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. 2014 Mar 31;9(4):e28645. doi: 10.4161/psb.28645

Table 2. Most Prominent Ions (Average Ion Counts > 5.0 (0.09%)) Showing Linear or Exponential Trends.

Decreasing with Treatment
ID Ret. Time Mass Line of Best fit Equation R2
1 6.9558 340.0927 Linear y = -0.2425x + 29.744 0.9929
2 6.0175 241.0592 Linear y = -0.2425x + 29.744 0.9929
3 13.5308 334.1383 Linear y = -2.1247x + 28.83 0.8788
4 1.7363 213.0418 Linear y = -1.0884x + 21.545 0.9578
5 8.9819 434.1102 Exponential y = 22.657e^-0.087x 0.9545
6 6.7 233.113 Exponential y = 16.501e^-0.016x 0.9827
7 1.8035 258.0591 Linear y = -2.3942x + 26.101 0.9964
8 13.5301 233.1084 Linear y = -1.4829x + 21.035 0.8013
9 6.0152 276.0961 Linear y = -0.4833x + 14.438 0.9926
10 6.2874 227.0927 Linear y = -0.7726x + 15.239 0.9506
11 6.0038 517.1004 Linear y = -1.4169x + 18.422 0.9510
12 15.1925 505.1853 Linear y = -1.4143x + 15.892 0.8489
13 1.6532 349.1665 Exponential y = 16.153e^-0.169x 0.9935
14 7.4304 480.1565 Linear y = -0.6014x + 11.605 0.9843
15 9.9656 434.1394 Exponential y = 9.5567e^-0.04x 0.9144
16 15.2634 375.0408 Linear y = -0.6859x + 11.272 0.9619
17 8.5777 399.0983 Exponential y = 9.3338e^-0.046x 0.9243
18 6.6231 352.0954 Linear y = -0.2273x + 8.1602 0.9322
19 9.8383 345.0929 Linear y = -0.3421x + 8.5447 0.9997
20 12.416 284.1271 Exponential y = 18.081e^-0.391x 0.9544
21 5.658 406.0925 Linear y = -0.8878x + 10.342 0.9593
22 6.0164 205.0516 Linear y = -0.2074x + 6.8009 0.9392
23 2.0929 284.0493 Linear y = -0.5524x + 8.4621 0.9663
24 14.6013 284.1245 Linear y = -0.6884x + 9.1244 0.9908
25 A 3.9644 166.0617 Linear y = -0.14x + 6.3751 0.9986
26 8.6167 210.11 Exponential y = 7.8604e^-0.079x 0.9960
27 8.9827 399.0978 Exponential y = 8.0114e^-0.086x 0.9980
28 1.7025 387.1295 Linear y = -0.7391x + 9.0713 0.9968
29 8.4876 169.1004 Linear y = -0.6547x + 8.467 0.7922
30 22.367 235.1366 Linear y = -0.7584x + 8.9401 0.9744
31 10.7236 582.172 Linear y = -0.7747x + 8.91 0.9526
32 9.8427 327.0888 Linear y = -0.2612x + 6.339 0.9882
Increasing with Treatment
ID Ret. Time Mass Line of Best fit Equation R2
21 9.5537 199.0965 Linear y = 1.1095x + 34.389 0.9410
22 1.8738 380.991 Linear y = 1.3206x + 33.245 0.9778
23 13.9895 215.116 Linear y = 1.1765x + 31.268 0.9722
24 6.3407 245.0937 Exponential y = 28.216e^0.0427x 0.7988
25 5.61 355.004 Linear y = 1.8953x + 14.458 0.9560
26 5.1247 205.0568 Exponential y = 5.5215e^0.1735x 0.8681
27 8.4323 354.1065 Linear y = 0.2937x + 17.805 0.8137
28 8.5697 516.9717 Linear y = 3.1768x - 0.6695 0.9845
29 B 5.3907 193.0583 Exponential y = 8.7431e^0.0777x 0.8284
30 7.9198 399.0968 Exponential y = 8.8366e^0.078x 0.9997
31 5.6303 355.0657 Linear y = 0.7487x + 7.7082 0.9981
32 5.3821 390.1043 Exponential y = 7.8235e^0.0536x 0.8414
33 5.1447 341.0321 Exponential y = 5.4765e^0.1026x 0.8517
34 9.8245 523.021 Exponential y = 2.0228e^0.2333x 1
35 1.742 290.7999 Linear y = 0.9645x + 4.58 0.9966
36 C 5.5567 355.0838 Linear y = 0.5845x + 5.6911 0.9561
37 11.4011 453.0584 Exponential y = -0.6014x + 11.605 0.9843
38 7.8306 534.1533 Linear y = 0.4806x + 5.0447 0.7993
39 5.1249 188.0433 Exponential y = 1.9949e^0.1862x 0.8455
40 1.8441 218.9918 Exponential y = 3.5563e^0.1186x 0.9998
41 1.5434 184.9998 Linear y = 0.3741x + 5.2801 0.9420
42 5.131 246.0826 Exponential y = 2.4741e^0.1565x 0.7967
43 8.0024 194.0502 Exponential y = 4.3066e^0.0751x 0.7676
44 22.1758 593.2024 Linear y = 1.3187x - 0.1007 0.9993
45 12.5212 469.1415 Exponential y = 1.108e^0.2394x 0.9468
46 11.8184 418.1451 Exponential y = 4.0479e^0.06x 0.8826
47 12.2741 416.1586 Exponential y = 0.3947e^0.3309x 0.9005
48 6.3831 422.0663 Exponential y = 2.3529e^0.1352x 0.9879
49 1.5636 122.5363 Linear y = 0.1846x + 4.1701 0.9528
50 9.9414 659.1121 Exponential y = 1.3175e^0.1987x 0.9541
*

Putative Identifications: A = Phenylalanine, B = Scopoletin/Isoscopoletin, C = Scopolin*