Tips and Tricks - Bruker · PDF fileTips and Tricks (for Acquisition ... • Forward Linear Prediction ... 130 125 120 115 110 105 ppm 64 256 Example: Forward Linear Prediction: HSQC

Sandra Loss, Rainer Kerssebaum, Bruker BioSpin

Tips and Tricks(for Acquisition & Processing - leading to good spectra)

Innovation with Integrity La Jolla, September 2016

2Bruker Users Meeting @SMASH, La Jolla, September 2016

Topics

• Window Functions

• Spectral Resolution and Zero Filling

• Folding and Filters

• Forward Linear Prediction

• Backward Linear Prediction


Window Functions


Basics

time domain frequency domain

FT

decay lineshape

amplitude integral

period frequency


Lineshape

FT

FT


Window Function

• The intensity of each FID-datapoint is multiplied with a intensity

value defined by the window function

• Application:

sensitivity gain

enhancement of resolution

dealing with truncation artifacts

interesting signals may be increased, non-interesting

suppressed (example: COSY-diagonal)

without window function exponential function gaussian function


Sensitivity Gain - Exponential Function

• Improvement of signal to noise with an exponential function:

beginning of FID: dominated by signal

end of FID: dominated by noise

WDW: EM LB > 0 (speed of decay of exponential)

processing with EFP

no

window

function

exponential

function

FT


• Improvement of resolution with Gaussian function:

slow decay of FID: sharp line(s)

fast decay of FID: broad line(s)

WDW: GM LB < 0 0 < GB < 1 (defines position of maximum)

processing with GFP

Improvement of Resolution - Gaussian Func.

8

Gaussian

function

FT

no

window

function


• Improvement of Resolution without S/N loss with Traficante function:

WDW: TRAF 0 < LB < 1

Traficante

function

FT

Improvement of Resolution – Traficante Func.

no

windows

function


Spectral Resolution and Zero Filling


Spectral Resolution

AQ

DW AQ = DW*TD

AQ = TD/(2*SWH)

DW = 1/(2*SWH)


Spectral Resolution


Important Parameters


Spectral Resolution


Spectral Resolution

TD

t

TDeff


Zero-filling


TD = 64k

SI = 32 k

TD = 64k

SI = 64k

TD = 64k

SI = 128k

Zero-filling


Spectral Resolution


Folding and Filtering


Folding

Nyquist theorem: min. 2 datapoints / period must be sampled


Folding

sw


Antialiasing Filter

sw


Oversampling and digital Filter

AQ = DW*TD DW = 1/(2*SWH)



AQ = DW*TD AQ = TD/(2*SWH)






