SinRX

Software for qualitative and quantitative analysis of energy dispersive X-ray Fluorescence (ED-XRF) spectra acquired on bulk and layered samples

General information


Windows 10 / 8.1 / 8 / 7 / Vista / XP 32 & 64 bit 

Spectra acquisition

Multiple X-rays source
and
DPP supported

Spectra analysis

Multiple file formats supported:
mca, csv, spx, snx.

General Information

  • Runs on Windows 7, 8 or 10, both 32 and 64 bit
  • Analysis of spectra stored in different file formats such as mca, csv, spx, snx
  • Three possible levels of password protected access (end user, administrator, maintenance)
  • Acquisition and analysis within just one software appplication.

Spectra Acquisition

Determination of the composition of industrial alloys

  • Spectra acquisition from different models of DPP’s and by using different X-ray sources
  • Possibility to save the acquired spectra in different file formats (csv, snx)
  • Display of: spectra in linear or logarithmic scaler; fit; background, residuals.

Calibration of the energy scale

  • through automatic or manual peak identification, and linear regression

Smoothing of the spectrum through:

  • Savitzky-Golay filter;
  • Moving average filter.

Background subtraction through:

  • Orthogonal polynomials;
  • Peak stripping.

Spectra Analysis

Qualitative Analysis

Fitting

  • Trough gaussian curves using a Levenberg-Marqaart algorithm;
  • Possibility to use modified gaussian curves to take into account the line shelf and tail.

Automatic identification of elements through peak analysis:

  • blacklist of elements to avoid misidentification;
  • manual adjustments of composition;
  • automatic identification of “sum” and “escape” peaks.

Quantitative Analysis

Standardless - fundamental parameters of:

  • Different tube spectra models available (for both end-window and side-window X-ray tubes);
  • Detector efficiency spectrum adjustable by the user;
  • Possibility to choose and save the geometry of analysis.

Analysis with standards:

  • Empirical calibration curves (linear and quadratic models), with or without interelement corrections;
  • Lucas-Tooth algorithm;
  • Lachance-Traill algorithm;
  • Modified fundamental parameters analysis to take into account the results on standard sample;
  • Combined methods: FP + empirical calibration.


Further analysis and report

Smart analysis:

  • for the rapid identification of the material, with possibility of creating a customized database of materials;

Matching analysis:

  • suggests, among all samples saved in the database, the one whose spectrum has the greatest likeness to the sample under analysis.

Empirical Studies:

  • Displaying of elements concentrations vs. corresponding peak areas;
  • Linear or quadratic fit of the points;
  • Inverse analysis to determine the concentration of the elements of an unknown sample.

Report and databases

  • Database of customers and samples;
  • Database of reference materials;
  • Database of standard samples for quantitative analysis;
  • Database of users;
  • Database of applications for optimized analysis
  • Creation of customized reports.