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Frequently asked questions in near-infrared spectroscopy analysis – Part 2

Frequently asked questions in near-infrared spectroscopy analysis – Part 2

Whether you are new to the technique, a seasoned veteran, or merely just curious about near-infrared spectroscopy (NIRS), Metrohm is here to help you to learn all about how to perform the best analysis possible with your instruments.

In this series, we will cover several frequently asked questions regarding both our laboratory NIRS instruments as well as our line of Process Analysis NIRS products.

Did you miss Part 1 in this series? Find it here!

1. What are typical detection limits for liquid samples and for solid samples?

The detection limit varies depending on the substance analyzed, the complexity of the sample matrix, and the sensitivity of both the reference and NIR technology used. NIR spectroscopy systems using dispersive technology are the most sensitive. Using such a system to analyze a simple sample in which the parameter of interest is a strong absorber will allow low detection limits.

For example, water in solvents can be detected down to about 10 mg/L in both offline and online/inline measurements. For more complex matrices (e.g., solids and slurries), detection limits are about 1000 mg/L (0.1%).

For more information about the differences between solid and liquid samples for NIRS analysis, as well as the different methods best suited for such matrices, read our blog post «Benefits of NIR spectroscopy: Part 1» here!

2. What accuracy can I achieve with NIR spectroscopy?

The accuracy of a near-infrared spectroscopic method depends on the accuracy of the reference/primary method. A highly accurate primary method will result in the development of a highly accurate NIR method, while a less accurate primary method lowers the accuracy of the related NIR method. This is because the NIR data and primary data are correlated in the prediction model. A good prediction model will have approximately 1.1x the accuracy of the primary method over the prediction range.

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

3. How are instruments calibrated and how often do I need to recalibrate an instrument?

Instruments are calibrated using certified NIST standards. For dispersive systems measuring in reflection mode, NIST SRM 1920 standards are used to calibrate the wavelength / wavenumber axis. Certified reflection standards with a defined reflectance made of ceramic can be used to calibrate the absorbance axis.

In transmission mode, typically NIST SRM 2065 or 2035 are used for the wavelength / wavenumber calibration, and air for the absorbance axis.

A calibration should be performed after each hardware modification (e.g., lamp exchange) and annually as part of a service interval. Ideally, the spectroscopy software guides user through the complete calibration processes.

Find the calibration tools for your Metrohm NIRS instruments here!

Metrohm NIRS reflection standard, set of 2.

4. How do I validate my instrument and how frequently should validation be done?

The Metrohm NIRS DS2500 Solid Analyzer.

NIR spectroscopy software offers different tests to validate the performance of the instrument. The most common one is a basic performance test, which tests some crucial hardware parts as well as the wavelength/wavenumber calibration and the signal to noise (S/N) of the system.

For the regulated environment, further tests according to the USP <856> guidelines are typically implemented, including photometric linearity and noise at high and low light fluxes. Instrument performance tests should be performed on a regular basis, with the frequency depending on risk assessment.

5. What sample types or parameters are not suitable for analysis with NIR spectroscopy?

Samples containing a high amount of carbon black cannot be analyzed by NIR spectroscopy because carbon black absorbs almost all NIR light.

Further, most inorganic substances have no absorbance bands in the NIR spectral region and are therefore not suitable for NIR analysis.

Find out more about the molecules and functional groups which are active in the NIR region of the electromagnetic spectrum in our previous blog post: «Benefits of NIR spectroscopy: Part 2».

Carbon black is not a suitable sample to be measured by NIR technology.

Are you looking for more spectroscopy applications? Check out the Metrohm Application Finder to download free applications across a variety of industries!

6. My industrial process is full of harsh chemicals, so manual sampling is not desirable. Is it possible to perform inline NIR analysis in hazardous areas?

Yes, and we have the right solutions for you. Metrohm not only manufactures instruments for laboratory analysis, but we also cater to the industrial process world! Metrohm Process Analytics offers two versions of process NIRS systems: the NIRS Analyzer Pro and the NIRS XDS Process Analyzer, the latter being the ideal solution for hazardous environments.

Metrohm Process Analytics offers two lines of near-infrared spectroscopic process analyzers: the NIRS Analyzer PRO and the NIRS XDS Process Analyzer.

NIRS is a robust and extremely versatile method, which enables simultaneous, «real-time» monitoring of diverse process parameters with a single measurement. The use of fiber-optics in NIRS means that the process analyzer and measuring point can be spatially separated – even by hundreds of meters if required. In fact, remote monitoring can be achieved at large distances without significant impact to S/N ratios. This is a huge advantage in environments with challenging explosion protection requirements. Fiber-optic probes and flow cells can be placed in very harmful working environments, while the spectrometer and analysis PC remain safe and secure in a shelter. When a shelter is not available, the NIRS XDS Process Analyzer can be directly placed in the hazardous area (ATEX Zone 2 or Class1Div2).

