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Moisture Analysis – Karl Fischer Titration, NIRS, or both?

Moisture Analysis – Karl Fischer Titration, NIRS, or both?

In addition to the analysis of the pH value, weighing, and acid-base titration, measurement of water content is one of the most common determinations in laboratories worldwide. Moisture determination is important for nearly every industry, e.g., for lubricants, food and feed, and pharmaceuticals.

Figure 1. Water drops in a spider web

For lubricants, the water concentration is very important to know because excess moisture expedites wear and tear of the machinery. For food and feed, moisture content must be within a narrow range so that the food does not taste dry or stale, nor that it is able to provide a breeding ground for bacteria and fungi, resulting in spoilage. For pharmaceuticals, the water content in solid dosage forms (tablets) and lyophilized products is monitored closely. For the latter, the regulations state that the moisture content needs to be below 2%.

Karl Fischer Titration

Karl Fischer (KF) Titration for water determination was introduced back in the 1930’s, and to this day remains one of the most tried and trusted methods. It is a fast and highly selective method, which means that water, and only water, is determined. KF titration is based on the following two redox reactions.

In the first reaction, methanol and sulfur dioxide react to form the respective ester. Upon addition of iodine, the ester is oxidized to the sulfate species in a water-consuming reaction. The reaction finishes when no water is left.

Figure 2. Manual sample injection for volumetric KF Titration

KF titration can be used for the determination of the water content in all sample types: liquids, solids, slurries, or even gases. For concentrations between 0.1% and 100%, volumetric KF titration is the method of choice, whereas for lower moisture content between 0.001% and 1%, coulometric KF titration is recommended.

Depending on the sample type, its water content, and its solubility in the KF reagents, the sample can either be added directly to the titration vessel, or would first need to be dissolved in a suitable solvent. Suitable solvents are those which do not react with the KF reagents — therefore aldehydes and ketones are ruled out. In case the sample is dissolved in a solvent, a blank correction with the pure solvent also needs to be performed. For the measurement, the sample is injected directly into the titration vessel using a syringe and needle (Fig. 2). The endpoint is detected by a polarized double Pt pin electrode, and from this the water concentration is directly calculated.

Insoluble or hygroscopic samples can be analyzed using the gas extraction technique with a KF Oven. Here, the sample is sealed in small vial, and the water is evaporated by heat then is subsequently carried to the titration cell.

Figure 3. Fully automated KF Titration with the Metrohm 874 KF Oven Sample Processor

For more information, download our free Application Bulletins: AB-077 for volumetric Karl Fischer titration and AB-137 for coulometric Karl Fischer analysis.

If you would like some deeper insight, download our free monograph: “Water determination by Karl Fischer Titration”. 

Near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) is a technique that has been used for myriad applications in the areas of food and feed, polymers, and textiles since the 1980’s. A decade later, other segments began using this technique, such as for pharmaceutical, personal care, and petroleum products.

NIRS detects overtones and combination bands of molecular vibrations. Among the typical vibrations in organic molecules for functional groups such as -CH, -NH, -SH, and -OH, it is the -OH moiety which is an especially strong near infrared absorber. That is also the reason why moisture quantification is one of the key applications of NIR spectroscopy.

For a further explanation, read our previous blog entry on this subject: Benefits of NIR spectroscopy: Part 2.

NIR spectroscopy is used for the quantification of water in solids, liquids, and slurries. The detection limit for moisture in solids is about 0.1%, whereas for liquids it is in the range of 0.02% (200 mg/L), However, in special cases (e.g., water in THF), moisture detection limits of 40–50 mg/L have been achieved.

This technique does not require any sample preparation, which means that samples can be used as-is. Solid samples are measured in high quality disposable sample vials, whereas liquids are measured in high quality disposable cuvettes. Figure 4 displays how the different samples are positioned on the analyzer for a measurement.

Detailed information about the NIRS technique has been described in our previous blog article: Benefits of NIR spectroscopy: Part 1.

Figure 4. Solid (left) and liquid (right) sample positioning for NIR measurements

NIRS is a secondary technique, meaning it can only be used for routine analysis for moisture quantification after a prediction model has been developed. This can be understood by an analogy to HPLC, for which measuring standards to create a calibration curve is among the initial steps. The same applies to NIRS: first, spectra with known moisture content must be measured and then a prediction model is created.

