A Multi-class Probabilistic Neural Network for Pathogen Classification
| Autor: | Kun Xiang, Walker H. Land, Yinglei Li, William S. Ford, Robert Congdon, Omowunmi A. Sadik |
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| Rok vydání: | 2013 |
| Předmět: |
differential evolution (DE)
Class (computer programming) business.industry Computer science probabilistic neural network (PNN) Food spoilage computer.software_genre Machine learning C. Albicans Task (project management) Set (abstract data type) Kernel (linear algebra) Probabilistic neural network classification Kernel (statistics) S. Epidermis E.coli General Earth and Planetary Sciences Artificial intelligence Data mining business computer General Environmental Science |
| Zdroj: | Complex Adaptive Systems |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2013.09.284 |
| Popis: | Recent outbreaks of listeria, salmonella, and other pathogens have reinforced the need for more rigorous testing of food products. Millions are spent each year testing food. Certifying the safety of the food is a challenging task using traditional testing methods. Current methods require long incubation times before the first results are observed and still only represent a small fraction of the food that is sold. Long analysis methods also lead to loss of consumables. 18.9 billion pounds of produce are lost a year to spoilage. A fast and effective method is needed to decrease the amount of time necessary to test the safety of food. The goal is to provide accurate sample classification as quickly as possible, thus allowing pathogen-free product to be shipped to market with the shortest delay possible. An autonomous electrochemical sensor was combined with a powerful multi-class Probabilistic Neural Network (PNN) system to classify four species of organisms (E. Coli #25922, E. Coli # 11775, S. Epidermis #12228, or C. Albicans #10231). We used an evolutionary based kernel optimization algorithm to optimize the kernel parameters, and trained the system on data sampled from four different organisms. The trained and optimized model was validated on a set containing several samples that were not used to train the network. We showed that the network was able to correctly classify unknown samples in a shorter period than the industry standard of 24hours, thus providing a potential benefit to the agriculture industry. |
| Databáze: | OpenAIRE |
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