Over the past decades, there has been increase in the consumption of the fresh tomato in the United States; this has been attributed to the nutritional benefits of fresh tomato, its widespread use in cooking and its availability throughout the year. In a Food and Agricultural Organization report, the United States was ranked as one of the largest producers of the fresh tomato in the world. In spite of its large production capacity, large quantities of the tomatoes are still being imported to the United States annually from Mexico. Series of multistate outbreaks of Salmonella infection have been associated with consumption of the fresh tomatoes; traceback of the tomatoes implicated in salmonellosis has been traced to tomatoes grown domestically. However, a survey conducted by U.S. Department of Agriculture on both domestic and imported tomatoes determined that imported fresh tomato was Salmonella positive. The purpose of this study was to determine the microbiological quality of fresh tomatoes imported from Mexico to the United States. The study consisted of sampling surfaces of cleaned tomatoes in Mexico prior to packing and shipping to the United States, and sampling of the tomato wash water at the end of the work shift at a Mexican tomato packinghouse. Four tomatoes were randomly sampled prior to packing, and they were rinsed with Universal Preenrichment Broth (UPB), this was repeated 10 times per working shift, with 2 shifts per day. 102 l of tomato wash water were collected and sampled with the aid of Modified Moore’s Swab (MMS) and membrane filter. The tomato wash water was collected at the end of shift twice daily. Both fruit and wash water samplings were repeated 3 times during the tomato harvesting season. Both the tomato UPB rinsates and the membrane filter were assayed for the E. coli and enterococci populations. Additionally, the tomato UPB rinsates and MMS were assayed for the presence of Salmonella. The results of the microbiological analysis on the UPB rinsates showed that no Salmonella was present, E. coli was not detectable (< 1.0), and the mean populations of enterococci were log 3.8, 2.6, and 1.0 CFU/g in sampling trials 1, 2, and 3 respectively. In the tomato wash water, no Salmonella was present, and no E. coli and enterococci were detected. Therefore, it was concluded that the microbiological quality of the tomatoes that were sampled and tested were high, this was due to the fact that all the samples collected tested negative to Salmonella analysis, and no E. coli was detected in any of the samples.

Since the passage of the Clean Water Act, concern about surface water quality has increased. Reducing exposure to pathogens and adverse impacts on human health because of contact with surface waters has become the focus of many regulatory agencies. Fecal pollution is often a cause of surface water impairment. Fecal indicators, such as fecal coliforms and Escherichia coli, are used as surrogates to evaluate the presence or absence of fecal pollution. However, a growing body of research has shown that these species lack key characteristics necessary to be adequate indicators. As such, explorations into the efficacy of indicator species in predicting fecal pollution in water are necessary. Sinking Creek is a tributary of the Watauga River Watershed, located in Northeast Tennessee. Approximately ten miles of Sinking Creek have been placed on the national 303(d) list for fecal pollution, denoting the presence of fecal contamination exceeding the regulatory limit. Salmonella and Aeromonas are two enteric pathogens that would be expected to be detected in fecally contaminated waters. The primary objective of this study was to detect the presence of Salmonella and Aeromonas in Sinking Creek. The secondary objective was to evaluate their relationship with fecal coliforms, E. coli, and water quality parameters. Six study sites along Sinking Creek were sampled and standard methods were used to enumerate Salmonella and Aeromonas. Samples for Salmonella were collected for 8 months, while samples for Aeromonas were collected for seven. Salmonella and Aeromonas were present in Sinking Creek. Salmonella had the highest concentration at site 2 (the most downstream site), and was detected during all months of the study except for November. Salmonella concentrations varied by site. Aeromonas was present only during colder months, and had the highest concentration at site 2. Both Salmonella and Aeromonas show qualitative relationships with water quality parameters, such as dissolved oxygen and conductivity. However, statistically significant correlations of Salmonella and Aeromonas with water quality parameters were not observed. The lack of statistical significance is partially due to large variability and a small data set. Neither Salmonella or Aeromonas exhibited a relationship with fecal coliforms or E. coli. Therefore, fecal coliforms and E. coli may not be adequate indicator species for the presence of Salmonella, Aeromonas and possibly other waterborne pathogens. Traditional indicator species may inflate risk of pathogen exposure. Thus, many water bodies may be unnecessarily deemed as impaired. The results from this study can be used to guide further research regarding covariates influencing pathogen densities at fecally contaminated sites, as well as to guide decisions regarding impaired surface waters and management techniques.

