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   현재위치 : HOME > 연구용소프트웨어 > 통계/그래프 > SYSTAT > Single License
 
 
PeakFit 4.12 (일반용)(교육용)(정부기관용)
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Peakfit의 매우 정교한 Nonlinear Curve Fitting으로 정확한 피크 분석과 분석의 결론을 위하여 반복적인 실험이나 고가의 장비를 사용하지 않고 소프트웨어적으로 분리할 수 있습니다. 보다 정교하게 보다 정확하게 피크를 분리 및 분석할 수 있습니다. Nonlinear Curve Fitting으로 노이즈를 줄이고 깔끔한 피크를 만들 수 있습니다.

PeakFit은 숨겨진 피크 위치를 확인은 세가지 방식으로 자동화할 수 있으며, 한가지의 방법이 다른 두가지 방법에 비해 훨씬 좋은 데이터 세트를 구할 수 있습니다. 세가지 방법은

  •  Residuals - initially places peaks by finding local maxima in a smoothed data stream
  •  Second Derivative - searches for local minima within a smoothed second derivative data  stream
  •  Deconvolution - uses a Gaussian response function with a Fourier deconvolution/ filtering algorithm.

PeakFit helps you separate overlapping peaks by statistically fitting numerous peak functions to one data set, which can help you find even the most obscure patterns in your data. The background can be fit as a separate polynomial, exponential, logarithmic, hyperbolic or power model. This fitted baseline is then subtracted before peak characterization data (such as areas) is calculated, which gives much more accurate results.

PeakFit is the automatic choice for Spectroscopy, Chromatography or Electrophoresis. AI Experts throughout the smoothing options and other parts of the program automatically help you to set many adjustments.PeakFit can even deconvolve your spectral instrument response so that you can analyze your data without the smearing that your instrument introduces. By Using 82 nonlinear peak models to choose from, you're almost guaranteed to find the best equation for your data.

PeakFit includes 18 different nonlinear spectral application line shapes, including the Gaussian, the Lorentzian, and the Voigt, and even a Gaussian plus Compton Edge model for fitting Gamma Ray peaks. As a product of the curve fitting process, PeakFit reports amplitude (intensity), area, center and width data for each peak. Overall area is determined by integrating the peak equations in the entire model.

The Voigt function is a convolution of both the Gaussian and Lorentzian functions. Most analysis packages that offer a Voigt function use an approximation with very limited precision. PeakFit actually uses a closed-form solution to precisely calculate the function analytically. PeakFit has four different Voigt functions, so you can fit the parameters you're most interested in, including the individual widths of both the Gaussian and Lorentzian components, and also the amplitude and area of the Voigt function. PeakFit's precise calculation of the Voigt function is crucial to the accuracy of your analysis.

주요 사양

  • Nonlinear Curve Fitting
  • Full Graphical Placement of Peaks
  • Highly Advanced Baseline Subtraction
  • PeakFit Saves You Precious Research Time
  • Publication-Quality Graphs and Data Output
  • PeakFit Offers Sophisticated Data Manipulation
  • PeakFit Automatically Places Peaks in Three Ways
  • Data Input
  • Data Preparation
  • Peak Autoplacement
  • Non - Linear Curve Fitting
  • Output and Export Options
  • Fitting Multiple Simultaneous Gaussian Functions
시스템 사양
  •  486 프로세서 이상
  •  윈도 95 이상
  •  8 MB RAM (12 MB 이상 권장)
  •  5MB 하드 디스크 공간

 

Why Should You Use Nonlinear Curve Fitting?

Nonlinear curve fitting is by far the most accurate way to reduce noise and quantify peaks. Many instruments come with software that only approximates the fitting process by simply integrating the raw data numerically. When there are shouldered, or hidden peaks, a lot of noise, or a significant background signal, this can lead to the wrong results. (For example, a spectroscopy data set may appear to have a peak with a 'raw' amplitude of 4,000 units -- but may have a shoulder peak that distorts the amplitude by 1,500 units! This would be a significant error.)

PeakFit helps you separate overlapping peaks by statistically fitting numerous peak functions to one data set, which can help you find even the most obscure patterns in your data. The background can be fit as a separate polynomial, exponential, logarithmic, hyperbolic or power model. This fitted baseline is then subtracted before peak characterization data (such as areas) is calculated, which gives much more accurate results. And any noise (like you get with electrophoretic gels or Raman spectra) that might bias raw data calculations is filtered simply by the nonlinear curve fitting process. Nonlinear curve fitting is essential for accurate peak analysis and accurate research.

