Limitations of on-site dairy farm regulatory debits as milk quality predictors.

In the United States, compliance with grade A raw fluid milk regulatory standards is assessed via laboratory milk quality testing and by on-site inspection of producers (farms). This study evaluated the correlation between on-site survey debits being marked and somatic cell count (SCC) or standard plate count (SPC) laboratory results for 1,301 Wisconsin grade A dairy farms in 2012. Debits recorded on the survey form were tested as predictors of laboratory results utilizing ordinary least squares regression to determine if results of the current method for on-site evaluation of grade A dairy farms accurately predict SCC and SPC test results. Such a correlation may indicate that current methods of on-site inspection serve the primary intended purpose of assuring availability of high-quality milk. A model for predicting SCC was estimated using ordinary least squares regression methods. Step-wise selected regressors of grouped debit items were able to predict SCC levels with some degree of accuracy (adjusted R2=0.1432). Specific debit items, seasonality, and farm size were the best predictors of SCC levels. The SPC data presented an analytical challenge because over 75% of the SPC observations were at or below a 25,000 cfu/mL threshold but were recorded by testing laboratories as at the threshold value. This classic censoring problem necessitated the use of a Tobit regression approach. Even with this approach, prediction of SPC values based on on-site survey criteria was much less successful (adjusted R2=0.034) and provided little support for the on-site survey system as a way to inform farmers about making improvements that would improve SPC. The lower level of correlation with SPC may indicate that factors affecting SPC are more varied and differ from those affecting SCC. Further, unobserved deficiencies in postmilking handling and storage sanitation could enhance bacterial growth and increase SPC, whereas postmilking sanitation will have no effect on SCC because somatic cells do not reproduce in stored milk. Results suggest that close examination, and perhaps redefinition, of survey debits, along with making the survey coincident with SCC and SPC sampling, could make the on-site survey a better tool for ensuring availability of high-quality milk.

Correlation between standard plate count and somatic cell count milk quality results for Wisconsin dairy producers.

The objective of this study was to determine if a correlation exists between standard plate count (SPC) and somatic cell count (SCC) monthly reported results for Wisconsin dairy producers. Such a correlation may indicate that Wisconsin producers effectively controlling sanitation and milk temperature (reflected in low SPC) also have implemented good herd health management practices (reflected in low SCC). The SPC and SCC results for all grade A and B dairy producers who submitted results to the Wisconsin Department of Agriculture, Trade, and Consumer Protection, in each month of 2012 were analyzed. Grade A producer SPC results were less dispersed than grade B producer SPC results. Regression analysis showed a highly significant correlation between SPC and SCC, but the R(2) value was very small (0.02-0.03), suggesting that many other factors, besides SCC, influence SPC. Average SCC (across 12 mo) for grade A and B producers decreased with an increase in the number of monthly SPC results (out of 12) that were ≤ 25,000 cfu/mL. A chi-squared test of independence showed that the proportion of monthly SCC results >250,000 cells/mL varied significantly depending on whether the corresponding SPC result was ≤ 25,000 or >25,000 cfu/mL. This significant difference occurred in all months of 2012 for grade A and B producers. The results suggest that a generally consistent level of skill exists across dairy production practices affecting SPC and SCC.

Predicting growth-no growth of Listeria monocytogenes on vacuum-packaged ready-to-eat meats.

Compliance with U.S. Department of Agriculture (USDA) composition-based labeling standards often has been regarded as evidence of the shelf stability of ready-to-eat (RTE) meats. However, the USDA now requires further proof of shelf stability. Our previous work included development of equations for predicting the probability of Staphylococcus aureus growth based on the pH and a(w) of an RTE product. In the present study, we evaluated the growth-no-growth during 21 degrees C storage of Listeria monocytogenes on 39 vacuum-packaged commercial RTE meat products with a wide range of pH (4.6 to 6.5), a(w) (0.47 to 0.98), and percent water-phase salt (%WPS; 2.9 to 34.0). Pieces of each product were inoculated with a five-strain cocktail of L. monocytogenes and vacuum packaged, and L. monocytogenes levels were determined immediately after inoculation and after storage at 21 degrees C for up to 5 weeks. L. monocytogenes grew on 13 of 14 products labeled "keep refrigerated" but not on any of the 25 products sold as shelf stable. Using bias reduction logistic regression data analysis, the probability of L. monocytogenes growth (Pr) could be predicted as a function of pH and a(w): Pr = exp[-59.58 + (4.67 x pH) + (35.05 x a(w))]/{1 + exp[-59.58 + (4.67 x pH) + (35.05 x a(w))]}. Pr also could be predicted as a function of pH and %WPS: Pr = exp[-20.52 + (4.10 x pH) - (0.51 x %WPS)]/{1 + exp[-20.52 + (4.10 x pH) - (0.51 x %WPS)]}. The equations accurately predicted L. monocytogenes growth (Pr values of 0.68 to 0.99) or no growth (Pr values of <0.01 to 0.26) and with our equations for predicting S. aureus growth will be useful for evaluating RTE meat shelf stability.

Mathematical approaches to estimating lag-phase duration and growth rate for predicting growth of Salmonella serovars, escherichia coli O157:H7, and Staphylococcus aureus in raw beef, bratwurst, and poultry.

This study was done to optimize accuracy of predicting growth of Salmonella serovars, Escherichia coli O157:H7, and Staphylococcus aureus in temperature-abused raw beef, poultry, and bratwurst (with salt but without added nitrite). Four mathematical approaches were used with experimentally determined lag-phase duration (LPD) and growth rate (GR) values to develop 12 versions of THERM (Temperature History Evaluation for Raw Meats; http://www.meathaccp.wisc.edu/ THERM/calc.aspx), a computer-based tool that calculates elapsing lag phase or growth that occurs in each entered time interval and sums the results of all intervals to predict growth. Each THERM version utilized LPD values calculated by linear interpolation, quadratic equation, piecewise linear regression, or exponential decay curve and GR values calculated by linear interpolation, quadratic equation, or piecewise linear regression. Each combination of mathematical approaches for LPD and GR calculations was defined as another THERM version. Time, temperature, and pathogen level (log CFU per gram) data were obtained from 26 inoculation experiments with ground beef, pork sausages, and poultry. Time and temperature data were entered into the 12 THERM versions to obtain pathogen growth. Predicted and experimental results were qualitatively described and compared (growth defined as > 0.3-log increase) or quantitatively compared. The 12 THERM versions had qualitative accuracies of 81.4 to 88.6% across 70 combinations of product, pathogen, and experiment. Quantitative accuracies within +/-0.3 log CFU were obtained for 51.4 to 67.2% of the experimental combinations; 82.9 to 88.6% of the quantitative predictions were accurate or fail-safe. Piecewise linear regression or linear interpolation for calculating LPD and GR yielded the most accurate THERM performance.

Predicting growth-no growth of Staphylococcus aureus on vacuum-packaged ready-to-eat meats.

U.S. Department of Agriculture (USDA) composition-based labeling standards for various ready-to-eat (RTE) meat products typically specify maximum product pH and/or moisture:protein ratio and less often maximum water activity (a(w)). Compliance with these standards often has been regarded as proof of shelf stability. However, the USDA now requires additional proof, e.g., challenge study results, of shelf stability. The pathogen most likely to grow on vacuum-packaged, reduced-moisture products is Staphylococcus aureus. Therefore, vacuum-packaged RTE products that do not support S. aureus growth at room temperature could be considered shelf stable. We developed mathematical equations for predicting whether S. aureus would grow under such conditions. Twenty-four commercial RTE meat products and 10 intentionally misprocessed products (insufficient drying, fermentation, and/or salt) were inoculated with a five-strain cocktail of S. aureus, vacuum packaged, and stored at 21 degrees C. Initial, 7-day, and 28-day S. aureus counts were recorded. Product pH, a(w), moisture:protein ratio, and percentage of water-phase salt (%WPS) also were determined. S. aureus grew only in the intentionally misprocessed products and in some commercial products labeled "keep refrigerated." Using bias reduction logistic regression data analysis, the probability of S. aureus growth (Pr) could be predicted by either of two equations. The first was based on pH and a(w) values: Pr = exp[-59.36 + (5.75 x pH) + (28.73 x a(w))]/{1 + [exp(-59.36 + (5.75 x pH) + (28.73 x a(w))]}. The second was based on pH and %WPS: Pr = exp[-26.93 + (5.38 x pH) + (-0.61 x %WPS)]/{1 + exp[-26.93 + (5.38 x pH) + (-0.61 x %WPS)]}. These equations accounted for observed S. aureus growth-no growth results and will be a useful tool for evaluating the shelf stability of RTE meats.

Predicting pathogen growth during short-term temperature abuse of raw sausage.

Lag-phase duration (LPD) and growth rate (GR) values were calculated from experimental data obtained using a previously described protocol (S. C. Ingham, M. A. Fanslau, G. M. Burnham, B. H. Ingham, J. P. Norback, and D. W. Schaffner, J. Food Prot. 70:1445-1456, 2007). These values were used to develop an interval accumulation-based tool designated THERM (temperature history evaluation for raw meats) for predicting growth or no growth of Salmonella serovars, Escherichia coli O157:H7, and Staphylococcus aureus in temperature-abused raw sausage. Data (time-temperature and pathogen log CFU per gram) were obtained from six inoculation experiments with Salmonella, E. coli O157:H7, and S. aureus in three raw pork sausage products stored under different temperature abuse conditions. The time-temperature history from each experiment was entered into THERM to predict pathogen growth. Predicted and experimental results were described as growth (> 0.3 log increase in CFU) or no growth (< or = 0.3 log increase in CFU) and compared. The THERM tool accurately predicted growth or no growth for all 18 pathogen-experiment combinations. When compared with the observed changes in log CFU values for the nine pathogen-experiment combinations in which pathogens grew, the predicted changes in log CFU values were within 0.3 log CFU for three combinations, exceeded observed values by 0.4 to 1.5 log CFU in four combinations, and were 1.2 to 1.4 log CFU lower in two combinations. The THERM tool approach appears to be useful for predicting pathogen growth versus no growth in raw sausage during temperature abuse, although further development and testing are warranted.