Chapters authored
Models Fitting to Pattern Recognition in Hyperspectral Images
Worldwide, the concern on food safety, for example, on agriculture products, has become a topic with huge relevance. Nowadays, hyperspectral imaging systems for rapid detection of dangerous agents have emerged in response to these needs. In this research project, we proposed a new algorithm for Salmonella typhimurium detection on tomato surfaces in visible range (400–1000 nm). Gaussian model was used as a way to take out a model that could be calculated its definite integral; the final result of this algorithm is the area under curve (AUC), which gives a quantitative approach of spectral signatures. Three doses (5, 10, and 15 μL) and a control response (0 μL) were spread out on 20 tomatoes’ surface. Subsequently, it was observed that some decrease responses with higher dose; also, numerically this pattern was seen with the help of AUC value. As a last step, a single factor analysis of variance showed no significance due to doses. Despite this outcome, the algorithm provides to be a good methodology for pathogen detection.
Part of the book: Hyperspectral Imaging in Agriculture, Food and Environment