Predicting Survival of Escherichia Coli O157:H7 in Dry Fermented Sausage Using Artificial Neural Networks
Bibliographic record
Abstract
The Canadian Food Inspection Agency requires the meat industry to ensure Escherichia coli(E.coli) O157:H7 does not survive in dry fermented sausage (salami) after a series of food borne illnessoutbreaks resulted from the presence of this pathogenic bacterium. The industry is in need of an alternativetechnique like predictive modeling for estimating bacterial viability because traditional microbiologicalenumeration is a time-consuming and laborious method. Testing the accuracy and speed of artificial neuralnetworks (ANNs) for this purpose is a current trend in predictive microbiological research, especially for onlineprocessing in industries. The data from study of interactive effects of different levels of pH, water activity (Aw),the concentration of allyl isothyocyanate (AIT) at various time intervals during sausage manufacture in reducingEscherichia coli O157:H7 were collected. Data were used to develop predictive models using GeneralRegression Neural Network (GRNN) (a form of ANN) and a statistical linear polynomial regression technique.Both models were compared for their prediction error using various statistical indices. GRNN predictions fortraining and test data sets had fewer and less serious errors when compared with the statistical modelpredictions. GRNN models were far superior and considerably superior respectively, for training and test setsthan the statistical model. Because it is simple, fast and quite accurate, ANN model can be used for onlineprocessing by research and development departments or quality control sections of meat processing industryto ensure product safety, and specifically for processing to eliminate Escherichia coli O157:H7 from dryfermented sausage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".