Obtain «real-time» results of your process without the need to take samples, reduce the risks of handling chemicals, and increase your profitability. Download our free brochure here for more information about safe operation of NIRS process analyzers in hazardous areas!

7. How is the maintenance of a NIRS process analyzer performed?

Maintenance is easy, fast, and not necessary to perform very often. NIRS is a reagentless analytical technique, so the only consumable to be replaced is the lamp, which needs replacement once per year.

Compared to other techniques like chromatography  (e.g., GC, IC) or titration, and also because NIR spectroscopic analysis does not degrade samples, there is no chemical waste which is produced. Additionally, thanks to our all-in-one software, automatic performance tests are performed regularly to guarantee that the analyzer is operating according to process specifications. The instrument can be left in the process without any further operator involvement. 

Metrohm Process Analytics NIRS process analyzers are maintenance-free systems that have been designed to guarantee high uptimes and low operational costs.

Are you searching for more process NIRS applications? Check out the Metrohm Application Finder to download them for free!

Want to learn more about NIR spectroscopy and potential applications? Have a look at our free and comprehensive application booklet about NIR spectroscopy.

Download our Monograph

A guide to near-infrared spectroscopic analysis of industrial manufacturing processes

Post written by Dr. Nicolas Rühl (Product Manager Spectroscopy at Metrohm International Headquarters, Herisau, Switzerland) and Dr. Alexandre Olive (Product Manager Process Spectroscopy at Metrohm Applikon, Schiedam, The Netherlands).

Frequently asked questions in near-infrared spectroscopy analysis – Part 1

Frequently asked questions in near-infrared spectroscopy analysis – Part 1

Whether you are new to the technique, a seasoned veteran, or merely just curious about near-infrared spectroscopy (NIRS), Metrohm is here to help you to learn all about how to perform the best analysis possible with your instruments.

In this series, we will cover several frequently asked questions regarding both our laboratory NIRS instruments as well as our line of Process Analysis NIRS products.

1. What is the difference between IR spectroscopy and NIR spectroscopy?

IR (infrared) and NIR (near-infrared) spectroscopy utilize different spectral ranges of light. Light in the NIR range is higher in energy than IR light (Figure 1), which affects the interaction with the molecules in a sample.

Electromagnetic Spectrum
Figure 1. The electromagnetic spectrum.

This energy difference has both advantages and disadvantages, and the selection of the ideal technology depends very much on the application. The higher energy NIR light is absorbed less than IR light by most organic materials, broadening the resulting bands and making it difficult to assign them to specific functional groups without mathematical processing.

However, this same feature makes it possible to perform analysis without sample preparation, as there is no need to prepare very thin layers of analyte or use ATR (attenuated total reflection). Additionally, NIRS can quantify the water content in samples up to 15%.

Want to learn more about how to perform faster quality control at lower operating costs by using NIRS in your lab? Download our free white paper here: Boost Efficiency in the QC laboratory: How NIRS helps reduce costs up to 90%.

The weaker absorption of NIR light leads to using long pathlengths for liquid measurements, which is particularly helpful in industrial process environments. Speaking of such process applications, with NIR spectroscopy, you can use long fiber optic cables to connect the analyzer to the measuring probe, allowing remote measurements throughout the process due to low absorbance of the NIR light by the fiber (Figure 2).

Electromagnetic Spectrum
Figure 2. Illustration of the long-distance measurement possibility of a NIRS process analyzer with the use of low-dispersion fiber optic cables. Many sampling options are available for completely automated analysis, allowing users to gather real-time data for immediate process adjustments.

For more information, read our previous blog post outlining the differences between infrared and near-infrared spectroscopy.

2. NIR spectroscopy is a «secondary technology». What does this mean?

To create prediction models in NIR spectroscopy, the NIR spectra are correlated with parameters of interest, e.g., the water content in a sample. These models are then used during routine quality control to analyze samples.

Values from a reference (primary) method need to be correlated with the NIR spectrum to create prediction models (Figure 3). Since NIR spectroscopy results depend on the availability of such reference values during prediction model development, NIR spectroscopy is therefore considered a secondary technology.

Electromagnetic Spectrum
Figure 3. Correlation plot of moisture content in samples measured by NIRS compared to the same samples measured with a primary laboratory method.