Quantitation Noise


Quantitation Noise


Quantitation Noise

rg = 1

rg = 57


Forward Linear Prediction



• only for truncated FID



t

si

tdeff lpbin

• only for truncated FID


Example: Forward Linear Prediction: HSQC

ppm

5.56.06.57.07.58.08.59.09.510.010.5 ppm

105

110

115

120

125

130

Current Data Parameters

NAME TXI_Calb_0102

EXPNO 10

PROCNO 1

F2 - Acquisition Parameters

Date_ 20020110

Time 4.03INSTRUM spectPROBHD 5mm TXI 1H-13C

PULPROG invif3gpsi

TD 2048

SOLVENT H2ONS 2

DS 8

SWH 8012.820 HzFIDRES 3.912510 Hz

AQ 0.1279076 secRG 512

DW 62.400 usec

DE 4.50 usec

TE 296.4 KCEN_HN1 0.00001523 sec

CEN_HN2 0.00003045 secCNST4 94.0000000

d0 0.00000300 sec

D1 1.00000000 sec

d11 0.03000000 sec

d13 0.00000400 sec

D16 0.00010000 secD24 0.00265957 sec

d26 0.00265957 secDELTA 0.00112250 sec

DELTA1 0.00110800 sec

IN0 0.00025000 sec

l3 128

======== CHANNEL f1 ========

NUC1 1H

P1 8.25 usecp2 16.50 usec

P28 1000.00 usec

PL1 0.00 dB

SFO1 500.1323506 MHz

======== CHANNEL f3 ========

CPDPRG3 garp64

NUC3 15N

P21 38.70 usecp22 77.40 usec

PCPD3 250.00 usec

PL3 -3.00 dBPL16 12.90 dBSFO3 50.6837130 MHz

====== GRADIENT CHANNEL =====

GPNAM1 SINE.100GPNAM2 SINE.100

GPNAM3 SINE.100GPX1 0.00 %

GPX2 0.00 %

GPX3 0.00 %

GPY1 0.00 %

GPY2 0.00 %

GPY3 0.00 %GPZ1 50.00 %

GPZ2 80.00 %GPZ3 8.10 %

P16 1000.00 usec

F1 - Acquisition parametersND0 2

TD 239

SFO1 50.68371 MHz

FIDRES 8.368201 Hz

SW 39.460 ppmFnMODE Echo-Antiecho

F2 - Processing parameters

SI 512SF 500.1300000 MHz

WDW SINE

SSB 2

LB 0.00 HzGB 0

PC 4.00

F1 - Processing parameters

SI 1024

MC2 echo-antiecho

SF 50.6777330 MHz

WDW SINE

SSB 2

LB 0.00 Hz

GB 0

1H-15N HSQC Calbindin D9k

ns=2


678910 ppm

256

678910 ppm

128

678910 ppm

64

horizontal projection (1H) No measured increments

No measured increments has no influence onto the resolution in the direct

dimension - S/N increases with TD[F1]



vertical projection (15N) No measured increments

256

105110115120125130 ppm

128

105110115120125130 ppm105110115120125130 ppm

64

vertical projection: experimental data without linear prediction



105110115120125130 ppm

LPFC

LPBIN=192

64 + 192

64 + 192

vertical projection (15N) No measured increments

105110115120125130 ppm105110115120125130 ppm

LPFC

LPBIN=0

64 + 64

105110115120125130 ppm

64

256



678910 ppmLPFC

LPBIN=0

64 + 192

678910 ppmLPFC

LPBIN=0

64 + 64


256

678910 ppm678910 ppm

64

TD[F1] 256 vs. 64 + LPfc


Forward Linear Prediction: Parameters

• ME_mod: LPfc (method: real time data)

LPmifc (constant time data)

• TDeff: number of used FID-points

TDeff = 0 TDeff = TD

• LPBIN: number of points after prediction

LPBIN = 0 LPBIN = TD (or TDeff)

• NCOEF: number of coefficients for prediction function

LPBIN points need at least 3 for each signal: frequency,

amplitude, relaxation

• SI: number of real datapoints after processing

t

si

tdeff lpbin


Forward Linear Prediction: Parameters

• LPBIN: 0 (double number of points)

Maximum: X * TD (or X * TDeff)

• NCOEF: 3 … 256 (number of signals * 3)

indirect dimension:

maximum number of signals

in one column * 3

typical values and limits

t

si

tdeff lpbin


Backward Linear Prediction



• is used if datapoints at the beginning of the FID are distorted

(this typically leads to baseline distortions)



• is used if datapoints at the beginning of the FID are distorted

(this typically leads to baseline distortions)



• first datapoints need to be corrected

lpbin

t

td

tdoff



• digitally filtered data need to be converted into “analog” type data using

command “convdta”

convdta


Backward Linear Prediction: Parameters

• ME_mod: LPbc (method)

• TDeff: number of used datapoints

TDeff = 0 TDeff = TD

• TDOFF: number of points to be replaced

(negative value: additional points)

should be even (complex data)

• LPBIN: number of input points for calculation

• NCOEF: number of coefficients in function for calculation of LPBIN

datapoints (für each signal 3: frequency, amplitude,

relaxation)



• TDOFF: 8 … 128

• LPBIN: 1k … 8k

• NCOEF: 30 … 2k (number of signals * 3)

typical values and limits






Innovation with Integrity

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Tips and Tricks - Bruker · PDF fileTips and Tricks (for Acquisition ... • Forward Linear Prediction ... 130 125 120 115 110 105 ppm 64 256 Example: Forward Linear Prediction: HSQC