50 May / June 2019 a)
for class separation [9]. Sometimes the class separability indicated by these distances is not apparent in the three-dimensional class projection plot.
b)
3. Discriminating power: This parameter helps variables to be identified that provide the most discrimination between the classes. A variable with larger discriminating power has greater influence on separating the classes than one with a small discriminating power. There does not appear to be a set threshold value above which a discriminating power is considered ‘good’, because these values vary strongly with interclass distance.
Results and Discussion
The SIFT-MS SCAN data (obtained using the NO+
reagent ion) for the strawberry flavour
Figure 2. SIFT-MS scan data obtained with the NO+ mix batch: (a) low m/z region and (b) higher m/z region.
reagent ion for the averaged replicates of each flavour
mixes are shown in Figure 2. The data in Figure 2 are the mean of the five replicate analyses, whereas the individual replicates are utilised for the subsequent statistical analyses in which these scans are utilised as ‘flavour fingerprints’.
1. Evaluation of the ability to discriminate different batches of flavour standards
SIFT-MS scan data obtained using the NO+
reagent ion can be used to rapidly
screen different flavour mix batches for acceptability. Figures 3 and 4 show the results obtained for flavour standards 1 (S1) and 2 (S2), respectively, following multivariate statistical analysis with the SIMCA algorithm. The analysis reveals that the different batches of S1 are significantly less consistent than those of S2, both visually in the class projections and quantitatively from the interclass distance metric (Figures 3 and 4).
Figure 3. Evaluation of the variability of different batches (A, B, and C) of flavour standard 1 (‘S1’) 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.
The multivariate statistical methodology utilised was Soft Independent Modelling by Class Analogy (SIMCA), which was developed byWold in the 1970s [8]. SIMCA applies principal component analysis (PCA) to the whole dataset and to each of the classes with the end goal of creating a model that discriminates each class from the others. The Infometrix® Inc. (Bothell,WA) implementation of the SIMCA algorithm in the Pirouette software package was employed here.
Prior to analysis using the Pirouette software package, SIFT-MS SCAN data were normalised (giving a sum of unity for all masses in the range), had the blank subtracted, and had masses with normalised
signals less than 0.000005 removed.
Three types of output from the SIMCA analysis are presented in this report:
1. Class projections: These three- dimensional plots show how each sample falls with respect to the three most important principal components derived from PCA on the entire data set. Each user-defined class shows the sample with the same color and a ‘cloud’ representing the calculated space in which all samples of the class are expected to lie. Better class separations lead to more confident assignment of unknown samples to a predefined class, if a suitable one exists.
2. Interclass distances: These are a measure of the separation between classes. A value of three (3) is usually considered acceptable
2. Evaluation of the ability to discriminate between standards
Figure 5 shows an evaluation of the ability of SIFT-MS to discriminate between the different flavour mixes. For this statistical analysis, the three batches for each of flavour standard 1 and 2 are grouped together into their parent classes. The separation obtained is very large, confirming the visual differences observable in the scan spectra (Figure 2). Chemically, these differences arise from different compositions of the flavour mixes that then give rise to different product ion profiles in the SIFT-MS mass spectra. The discriminating powers indicate the product ion m/z that discriminate these mixes most effectively. For example, it appears that 4-decanolide and methyl cinnamate are significant in this instance.
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