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Securing the physical quality and microbial safety of fresh foods has been a major focus in the food industry. To improve quality and increase the shelf life of fresh produce, disinfection methods have been developed. Titanium dioxide (TiO2) photocatalytic reactions under UV radiation produce hydroxyl radicals that can be used for disinfection of foodborne pathogenic bacteria. We investigated the effects of TiO2-UV photocatalytic disinfection on the shelf life of iceberg lettuce. Counts of natural microflora (total aerobic bacteria, coliforms, psychrotrophic bacteria, and yeasts and molds) and inoculated pathogenic bacteria (Escherichia coli, Listeria monocytogenes, Staphylococcus aureus, and Salmonella Typhimurium) on iceberg lettuce were determined after 20-min treatments with TiO2-UV, UV radiation, a sodium hypochlorite (NaOCl) solution, and tap water. TiO2-UV treatment reduced the number of microorganisms by 1.8 to 2.8 log CFU/g compared with reductions of 0.9 to 1.4 and 0.7 to 1.1 log CFU/g obtained with UV radiation and NaOCl treatments, respectively. Treatment with tap water was used as a control and resulted in no reductions. Counts of microflora for iceberg lettuce at 4 and 25 degrees C were determined during a 9-day period. TiO2-UV treatment resulted in 1.2- and 4.3-log increases in the counts of total aerobic bacteria at 4 and 25 degrees C, respectively, compared with 1.3- to 1.6-log and 4.4- to 4.8-log increases due to UV radiation and NaOCl treatments.

Researches on pattern recognition have been tremendously performed in various fields because of its wide use in both machines and human beings. Previously, traditional methods used to study pattern recognition problems were not strong enough to recognize patterns accurately as compared to optimization algorithms. In this study, we employ both traditional based methods to detect the edges of each pattern in an image and apply convolutional neural networks to classify the right and wrong pattern of the cropped part of an image from the raw image. The results indicate that edge detection methods were not able to detect clearly the patterns due to low quality of the raw image while CNN was able to classify the patterns at an accuracy of 84% within 1.5 s for 10 epochs.

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