For more information about how Karl Fischer titration and NIR spectroscopy work in perfect synergy, download our brochure: Water Content Analysis – Karl Fischer titration and Near-Infrared Spectroscopy in perfect synergy.

Read our previous blog posts to learn more about NIRS as a secondary technique.

3. What is a prediction model, and how often do I need to create/update it?

In NIR spectroscopy, prediction models interpret a sample’s NIR spectrum to determine the values of key quality parameters such as water content, density, or total acid number, just to name a few. Prediction models are created by combining sample NIR spectra with reference values from reference methods, such as Karl Fischer titration for water content (Figure 3).

A prediction model, which consists of sufficient representative spectra and reference values, is typically created once and will only need an update if samples begin to vary (for example after a change of production process equipment or parameter, raw material supplier, etc.).

Want to know more about prediction models for NIRS? Read our blog post about the creation and validation of prediction models here.

4. How many samples are required to develop a prediction model?

The number of samples needed for a good prediction model depends on the complexity of the sample matrix and the molecular absorptivity of the key parameter.

For an «easy» matrix, e.g., a halogenated solvent with its water concentration as the measurement parameter, a sample set of 1020 spectra covering the complete concentration range of interest may be sufficient. For applications that are more complex, we recommend using at least 40–60 spectra in order  to build a reliable prediction model.

Find out more about NIRS pre-calibrations built on prediction models and how they can save time and effort in the lab.

5. Which norms describe the use of NIR in regulated and non-regulated industries?

Norms describing how to implement a near-infrared spectroscopy system in a validated environment include USP <856> and USP <1856>. A general norm for non-regulated environments regarding how to create prediction models and basic requirements for near-infrared spectroscopy systems are described in ASTM E1655. Method validation and instrument validation are guided by ASTM D6122 and ASTM D6299, respectively.

Figure 4. Different steps for the successful development of quantitative methods according to international standards.

For specific measurements, e.g. RON and MON analysis in fuels, standards such as ASTM D2699 and ASTM D2700 should be followed.

For further information, download our free Application Note: Quality Control of Gasoline – Rapid determination of RON, MON, AKI, aromatic content, and density with NIRS.

6. How can NIRS be implemented in a production process?

Chemical analysis in process streams is not always a simple task. The chemical and physical properties such as viscosity and flammability of the sample streams can interfere in the analysis measurements. Some industrial processes are quite delicate—even the slightest changes to the process parameters can lead to significant variability in the properties of final products. Therefore, it is essential to measure the properties of the stream continuously and adjust the processing parameters via rapid feedback to assure a consistent and high quality product.

Figure 5. Example of the integration of inline NIRS analysis in a fluid bed dryer of a production plant.

Curious about this type of application? Download it for free from the Metrohm website!

The use of fiber optic probes in NIRS systems has opened up new perspectives for process monitoring. A suitable NIR probe connected to the spectrometer via optical fiber allows direct online and inline monitoring without interference in the process. Currently, a wide variety of NIR optical probes are available, from transmission pair probes and immersion probes to reflectance and transflectance probes, suitable for contact and non-contact measurements. This diversity allows NIR spectroscopy to be applied to almost any kind of sample composition, including melts, solutions, emulsions, and solid powders.

Selecting the right probe, or sample interface, to use with a NIR process analyzer is crucial to successful process implementation for inline or online process monitoring. Depending upon whether the sample is in a liquid, solid or gaseous state, transflectance or transmission probes are used to measure the sample, and specific fitting attachments are used to connect the probes to the reactor, tank, or pipe. With more than 45 years of experience, Metrohm Process Analytics can design the best solutions for your process. 

Visit our website to find a selection of free Application Notes to download related to NIRS measurements in industrial processes.

7. How can product quality be optimized with process NIRS?

Regular control of key process parameters is essential to comply with certain product and process specifications, and results in attaining optimal product quality and consistency in any industry. NIRS analyzers can provide data every 30 seconds for near real-time monitoring of production processes.

Figure 6. The Metrohm Process Analytics NIRS XDS Process Analyzer, shown here with multiplexer option allowing up to 9 measuring channels. Here, both microbundle (yellow) and single fiber (blue) optical cables are connected, with both a reflectance probe and transmission pair configured.

Using NIRS process analyzers is not only preferable for 24/7 monitoring of the manufacturing process, it is also extremely beneficial for inspecting the quality of raw materials and reagents. By providing data in «real-time» to the industrial control system (e.g., DCS or PLC), any process can be automated based on the NIRS data. As a result, downtimes are reduced, unforeseen situations are avoided, and costly company assets are safeguarded.

