (aw). Our results suggest that the sharply reduced water activity of oil resulting from a rise in temperature could cause desiccation of bacteria. This is a possible explanation for the protective effect of oil in thermal processing.

Gas chromatography-mass spectrometry combined with multivariate data analysis as a tool for differentiating between processed orange juice samples on the basis of their volatile markers.

The contents of volatile aroma compounds in orange juice vary after high hydrostatic pressure (HHP) processing and pasteurization, and hence gas chromatography-mass spectrometry has potential for discriminating between orange juice samples according to the processing method. Effects of HHP processing and pasteurization on orange juice were investigated immediately after processing and after storage for 28 days at 4 °C. Processing decreased the contents of most terpenes and esters and increased those of alcohols and aldehydes. Volatile compounds that could serve as discriminant indicators of HHP processing and pasteurization were heptanal, (E)-2-heptenal, (E)-2-nonenal, and d-carvone and ß-terpineol, p-mentha-1,5-dien-8-ol, carveol, and ß-copaene, respectively. The discrimination abilities of pH, ascorbic acid, total soluble solids, and color were compared with those of volatile compounds, which were found to be lower. The compounds d-carvone and ß-terpineol could be used as discriminant indicators of HHP-treated and pasteurized orange juice, respectively, throughout the storage period.