The development of prediction models has been described in detail in our previous blog article: Benefits of NIR spectroscopy: Part 3.

The schematic outline is shown in Figure 5.

Figure 5. Workflow for NIR Method implementation for moisture analysis

For creation of the calibration set, around 30–50 samples need to be measured with both NIRS and KF titration, and the values obtained from KF titration must be linked to the NIR spectra. The next steps are model development and validation (steps 2 and 3 in Figure 5), which are quite straightforward for moisture analysis. Water is a strong NIR absorber, and its peaks are always around 1900–2000 nm (combination band) and 1400–1550 nm (first overtone). This is shown in Figure 6 below.

Figure 6. NIR Spectra of moisturizing creams, showing the absorptions related to H2O at 1400–1550 nm and 1900–2000 nm

After creation and validation of the prediction model, near-infrared spectroscopy can be used for routine moisture determination of that substance. The results for moisture content will be obtained within 1 minute, without any sample preparation or use of chemicals. Also, the analyst does not need to be a chemist, as all they need to do is place a sample on the instrument and press start.

You can find even more information about moisture determination by near-infrared spectroscopy in polyamides, caprolactam, lyophilized products, fertilizers, lubricants, and ethanol/hydrocarbon blends below by downloading our free Application Notes.

Your choice for moisture measurements: KF Titration, NIRS, or both!

As summarized in Table 1, KF Titration and NIR Spectroscopy each have their advantages. KF Titration is a versatile method with a low level of detection. Its major advantage is that it will always work, no matter if you have a sample type that you measure regularly or whether it is a sample type that you encounter for the first time.

Table 1. Overview of characteristics of moisture determination via titration and NIR spectroscopy

NIR spectroscopy requires a method development process, meaning it is not suitable for sample types that always vary (e.g., different types of tablets, different types of oil). NIRS however is a very good method for sample types that are always identical, for example for moisture content in lyophilized products or for moisture content in chemicals, such as fertilizers.

For the implementation of a NIR moisture method, it is required that samples are measured with KF titration as the primary method for the model development. In addition, during the routine use of a NIR method, it is important to confirm once in a while (e.g., every 50th or every 100th sample) with KF Titration that the NIR model is still robust, and to ensure that the error has not increased. If a change is noticed, extra samples need to be added to the prediction model to cover the observed sample variation.

In conclusion, both KF Titration and NIR spectroscopy are powerful techniques for measuring moisture in an array of samples. Which technique to use depends on the application and the individual preference of the user.

For more information

Download our free whitepaper:

Karl Fischer titration and near-infrared spectroscopy in perfect synergy

Post written by Dr. Dave van Staveren (Head of Competence Center Spectroscopy), Dr. Christian Haider (Head of Competence Center Titration), and Iris Kalkman (Product Specialist Titration) at Metrohm International Headquarters, Herisau, Switzerland.

Benefits of NIR spectroscopy: Part 4

Benefits of NIR spectroscopy: Part 4

This blog post is part of the series “NIR spectroscopy: helping you save time and money”.

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):

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 (Fig. 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 (Fig. 1: Step 2). After validation by the software, a prediction model is available for use in routine measurements.

Figure 1. Workflow for NIR method implementation.

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 1.

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

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.

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

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.

Below we will go through each of these cases and provide recommendations:

    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 (Fig. 3 – bottom).

    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 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.

    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.

    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.

    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 (click to enlarge).

    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.

    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

    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
    * The pre-calibrations of PE, PP, PET and PA 6 will be available prior to May 2020.
    Table 2. Overview of available pre-calibrations for the Metrohm Vision Air software.

    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.

    To learn more

    about pre-calibration for selected NIRS applications,

    come visit our website!

    Post written by Dr. Dave van Staveren, Head of Competence Center Spectroscopy at Metrohm International Headquarters, Herisau, Switzerland.

    To automate or not to automate? Advantages of PAT: Part 1

    To automate or not to automate? Advantages of PAT: Part 1

    I have to admit that the technological world of process analysis seemed foreign for me for a while. When I first heard about process automation, I imagined futuristic robots that do the work, similar to modern science fiction films. Perhaps many people might have the same impression.

