Select Page
Five myths about online dispersive NIR spectroscopy, FT-NIR, and FT-IR – Part 1

Five myths about online dispersive NIR spectroscopy, FT-NIR, and FT-IR – Part 1

Spectroscopy is not just spectroscopy—or is it?

When talking with our project partners and customers, the topic of near-infrared (NIR) spectroscopy is often automatically associated with FT-NIR spectroscopy. So, why isn’t it just called NIR? What is the difference between IR and NIR? Some of you might even wonder: “Can I replace an old IR analyzer with NIR hardware?” And additionally: “Why should I replace the IR with a NIR process analyzer?”

This two-part series aims to explain the differences between these techniques and dispel some myths.

Click below to jump directly to a section:

A brief historical overview


The NIR wavelength range has a long history. As early as the 1880s, organic components were investigated in the NIR range and the strong –OH band relating to the presence of water was discovered as a very important piece of information. Shortly after, measurement of oils from the agricultural industry and investigations into various polymers followed. Some of the first industrial applications of dispersive NIR spectrometers were in the food and agricultural industries. In such applications, parameters including moisture, protein content, and fat content were analyzed quantitatively.

On the other hand, some strong advantages came from using the infrared (IR) wavelength range—high structural sensitivity and specificity—making it possible to obtain precise fingerprints for structural identification.

Figure 1. Historical punch card for assigning various spectral features to acetyl chloride in the infrared wavelength region [1]. (Click to enlarge image)
For more information about the differences between IR and NIR spectroscopy, read our previous blog posts.

The hardware for NIR and IR analysis was fundamentally different. At that time, even though the evaluation of NIR spectra seemed to be too difficult and ambiguous due to the broad overlapping peaks, there was one major advantage: robust and cheap materials could be used for NIRS (e.g., PbS detectors, tungsten lamps, and simple glass materials for the optics). Since the NIR-bands were broad and overlapped, users were limited to only the essential information and therefore did not need higher resolution, so simple dispersive gratings (monochromator gratings) were sufficient.

For IR, Fourier transform infrared spectrometers (FT-IR) were used which operated based on Michelson interferometers. This was necessary to obtain the spectral resolution needed for structural interpretation (e.g., distinguishing the isomers 1-propanol and 2-propanol at about 2700 nm for the first time). These spectrometers were introduced to the market in the 1960s. Due to the high costs for interferometers, special optics, and lasers, they were mainly used for research purposes.


Significant progress has been made in the field of spectroscopy due to the development of more powerful computers in the 1980s and 1990s in combination with chemometrics. While IR spectroscopy, due to its high data point density, was still far behind in the application of computer-based chemometric methods, NIR spectrometers were already able to benefit from fast evaluation methods.

Chemometric tools combined with the hardware benefits of NIR technology led many manufacturers to transfer their existing FT-IR measurement technology to the NIR range. And other companies? They just used and improved their already existing measurement technology to achieve perfect synergy between spectrometer and chemometrics.

Now that some of the background has been laid out, it’s time to answer some myths about NIR, FT-NIR, and FT-IR spectroscopy.

Myth 1: NIR spectroscopy always means FT-NIR

This is a persistent myth that you could easily miss unless you look closely. What exactly does the «FT» mean and why doesn’t everyone use it to describe NIR spectroscopy?

When using a FT-NIR spectrometer, first an interferogram is generated—not a spectrum in that sense. The conversion of the interferogram into a spectrum is done by applying a mathematical operation, the Fourier transform (FT). This transforms the path-dependent information (e.g., relative mirror position of two mirrors in the spectrometer) into a frequency-dependent function. This means FT-NIR is nothing more than the methodology of generating the spectrum in the NIR wavelength range.

As before, it is NIR spectroscopy that provides the same information as dispersive NIR spectroscopy or diode array spectroscopy. FT-NIR uses the interferences produced by an interferometer to extract single wavelengths from white light (halogen lamp), while dispersive spectrometers use gratings. Gratings are produced by very modern lithographic techniques and offer the highest precision (of wavelength accuracy).

