BACKGROUND: Penicillium digitatum and Penicillium italicum are responsible for one the most serious diseases occurring during storage of citrus fruits. Its early detection allows a relevant increase in shelf life, and in situ monitoring of fungal infections represents a very efficient tool to improve storage quality. In the case of metabolic alterations due to physiological or fungal pathologies, olfactometric analysis allows the detection of specific volatile biomarkers, thus providing an effective tool for postharvest quality control of fruits and vegetables. RESULTS: A total of 300 Valencia oranges were analysed with an electronic nose and results were screened by a multivariate classification technique, partial least squares discriminant analysis, in order to investigate whether the electronic nose could distinguish between Penicillium-infected and non-infected samples and to evaluate the efficiency of the group classifications. High percentages of correct classification were obtained at low levels of infection (100% for 2-5% infection in an independent test). CONCLUSION: The electronic nose may be successfully applied as a reliable, non-destructive and non-contact indirect technology for the identification of fungal strains in storage rooms, especially when the infection occurs in small percentages that are not easily identifiable by classic methodologies of inspection.
BACKGROUND:Penicillium digitatum and Penicillium italicum are responsible for one the most serious diseases occurring during storage of citrus fruits. Its early detection allows a relevant increase in shelf life, and in situ monitoring of fungal infections represents a very efficient tool to improve storage quality. In the case of metabolic alterations due to physiological or fungal pathologies, olfactometric analysis allows the detection of specific volatile biomarkers, thus providing an effective tool for postharvest quality control of fruits and vegetables. RESULTS: A total of 300 Valencia oranges were analysed with an electronic nose and results were screened by a multivariate classification technique, partial least squares discriminant analysis, in order to investigate whether the electronic nose could distinguish between Penicillium-infected and non-infected samples and to evaluate the efficiency of the group classifications. High percentages of correct classification were obtained at low levels of infection (100% for 2-5% infection in an independent test). CONCLUSION: The electronic nose may be successfully applied as a reliable, non-destructive and non-contact indirect technology for the identification of fungal strains in storage rooms, especially when the infection occurs in small percentages that are not easily identifiable by classic methodologies of inspection.