The Effects of High Pressure Processing on Peanut Sauce Inoculated with Salmonella

Recent Salmonella outbreaks have prompted the need for new processing options for peanut products. Traditional heating kill-steps have shown to be ineffective in lipid-rich matrices such as peanut products. High pressure processing is one such option for peanut sauce because it has a high water activity, which has proved to be a large contributing factor in microbial lethality due to high pressure processing. Four different formulations of peanut sauce were inoculated with a five strain Salmonella cocktail and high pressure processed. Results indicate that increasing pressure or increasing hold time increases log10 reductions. The Weibull model was fitted to each kill curve, with b and n values significantly optimized for each curve (p-value < 0.05). Most curves had an n parameter value less than 1, indicating that the population had a dramatic initial reduction, but tailed off as time increased, leaving a small resistant population. ANOVA analysis of the b and n parameters show that there are more significant differences between b parameters than n parameters, meaning that most treatments showed similar tailing effect, but differed on the shape of the curve. Comparisons between peanut sauce formulations at the same pressure treatments indicate that increasing amount of organic peanut butter within the sauce formulation decreases log10 reductions. This could be due to a protective effect from the lipids in the peanut butter, or it may be due to other factors such as nutrient availability or water activity. Sauces pressurized at lower temperatures had decreased log10 reductions, indicating that cooler temperatures offered some protective effect. Log10 reductions exceeded 5 logs, indicating that high pressure processing may be a suitable option as a kill-step for Salmonella in industrial processing of peanut sauces. Future research should include high pressure processing on other peanut products with high water activities such as sauces and syrups as well as research to determine the effects of water activity and lipid composition with a food matrix such as peanut sauces.

Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.