    There is often a great deal of uncertainty about what the expression «we automate your process» actually means. In this blog series, I want to show you that process analytical technology (PAT) is less complicated than expected and offers several advantages for users.

    What does process analytical technology (PAT) mean? 

    I was once told in conversation:

    «Process analytics is for everyone who believes that they don’t need it.»

    There is definitely truth in this statement, and it certainly shows the abundance of application possibilities. At the same time, it should be considered that in the future, users of process analytical technology will not only invest in conventional measurement technologies (e.g., direct measurement, TDLAS, GC), but also increasingly in the determination of substance properties and material compositions.

    Pollution (gases and aerosols) in ambient air are especially harmful to human health. These substances can continuously and reliably be monitored by process analyzers.

    PAT serves to analyze, optimize, and ultimately control processes and their critical parameters. This control makes a major contribution to quality assurance and the overall process reliability at the manufacturer. Thinking back to some well-known chemical disasters (e.g. Minamata, Toulouse, or Tianjin) in which poisonous substances were released, causing immense damage to people and the environment, the importance regarding regular monitoring of critical parameters becomes abundantly clear. The list of analytes that can and must be monitored is long, ranging from contamination in wastewater due to municipal or industrial wastewater treatment plants, to pharmaceutical agents, to gases and aerosols in the ambient air.

    From Lab to Process

    Considering the history of manufacturing and other industrial processes, it is clear that the ultimate goal is to increase throughput in ever-shorter timeframes, with an eye on safety measures and minimization of costs where possible. Independence through automation and fast, reliable data transfer is a high priority.

    In order to make the process economically viable along the entire value chain, the resulting products should be manufactured at the highest quality in a short time and with minimal raw material and energy usage. For 24/7 operations in particular, knowledge of the composition of the starting materials and intermediate products (or rather, any impurities) is essential for optimal process control and reliability.

    How can reliable process monitoring be ensured around the clock? Very few companies have company laboratories with an actual 3-shift operation, and often send their samples to external laboratories. Additionally, the samples are sometimes taken with longer time intervals between them. This carries various risks.

    On one hand, the time lost between the sampling event and receiving the results from the analysis is enormous. It is only possible to react to fluctuations and deviations from target concentrations or limit values ​​with a certain delay. On the other hand, working and environmental conditions are not comparable and can lead to changes in the sample. Oxidation, pressure or temperature changes, introduction of moisture, and many other factors can change a sample’s original properties during transport, waiting periods, and manual laboratory analysis.

    Example trend graph comparing process deviations mitigated by manual control (grey) and fully automatic process control (orange) via PAT.

    Process analyzers: automated operation around the clock

    Analyses, which are usually carried out manually, are automated by using industrial process analyzers. The samples are automatically removed from critical points in the production process and processed further. The information obtained is used to control the process without any delay, as the data can be transferred immediately to a central computing system at the plant. Automated analysis right at the sample point allows for increased accuracy and reproducibility of the data.

    In practice, this entails rerouting a partial stream from the process in question to be fed to the analyzer by means of valves, peristaltic pumps, or bypass lines. Each sample is therefore fresh and correlates to the current process conditions. Probes can also be integrated directly into the process for continuous inline measurement.

    The analysis is performed using common titration, spectroscopy, ion chromatography, or electrochemical methods known from the laboratory, which are optimally integrated into the process analyzer for each individual application requirement. The methods can be used in combination, allowing several measuring points to be monitored in parallel with one system. Thanks to the process analyzers that are specifically configured and expandable for the application, the optimal conditions for stable process control are obtained.

    Spectroscopic methods have become particularly well-established in recent years for process analysis and optimization purposes. In contrast to conventional analysis methods, near-infrared (NIR) spectroscopy shows a number of advantages, especially due to the analysis speed. Results can be acquired within a few seconds and transferred directly to the chemical control system so that production processes can be optimized quickly and reliably. Samples are analyzed in situ, completely without the use of chemicals, in a non-destructive manner, which means further added value for process safety.

