Conducting inferential statistics for low microbial counts in foods using the Poisson-gamma regression

Gonzales-Barrón, Úrsula; Cadavez, Vasco; Butler, Francis

doi:10.1016/j.foodcont.2013.09.032

Cited by 16 publications

(12 citation statements)

References 16 publications

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“…These regression models have been recently adopted to analyze microbial count data taking into other covariates into account. [30, 31] In addition, we performed principal component analyses at class, family and species levels where relative abundance of each bacterial group was square root transformed as it performed better than other transformations, such as natural log or arcsine. Analysis of variance was used to compare principal component scores as well as UniFrac distances of principal coordinates between subject groups with adjustment for selected covariates where first coordinate distances were natural log-transformed to improve deviation from normal distribution.…”

Section: Methodsmentioning

confidence: 99%

Oral microbiome and history of smoking and colorectal cancer

Kato

Vasquez

Moyerbrailean

et al. 2016

JER

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Background The equilibrium of oral microbiome may be altered by environmental factors, including cigarette smoking. Several recent studies also suggest that oral pathogens causing periodontal disease, such as Fusobacterium nucleatum, are involved in pathogenesis of colorectal cancer. Methods For this study oral rinse DNA samples from 190 participants in a population-based case-control study for colorectal cancer were used to amplify a V3-V4 region of bacterial 16S rRNA gene. The amplicons were sequenced using Illumina MiSeq paired end chemistry on two runs, yielding approximately 35 million filtered reads which were assigned to bacterial phyla. Results No association was found between Fusobacterium abundance or presence and colorectal cancer. However, adjusted for age and experimental batch, colorectal cancer history was associated with increased presence of genus Lactobacillus and increased relative abundance of Rothia by 28% and current smoking was associated with a 33% decrease in relative counts of Betaproteobacteria (primarily Neisseria) and 23% increase in relative abundance of Veillonellaceae family. We also found that smoking had significant effects on the 2nd component scores and 2nd coordinate distances in principal component and coordinate analyses. Conclusions It remains to be elucidated whether the observed differences can be translated into biochemical changes in oral environment, thus potentially affecting oral health.

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Section: Methodsmentioning

confidence: 99%

Oral microbiome and history of smoking and colorectal cancer

Kato

Vasquez

Moyerbrailean

et al. 2016

JER

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“…As the untransformed L. monocytogenes data (CFU/g) was overdispersed (variance ≫ mean), consisting of low microbial counts and large proportion of zero counts (non-detections), a Poisson-gamma (negative binomial) count data model was opted for. Earlier, Gonzales- Barron, Cadavez, and Butler (2014) demonstrated that this type of count data models along with their zero-modified counterparts are much more suitable for inferential assessment than normality-based regression models when analysing over-dispersed microbiological data. Thus, in order to appraise the same fixed effects as in Eqs.…”

Section: Analysis I: Associations Between Physicochemical Properties and Microbial Counts Along Processingmentioning

confidence: 99%

Relating physicochemical and microbiological safety indicators during processing of linguiça , a Portuguese traditional dry-fermented sausage

Gonzales-Barrón

Cadavez

Pereira

et al. 2015

Food Research International

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Linguiça is a Portuguese traditional fermented sausage whose microbiological quality and safety can be highly variable. In order to elucidate risk factors and the particularities of the manufacturing technology that explain the between-batch variability in total viable counts (TVC), Enterobacteriaceae, Staphylococcus aureus and Listeria monocytogenes in the product; microbiological and physicochemical characterisation of linguiça at five stages of production (i.e., raw pork meat, mixed with ingredients, macerated, smoked and ripened) was carried out. A total of six production batches were surveyed from two factories; one utilised curing salts and polyphosphate in their formulation (Factory II). The delayed fermentation in the nitrite-formulated sausages was partly responsible for the increase (p<0.01) in Enterobacteriaceae, S. aureus and L. monocytogenes from raw meat (3.21logCFU/g, 1.30logCFU/g and 22.2CFU/g, respectively) to the end of maceration (4.14logCFU/g, 2.10logCFU/g and 140CFU/g, respectively) while the better acidification process in the nitrite-free sausages (Factory I) led to lower counts of S. aureus (2.64logCFU/g) and L. monocytogenes (10CFU/g) in the finished products. In Factory II, although L. monocytogenes entered the chain at the point of mixing, it became steadily inactivated during smoking and ripening (<50CFU/g), despite the initially-delayed fermentation. Nitrite had a strong effect on reducing Enterobacteriaceae throughout smoking (r=-0.73) and ripening (r=-0.59), while it failed to control the growth of S. aureus. The main hurdle preventing the development of S. aureus in linguiça is the pH, and other factors contributing to its control are: longer ripening days (p=0.019), low S. aureus in raw meat (p=0.098), properly-washed casings (p=0.094), and less contamination during mixing (p=0.199). In the case of L. monocytogenes, at least three hurdles hinder its development in linguiça: low a (p=0.004), low pH (p=0.040) and nitrite (p=0.060), and other factors contributing to its control are: longer ripening (p=0.072) and maceration (p=0.106) periods, lower a at the end of smoking (p=0.076) and properly-washed casings (p=0.099). Results have shown that there is a need to standardise the productive process of linguiça, to optimise the initial acidification process, and to reinforce proper programmes of quality control of ingredients and good hygiene practices, so as to minimise the introduction of Enterobacteriaceae and pathogens from external sources.

