Identifying volatile and non‐volatile organic compounds to discriminate cultivar, growth location, and stage of ripening in olive fruits and oils

Abstract

BACKGROUND There is increasing consumer demand for olive oil to be traceable. However, genotype, environmental factors, and stage of maturity, all affect the flavor and composition of both the olives and olive oil. Few studies have included all three variables. Key metabolites include lipids, phenolics, and a wide range of volatile organic compounds (VOCs), which provide the olives and oil with their characteristic flavor. Here we aim to identify markers that are able to discriminate between cultivars, that can identify growth location, and can discriminate stages of fruit maturity. ‘Nocellara messinese’ and ‘Carolea’ olive fruits were grown at three locations differing in altitude in Calabria, Italy, and harvested at three stages of maturity. Oil was analyzed from the two most mature stages. RESULTS Nine and 20 characters discriminated all fruit and oil samples respectively, and relative abundance of two fatty acids distinguished all oils. Whole VOC profiles discriminated among the least mature olives, and oil VOC profiles discriminated location and cultivar at both stages. Three VOCs putatively identified as hexanal, methyl acetate, and 3‐hexen‐1‐ol differentiated all samples of oils from the most mature fruit stage. CONCLUSION The results confirm that interactions of location, cultivar and fruit maturity stage are critical for the overall pattern of aroma compounds, and identify potential markers of commercial relevance. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Ester‐related volatile compounds reveal the diversity and commonalities of different types of late‐ripening peaches

“…Then each peak was matched with its recorded mass spectra in the NIST14 library and confirmed by linear retention index (LRI) (Li et al., 2022; Wang et al., 2022). Volatile compounds scoring >80% in forward and backward fit and a retention index match of ±15 were used to putatively identified volatile compounds (Greco et al., 2022). LRI was calculated using a mixture of n ‐alkanes (C6‐C30) (Sigma‐Aldrich) as follows: $$$$\begin{equation*}{\mathrm{LRI}} = 100 \times \left( {n + \frac{{{T_x} - {T_n}}}{{{T_{n + 1}} - {T_n}}}} \right)\end{equation*}$$$$where T x , T n , and T n+ 1 are the retention times of compound x , alkane n , and alkane n + 1, respectively ( T n < T x < T n+ 1 ).…”

“…Then each peak was matched with its recorded mass spectra in the NIST14 library and confirmed by linear retention index (LRI) (Li et al, 2022;Wang et al, 2022). Volatile compounds scoring >80% in forward and backward fit and a retention index match of ±15 were used to putatively identified volatile compounds (Greco et al, 2022). LRI was calculated using a mixture of n-alkanes (C6-C30) (Sigma-Aldrich) as follows:…”

Maturity Grading and Identification of Camellia oleifera Fruit Based on Unsupervised Image Clustering

“…Based on the facts above, several researchers have attempted to produce a technological breakthrough that can non-invasive identify the maturity of oil fruits such as oil palm [ 24 , 25 , 26 ] and olive [ 27 , 28 ]. Tugnolo et al [ 29 ] used visible/near-infrared imaging in a study that automatic identifying the maturity of olive fruits.…”