    The many advantages of PAT

    Automation in the context of process analysis technology does not always have anything to do with futuristic robots. Instead, PAT offers companies a number of advantages:

     

    • Fully automatic, 24/7 monitoring of the process
    • Timely and automatic feedback of the analysis results to the system control for automatic process readjustment
    • Reduction in fluctuations of product quality
    • Increased process understanding to run production more efficiently
    • Independent of your own laboratory (or contract lab)
    • Complete digital traceability of analysis results
    • Total solution concepts including sample preconditioning, saving time and increasing safety

    What’s next?

    In our next post in this series, you will discover the role process analysis technology plays in digital transformation with regard to «Industry 4.0».

    Want to learn more about the history of process analysis technology at Metrohm? Check out our previous blog post:

    Read what our customers have to say!

    We have supported customers even in the most unlikely of places⁠—from the production floor to the desert and even on active ships!

    Post written by Dr. Kerstin Dreblow, Product Manager Wet Chemical Process Analyzers, Deutsche Metrohm Prozessanalytik (Germany), with contributions from Dr. Alyson Lanciki, Scientific Editor at Metrohm International Headquarters (Switzerland).

    Benefits of NIR spectroscopy: Part 4

    Benefits of NIR spectroscopy: Part 3

    This blog post is part of the series “NIR spectroscopy: helping you save time and money”. 

    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.

    Here, we describe how to implement a NIR method in your laboratory, exemplified by a real case. Let’s begin by making a few assumptions:

    • your business produces polymeric material and the laboratory has invested in a NIR analyzer for rapid moisture measurements (as an alternative to Karl Fischer Titration) and rapid intrinsic viscosity measurements (as an alternative to measurements with a viscometer)
    • your new NIRS DS2500 Analyzer has just been received in your laboratory

    The workflow is described in Figure 1.

    Figure 1. Workflow for NIR spectroscopy method implementation (click to enlarge).

    Step 1: Create 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.

    Figure 2. Display of 10 NIR measurements linked with intrinsic viscosity and moisture reference values obtained with KF titration and viscometry (click to enlarge).

    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).

      Figure 3. Example of the intensifying effect on spectra information by using mathematical calculation: a) without any mathematical optimization and b) with applied second derivative highlighting the spectra difference at 1920 nm and intensifying the peaks near 2010 nm (click to enlarge).
      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.

      Figure 4. Correlation plot and Figures of Merit (FOM) for the prediction of water in polymer samples using NIR spectroscopy. The «split set» function in the Metrohm Vision Air Complete software package allows the generation of a validation data set, which is used to validate the prediction model (click to enlarge).
      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.

      Figure 5. Overview of a selection of NIR predicted results, with clear pass (no box) and fail (red box) indications (click to enlarge).
      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).

      Figure 6. Trend chart of NIR moisture content analysis results. The parallel lines indicate defined warning (yellow) and action (red) limits (click to enlarge).

      Summary

      The majority of effort needed to implement NIR 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 would 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 of the series. Stay tuned, and don’t forget to subscribe!

      For more information

      If you want to learn more about selected NIR applications in the polymer industry, visit our website!

      We offer NIRS analyzers suitable for laboratory work as well as for harsh industrial process conditions.

      Post written by Dr. Dave van Staveren, Head of Competence Center Spectroscopy at Metrohm International Headquarters, Herisau, Switzerland.

      Benefits of NIR spectroscopy: Part 4

      Benefits of NIR spectroscopy: Part 2

      This blog post is part of the series “NIR spectroscopy: helping you save time and money”. 

      Infrared spectroscopy and near infrared spectroscopy – is there a difference?

      This is the second installment in our series about NIR spectroscopy. In this post, you will learn the background of NIR spectroscopy on a higher level and determine why this technique might be more suitable than infrared spectroscopy for your analytical challenges in the laboratory and in the process.

      Spectroscopy… what is that?

      A short yet accurate definition of spectroscopy is «the interaction of light with matter». We all know that light certainly influences matter, especially after spending a long day outside, unprotected. We experience a sunburn as a result if we are exposed to the sun for too long.

      A characteristic of light is its wavelength, which is inversely correlated to its energy. Therefore, the smaller the wavelength, the more energy there is. The electromagnetic spectrum is shown in Figure 1. Here you can see that the NIR region is nestled in between the visible region (at higher energy) and the infrared region (at lower energy).