PeakFit Offers Sophisticated Data Manipulation

With PeakFit's visual FFT filter, you can inspect your data stream in the Fourier domain and zero higher frequency points -- and see your results immediately in the time-domain. This smoothing technique allows for superb noise reduction while maintaining the integrity of the original data stream. PeakFit also includes an automated FFT method as well as Gaussian convolution, the Savitzky-Golay method, and the Loess algorithm for smoothing. AI Experts throughout the smoothing options and other parts of the program automatically help you to set many adjustments. And, PeakFit even has a digital data enhancer, which helps to analyze your sparse data. Only PeakFit offers so many different methods of data manipulation.

 

 
Highly Advanced Baseline Subtraction

PeakFit's non-parametric baseline fitting routine easily removes the complex background of a DNA electrophoresis sample. PeakFit can also subtract eight other built-in baseline equations, or it can subtract any baseline you've developed and stored in a file.

 



 
Full Graphical Placement of Peaks
 

If PeakFit's auto-placement features fail on extremely complicated or noisy data, you can place and fit peaks graphically with only a few mouse clicks. Each placed function has "anchors" that adjust even the most highly complex functions, automatically changing that function's specific numeric parameters. PeakFit's graphical placement options handle even the most complex peaks as smoothly as Gaussians.

 
Publication-Quality Graphs and Data Output
 

Every publication-quality graph (see above) was created using PeakFit's built-in graphic engine -- which now includes print preview and extensive file and clipboard export options. The numerical output is customizable so that you see only the content you want.

 
PeakFit Saves You Precious Research Time
 

For most data sets, PeakFit does all the work for you. What once took hours now takes minutes ?with only a few clicks of the mouse! It뭩 so easy that novices can learn how to use PeakFit in no time. And if you have extremely complex or noisy data sets, the sophistication and depth of PeakFit뭩 data manipulation techniques is unequaled.

 
PeakFit Automatically Places Peaks in Three Ways
 

PeakFit uses three procedures to automatically place hidden peaks; while each is a strong solution, one method may work better with some data sets than the others.

  1. The Residuals procedure initially places peaks by finding local maxima in a smoothed data stream. Hidden peaks are then optionally added where peaks in the residuals occur.

  2. The Second Derivative procedure searches for local minima within a smoothed second derivative data stream. These local minima often reveal hidden peaks.

  3. The Deconvolution procedure uses a Gaussian response function with a Fourier deconvolution/ filtering algorithm. A successfully deconvolved spec-trum will consist of 뱒harpened?peaks of equivalent area. The goal is to enhance the hidden peaks so that each represents a local maximum.

 

 

GENERAL FEATURES

Data Input

  • ASCII
  • Excel
  • Lotus 123
  • Quattro Pro Windows
  • SigmaPlot
  • AIA Chromatography
  • dBase III+, IV
  • DIF
  • ASCII and Spreadsheet - like Editors
  • Averaging Digital Filter

Data Preparation

  • Gaussian Deconvolution to remove Spectrophotometer Instrument Response smearing
  • Exponential Deconvolution to remove Chromatographic Detector Response smearing
  • Smoothing (Savitsky Golay, FFT Filter, Loess, Gaussian Convolution)
  • Real - time FFT/Time Domain Graphical Editor
  • Dual Graph Data Sectioning with Graphical data point exclusion
  • Non - Parametric Digital Filter to Filter or augment data
  • Compare with Reference
  • Subtract Baseline Imported from File
  • Data Transforms
  • Area Normalization
  • Inspect Second and Fourth Derivatives
  • Data Weighting

Peak Autoplacement

  • Automatic by Local Maxima and Residuals
  • Automatic by Second Derivatives
  • Automatic by Deconvolved Local Maxima
  • Graphical Placement and Adjustments
  • Manual Parameter Adjustments
  • Share and Lock Parameters
  • Constant or Variable Widths and/or Shape in a single step

Non - Linear Curve Fitting

  • Marquardt - Levenburg Algorithm
  • Least - Squares and 3 Robust (Maximum Likelihood) methods
  • Up to 100 Peaks and 1000 Parameters
  • Intelligent Constraints to insure Fit Integrity
  • Sparse Curvature Matrix for Faster Fitting
  • Both Numeric and Graphical Fitting Options
  • Zoom - in or Toggle Points during Fitting