Table 1. Comparison of FT-NIR and dispersive NIR spectroscopy

Parameter FT-NIR NIR
Wavelength splitting Mathematical calculation (Fourier transformation) from the phase shift of two incident light beams (interferogram) Diffraction or dispersion, movement by a digital encoder
Resolution depends on… Setting the maximum offset of the moving mirror Number of lines of the monochromator grid, slit width, encoder quality
Moving elements Yes (motor of the interference mirror) Yes (motor of the grid)
Wavelength range 12500 cm-1–4000 cm-1

(800–2500 nm)

800–2500 nm

(expandable to 400 nm)

Noise Depending on the resolution, higher than dispersive NIR with comparable setup Depending on the resolution, lower than FT-NIR with comparable setup
Method transferability (i.e., to other spectrometers) Yes Yes (due to the Metrohm calibration concept)


Consumables Laser source, halogen lamp, and desiccant Halogen lamp
Compared to a dispersive analyzer, a FT-NIR spectrometer uses a laser to control the position of the interferometer mirror. This laser must be changed periodically, however this task is generally not done by the end-user themselves compared to halogen lamp, which is easily replaceable.

Looking a little bit deeper into the details of Table 1, it is clear that dispersive spectroscopic instruments are more suitable for industrial process applications. Why? A low acquisition time is critical for real-time measurements with the least time loss. Compared to a FT-NIR instrument, the acquisition time is lower for a dispersive analyzer (leading to faster results) at the same resolution.

If you have ever wondered why you might need to calculate a Fourier transform, you will know after the next myth is answered.

Myth 2: Method transfer is only possible with FT-NIR spectrometers and not with dispersive spectrometers

Where does this myth originate?

Visualize the internal structure of an interferometer. A He-Ne laser is used as the reference measurement for the precise determination of the mirror position and thus also obtains an exact spectrum with high wavelength reproducibility at the same spatial coordinate by the Fourier transformation.

What is different about the dispersive spectrum?

In this case, the spectrum is not calculated in a mathematically complex way but is recorded directly via the dispersing element (the monochromator) on the detector. Here, a high-resolution state of the art grating and an accurate digital encoder that is precisely matched with the detector play an important role. NIRS XDS Process Analyzers from Metrohm Process Analytics (Figure 2) use wavelength standards to achieve both high wavelength precision and reproducibility and to ensure the transferability of the developed method.

Figure 2. The NIRS XDS Process Analyzer from Metrohm Process Analytics. 
Wavelength Precision <0.015 nm Valid
975.880 nm 0.0012 nm Yes
1221.342 nm 0.0005 nm Yes
1678.040 nm 0.0012 nm Yes
Figure 3. Results of the wavelength reproducibility test of a NIRS XDS Process Analyzer during a performance test. The precision meets the very narrowly defined testing specifications.
By using a wavelength and reference standard built into the spectrometer, additional diagnostics can be carried out at routine intervals (either as part of maintenance or automated within regular process operation) to check the wavelength accuracy and precision.

Due to the standardization concept with reference standards and wavelength standards, methods can be transferred without much effort even when changing or adjusting accessories (e.g., longer optical fibers, partially changed probes).

To summarize: a good standardization concept with NIST certified reference and wavelength standards as well as internally installed standards allow robust method transfers to other spectrometers and excellent long-term stability in the production process.

Myth 3: Many applications cannot be measured with dispersive NIRS, but require well-resolved FT-NIR spectroscopy

The Michelson interferometer and the monochromator grating were both developed in the 1800s. Both of these technologies have been used industrially since the advancement of computer technology and utilize the same light sources, detectors, optical fibers, and probes.

Monochromator gratings now consist of, e.g., a holographic concave diffraction grating with an optimized image plane to avoid aberrations and stray light. Holographic gratings are created by etching interference lines via laser into a photoresist layer. An advantage of this is very high spectral resolution, which together with a detailed adjustable encoder (and other components of the monochromator), provides very good resolution with the NIR spectrometer. For example, the NIRS XDS Process Analyzer (Figure 2) has a real resolution of 8.75 nm.

