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Stagonospora nodorum blotch (SNB) is an\ud important wheat disease caused by Parastagonospora\ud nodorum (Berk.) Studies in Mycology, 75: 307–390\ud (2013). This necrotrophic pathogen can attack common\ud and durum wheat, causing grain yield, quality and safety\ud worsening. This work aims to studying a novel diagnostic\ud technique for early detecting P. nodorum attack and\ud infection progression in three Italian commercial varieties\ud of durum wheat. Plants of these wheat varieties were\ud inoculated in field with a spore suspension of\ud P. nodorum. Different experimental conditions were\ud used: P. nodorum was inoculated in plants at different\ud growth stage (GS); GS32 (Inf A) and GS 73 (milk\ud development - Inf B). To consider natural contaminating\ud mycoflora interference for the subsequent analysis, noninoculated\ud controls, fungicide-treated (T) or non-treated\ud (NT) were used. Inf A produced symptoms both on\ud leaves and ears which appeared to be more severe compared\ud to those induced by Inf B, even if the latter\ud affected more thoroughly wheat yield in at least one\ud variety of wheat. An early detection of SNB could be\ud helpful to manage a targeted control strategy.\ud Hyperspectral imaging analyses showed that this rapid\ud and non-invasive technique allows differentiation between\ud spectra of healthy and diseased tissues already\ud 48 h after inoculation. qPCR confirmed hyperspectral\ud results. The results showed that marked differences\ud emerge in the ability of the varieties to tolerate the\ud SNB disease especially regarding grain yield and quality.\ud Compared with classical approaches, the\ud hyperspectral imaging analysis may represent a useful\ud tool for early detection of P. nodorum on wheat

Single kernels of durum wheat have been analyzed by hyperspectral imaging (HSI). Such an approach is based on the utilization of an integrated hardware and software architecture able to digitally capture and handle spectra as an image sequence, as they results along a pre-defined alignment on a surface sample properly energized. The study was addressed to investigate the possibility to apply HSI techniques for classification of different types of wheat kernels: vitreous, yellow berry and fusarium-damaged. Reflectance spectra of selected wheat kernels of the three typologies have been acquired by a laboratory device equipped with an HSI system working in near infrared field (1000-1700 nm). The hypercubes were analyzed applying principal component analysis (PCA) to reduce the high dimensionality of data and for selecting some effective wavelengths. Partial least squares discriminant analysis (PLS-DA) was applied for classification of the three wheat typologies. The study demonstrated that good classification results were obtained not only considering the entire investigated wavelength range, but also selecting only four optimal wavelengths (1104, 1384, 1454 and 1650 nm) out of 121. The developed procedures based on HSI can be utilized for quality control purposes or for the definition of innovative sorting logics of wheat. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE)