Investigating of Tomato Pastes Microbial Contamination in Iran and Isolation and Identification of Alicyclobacillus acidocaldarius by PCR Method

Introduction Tomato paste is one of the processed tomato products that has a long shelf life and is used as an important food ingredient all over the world. According to global statistics, Iran is among the top ten producers of tomato paste in the world, Iran ranks fourth to fifth in the world in the field of aseptic paste production. Alicyclobacillus bacteria are considered as a risk for pasteurized acidic food industries. These bacteria enter the product through soil-contaminated fruits, production equipment of the factories and finally produce metabolites such as guaiacol, causing an unpleasant taste in the product.   Materials and Methods  In order to investigate the microbial contamination of canned tomato paste in the country, 46 samples of canned tomato paste in the amount of 184 cans of 800 grams were purchased from the market. Regarding the purchase of samples from the market, we tried to buy a different production date and production series for each sample (approximately 4 cans for each brand from each production series). The purchased samples were sent to the Microbiology Department of the Standard Research Institute laboratory for microbiology tests. At the same time, the culture media of thermophilic bacteria (Orange Serum Agar, Thermoacidurans Agar from 4 available brands) were tested for performance control. The canned tomato paste samples were incubated at 30°C ± 1°C for 14 days and 55°C ± 1°C for 7 days.   Results and Discussion  The contents of both examined samples were tested separately for thermophilic bacteria, mesophilic bacteria, mold and yeast. Out of the 46 samples prepared with different production dates and production series, which were 46 cans of tomato paste, 28 samples were positive in terms of contamination with thermophilic bacteria. According to the number of contaminated samples, it was found that 60.86% of the samples were contaminated. Colonies grown on Thermoacidurans Agar medium were examined morphologically. For further investigations, gram staining was performed. All the stained colonies morphologically showed the form of gram-positive rod-shaped bacilli. Biochemical tests including catalase and oxidase were performed to identify Alicyclobacillus species. All the grown colonies were catalase positive and oxidase negative. The final identification of the species was done by performing molecular tests based on specific primers designed from Alicyclobacillus gene. These tests were performed in three stages: genomic DNA extraction, polymerase chain reaction and electrophoresis. Using the PCR method, the grown colonies were analyzed for two types of bacteria, Alicyclobacillus acidocaldarius and Bacillus coagulans. According to the results obtained from sequencing with designed primers in the NCBI database, it showed 100% similarity with the registered sequences, which are all different strains of the Alicyclobacillus acidocaldarius species. None of the colonies were detected as Bacillus coagulans species. Since Alicyclobacillus acidocaldarius was isolated from soil for the first time, the presence of these bacteria in the product indicates the contamination of raw materials with soil.   Conclusion In this research, the presence of Alicyclobacillus bacteria in canned tomato paste was confirmed. Due to the high heat resistance of this bacteria, there is a possibility of the presence of Alicyclobacillus in the all stages of tomato paste production, which have entered the product through the soil, and 95°C ± 3°C pasteurization temperature in 30 minutes is not effective in removing this bacteria completely. Most acidophilus thermophilic bacteria, such as Alicyclobacillus family, are not pathogenic bacteria. Their presence in food may make the food taste bad or smelly, but it does not pose a risk to the health of the consumer. Therefore, in order to reduce the risk of spoilage and to prevent the growth of bacterial spores in the product, it is essential not to expose the product to high temperatures for a long time. It is also necessary to perform rapid cooling after heat treatment and keep the product at a temperature below 30°C.   Acknowledgement This article is the result of a common research project of Microbiology and Biology Research Group of Standard Research Institute and Kermanshah Standard Regional Research Group. We hereby thank and appreciate the cooperation of the microbiology research group of the Standard Research Institute and the Kermanshah General Directorate of Standards. We are also very grateful to Rogin Talk Company as the employer of this project

Bayesian Inference For The Segmented Weibull Distribution

In this paper, we introduce a Bayesian approach for segmented Weibull distributions which could be a good alternative to analyze medical survival data in the presence of censored observations and covariates. With the obtained Bayesian estimated change-points we could get an excellent fit of the proposed model to any data sets. With the proposed methodology, it is also possible to identify survival times intervals where a covariate could have significantly different effects when compared to other lifetime intervals, an important point under a clinical view. The obtained Bayesian estimates are obtained using standard Markov Chain Monte Carlo methods. Some examples with real data sets illustrate the proposed methodology and its potential clinical value

Classification of Spanish ports using cluster analysis

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Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods

This paper aims to analyze the green efficiency performance of the logistics industry in China’s 30 provinces from 2008 to 2017. We first evaluate the green efficiency of the logistics industry through the non-directional distance function (NDDF) method. Then, we use the functional clustering method funHDDC, which is one of the popular machine learning methods, to divide 30 provinces into 4 clusters and analyze the similarities and differences in green efficiency performance patterns among different groups. Further, we explore the driving factors of dynamic changes in green efficiency through the decomposition method. The main conclusions of this paper are as follows: (1) In general, the level of green efficiency is closely related to the geographical location. From the clustering results, we can find that most of the eastern regions belong to the cluster with higher green efficiency, while most of the western regions belong to the cluster with lower green efficiency. However, the green efficiency performance in several regions with high economic levels, such as Beijing and Shanghai, is not satisfactory. (2) Based on the analysis of decomposition results, the innovation effect of China’s logistics industry is the most obvious, but the efficiency change still needs to be improved, and technical leadership should be strengthened. Based on these conclusions, we further propose some policy recommendations for the green development of the logistics industry in China

Intuitionistic Fuzzy Time Series Functions Approach for Time Series Forecasting

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods

Statistical Inference on Middle-Censored Data in a Dependent Setup

In this article, we deal with a dependent middle censoring with a censoring interval of fixed length, where the lifetime and lower bound of censoring interval are variables with a Marshall-Olkin bivariate exponential distribution. In this setup, we derive maximum likelihood estimates of the unknown parameters, using some iterative method. We also propose the Bayes estimates of the parameters under gamma priors and the squared error loss function. Finally, a Monte Carlo simulation is carried out to compare these estimators

Dependent right censorship in the Marshall-Olkin bivariate Weibull distribution

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