Chemical, enzymatic or microbial activities from the surrounding environment and the food itself can cause spoilage to food products. In the meantime, the recent surge in world population, calls forfood products to be stored and delivered from one place to another place. During delivery, food products will start to deteriorate, losetheir appearance and decrease in nutritional values. Thus, the presence of food preservation methods such as heating, pickling, edible coating, drying, freezing and high-pressure processing can solve this problem by extending the food products" shelf life, stabilize their quality, maintaining their appearance and their taste. There are two categories of food preservations, the modern technology preservation method and the conventional preservation method. In the meantime, conventional food preservations usually use natural food preservatives. Meanwhile, the use of the synthetic preservative such as sulphites, benzoates, sorbates etc. for food preservation can cause certain health problems. In this light, replacing these synthetic preservatives with natural preservatives such as salt, vinegar, honey, etc. are much safer for human and environment. Furthermore, natural preservatives are easy to obtain since the sources are from plant, animal and microbes origin. This review paper focuses on preservation methodsand the natural preservatives that are suitable to be used for food preservation.
Asphalt cracks are one of the major road damage problems in civil field as it may potentially threaten the road and highway safety. Crack detection and classification is a challenging task because complicated pavement conditions due to the presence of shadows, oil stains and water spot will result in poor visual and low contrast between cracks and the surrounding pavement. In this paper, the network proposed a fully automated crack detection and classification using deep convolution neural network (DCNN) architecture. First, the image of pavement cracks manually prepared in RGB format with dimension of 1024x768 pixels, captured using NIKON digital camera. Next, the image will segmented into patches (32x32 pixels) as a training dataset from the original pavement cracks and trained DCNN with two different filter sizes: 3x3 and 5x5. The proposed method has successfully detected the presence of crack in the images with 98%, 99% and 99% of recall, precision and accuracy respectively. The network was also able to automatically classify the pavement cracks into no cracks, transverse, longitudinal and alligator with acceptable classification accuracy for both filter sizes. There was no significant different in classification accuracy between the two different filters. However, smaller filter size need more processing training time compared to the larger filter size. Overall, the proposed method has successfully achieved accuracy of 94.5% in classifying different types of crack.
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