Benefits of NIR spectroscopy: Part 3

How to implement NIRS in your laboratory workflow

This is the third installment in our series about NIR spectroscopy. In our previous installments of this series, we explained how this analytical technique works from a sample measurement point of view and outlined the difference between NIR and IR spectroscopy.

Find out more about the NIRS DS2500 Analyzer here!

Step 1: Create a calibration set

NIR spectroscopy is a secondary method, meaning it requires «training» with a set of spectra corresponding to parameter values sourced from a primary method (such as titration). In the upcoming example for analyzing moisture and intrinsic viscosity, the values from the primary analyses are known. These calibration set samples must cover the complete expected concentration range of the parameters tested for the method to be robust. This reflects other techniques (e.g., HPLC) where the calibration standard curve needs to span the complete expected concentration range. Therefore, if you expect the moisture content of a substance to be between 0.35% and 1.5%, then the training/calibration set must cover this range as well.

After measuring the samples on the NIRS DS2500 Analyzer, you need to link the values obtained from the primary methods (Karl Fischer titration and viscometry) on the same samples to the NIR spectra. Simply enter the moisture and viscosity values using the Metrohm Vision Air Complete software package (Figure 2). Subsequently, this data set (the calibration set) is used for prediction model development.

Step 2: Create and validate prediction models

Now that the calibration set has been measured across the range of expected values, a prediction model must be created. Do not worry – all of the procedures are fully developed and implemented in the Metrohm Vision Air Complete software package.

First, visually inspect the spectra to identify regions that change with varying concentration. Often, applying a mathematical adjustment (such as the first or second derivative) enhances the visibility of the spectral differences (Figure 3).

Univariate vs. multivariate data analysis

Once visually identified, the software attempts to correlate these selected spectral regions with values sourced from the primary method. The result is a correlation diagram, including the respective figures of merit, which are the Standard Error of Calibration (SEC, precision) and the correlation coefficient (R²) shown in the moisture example in Figure 4. The same procedure is carried out for the other parameters (in this case, intrinsic viscosity).

This process is again similar to general working procedures with HPLC. When creating a calibration curve with HPLC, typically the peak height or peak intensity (surface) is linked with a known internal standard concentration. Here, only one variable is used (peak height or surface), therefore this procedure is known as «univariate data analysis».

On the other hand, NIR spectroscopy is a «multivariate data analysis» technology. NIRS utilizes a spectral range (e.g., 1900–2000 nm for water) and therefore multiple absorbance values are used to create the correlation.

How many spectra are needed?

The ideal number of spectra in a calibration set depends on the variation in the sample (particle size, chemical distribution, etc.). In this example, we used 10 polymer samples, which is a good starting point to check the application feasibility. However, to build a robust model which covers all sample variations, more sample spectra are required. As a rule, approximately 40–50 sample spectra will provide a suitable prediction model in most cases.

This data set including 40–50 spectra is also used to validate the prediction model. This can be done using the Metrohm Vision Air Complete software package, which splits the data set into two groups of samples:

1. Calibration set 75%
2. Validation set 25%

As before, a prediction model is created using the calibration set, but the predictions will now be validated using the validation set. Results for these polymer samples are shown above in Figure 4.

Users who are inexperienced with NIR model creation and do not yet feel confident with it can rely on Metrohm support, which is known for its high quality service. They will assist you with the prediction model creation and validation.

Step 3: Routine Analysis

The beauty of the NIRS technique comes into focus now that the prediction model has been created and validated.

Polymer samples with unknown moisture content and unknown intrinsic viscosity can now be analyzed at the push of a button. The NIRS DS2500 Analyzer will display results for those parameters in less than a minute. Typically, the spectrum itself is not shown during this step—just the result—sometimes highlighted by a yellow or red box to indicate results with a warning or error as shown in Figure 5.

Display possibilities

Of course, the option also exists to display the spectra, but for most users (especially for shift workers), these spectra have no meaning, and they can derive no information from them. In these situations only the numeric values are important along with a clear pass/fail indication.

Another display possibility is the trend chart, which allows for the proactive adjustment of production processes. Warning and action limits are highlighted here as well (Figure 6).

Summary

The majority of effort needed to implement NIRS in the laboratory is in the beginning of the workflow, during collection and measurement of samples that span the complete concentration range. The prediction model creation and validation, as well as implementation in routine analysis, is done with the help of the Metrohm Vision Air Complete software package and can be completed within a short period. Additionally, our Metrohm NIRS specialists will happily support you with the prediction model creation if you require assistance.

At this point, note that there are cases where NIR spectroscopy can be implemented directly without any prediction model development, using Metrohm pre-calibrations. These are robust, ready-to-use operating procedures for certain applications (e.g., viscosity of PET) based on real product spectra.

We will present and discuss their characteristics and advantages in the next installment. Click here to go directly to the final post in the series!

Your knowledge take-aways

Learn more about selected NIR applications in the polymer industry.