Table 2.
Analyte | εb | Protonated mass (m/z)c | Linear range (ng/mL) | Sloped | r2e |
---|---|---|---|---|---|
E1 | 2138 | 504.22031 | 0.025–100 | 2.26 × 10−4 | 0.999 |
E2 | 2344 | 506.23596 | 0.025–100 | 1.14 × 10−4 | 0.998 |
E3, 16epi-E3, 17epi-E3 | 2188 | 522.23087 | 0.05–100 | 7.70 × 10−5 | 0.999 |
16α-OHE1 | 2138 | 520.21522 | 0.05–100 | 9.84 × 10−5 | 0.999 |
16-ketoE2 | 2344 | 520.21522 | 0.05–100 | 7.29 × 10−5 | 0.999 |
2-OHE1 | 2138 | 753.26628 | 0.05–50 | 3.52 × 10−4 | 0.999 |
2-OHE2 | 2344 | 755.28193 | 0.05–50 | 3.40 × 10−4 | 0.999 |
2-MeOE1 | 2138 | 534.23087 | 0.05–50 | 5.94 × 10−5 | 0.999 |
2-MeOE2 | 2344 | 536.24652 | 0.05–50 | 2.71 × 10−5 | 0.998 |
2-OH-3-MeE1 | 2138 | 534. 23087 | 0.05–50 | 5.90 × 10−5 | 0.998 |
4-OHE1 | 2138 | 753.26628 | 0.05–50 | 3.64 × 10−4 | 0.999 |
4-OHE2 | 2344 | 755.28193 | 0.05–50 | 7.18 × 10−4 | 0.998 |
4-MeOE1 | 2138 | 534. 23087 | 0.05–50 | 6.40 × 10−5 | 0.998 |
4-MeOE2 | 2344 | 536.24652 | 0.05–50 | 3.37 × 10−5 | 0.998 |
P | 16982 | 315.23186 | 0.05–100 | 9.28 × 10−5 | 0.998 |
F | 15500 | 363.21660 | 1–1,000 | 2.93 × 10−3 | 0.998 |
E | 15136 | 361.20095 | 1–1,000 | 1.97 × 10−3 | 0.996 |
Internal Standards | |||||
E1-d4 | 2138 | 508.24542 | |||
E2-d3 | 2344 | 509.25479 | |||
2-MeOE2-d5 | 2344 | 541.27791 | |||
4-OHE2-d5 | 2344 | 760.31331 | |||
16α-OHE1-d5 | 2138 | 525.24661 |
using orbitrap MS (model Exactive, Thermo Scientific) that allowed exact mass measurements
molar extinction coefficient in methanol at 280 nm except for F (241 nm), E (238 nm), P (249 nm) used from CRC Handbook of Chemistry and Physics, 52nd ed. 1971; ε-values of E1 metabolites, E2 metabolites, and E3 metabolites were deduced from E1, E2, and E3, respectively
theoretical (monoisotopic) exact mass of each protonated analyte used to monitor in positive mode in a +/− 5 ppm range to account for mass spectrometric inaccuracies; all analytes dansylated except for P, F, E
slope of linear regression after forcing curve through zero (no intercept); relative standard error<6%
r2 = coefficient of determination for linear regression