      Figure 1. The electromagnetic spectrum. (Click to enlarge.)

      Light from both the infrared (IR) and near-infrared (NIR) region (800–2500nm) of the electromagnetic spectrum induces vibrations in certain parts of molecules (known as functional groups). Thus IR and NIR belong to the group of vibrational spectroscopies. In Figure 2, several functional groups and molecules which are active in the NIR region are shown.

        Figure 2. Major analytical bands and relative peak positions for prominent near-infrared absorptions. Most chemical and biological products exhibit unique absorptions that can be used for qualitative and quantitative analysis. (Click to enlarge.)

        The difference in the vibrations induced by IR or NIR spectroscopy is due to the higher energy of NIR wavelengths compared to those in the IR region.

        Vibrations in the infrared region are classified as fundamental—meaning a transition from the ground state to the first excited state. On the other hand, vibrations in the near infrared region are either combination bands (excitation of two vibrations combined) or overtones. Overtones are considered vibrations from the ground state to a level of excitation above the first state (see Figure 3). These combination bands and overtones have a lower probability of occurring than fundamental vibrations, and consequently the intensity of peaks in the NIR range is lower than peaks in the IR region.

        Figure 3. Schematic representation of the processes occurring with fundamental vibrations and with overtones. (Click to enlarge.)

        This can be better understood with an analogy about climbing stairs. Most people climb one step at a time, but sometimes you see people in a hurry taking two or three stairs at once. This is similar to IR and NIR: one step (IR – fundamental vibrations) is much more common compared to the act of climbing two or more stairs at a time (NIR – overtones). Vibrations in the NIR region are of a lower probability than IR vibrations and therefore have a lower intensity.

        Theory is fine, but what does this mean in practice?

        The advantages of NIR over IR derived from the theoretical outline above are:

        1. Lower intensity of bands with NIR, therefore less detector saturation.

        For solids, pure samples can be used as-is in a vial suitable for NIR analysis. With IR analysis, you either need to create a KBr pellet or carefully administer the solid sample to the Attenuated Total Reflectance (ATR) window, not to mention cleaning everything thoroughly afterwards.

        For liquids, NIR spectra should be measured in disposable 4 mm (or 8 mm) diameter vials, which are easy to fill, even in the case of viscous substances. IR analysis requires utilization of very short pathlengths (<0.5 mm) which require either costly quartz cuvettes or flow cells, neither of which are easy to fill.

        2. Higher energy light with NIR, therefore deeper sample penetration.

        This means NIR provides information about the bulk sample and not just surface characteristics, as with infrared spectroscopy.

        However, these are not the only advantages of NIR over IR. There are even more application related benefits:

        3. NIR can be used for quantification and for identification.

        Infrared spectroscopy is often used for detecting the presence of certain functional groups in a molecule (identification only). In fact, quantification is one of the strong points of utilizing NIR spectroscopy (see below).

        4. NIR is versatile.

        NIR spectroscopy can be used for the quantification of chemical substances (e.g. moisture, API content), determination of chemical parameters (e.g. hydroxyl value, total acid number) or physical parameters (e.g. density, viscosity, relative viscosity and intrinsic viscosity). You can click on these links to download free application notes for each example.

        5. NIR also works with fiber optics.

        This means you can easily transfer a method from the laboratory directly into a process environment using an analyzer with a long, low-dispersion fiber optic cable and a rugged probe. Fiber optic cables are not possible to use with IR due to physical limitations.

        NIR ≠ IR

        In summary, NIR is a different technique than IR, although both are types of vibrational spectroscopy. NIR has many advantages over IR regarding speed (easier handling, no sample preparation needed), providing information about the bulk material as well as its versatility. NIR allows for the quantification of different kinds of chemical and physical parameters and can also be implemented in a process environment.

        In the next installment of this series, we will focus on the process of implementing a NIR spectrometer in your laboratory workflow, using a specific example.

        For more information

        about NIRS solutions provided by Metrohm, visit our website!

        We offer NIRS analyzers suitable for laboratory work as well as for harsh industrial process conditions.

        Post written by Dr. Dave van Staveren, Head of Competence Center Spectroscopy at Metrohm International Headquarters, Herisau, Switzerland.