Output and Export Options

  • File Export with full Generated Data: Lotus 123, Excel, Quattro Pro Windows, SigmaPlot, and ASCII.
  • Graphs to clipboard or file in BMP or WMF formats
  • All Numeric data in Graph to Clipboard in Spreadsheet Format

 

SINGLE CHANNEL ANALYSIS - FITTING MULTIPLE SIMULTANEOUS GAUSSIAN FUNCTIONS :

We are interested in the regulation of large conductance Ca2+-activated potassium channels of CNS origin. We are using single channel recording techniques to study the effects of a variety of signal transduction elements on the activity of these important channels. Regulation of activity can be compared using the open probability - the amount of time the channel(s) are open. Experiments are performed under control conditions and in the presence of the desired bioactive substance in the same patch. When the patch contains only 1 or 2 channels, the determination of open probability is relatively simple. However, there are often 4-8 channels in a given patch. Determination of the open probability requires the deconvolution of an amplitude histogram comprised of multiple Gaussian distributions. There is a unique Gaussian distribution associated with each channel state. In the present example there are 5 channels. Therefore there are 6 Gaussian distributions in the amplitude histogram; all channels closed (level 0 on the activity records), 1 channel open (level 1), 2 channels open (level 2), 3 channels open (level 3), 4 channels open (level 4) and 5 channels open (level 5). Often there is significant overlap between distributions and if the time spent with all channels open is small (in this example all channels are open approximately 1% of the time) it is difficult to solve for this distribution.

The excised patch configuration of the patch-clamp technique was used for data acquisition. Electrodes were fabricated in two steps and were fire polished to a final tip resistance between 3.0 and 5M . Recordings were performed at room temperature with a patch clamp amplifier. The recordings in this example were conducted with the patch depolarized to +20 mV. Single channel data were stored initially on digital audiotape.

Single channel data were digitized at a sampling rate of 2 kHz and filtered at 1 kHz using a 4-pole low pass Bessel filter. Each data file contains approximately 100,000 events. Amplitude histograms are constructed from the digitized single channel data using commercially available software. Single channel records and the amplitude histogram data were exported to SigmaPlot and PeakFit as ASCII files. Line plots were created in SigmaPlot for the desired number of activity records (3 in the present example) and annotated as desired.

 

A vbar graph was created for the amplitude histogram data. The activity records and amplitude histogram were visually inspected to determine the most likely number of distributions contained in the data.


 
 

The amplitude histogram was imported into PeakFit and fitted using a sum of Gaussian distribution functions. PeakFit Method 1. was selected for analysis and several hidden peaks were found. These arise from the overlap in distributions and were removed to give a function with 6 Gaussian peaks.

 
 

The data were then fit automatically. Estimates for individual peak areas, means and standard deviations were obtained from the peak fit results.

 
 

Equation 1. was used to calculate the open probability times the number of channels (NP0). i is the number of open channels, N is the total number of channels in the patch and Ai is the area associated with each channel state as determined from the curve-fit individual peak areas. The open probability (P0) was then calculated as NP0/N.

 

 
In the present example, NP0= 1.11 and P0=0.22.

Author : Donald D. Denson, Ph.D., Department of Anesthesiology, Emory University School of Medicine.
 
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관리자에 의해서 부합되지 않은 상품평은 삭제될 수도 있습니다.
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제품을 구입하시는데 있어 궁금하신사항이 있으시면 문의를 남겨주세요.
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해당 상품의 질문이 없습니다.
 
 
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- 다른 제품으로 교환, 또는 이상이 없는 제품과 함께 교환을 원하실 경우 구매자께서 운송비를 부담합니다.

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구입 후 30일 이내에는 반품과 환불이 가능하나, 소프트웨어 및 Document 들, 메뉴얼이나 등록카드 등 제반 내용물들이 손상되지 않아야 하며, 사용 흔적이 없어야 합니다.



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구입제품의 이상이 있을 경우 (색상,사이즈,다른상품교환

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!! 주의사항

비닐포장 및 Tag의 폐기 또는 훼손 등으로 상품 가치가 멸실된 경우에는 제한.

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5만원 이상 구입시 무료배송입니다.

(제주도를 포함한 도서,산간지역은 항공료 또는 도선료가 추가됩니다.)
 
결제방법은 신용카드, 국민/BC(ISP), 무통장입금, 적립금이 있습니다.

정상적이지 못한 결제로 인한 주문으로 판단될 때는 임의로 배송이 보류되거나,주문이 취소될 수 있습니다.

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상담 및 문의전화    02-711-3631/3 ( smhan@sigmasoft4u.com )

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