nt was similar between Ontario, Georgia, Nebraska, and Iowa (Figure 4). Likewise, the allergen content was similar between Pennsylvania, and Kansas. Cultivar 92B12 grown in Ontario had the lowest level of total allergens whereas; cultivars 92M72 and 93B15 grown in Pennsylvania had the highest levels of total allergens (Figure 4).
The following can be seen from Figure 5: (1) Across the 24 samples (four cultivars at six sites), Glycinin G2, Glycinin G4, and Beta conglycinin α were positively correlated (a small acute angle), and KTI 3-1 and KTI 3-2 were positively correlated (an acute angle). Glycinin G3 was negatively correlated with Glycinin G2, KTI 1, KTI 3, Beta conglycinin α, and Glycinin G4 (obtuse angles). (2) Samples tended to be clustered together by sites. All four cultivars from the GA site were at the lower left quadrant of the biplot. All four cultivars from the ON site were at the left part of the biplot. All four cultivars from the PA site were at the upper part of the biplot. All four cultivars from the KS site were at the right part of the biplot. All four cultivars from IA and NE sites were at the lower part of the biplot and showed similar pattern by cultivar. From PCA analysis it can be concluded that variation among sites (i.e., environment) was the dominant variation for allergens.
In this study, eight allergens were quantified in 24 samples with each sample measured four times. In order to perform this ambitious study we addressed the individual seed variability by taking large samplings to average this component. This was performed using standard procedure for random sampling of material from test plots. It should also be noted that subsequent quantification was performed on single representative AQUA™ peptides as explained in the Section “Materials and Methods.”
Once protein was extracted, processing from protein dissolution to calculations for absolute quantity took approximately 6 days, with an average percent coefficient of variance for the experiment at 10.0%. In order to achieve this rapid turnover, some assumptions had to be made. Firstly, we assumed that replicate injections could be forgone if replicate extractions were performed for each sample. The experimental design employed replicate extractions to assess variation at the protein extraction level. Assuming that the variation at the extraction level was greater than the variation at the injection level (largely controlled for with AQUA™ peptides, and previously assessed at 3–5%), we were satisfied that we were accounting for the majority of experimental variation by comparing replicate extractions without replicate injections. Secondly, we assumed that single point quantification can accurately assess peptide abundance. For our quantification experiments, protein quantities were calculated based on a single simultaneous measurement of both AQUA™ and endogenous peptide intensities. Ideally, the analysis would be repeated for each sample using a dilution strategy, where the AQUA™ peptide or the endogenous matrix is diluted and subsequent measurements are made of these samples to assess the linearity of response. To avoid this necessity, we assessed the linearity of our endogenous peptides with a twofold dilution series from 2.0 to 0.0625 μg/injection (2.0 μg maximum capacity peptide traps) of a Williams 82 soybean protein sample using a constant amount of AQUA™ peptide for all dilutions. The ratio of endogenous: AQUA™ peak areas were used to assess linearity. The scatter plot of “measured fold change” versus “actual fold change” shows slopes that on average are within 5.70 ± 3.82% of the optimal value of 1, with an average R2 value of 0.9969 ± 0.0042 (Figure A1 in Appendix). With this information in mind, we decided to use 1 μg injections allowing for at least a twofold increase, and a 16-fold decrease in any of the endogenous peptides without extrapolating past the linear range (2.0–0.0625 μg). To maximize the efficacy of our internal standards, we calibrated the amount of each AQUA™ to approximately match the amount of the endogenous an