| Autor: |
Wiebke Kaziur-Cegla, Maik A. Jochmann, Karl Molt, Andreas Bruchmann, Torsten C. Schmidt |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Food Chemistry: X, Vol 14, Iss , Pp 100337- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2590-1575 |
| DOI: |
10.1016/j.fochx.2022.100337 |
| Popis: |
Honey is the oldest and nowadays widely used natural sweetener for food worldwide. Its composition is associated with its botanical and geographical origin and honey is often mislabeled and has a high potential for food fraud. Thus, quick easy and sensitive analyses are required. For the first time, we developed and applied an automated, fast, sensitive and robust, in-tube extraction dynamic headspace in-tube extraction-dynamic headspace (ITEX-DHS) method for a variety of Honey containing VOCs in connection with GC–MS. Another advantage of ITEX is, that it is a green analytical solventless method. The method provides very low method detection limits (MDL) from 0.8 to 47 ng g−1 for VOCs in honey samples as well as very good repeatabilities with averages below 9 % RSD. Recoveries are between 83 and 100 %. Only octanal possess a repeatability 13 % and a recovery of 62 % due to its high polarity. 38 honey samples were measured after method validation. Four acacia honeys (A), six forest honeys (F) and 22 blossom honeys (B). The type of six honeys was not known (U) but could be predicted with the help of a linear discriminant analysis (LDA). The LDA was carried out with the three groups (A, B, F) leading to a proportion of correct predictions of 90.6 %. With the help of a scatterplot, two of the unknown samples were classified as forest honeys and four of them as blossom honeys. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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