h five points of smoothing, and a baseline window of 20. All other values were left as default. Results were exported from LCQuan as tab delimited text files which were imported into MS-Excel (2007) for calculations. Response ratios (unlabeled peptide area/labeled peptide area) were multiplied by the mole amount of each AQUA™ present per microgram of sample to obtain endogenous mole quantities. Endogenous mole quantities were multiplied by the molecular weight of the protein (Table A1 in Appendix), and grams per microgram was converted to microgram protein per milligram of digest by dimensional analysis.
Linear ranges of detection for all unlabeled peptides of interest were determined with a twofold dilution series of soybean total protein digest using 0.5 M urea buffer (similar to final digest buffer) and AQUA™ internal standards at 100 fmol on column. Injection amounts for the dilution series were from 2 μg down to 125 ng on column. Using the peptide area ratios (unlabeled/labeled) attained from this initial standard curve, linear ranges were determined for each peptide by manually inspecting the response ratio changes between dilutions, using a twofold change in area ratio as an indicator of a perfect response between two dilutions. Slight deviations from this ideality were accounted for by ensuring the internal standard peptide was within 10-fold of the endogenous peptide signal (response of 10–0.1) for all peptides. Final quantitative analyses were performed with labeled standards at these concentrations. In most cases 100 fmol/μg of soy digest was appropriate. AQUA™ internal standard peptides were also analyzed for their linearity of response using a twofold dilution series from 2 pmol/injection down to 0.061 fmol/injection. This dilution was performed using total soy protein digest material at 200 ng/μL (typical endogenous peptide concentrations). All peptides, endogenous and AQUA™, were used within their linear ranges.
Technical variation was quantified by coefficient of variation (%CV). The %CV for a single variable (i.e., soybean allergen) aims to describe the measurement dispersion of the variable in a way that does not depend on the variable’s measurement unit. The higher the %CV is, the greater the measurement dispersion is in the variable. For each soybean allergen, %CV was calculated for each cultivar in each site using the following formula:
where %CVij is the %CV for the ith cultivar at the jth site (i = 1, 2, 3, 4, j = 1, 2, …, 6), and s(yij) and y¯yij are sample standard deviation and sample mean of four replicate individual data values yijk (k = 1, 2, 3, 4), respectively.
For a given variable, genotypic variation and environmental variation were quantified relative to residual variation using ANOVA-based F-test. Genotypic variation and environmental variation were also assessed using %CV, which represents normalized variation to the overall mean of the variable. ANOVA was conducted using the following statistical model:
where yijk denotes the kth measurement for the ith cultivar at the jth site (i = 1, 2, 3, 4, j = 1, 2, …, 6, k = 1, 2, 3, 4); U denotes the overall mean (fixed effect); Gi denotes the ith genotype effect (fixed effect); Ej denotes the jth environment effect (fixed effect); and eijk denotes the residual term associated with yijk (random error). The assumption for residual terms is eijk ∼ MVN(0, Σ) where MVN denotes multivariate normal and Σ denotes variance-covariance matrix. The Compound Symmetry model was utilized to fit the residual variance-covariance structure for repeated measurements on the same subject (a subject was defined as a unique cultivar by site combination).
A PCA analysis was conducted to explore the patterns of multivariate data and a biplot (Gabriel, 1971) was utilized to provide results in graphic presentation (Figure 5). The biplot visualizes the relationships among allergens and identifies allergens that are positively or negatively associated. It is also useful to visualize the allergen profiles of samples (site and cultivar combinations).
Small technical variati