In comparison, higher resolutions can be achieved with interferometers, but this can also decrease the signal-to-noise (S/N) ratio. Usually, resolutions of approximately 8 cm-1 or 16 cm-1 are used, which corresponds to 10–25 nm at 2500 nm.

Parameter FT-NIR Dispersive NIR (Metrohm)
Wavelength range (nm) 800–2200

(12,500–4545 cm-1)


400–2200 (optional)

Wavelength precision (nm) ~0.01 ~0.005
Wavelength accuracy (nm) ~0.05–0.2 ~0.05
Figure 4. Noise spectra recorded with a Metrohm dispersive NIRS analyzer and a typical FT-NIR spectrometer.
Higher resolutions are usually not required for the majority of applications since harmonics/overtones and combination bands of pure substances in the NIR wavelength range have a broad bandwidth. The absorption peak with the smallest bandwidth currently known in the NIR region is talc at slightly more than 10 nm.

Very similar overlapping information (e.g., –OH bands or –COOH bands) are separated by using chemometric methods and are evaluated individually and specifically.

Another example that shows how powerful dispersive NIR spectroscopy is compared to FT techniques and IR spectroscopy can be seen in the separation of xylene isomers in a mixture of several aromatics/hydrocarbons (Figure 5).

Figure 5. Xylene isomers present in a mixture of aromatics and other hydrocarbons. (L) Raw spectra of all components from 800–2200 nm. (R) Raw spectra of xylene isomers show significant molecular vibration variations in the –CH region from 1700–1850 nm.
Figure 5 shows that the three xylene isomers can be clearly distinguished spectroscopically. Applying chemometrics elaborates the information even more and ultimately all six components can be determined individually and quantitatively. In a production process, the real-time reaction monitoring can be performed for all six components (Figure 6).
Figure 6. Graph showing real-time monitoring during a feed separation process of a mixture including three xylene isomers.
The application shown in Figures 5 and 6 has often been implemented with IR photometers in the past. We have now shown that the application can not only be transferred to the NIR wavelength range, but that even minimal structural differences between the functional groups of molecules can be detected by dispersive NIR spectroscopy.

To summarize: dispersive spectrometers have very good spectral resolution—in some cases better than FT-NIR spectrometers—and can even distinguish between different isomers in complex mixtures, or between very similar components like –OH and –COOH functional groups.


In the first part of this series, we went more into detail about the practical differences between FT-NIR and dispersive NIR spectroscopy. Three myths were discussed: that NIR spectroscopy always means FT-NIR—False, method transfer is only possible with FT-NIR spectrometers and not with dispersive spectrometers—False, and many applications cannot be measured with dispersive NIRS, but require well-resolved FT-NIR spectroscopy—False.

Some myths should no longer be kept alive because they are not facts!

We have also compared FT-NIR spectrometers to dispersive spectrometers when used in a process environment. Some critical points to remember: the dispersive analyzer is less sensitive to vibrations; less maintenance is necessary, and the grating is post-dispersive and therefore less prone to pollution due to lower ambient light.

To prove those arguments, Part 2 will show that, contrary to some expectations, it is possible to replace IR measurement techniques in industrial processes with easy-to-implement NIR measurement techniques. We will dispel some more myths and go into detail of an extremely low water content measurement in a process with the support of our primary analysis methods.


[1] Baker, A. W.; Wright, N.; Opler, A. Automatic Infrared Punched Card Identification of Mixtures. Anal. Chem. 1953, 25 (10), 1457–1460. doi:10.1021/ac60082a011

Find your process application

Our Applications Book contains hundreds of sold process applications from our more than 10,000 installed analyzers over several industries!

Post written by Sabrina Hakelberg, Product Manager NIRS Process Analyzers, Deutsche Metrohm Prozessanalytik (Germany).

