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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
Published on: September 2, 2020
Multiple internal standard normalization for improving HS-SPME-GC-MS quantitation in virgin olive oil volatile
Martina Fortini1, Marzia Migliorini1, Chiara Cherubini1
1PromoFirenze, Azienda Speciale della CCIAA di Firenze, Divisione Laboratorio Chimico, Via Orcagna 70, 50121 Firenze, Italy.
Abstract:
The commercial value of virgin olive oils (VOOs) strongly depends on their classification, also based on the aroma of the oils, usually evaluated by a panel test. Nowadays, a reliable analytical method is still needed to evaluate the volatile organic compounds (VOCs) and support the standard panel test method. To date, the use of HS-SPME sampling coupled to GC-MS is generally accepted for the analysis of VOCs in VOOs. However, VOO is a challenging matrix due to the simultaneous presence of: i) compounds at ppm and ppb concentrations; ii) molecules belonging to different chemical classes and iii) analytes with a wide range of molecular mass. Therefore, HS-SPME-GC-MS quantitation based upon the use of external standard method or of only a single internal standard (ISTD) for data normalization in an internal standard method, may be troublesome. In this work a multiple internal standard normalization is proposed to overcome these problems and improving quantitation of VOO-VOCs. As many as 11 ISTDs were used for quantitation of 71 VOCs. For each of them the most suitable ISTD was selected and a good linearity in a wide range of calibration was obtained. Except for E-2-hexenal, without ISTD or with an unsuitable ISTD, the linear range of calibration was narrower with respect to that obtained by a suitable ISTD, confirming the usefulness of multiple internal standard normalization for the correct quantitation of VOCs profile in VOOs. The method was validated for 71 VOCs, and then applied to a series of lampante virgin olive oils and extra virgin olive oils. In light of our results, we propose the application of this analytical approach for routine quantitative analyses and to support sensorial analysis for the evaluation of positive and negative VOOs attributes.
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