df = Degrees of freedom; A0 = intercept coefficient; A1 = slope coefficient; Err[Ai] = standard error of coefficient ‘Ai’; p -value = probability Ai ≠ 0 (< 0.05); R² = adjusted correlation coefficient; σy = standard error of the y-estimate; F-ratio = variance ratio (model/residuals). SeeTable 1 for abbreviations.
errors of the y-estimate (σy ≤ 0.2) for calculating ln(IPR) directly from T data. Results from ANOVA demonstrated high variance (F-) ratios (model/residuals), indicating that the goodness
of fit of the model equations was high. However, examination of the data in Figure 1 revealed small degrees of curvature in the data for MeC18:2. Figure S1 is a residual plot in the supporting information (residuals = IPR – IPA[IP calculated from the corresponding Model A equation]) that revealed a non-random pattern, suggesting that a linear model may be insufficient for correlating IPR to T. When the data for MeC18:2 were fitted to a second-order polynomial by regression analysis, the results showed that R² increased from 0.985 to 0.997 and F-ratio from 328 to 850 relative to the linear model summarized inTable 2 . All three regression coefficients had p -value ≤ 0.02 and σy decreased from 0.1 to 0.06. Nevertheless, it was desired to maintain consistency in the application of Model A to correlate IPR-T data for the five FAME studied herein. Therefore, the linear model equations summarized inTable 2 were employed to calculate shelf-life data at 25 °C for all FAME, including MeC18:2.
Shown in Figure 2 is a confirmation graph of IPA values versus experimental IPR values. The dashed line drawn through the data represents the IPA = IPR line. All data points appear in