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“…In preharvest practice, a hurdle model can be considered the most optimal modeling choice because modeling predictions can be directly compared and verified against observations. Nevertheless, only a small number of previous studies have applied this model in the food safety field, such as on samples from beef carcasses (24,54). To our knowledge, there are no other studies that investigated risk factors of produce contamination with a microorganism using a hurdle model.…”

Section: Discussionmentioning

confidence: 99%

Multifactorial Effects of Ambient Temperature, Precipitation, Farm Management, and Environmental Factors Determine the Level of Generic Escherichia coli Contamination on Preharvested Spinach

Park

Navratil

Gregory³

et al. 2015

Appl Environ Microbiol

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A repeated cross-sectional study was conducted to identify farm management, environment, weather, and landscape factors that predict the count of generic Escherichia coli on spinach at the preharvest level. E. coli was enumerated for 955 spinach samples collected on 12 farms in Texas and Colorado between 2010 and 2012. Farm management and environmental characteristics were surveyed using a questionnaire. Weather and landscape data were obtained from National Resources Information databases. A two-part mixed-effect negative binomial hurdle model, consisting of a logistic and zero-truncated negative binomial part with farm and date as random effects, was used to identify factors affecting E. coli counts on spinach. Results indicated that the odds of a contamination event (non-zero versus zero counts) vary by state (odds ratio [OR] ‫؍‬ 108.1). Odds of contamination decreased with implementation of hygiene practices (OR ‫؍‬ 0.06) and increased with an increasing average precipitation amount (mm) in the past 29 days (OR ‫؍‬ 3.5) and the application of manure (OR ‫؍‬ 52.2). On contaminated spinach, E. coli counts increased with the average precipitation amount over the past 29 days. The relationship between E. coli count and the average maximum daily temperature over the 9 days prior to sampling followed a quadratic function with the highest bacterial count at around 24°C. These findings indicate that the odds of a contamination event in spinach are determined by farm management, environment, and weather factors. However, once the contamination event has occurred, the count of E. coli on spinach is determined by weather only. Foodborne disease outbreaks associated with produce impose a considerable public health burden (1). Among different produce commodities, leafy green vegetables have been identified as a group of high concern from a microbiological safety perspective due to their being implicated in multiple outbreaks of foodborne disease with high numbers of illnesses worldwide (2). Indeed, leafy green vegetables are commonly grown in open farm fields, where they may be exposed to microbial contamination from soil, manure fertilizer, irrigation water, and intrusions of wild or domestic animals, and they are likely to be consumed fresh or minimally processed. Enteric foodborne pathogens, such as Salmonella and Escherichia coli O157:H7, have been the main causative agents responsible for foodborne outbreaks associated with leafy green vegetables in the United States (3). These pathogens are spread in the environment through feces of infected animals and humans (4). While contamination of leafy greens with these foodborne pathogens has major consequences (2), fortunately it occurs at a low frequency (5, 6). The low frequency and heterogeneous distribution of these pathogens in the produce field make their detection difficult, costly, and time-consuming. Instead indicator organisms are routinely used by the industry, environmental agencies, and public health organizations to verify effective implementation of good ag...

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Conducting inferential statistics for low microbial counts in foods using the Poisson-gamma regression

Cited by 16 publications

References 16 publications

Oral microbiome and history of smoking and colorectal cancer

Oral microbiome and history of smoking and colorectal cancer

Relating physicochemical and microbiological safety indicators during processing of linguiça , a Portuguese traditional dry-fermented sausage

Multifactorial Effects of Ambient Temperature, Precipitation, Farm Management, and Environmental Factors Determine the Level of Generic Escherichia coli Contamination on Preharvested Spinach

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