Cocoa smoky off-flavour: A MS-based analytical decision maker for routine controls. (30th January 2021)
- Record Type:
- Journal Article
- Title:
- Cocoa smoky off-flavour: A MS-based analytical decision maker for routine controls. (30th January 2021)
- Main Title:
- Cocoa smoky off-flavour: A MS-based analytical decision maker for routine controls
- Authors:
- Scavarda, Camilla
Cordero, Chiara
Strocchi, Giulia
Bortolini, Cristian
Bicchi, Carlo
Liberto, Erica - Abstract:
- 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 defectiveHighlights: 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. … (more)
- Is Part Of:
- Food chemistry. Volume 336(2021)
- Journal:
- Food chemistry
- Issue:
- Volume 336(2021)
- Issue Display:
- Volume 336, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 336
- Issue:
- 2021
- Issue Sort Value:
- 2021-0336-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-01-30
- Subjects:
- Cocoa volatiles -- Smoky off-flavour -- Phenolic derivatives -- HS-SPME-MS-enose -- Chemometrics -- HS-SPME-GC–MS
Food -- Analysis -- Periodicals
Food -- Composition -- Periodicals
664 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03088146 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.foodchem.2020.127691 ↗
- Languages:
- English
- ISSNs:
- 0308-8146
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3977.284000
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British Library HMNTS - ELD Digital store - Ingest File:
- 14330.xml