Furthermore, the included software on Metrohm Process Analytics NIRS instruments has a built-in chemometric package which allows qualification of a product even while it is still being produced. A report is then generated which can be directly used by the QC manager. Therefore, the product quality consistency is improved leading to potential added revenues.

Do you want to learn more about improving product quality with online or inline NIRS analysis? Take a look at our brochure!

In the next part, we cover even more of your burning questions regarding NIRS for lab and process measurements:

Want to learn more about NIR spectroscopy and potential applications? Have a look at our free and comprehensive application booklet about NIR spectroscopy.

Download our Monograph

A guide to near-infrared spectroscopic analysis of industrial manufacturing processes

Post written by Dr. Nicolas Rühl (Product Manager Spectroscopy at Metrohm International Headquarters, Herisau, Switzerland) and Dr. Alexandre Olive (Product Manager Process Spectroscopy at Metrohm Applikon, Schiedam, The Netherlands).

Upgrade your lab skills online

Upgrade your lab skills online

At the moment, times are strange, as many people are kept home to keep each other safe and healthy. Some of you are still able to work in your office or laboratories, but others are trying to find constructive ways to keep focused and stay connected.

During this time, one way to keep your skills sharp, or even to learn new ones, is by watching informative webinars. Level up in your laboratory expertise!

Below, we have a selection of some excellent free webinars from Metrohm to keep you on top of your game – no matter which technique you use. Application examples, practical information on handling, care, and troubleshooting, and more – our webinars provide very useful information dealing with various techniques and industries.

We offer several on-demand webinars about subjects such as the fundamentals of titration, troubleshooting, and the synergy between titration and near-infrared spectroscopy (also see our related blog post on this topic).

This important segment of titration is especially important for accurate moisture determinations.

On-demand webinars available include fundamentals and troubleshooting, as well as others for more in-depth knowledge.

NIRS is a fast, nondestructive, reagent-free technique, used in several markets (e.g., pharmaceuticals, petrochemicals, polymers, and personal care).

We have many interesting webinars not only focused on these industries, but also for quality control, process analytical technology (PAT),  and about the combination with the primary method of titration (also see our related blog post on this topic).

Raman spectroscopy is a handy tool for quick, reagent-free identification of raw materials, illicit substances, and hazardous chemicals – even from a distance.

Watch this webinar to learn how accurate, reliable, and portable screening tools can help to detect substandard and falsified medical products.

Aside from providing information about how Metrohm ion chromatography (IC) can be used for multiple applications in different markets, we also offer free webinars about sample preparation and automatic calibration to help save you valuable time when you’re back in the lab!

The measurement of pH is one of the most commonly performed determinations in chemical analysis. Why not learn some of the basics, or perhaps some troubleshooting techniques with our free webinars to impress your colleagues? If you are looking to avoid the most common mistakes in pH measurement, be sure to check out our blog post as well.

Our electrochemistry webinars cover a variety of topics to enhance your knowledge in this area. From corrosion analysis to electrocatalysis research, we have you covered.

If you’re more interested in screen-printed electrodes (SPEs) and biosensing applications, we have something for you, too!

I hope you find these webinars informative. If you’re interested in further educational opportunities from Metrohm, check out the Metrohm Academy. Stay safe, stay healthy, and always keep learning!

Post written by Dr. Alyson Lanciki, Scientific Editor at Metrohm International Headquarters, Herisau, Switzerland.

Benefits of NIR spectroscopy: Part 4

Benefits of NIR spectroscopy: Part 4

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

Have you read the other parts in this series? If not, check them out below!

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
    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.

    Improving your conductivity measurements

    Improving your conductivity measurements

    Have you ever performed a conductivity measurement and obtained incorrect results? There are several possible reasons for this. In this post, I want to show you how you may overcome some of these issues.

    By itself, conductivity measurement is performed quite easily. One takes a conductivity cell and a suitable measuring device, inserts the conductivity cell into the sample solution and reads the value given. However, there are some challenges such as choosing the right sensor, the temperature dependency of conductivity, or the CO2 uptake, which falsify your results.

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

     

    So many measuring cells – which one to use?

    The first and most important question about conductivity measurement is: which sensor is the most suitable for your application? The measuring range is dependent on the cell constant of your conductivity cell, and therefore this choice requires a few considerations:

    • What is the expected conductivity of my sample?
    • Do I have a broad range of conductivities within my samples?
    • What is the amount of sample I have available for measurement?

    There are different types of conductivity measuring cells available on the market. Two-electrode cells have the advantage that they can be constructed within a smaller geometry, and are more accurate at low conductivities. On the other hand, other types of measuring cells show no influences towards polarization, have a larger linear range, and are less sensitive towards contaminations.

