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A commercial electronic nose (e-nose) equipped with a metal oxide sensor array was trained to recognize volatile compounds emitted by potatoes experimentally infected with Ralstonia solanacearum or Clavibacter michiganensis subsp. sepedonicus, which are bacterial agents of potato brown and ring rot, respectively. Two sampling procedures for volatile compounds were tested on pooled tubers sealed in 0.5-1 L jars at room temperature (laboratory conditions): an enrichment unit containing different adsorbent materials (namely, Tenax(®) TA, Carbotrap, Tenax(®) GR, and Carboxen 569) directly coupled with the e-nose (active sampling) and a Radiello(™) cartridge (passive sampling) containing a generic Carbograph fiber. Tenax(®) TA resulted the most suitable adsorbent material for active sampling. Linear discriminant analysis (LDA) correctly classified 57.4 and 81.3% total samples as healthy or diseased, when using active and passive sampling, respectively. These results suggested the use of passive sampling to discriminate healthy from diseased tubers under intermediate and real scale conditions. 80 and 90% total samples were correctly classified by LDA under intermediate (100 tubers stored at 4°C in net bag passively sampled) and real scale conditions (tubers stored at 4°C in 1.25 t bags passively sampled). Principal component analysis (PCA) of sensorial analysis data under laboratory conditions highlighted a strict relationship between the disease severity and the responses of the e-nose sensors, whose sensitivity threshold was linked to the presence of at least one tuber per sample showing medium disease symptoms. At intermediate and real scale conditions, data distribution agreed with disease incidence (percentage of diseased tubers), owing to the low storage temperature and volatile compounds unconfinement conditions adopted.

ELECTRONIC NOSE AS A RAPID AND INNOVATIVE TOOL FOR THE DIAGNOSIS OF GRAPEVINE CROWN GALL The electronic nose (E-nose) is an innovative device developed for microbiological, food/drink and environmental safety, and medical applications. E-nose comprises appropriate sensor arrays, able to detect and discriminate several flavours and odours from different sources. In the last decade, E-nose is being applied to recognize plant diseases caused from different quarantine bacterial pathogens such as Erwinia amylovora, Ralstonia solanacearum and Clavibacter michiganensis subsp. sepedonicus. In grapevines, tumourigenic strains of Agrobacterium vitis cause crown gall disease which may give serious problems in nurseries and vineyards. For the first time, a portable E-nose (PEN3) was used to discriminate between galled and healthy vines (controls), experimentally inoculated with two different strains of A. vitis and water, respectively. E-nose data were related to the increase of inoculation site diameters measured 9 months after inoculation with the pathogen. Preliminary gas chromatography-mass spectrometry (GC-MS) analysis were performed to identify volatile species from grapevine samples and to optimize experimental conditions for sensorial analysis. Solid phase microextraction (SPME) CAR/PDMS fibers were used to concentrate trace level vapour emissions. Spectra from tumoured vines and controls had almost identical volatile compositions, except the key compound styrene detected in infected samples. In plants, styrene formation is compatible with decarboxylation of cinnamic acid involved in secondary metabolism of plants. All data (E-nose sensors responses and diameter increases) from samples were processed with Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA application allowed a discrete separation of the samples into two groups, according to the presence or absence of disease symptoms. Moreover, the LDA model allowed the correct sample classification between healthy and galled vines (83.3%, cross validation). Tumours caused by the two strains of the pathogen were not discriminated according to the volatile compound composition identified by GC-MS. Although a larger number of grapevine samples should be analysed to create a more robust model, our results give interesting clues to go further with research on the diagnostic potential of this innovative system associated with multi-dimensional chemometric techniques. Keywords: E-nose; Agrobacterium vitis; GC-MS.