More than 270,000 km of rivers and streams are impaired due to fecal pathogens, creating an economic and public health burden. Fecal indicator organisms such as Escherichia coli are used to determine if surface waters are pathogen impaired, but they fail to identify human health risks, provide source information, or have unique fate and transport processes. Statistical and machine learning models can be used to overcome some of these weaknesses, including identifying ecological mechanisms influencing fecal pollution. In this study, canonical correlation analysis (CCorA) was performed to select parameters for the machine learning model, Maxent, to identify how chemical and microbial parameters can predict E. coli impairment and F+-somatic bacteriophage detections. Models were validated using a bootstrapping cross-validation. Three suites of models were developed; initial models using all parameters, models using parameters identified in CCorA, and optimized models after further sensitivity analysis. Canonical correlation analysis reduced the number of parameters needed to achieve the same degree of accuracy in the initial E. coli model (84.7%), and sensitivity analysis improved accuracy to 86.1%. Bacteriophage model accuracies were 79.2, 70.8, and 69.4% for the initial, CCorA, and optimized models, respectively; this suggests complex ecological interactions of bacteriophages are not captured by CCorA. Results indicate distinct ecological drivers of impairment depending on the fecal indicator organism used. Escherichia coli impairment is driven by increased hardness and microbial activity, whereas bacteriophage detection is inhibited by high levels of coliforms in sediment. Both indicators were influenced by organic pollution and phosphorus limitation.

Background: The leading cause of surface water impairment in United States’ rivers and streams is pathogen contamination. Although use of fecal indicators has reduced human health risk, current approaches to identify and reduce exposure can be improved. One important knowledge gap within exposure assessment is characterization of complex fate and transport processes of fecal pollution. Novel modeling processes can inform watershed decision-making to improve exposure assessment. Methods: We used the ecological model, Maxent, and the fecal indicator bacterium Escherichia coli to identify environmental factors associated with surface water impairment. Samples were collected August, November, February, and May for 8 years on Sinking Creek in Northeast Tennessee and analyzed for 10 water quality parameters and E. coli concentrations. Univariate and multivariate models estimated probability of impairment given the water quality parameters. Model performance was assessed using area under the receiving operating characteristic (AUC) and prediction accuracy, defined as the model’s ability to predict both true positives (impairment) and true negatives (compliance). Univariate models generated action values, or environmental thresholds, to indicate potential E. coli impairment based on a single parameter. Multivariate models predicted probability of impairment given a suite of environmental variables, and jack-knife sensitivity analysis removed unresponsive variables to elicit a set of the most responsive parameters. Results: Water temperature univariate models performed best as indicated by AUC, but alkalinity models were the most accurate at correctly classifying impairment. Sensitivity analysis revealed that models were most sensitive to removal of specific conductance. Other sensitive variables included water temperature, dissolved oxygen, discharge, and NO3. The removal of dissolved oxygen improved model performance based on testing AUC, justifying development of two optimized multivariate models; a 5-variable model including all sensitive parameters, and a 4-variable model that excluded dissolved oxygen. Discussion: Results suggest that E. coli impairment in Sinking Creek is influenced by seasonality and agricultural run-off, stressing the need for multi-month sampling along a stream continuum. Although discharge was not predictive of E. coli impairment alone, its interactive effect stresses the importance of both flow dependent and independent processes associated with E. coli impairment. This research also highlights the interactions between nutrient and fecal pollution, a key consideration for watersheds with multiple synergistic impairments. Although one indicator cannot mimic the plethora of existing pathogens in water, incorporating modeling can fine tune an indicator’s utility, providing information concerning fate, transport, and source of fecal pollution while prioritizing resources and increasing confidence in decision making. Methods We used the ecological model, Maxent, and the fecal indicator bacterium Escherichia coli to identify environmental factors associated with surface water impairment. Samples were collected August, November, February, and May for 8 years on Sinking Creek in Northeast Tennessee and analyzed for 10 water quality parameters and E. coli concentrations. Univariate and multivariate models estimated probability of impairment given the water quality parameters. Model performance was assessed using area under the receiving operating characteristic (AUC) and prediction accuracy, defined as the model’s ability to predict both true positives (impairment) and true negatives (compliance). Univariate models generated action values, or environmental thresholds, to indicate potential E. coli impairment based on a single parameter. Multivariate models predicted probability of impairment given a suite of environmental variables, and jack-knife sensitivity analysis removed unresponsive variables to elicit a set of the most responsive parameters. Results Water temperature univariate models performed best as indicated by AUC, but alkalinity models were the most accurate at correctly classifying impairment. Sensitivity analysis revealed that models were most sensitive to removal of specific conductance. Other sensitive variables included water temperature, dissolved oxygen, discharge, and NO3. The removal of dissolved oxygen improved model performance based on testing AUC, justifying development of two optimized multivariate models; a 5-variable model including all sensitive parameters, and a 4-variable model that excluded dissolved oxygen. Discussion Results suggest that E. coli impairment in Sinking Creek is influenced by seasonality and agricultural run-off, stressing the need for multi-month sampling along a stream continuum. Although discharge was not predictive of E. coli impairment alone, its interactive effect stresses the importance of both flow dependent and independent processes associated with E. coli impairment. This research also highlights the interactions between nutrient and fecal pollution, a key consideration for watersheds with multiple synergistic impairments. Although one indicator cannot mimic theplethora of existing pathogens in water, incorporating modeling can fine tune an indicator’s utility, providing information concerning fate, transport, and source of fecal pollution while prioritizing resources and increasing confidence in decision making.

More than 270,000 km of rivers and streams are impaired due to fecal pathogens, creating an economic and public health burden. Fecal indicator organisms such as Escherichia coli are used to determine if surface waters are pathogen impaired, but they fail to identify human health risks, provide source information, or have unique fate and transport processes. Statistical and machine learning models can be used to overcome some of these weaknesses, including identifying ecological mechanisms influencing fecal pollution. In this study, canonical correlation analysis (CCorA) was performed to select parameters for the machine learning model, Maxent, to identify how chemical and microbial parameters can predict E. coli impairment and F+-somatic bacteriophage detections. Models were validated using a bootstrapping cross-validation. Three suites of models were developed; initial models using all parameters, models using parameters identified in CCorA, and optimized models after further sensitivity analysis. Canonical correlation analysis reduced the number of parameters needed to achieve the same degree of accuracy in the initial E. coli model (84.7%), and sensitivity analysis improved accuracy to 86.1%. Bacteriophage model accuracies were 79.2, 70.8, and 69.4% for the initial, CCorA, and optimized models, respectively; this suggests complex ecological interactions of bacteriophages are not captured by CCorA. Results indicate distinct ecological drivers of impairment depending on the fecal indicator organism used. Escherichia coli impairment is driven by increased hardness and microbial activity, whereas bacteriophage detection is inhibited by high levels of coliforms in sediment. Both indicators were influenced by organic pollution and phosphorus limitation.

