Assessment of Strawberry Aroma through SPME/GC and ANN Methods. Classification and Discrimination of Varieties

Abstract

To provide an efficient and running analytical tool to strawberry plant breeders who have to characterize and compare the aromatic properties of new cultivars to those already known, a HS-SPME/GC-MS analysis method has been coupled with a statistical treatment method issued from the current development of artificial neuron networks (ANN), and more specifically, the unsupervised learning systems called Kohonen self-organizing maps (SOMs). So, 70 strawberry samples harvested at CIREF from 17 known varieties have been extracted by using a DVB/Carboxen/PDMS SPME fiber according to the headspace procedure, and then chromatographed. A panel of 23 characteristic aromatic constituents has been selected according to published results relative to strawberry aroma. The complex resulting matrix, collecting the relative abundance of the 23 selected constituents for each sample, has been input into the SOM software adapted and optimized from the Kohonen approach described by one of the authors. After a period of training, the self-organized system affords a map of virtual strawberries to which real samples are compared and plotted in the best matching unit (BMU) of the map. The efficiency for discriminating the real samples according to their variety is dependent on the number of units selected to define the map. In this case, a 24-unit map allowed the complete discrimination of the 17 selected varieties. Moreover, to test the validity of this approach, two additional samples were blind-analyzed and the results were computed according to the same procedure. At the end of this treatment, both samples were plotted into the same unit as those of the same variety used for training the map.

Artificial Neural Networks in Fruits: A Comprehensive Review

“…In order to provide an efficient and running analytical tool to strawberry plant breeders who have to characterize and compare the aromatic properties of new cultivars to those already known, a HS−SPME/GC−MS analysis method has been coupled with a statistical treatment method issued from the current development of ANN, and more specifically, the unsupervised learning systems called Kohonen self-organizing maps (SOMs) [43]. Seventy strawberry samples harvested at CIREF from 17 known varieties were extracted by using a DVB/Carboxen/PDMS SPME fiber according to the headspace procedure, and then chromatographed.…”

Volatile compounds in noni (Morinda citrifolia L.) at two ripening stages

“…This fiber was selected according to the best results for the extraction of fruit volatiles (PAWLISZYN, 1999;HARMON, 2002). For each extraction, 10 g of pulp (without seeds) and 20 mL of 20% NaCl solution (to inhibit enzymatic reactions and to favour the transfer of the analytes from the aqueous solution to the headspace) were blended in a Braun MR 400 juicer for 2 minutes and then centrifuged at 3000 rev/min for 10 minutes, a procedure essentially identical to those previously used for other fruits (FALLIK et al, 2001;URRUTY et al, 2002;SERVILI et al, 2006). An 8 mL aliquot of the supernatant was transferred into a 15 mL Teflon-lined septum cap vial equipped with a tefloncoated magnetic bar.…”

Analysis of strawberry volatiles using comprehensive two-dimensional gas chromatography with headspace solid-phase microextraction

“…Some have used whole fruits, with extraction times varying from 5 to 45 min, and equilibration times from 5 min to 2 h at temperatures up to 30 • C [10,15,18,19]. Others have used chopped or puréed fruits, with or without the addition of salts and internal standards, with or without stirring [18,[20][21][22][23]. Little has been published on the reproducibility and repeatability of each method.…”