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3. Evaluation of ability to classify unknowns
Three unidentified flavour mixes were supplied for evaluation using SIFT-MS. To identify the mix group to which they belong (if any), each of the ‘unknown’ samples (U1, U2, and U3) was added as a new class in the SIMCA analysis. The results obtained are summarised in Figure 6. Based on the interclass distances determined (and confirmed visually in the class projection plot):
• Unknown 1 (U1) is another batch of flavour standard 1 (S1). The interclass distance between U1 and S1 is greater than 3, but as shown in Figure 3, the three batches (identified as S1a, S1b, and S1c) are all readily differentiable based on their volatile profiles. That is, there is a lot of variability in the batches of the S1 mix.
• Unknown 2 (U2) is identified as another batch of flavour standard 2 (S2), because the interclass distance is very small.
Figure 4. Evaluation of the variability of different batches (A, B, and C) of flavour standard 2 (‘S2’) using SIFT- MS in scan mode coupled with SIMCA multivariate statistical analysis. Class projections, interclass distances, and the top 10 variables (m/z) for discrimination of the samples are shown.
• Unknown 3 (U3) is extremely different from all other flavour mixes, as indicated by large interclass distances with all other samples. It represents a completely different (i.e. a third) flavour mix.
Assignments of unknown samples U1 and U2 to S1 and S2, respectively, were confirmed by adding them to the S1 and S2 data sets and reprocessing with SIMCA. Further confirmation of these assignments was provided by the customer. They likewise observed significant variation in S1 batches using gas chromatographic analysis and attributed it to degradation of the flavour mixes.
Conclusions
This study demonstrates that untargeted SIFT-MS analysis coupled with multivariate statistical analysis can rapidly screen strawberry flavour mixes to ensure that they fall within the required specification prior to their use in foods, beverages, and nutritional formulations. Automated static headspace-SIFT-MS analyses samples in less than one minute using a fingerprinting approach (full scan mode).
Figure 5. Evaluation of the ability of SIFT-MS coupled with SIMCA multivariate statistical analysis to discriminate between flavour standards 1 and 2 (S1 and S2). Class projections, interclass distances, and the top 10 variables (m/z) for discrimination of the samples are shown.
The combined instrumental and statistical approach utilised here has potential to facilitate enhanced quality control through rapid, economical screening of food ingredients.
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