How pre-calibrations assist quick implementation of NIRS

This is part four in our series about NIR spectroscopy. In this installment, it is outlined in which cases NIRS can be implemented directly in your laboratory without the need for any method development. This means that for these applications your instrument is immediately operational to deliver accurate results – right from day one. At the end of this blog post, we provide an overview of several applications for which it is possible to get immediate results from the beginning.

The following topics will be covered in the rest of this post (click to jump to the topic):

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Figure 1. Workflow for NIR method implementation.

Introduction

In our last installment (Part 3: How to implement NIRS in your laboratory workflow), we showed how a newly received NIR spectrometer can become operational with a real application example. This process is depicted here in Figure 1.

The majority of work consists of creating a calibration set. Approximately 40–50 samples across the expected parameter range must be measured by a primary method, and resulting values need to be linked to the NIR spectra recorded for the same samples (Figure 1: Step 1).

Thereafter, a prediction model needs to be created by visually identifying  the spectral changes and correlating these changes to the values obtained from the primary method (Figure 1: Step 2). After validation by the software, a prediction model is available for use in routine measurements.

The process described above requires some effort and is of significant duration because in many cases, the samples spanning the concentration range first need to be produced and collected. Therefore, it would be very beneficial if steps 1 and 2 could be omitted so that the analyzer can be used immediately from day one.

This is not just wishful thinking, but rather the reality for specific applications with the use of pre-calibrations.

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Figure 2. Workflow for NIRS method implementation with a pre-calibration.

What are pre-calibrations?

Pre-calibrations are prediction models that can be deployed immediately and provide satisfying results right from the beginning. These models are based on a large number (between 100–600) of real product spectra covering a wide parameter range.

This means that steps 1 and 2 (Figure 1) are not required and instead the pre-calibration predication model can be used directly for routine analysis, as illustrated in Figure 2.

How do pre-calibrations work?

Each pre-calibration comes as a digital file that must be imported into the Metrohm Vision Air software. After installation of a new instrument (including the Vision Air software), a method needs to be created containing measurement-specific settings, such as measurement temperature and which sample vessel is used, followed by importing the pre-calibration and linking it to the method.

That’s all that is needed!

The instrument is now ready to deliver reliable results for routine measurements. It is advised to measure a few control samples of known values to confirm that the pre-calibration provides acceptable results.

Optimizing the pre-calibration

In some cases, the results obtained on control samples with the pre-calibration are not completely acceptable. There could be various reasons for this and in general, three different cases can be distinguished:

  1. The results obtained with the control samples deviate only slightly from the expected values.
  2. The results are acceptable, but the standard error is somewhat on the larger side.
  3. The results deviate significantly.

We will go through each of these cases below and provide recommendations.

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Figure 3. Top: correlation between measured control samples (orange dots) and the pre-calibration prediction model (blue line). Bottom: correlation between the values after slope-bias correction (orange dots) and the pre-calibration prediction model (blue line).

Case 1:

  • The results obtained with the control samples deviate only slightly from the expected values.

If the value obtained from the control samples deviates only slightly, a slope-bias correction is the recommended solution. The process is illustrated in Figure 3.

In the top diagram, you see that the values from the pre-calibration deviate consistently over the whole range. In this situation, it is possible to perform a slope-bias correction on the measured model in the Vision Air software. After this has been done, the results fit very well (Figure 3 – bottom).

Case 2:

  • The results are acceptable, but error is somewhat on the larger side.

In most cases, this behavior is observed if the range of the pre-calibration is much larger than the range that the analyst is interested in.

Consider for example, measurement of a value at the lower end of the overall range. The error of the pre-calibration is calculated over the entire range, and therefore the impact of the average error (SECV = standard error of cross validation) is much larger on values on the lower end compared to values in the middle of the complete range. This is exemplified in Figure 4 and Table 1.

Figure 4. Pre-calibration correlation plot of the kappa number (a pulp & paper parameter) over the extended range 0–200 (left), and the smaller range 0–36 (right).

Table 1. Figures of merit for the different regions of the pre-calibration from Figure 4. Note the much smaller SECV for the range 0–36 compared to the SECV for the full range of 0–200.

Range R2 SEC SECV
0–200 0.996 3.8 3.9
0–36 0.994 0.77 0.81
32–109 0.986 3.3 3.8
91–200 0.977 3.6 3.7

The recommended action in this case is to remove certain ranges of the pre-calibration, leaving in only the range of interest.

From Table 1, it is clear that the SECV for the whole range (0–200) is much higher than the SECV of the smaller range (0–36). This means that when removing the samples corresponding to the higher ranges from the pre-calibration (leaving only the range of 0–36 in), the resulting modified pre-calibration gives a lower SECV.

Case 3:

  • The results deviate significantly.

There could be several reasons behind this, so we will select two examples.

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Figure 5. Correlation between measured control samples (blue dots) and the pre-calibration model (dotted red line) for the hydroxyl number in polyols. This data is based on a real customer example.

In the first example, consider the possibility that the provided samples for analysis are proprietary. For instance, certain manufacturers produce unique, patented polyols. These proprietary substances are not included among the standard collection of sample spectra in the pre-calibration. Thus, the pre-calibration does not provide acceptable results for such proprietary samples.

Another example is shown in Figure 5. Here it can be observed that the values from the primary method (blue dots) deviate significantly from the values obtained from the pre-calibration model.

This example is taken from a real customer case which we have observed.

At first, we were a bit puzzled when checking the results, but the reason became clear after speaking with our customer. They had chosen to measure the primary values (hydroxyl number) via manual titration and not, as recommended, with an automatic titrator from Metrohm.

Therefore, the reason that the fit of the control samples is unsatisfying is due to the poor accuracy of manual titration of the control samples and has nothing to do with the quality of the pre-calibration.

For more information about this topic, read our blog post below about the main error sources in manual titration.

«Analyze This»: The main error sources in manual titration

Looking for your pre-calibration?

Metrohm offers a selection of pre-calibrations for a diverse collection of applications. These are listed in Table 2 together with the most important parameters of the pre-calibration. Click on the links to get more information.

Metrohm NIRS pre-calibration options

Table 2. Overview of available pre-calibrations for the Metrohm Vision Air software.

Pre-calibration Selected Important Parameters
Polyols Hydroxyl number (ASTM D6342)
Gasoline RON, MON, anti-knock index, aromatics, benzene, olefins
Diesel Cetane index, density, flash point
Jet Fuel Cetane, index, density, aromatics
Palm oil Iodine value, free fatty acids, moisture
Pulp & Paper Kappa number, density, strength parameters
Bio-methane Potential (BMP) BMP (of biological waste)
Polyethylene (PE) Density, intrinsic viscosity
Polypropylene (PP) Melt Flow Rate
Polyethylene Terephthalate (PET) Intrinsic viscosity, acid number, and others
Polyamide (PA 6) Intrinsic viscosity, NH2 and COOH end groups

Conclusion

Pre-calibrations are prediction models based on a large number of real product spectra. These allow users to skip the initial model development part and make it possible to use the instrument from day one, saving both time and money.

Author
van Staveren

Dr. Dave van Staveren

Head of Competence Center Spectroscopy
Metrohm International Headquarters, Herisau, Switzerland

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