Discrimination of four different postharvest treatments of ¡®Red Delicious¡¯ apples based on their volatile organic compound (VOC) emissions during shelf-life measured by proton transfer reaction mass spectrometry (PTR-MS)
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- 作者：Flavio Ciesa ; Josef Dalla Via ; Armin Wisthaler ; Angelo Zanella ; Walter Guerra ; Tomas Mikoviny ; Tilmann D. M?rk ; Michael Oberhuber
- 关键词：Apple (Malus ; × ; ; domestica Borkh.) ; &lsquo ; Red Delicious&rsquo ; Shelf-life ; PTR-MS ; Volatile organic compounds ; Storage techniques
- 刊名：Postharvest Biology and Technology
- 出版年：2013
- 出版时间：December, 2013
- 年：2013
- 卷：86
- 期：Complete
- 页码：329-336
- 全文大小：1314 K
文摘
Storage methods extend the postharvest life of apples from weeks to up to one year; however, these methods also alter the production of volatile organic compounds (VOCs), which amongst others, are important for aroma attributes. While the impact of storage on particular aroma components has been established, high throughput methods for determining the storage history during shelf-life are elusive. Here we show the potential of proton transfer reaction-mass spectrometry (PTR-MS), an MS-based metabolic fingerprinting technique, for characterizing fruit in the postharvest chain. The VOC fingerprint of apples (Malus ¡Á domestica Borkh. ¡®Red Delicious¡¯) was analyzed by PTR-MS during four weeks of shelf-life ripening after storage under four different storage conditions: ULO (ultra-low oxygen), DCA-CF (dynamic controlled atmosphere monitored by chlorophyll fluorescence), RLOS (repeated low oxygen stress) and 1-MCP (1-methylcyclopropene) in ULO. PTR fingerprint mass spectra of the apple headspace, obtained in short time without sample preparation or preconcentration, were sufficient to discriminate the four storage conditions during shelf-life. Moreover, we were able to monitor the changes in quality-critical VOC classes, including esters and terpenes, during shelf-life and observe the differential impact of the storage history on these VOCs. This work emphasizes the potential of PTR-MS as a valuable addition to targeted GC-MS-based approaches in postharvest research.