XRF Data Differences: Quantitative, Semi-Quantitative, and Qualitative Data

XRF data can be generated in 3 basic forms. The form that will provide the most information or help the user attain a certain goal depends on the application, the sample type, and how the information is meant to be used. Novice users of XRF can often find it complicated to make a decision regarding which type of data is appropriate for their particular uses. Without the proper training, users sometimes rely on quantitative data from an XRF instrument in a situation where that data will lack accuracy or repeatability. Conversely, users who know they cannot rely on quantitative data provided by an instrument in a certain situation may get discouraged, because they have not been trained to compare samples using semi-quantitative data processing. Click here to discuss your data analysis needs with our specialists!

Understanding each type of data, what it represents, how it can be used, and for what types of samples it is appropriate; is essential to taking your XRF skills beyond simple point-and-shoot—and to making sure you are getting the data you need even when simple point-and-shoot operation will suffice! Here we offer a short primer on each data type.

The raw spectrum is called qualitative data because it informs the user as to which elements are present in a sample, but does not contain information regarding how much of each element is present unless the data is processed further. Raw XRF data is excellent for analysis of samples that are non-uniform / nonhomogeneous, or for samples where the question of interest is what is in the sample, but how much does not matter. It can also be used to check any questionable quantitative data, because while mathematical data processing can potentially contain mistakes, the raw spectrum never lies; if a peak is present at a certain fluorescent energy, the element to which that energy corresponds is certainly present in the sample.

Quantitative XRF Data: Calibrations and Quantification

Quantitative XRF data informs the user as to the absolute quantity of an element present in a sample. This sort of data contains a number and a unit—usually ppm (parts per million) or % weight. Calibrations are created in order to make raw qualitative data into quantitative data. There are several types of calibrations. Two typical types are FP (fundamental parameters) and empirical calibrations. Calibrations are made by using samples with known concentrations of elements of interest to create a calibration curve that relates the specific known concentrations to peak heights. This curve can then be used to quantify samples of unknown concentrations by relating the peak height to the curve built from the known samples. There are some significant differences in how exactly these calibrations are created and how they work depending on the calibration type. Some rely more heavily on math, while others are purely made by analyzing known samples that are similar to the unknown samples the user wants to quantify.

Quantitative data can be calculated and reported completely by the instrument, with no additional input required from the user. However, it is important that users understand when quantitative data is reliable. For example, if you attempt an analysis of metal samples using a calibration that was intended for the analysis of soil samples, your instrument will generate numbers, but the numbers will be meaningless. For accurate quantitative data, the following four conditions must be met:

• Sample must be homogeneous (no layers, no rust, no inclusions, etc.)
• Calibration used for quantification must be appropriate to the material of the sample analyzed
• The sample must meet “infinite thickness” conditions, meaning that the sample must be thick enough to attenuate all primary x-rays from the XRF instrument, without any of the primary x-rays escaping out the other side of the sample
• There must be samples of known concentration available in order to check and/or create the calibration
  
  

Semi-Quantitative Data

In some instances the conditions for reliable quantitative data are not met, but qualitative data is not enough to answer the questions at hand. In these situations, there is a third option. Semi-quantitative data processing allows the user to compare spectral data from samples in order to obtain information regarding the relative concentrations of elements from sample to sample. While this method does not provide absolute concentration values, it can be used to ascertain relative element concentrations between samples; for example, this method would provide information such as “sample A contains approximately 20% more Ag (silver) than sample B.” This sort of data is extracted by calculating the area under each peak of interest, which is equivalent to the number of counts. This type of data is appropriate in situations where a calibration and/or samples of known concentrations do not exist, but comparing the samples in terms of element concentration is necessary. Click here and learn how Bruker can assist you with your data analysis!