Optical study of laser biospeckle activity in leaves of Jatropha curcas L. A noninvasive analysis of foliar endophyte colonization

Currently, the detection of endophytic fungi is determined mostly by invasive methods, including direct isolation of fungal organismsfrom plant tissue in growth media, molecular detection of endophyticfungi DNA from plant material by PCR, or evaluation under microscopy techniques.In this work we explore the potential of laser biospeckle activity (LBSA) to be usedfor the detection of endophytic colonization of leaves of a promising energy crop, Jatropha curcas L. We compared the laser biospeckle activityof endophyte infected and uninfected J. curcas leaves. The differences between blade and veins (including midrib) of the studied leaves was validated and growth parameters of the studied plants were also analyzed using the normalized weigthed generalized differencescoefficient (nWGD). The obtained results showeda relationship between the endophytic burden of leaves and the LBS, suggesting that LSBA is a useful tools to detect endophytic colonization in situ.Also, the increasedwater movements inside leaves promoted by endophytic colonizationcould be explainby the obtained data

Characterization of spatial-temporal patterns in dynamic speckle sequences using principal component analysis

Speckle is being used as a characterization tool for the analysis of the dynamics of slow-varying phenomena occurring in biological and industrial samples at the surface or near-surface regions. The retrieved data take the form of a sequence of speckle images. These images contain information about the inner dynamics of the biological or physical process taking place in the sample. Principal component analysis (PCA) is able to split the original data set into a collection of classes. These classes are related to processes showing different dynamics. In addition, statistical descriptors of speckle images are used to retrieve information on the characteristics of the sample. These statistical descriptors can be calculated in almost real time and provide a fast monitoring of the sample. On the other hand, PCA requires a longer computation time, but the results contain more information related to spatial-temporal patterns associated to the process under analysis. This contribution merges both descriptions and uses PCA as a preprocessing tool to obtain a collection of filtered images, where statistical descriptors are evaluated on each of them. The method applies to slow-varying biological and industrial processes.Facultad de IngenieríaCentro de Investigaciones Óptica