    Figure 1 below shows you the wide application range of sensors with different cell constants. As a general rule: Sensors with a low cell constant are used for samples with a low conductivity and sensors with high cell constants should be used for high conductivity samples.

    Figure 1. Illustration of the range of applications for different conductometric measuring cells offered by Metrohm (click to enlarge).

    To get more information, check out our Electrode finder and select «conductivity measurement».

    Determination of the cell constant

    Each conductivity cell has its own conductivity cell constant and therefore needs to be determined regularly. The nominal cell constant is dependent of the area of the platinum contacts and the distance between the two surfaces:

    :  Cell constant in cm-1
    Aeff :  Effective area of the electrodes in cm2
    delectrodes :  Distance between the electrodes in cm

    However, no sensor is perfect and the effective cell constant does not exactly agree with the ideal cell constant. Thus, the effective cell constant is determined experimentally by measuring a suitable standard. Its measured conductivity is compared to the theoretical value:

    :  Cell constant in cm-1
    ϒtheor. :  Theoretical conductivity of the standard at the reference temperature in S/cm
    Gmeas :  Measured conductance in S

    With increasing lifetime usage, the properties of the measuring cell might change. Changing its properties means also changing its cell constant. Therefore, it is necessary to check the cell constant with a standard from time to time and to perform a redetermination of the cell constant if necessary.

    Temperature dependency of the conductivity

    Have you ever asked yourself why the conductivity is normally referred to at 20 °C or 25 °C in the literature? The reasoning is that the conductivity itself is very temperature-dependent and will vary with different temperatures. It is difficult to compare conductivity values measured at different temperatures, as the deviation is approximately 2%/°C. Therefore, please make sure you measure in a thermostated vessel or you use a temperature compensation coefficient.

    What is a temperature compensation coefficient anyway?

    The temperature compensation coefficient is a correction factor, which will correct your measured value at a certain temperature to the defined reference temperature. The factor itself depends on the sample matrix and is different for each sample.

    For example, if you measure a value of 10 mS/cm at 24 °C, then the device will correct your value with a linear correction of 2%/°C to 10.2 mS/cm to the reference temperature of 25 °C. This feature of linear temperature compensation is very common and is implemented in most devices.

    However, the temperature compensation coefficient is not linear for every sample. If the linear temperature compensation is not accurate enough, you can also use the feature of recording a temperature compensation function. There, you will measure the conductivity of your sample at different temperatures and afterwards fit a polynomial function though the measured points. For future temperature corrections, this polynomial function will be used, and more accurate results will be obtained.

    And… what about the conductivity standard?

    Figure 2. The blue curve shows the actual conductivity (mS/cm) and the orange line is a linear temperature compensation. The temperature compensation here varies from 2.39–4.04 %/°C.

    Which standard do I have to choose?

    In contrast to pH calibration, the conductivity cell only requires a one-point calibration. For this purpose, you need to choose a suitable standard, which has a conductivity value in the same range as your sample and is inert towards external influences.

    As an example, consider a sample of deionized water, which has an expected conductivity of approximately 1 µS/cm. If you calibrate the conductivity cell with a higher conductivity standard around 12.88 mS/cm, this will lead to an enormous error in your measured sample value.

    Most conductivity cells will not be suitable for both ranges. For such low conductivities (1 µS/cm), it is better to use a 100 µS/cm conductivity standard. While lower conductivity standards are available, proper handling becomes more difficult. For such low conductivities, the influence of CO2 influence increases.

    Last but not least: To stir or not to stir?

    This is a controversial question, as stirring has both advantages and disadvantages. Stirring enables your sample solution to be homogeneous, but it might also enhance the carbon dioxide uptake from ambient air.

    Either way, it does not matter if you choose to stir or not to stir, just make sure that the same procedure is applied each time for the determination of the cell constant, and for the determination of the conductivity of your sample. Personally, I recommend to stir slightly, because then a stable value is reached faster and the effect of carbon dioxide uptake is almost negligible.

    To summarize, it is quite easy to perform conductometric measurements, but some important points should be considered thoroughly before starting the analysis, like the temperature dependency, choice of suitable conductometric measuring cell, and the choice of calibration standard. Otherwise false results may be obtained.

    Curious about conductivity measurements?

    Read through our free comprehensive monograph:

    Conductometry – Conductivity measurement

    Additionally, you can download our free two-part Application Bulletin AB-102 – Conductometry below:   

    Post written by Iris Kalkman, Product Specialist Titration at Metrohm International Headquarters, Herisau, Switzerland.