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Made available in DSpace on 2016-12-08T16:24:09Z (GMT). No. of bitstreams: 1 PGCA11MA075.pdf: 737868 bytes, checksum: 5473c383aab70553ce6a1f7fcce2d52c (MD5) Previous issue date: 2011-11-11 / The aim of this study was to validate the use of n-alkanes C31, C32 and C33 on estimates of intake and digestibility and assess the accuracy of the use of indicators and acid detergent fiber (ADF), compared to the lignin in the studies of duodenal flow of nutrients. In addition, was to studied the use of indicators of fecal content in the estimation of digestibility and forage intake in lambs receiving ryegrass green. Two assays were conducted digestibility "in vivo" with 16 days each (11 for adaptation and 5 collection). The treatments consisted of diets composed by Italian ryegrass (Lolium multiflorum Lam.), in two successive cycles of grazing (28 and 35 days regrowth). The n-alkane C33 showed fecal recovery close to 1,0 in both tests and was more accurate in predicting the digestibility of C31. The intestinal flows of dry matter (DM) estimated from the C31, FDA or lignin were similar. In addition, the flow estimates obtained by internal ADF and lignin markers proved closely associated (linear coefficient = 1,016, r2 = 0,809). In local validation of the use of n-alkanes to estimate the consumption pair C33: C32 showed better relationship with the observed dry matter intake, since it held the correction for recovery rate of fecal C32. For the study of fecal indicators index the best estimates of consumption of organic matter (IOM) were obtained when using the amounts excreted nitrogen (g / day) and neutral detergent fiber (NDF, g / day) associated with the content forage NDF (r² = 0,78, rsd = 0,095). In conclusion, estimates of consumption and / or digestibility using the technique of n-alkanes should be preceded by local validation tests, studies of the intestinal flow of nutrients the FDA can be used instead of the lignin and estimates of forage intake in sheep may be made depending on the amount of nitrogen excreted per day and NDF / Os objetivos deste trabalho foram validar o uso dos n-alcanos C31, C32 e C33 em estimativas de consumo e digestibilidade e avaliar a exatidão do uso destes indicadores e da fibra em detergente ácido (FDA), em comparação à lignina, nos estudos de fluxo duodenal de nutrientes. Além disso, foi estudado o uso de indicadores de índice fecal na estimativa da digestibilidade e do consumo de forragem em ovinos recebendo azevém anual verde. Foram conduzidos dois ensaios de digestibilidade in vivo com 16 dias de duração cada (11 de adaptação e 5 de coleta). Os animais utilizados foram oito ovinos machos, castrados, mantidos em gaiolas de metabolismo. A dieta fornecida foi composta exclusivamente de azevém anual (Lolium multiflorum Lam.) verde em dois ciclos de desenvolvimento (28 e 35 dias de rebrote). O n-alcano C33 apresentou recuperação fecal próxima de 1,0 em ambos os ensaios e se mostrou mais exato na predição da digestibilidade aparente que o C31. Os fluxos intestinais de matéria seca (MS) estimados a partir do C31, FDA ou lignina foram semelhantes. Além disso, as estimativas de fluxo obtidas pelos marcadores internos FDA e lignina se mostraram estreitamente associadas (coeficiente linear = 1,016; r2 = 0,809). Na validação local da utilização dos n-alcanos para estimativa do consumo o par C33:C32 mostrou melhor relação com o consumo de matéria seca observado, desde que realizada a correção pra taxa de recuperação fecal do C32. Para o estudo com indicadores de índices fecais as melhores estimativas do consumo de matéria orgânica (CMO) foram obtidas quando se utilizou as quantidades excretadas de nitrogênio (g/dia) e fibra em detergente neutro (FDN, g/dia) associados ao teor de FDN na forragem (r² = 0,78; dpr = 0,095). Em conclusão, estimativas do consumo e/ou digestibilidade aparente utilizando a técnica dos n-alcanos devem ser precedidas de ensaios de validação local; nos estudos do fluxo intestinal de nutrientes o FDA pode ser utilizado em substituição a lignina e estimativas do consumo de forragem em ovinos podem ser efetuadas em função da quantidade de nitrogênio e FDN excretados por dia