Cocoa smoky off-flavour: A MS-based analytical decision maker for routine controls. (30th January 2021) - Scavarda, Camilla; Cordero, Chiara; Strocchi, Giulia; Bortolini, Cristian; Bicchi, Carlo; Liberto, Erica
Highlights: HS-SPME-MS-enose provides diagnostic fingerprints to discriminate defective cocoa samples. Low specificity of MS-enose is compensated by improved diagnostics, speed and cost compared to sensory approaches. Both bean and liquor cocoa samples can be classified as good or defective. SIMCA models provide higher sensitivity and specificity than PLS-DA classification. Quantitative GC–MS by MHE cross-validates results of the MS-enose analytical approach. Abstract: Cocoa smoky off-flavour is generated from an inappropriate artificial drying applied on beans to speeding up the post-harvest process and it can affect the quality of the chocolate. The sensory tests are time-consuming, and at present, a fast analytical method to detect this defect in raw materials is not yet available. This study applies a HS-SPME-MS-enose in combination with chemometrics to obtain diagnostic mass-spectral patterns to detect smoked samples and/or as analytical decision maker. SIMCA models provide the best classification results, compared to PLS-DA, with sensitivities exceeding 90% and a high class specificity range of 89–100% depending on the matrix investigated (beans or liquors). Resulting diagnostic ions were related to phenolic derivatives. The discrimination ability of the method has been confirmed by a quantitative analysis through HS-SPME-GC–MS. HS-SPME-MS-enose turned out to be a fast, cost-effective and objective approach for high throughput analytical screening to discard defective cocoa samples.