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Prediction of Penicillium expansum Spoilage and Patulin Concentration in Apples Used for Apple Juice Production by Electronic Nose Analysis

Karlshoj, K., Nielsen, P.V., Larsen, T.O.
Journal of agricultural and food chemistry 2007 v.55 no.11 pp. 4289-4298
apples, food quality, microbiological quality, prediction, food spoilage, apple juice, screening, quality control, electronic nose, high performance liquid chromatography, patulin, Penicillium expansum, volatile compounds, metabolites, product grading, sorting
Classification models for Penicillium expansum spoilage of apples and prediction models for patulin concentration in apples usable for apple juice production were made on the basis of electronic nose (e-nose) analysis correlated to HPLC quantification of patulin. A total of 15 Golden Delicious and 4 Jonagold apples were surface sterilized and divided into three groups per variety. The Golden Delicious group consisted of five apples each. Group 1 was untreated control, group 2 was surface inoculated with P. expansum, and group 3 was inoculated in the core with P. expansum. The apples were incubated at 25 °C for 10 days. E-nose analysis was performed daily. At day 10 the Golden Delicious apples were individually processed for apple juice production. During apple juice production the mash and juice were analyzed by e-nose, and samples were taken for patulin analysis by HPLC. The volatile metabolite profile was obtained by collection of volatile metabolites, on tubes containing Tenax TA, overnight between the 9th and 10th days of incubation and subsequent analysis of the collected compounds by GC-MS. Prediction models using partial least-squares, with high correlation, for prediction of patulin concentration in shredded apples as well as apple juice were successfully created. It was also shown that it is possible to classify P. expansum spoilage in apples correctly on the basis of soft independent modeling of class analogy classification of e-nose analysis data. To the authors' knowledge this is the first report of a regression model between e-nose data and mycotoxin content in which actual concentrations are reported. This implies that it is possible to predict mycotoxin production and concentration by e-nose analysis.