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Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal measurement procedures and experiments, and to provide maximum chemical information by analyzing chemical data.

In modern analytical chemistry and biochemistry, chemometric approaches have become famous in quantitative and qualitative analysis of samples from spectroscopic data. The process of data evaluation is called calibration and will be described in more detail in the following.

In calibration, indirect measurements are made from samples where the property or the amount of a property to be evaluated has been pre-determined, usually by an independent technique or reference measurement. These measurements, along with the pre-determined property or property levels, comprise a group known as the calibration set. This set is used to develop a model that relates the property or property level of a sample to the instrumental measurements. In some cases, the construction of the model is simple due to a certain relationship, such as Lambert Beer's Law in the application of UV, IR and NIR spectroscopy. Unlike spectroscopy, other cases can be much more complex, and it is in these cases where construction of the model is the time-consuming step. Once the model is constructed, it can predict sample properties or property levels based on measurements of new or even unknown samples.

The concepts of calibration have been well defined basically by the International Union of Pure and Applied Chemistry (IUPAC). For more comprehensive approaches several organizations like the International Dairy Federation (IDF) or others developed special methods. Please review the literature of chemometry for details.

The software focuses on two classical calibration approaches, which have been widely accepted in the world of analytical chemistry, the quantitative and the qualitative calibration.

Calibration Algorithms

Multivariate calibration allows for the analysis of several measurements from several samples. This compares to univariate calibration, which involves the use of a single instrumental measurement to determine a single sample property. Either method may contribute to a multi-step procedure where data is calibrated, validated (optional) and further samples predicted based on the calibration model.

Quantitative Calibration

The following quantitative calibration methods are available:

  1. Univariate calibration following Lambert Beer's Law

  2. Multivariate calibration

    • Partial Least Squares Regression (PLS1, PLS2 or SIMPLS)

    • Multiple Linear Regression (MLR, PCR)

Qualitative Calibration

The following qualitative calibration methods are available:


Creating Calibrations

Calibrations are constructed using a wizard which guides the user through the steps of a calibration. The Calibration Model Wizard supports the user in creating Univariate Calibrations and Multivariate Calibrations.

Predicting unknown Samples

After creation of calibration models they will be applied in routine analysis to predict unknown samples and their concentrations. The software provides several options to perform predictions and present prediction results:

Analysis of Variance

The robustness and reliability of the calibration model is reflected by the results of the analysis of variance. Several statistical values are available as shown in the following.

For calibration spectra the following statistics are calculated:

Standard Error of Calibration (SEC)

Root mean square error of Calibration (RMSEC)

Standard Error of cross validation (SECV)

Root mean square error of cross validation (RMSECV)

For validation spectra the following statistics are calculated:

Standard error of prediction (SEP)

Root mean square error of prediction (RMSEP)



K. Danzer and L.A. Currie
Guidelines for calibration in analytical chemistry
Part 1. Fundamentals and single component calibration
Pure & Applied Chemistry, 70 (1998) 993-1014.

K. Danzer, M. Otto and L.A. Currie
Guidelines for calibration in analytical chemistry
Part 2. Multispecies calibration
Pure & Applied Chemistry, 76 (2004) 1215-1225.

M. Otto
Chemometrics, Wiley-VCH, 1999

H. Martens, T. Naes
Multivariate Calibrtion, Wiley, 1989