USP  – simple automated analysis of ultrapure water

USP <645> – simple automated analysis of ultrapure water

H2O – two simple elements, oxygen (O) and hydrogen (H), fuse together to form one of the most important molecules in the world: water. Water is everywhere on Earth and it is vital for our health and survival. It often contains other ions like calcium, magnesium, and chloride which are essential for the human body to function. However, in specific situations, ultrapure water (UPW) is needed to prepare e.g., injections or other solutions used in hospitals. How is the quality of UPW ensured so that it is always suitable for such medical purposes? The answer to this comes from USP <645>. This standard explains how the water quality can be determined and how this analysis must be performed.


For this analysis, a measuring device capable of measuring the conductivity and the pH value is required. If a combined device is not available, then using two separate ones is also fine. Then, a pH electrode that is especially suitable for the determination of the pH value of water and a conductivity cell for measurement of low conductivities is necessary. In this case, the Aquatrode plus and the stainless steel conductivity cell are recommended.

The Aquatrode plus responds very quickly in ion-deficient matrices (such as ultrapure water) and thanks to its double junction system, the bridge electrolyte can be chosen freely.
The stainless steel conductivity cell has been specially designed for measurements in samples with low conductivity. With a cell constant of c = 0.1 cm-1, it is ideal for conductivities ranging from 0–300 µS/cm. The built-in temperature sensor makes handling even easier as no additional sensor is needed for the temperature measurement.

USP <645> procedure

Now that the necessary instrumentation has been introduced, it’s time to take a look at the standard procedure itself. At first glance this looks a bit difficult as it is a three-step analysis, or actually a four-step analysis if you count the calibration as well.

Step 1: First, calibrate the pH electrode and the stainless steel conductivity cell (sensor). The pH electrode is calibrated with pH 4 and 7 solutions, whereas the stainless steel sensor is calibrated with a 100 µS/cm standard.

Find the standard solutions you need here:

Step 2: After calibrating the sensors, both the temperature of the water and the conductivity are measured without temperature compensation. If the measured conductivity is lower than the value mentioned in the table of USP <645>, then the requirement for the conductivity is met and the water can be used for medical purposes. If this is not the case, then step 3 must be performed.

Step 3: 100 mL water is transferred to an external titration vessel where its temperature is adjusted to 25 ± 1 °C. The water is stirred vigorously to incorporate carbon dioxide present in the air. If the conductivity does not change by more than 0.1 µS/cm per 5 minutes, the value is noted for further evaluation. If this value is below 2.1 µS/cm, then the water is usable for medical purposes. If not, then proceed with step 4.

Step 4: The solution is tempered to 25 ± 1 °C. Once the temperature is stable, 0.3 mL of saturated KCl solution is added and the pH of the water is measured. The pH value must lie between pH 5 and 7. If this is not the case, the water does not meet the requirements and must be discarded. If the pH is measured between 5 and 7, then the conductivity must additionally be lower as mentioned in the USP <645> table. If this is the case, the analysis passes and the water can be used for medical purposes.

Automation of USP <645>

The analysis can be quite time-consuming and therefore Metrohm has provided a solution to make this process much easier. Our system combines all of these steps into one method, allowing you to perform walk-away automation and focus on more important tasks.

To expand the capabilities of the measuring system, the 856/867 modules can be exchanged with a modular OMNIS Titrator which can also be used for other standard potentiometric titrations.

Take a closer look at our automated analysis solution

Proper electrode immersion depth
  • Aquatrode plus for accurate pH measurement in ion-deficient matrices
  • Stainless steel conductivity cell for low conducting samples
  • Special holder for performing step 2 of the standard procedure
  • Thermostated vessel (for step 2)
  • DIS-Cover lid to prevent the sample from CO2 uptake (before step 3)
  • Rod stirrer to saturate the solution with carbon dioxide (for step 3)


With this complete system, standard analysis of UPW quality according to USP <645> is performed in a fully automatic and reliable manner. At the end of each analysis a clear message is received if the deionized water (UPW) has passed the test or not. Handling is very easy and allows users to check if an analysis passes or not with just a glance.

Download our Application Bulletin

Automatic conductometry in water samples with low electrical conductivity in accordance with USP<645>
Post written by Iris Kalkman (Product Specialist Titration) and Heike Risse (Product Manager Titration – Automation), Metrohm International Headquarters, Herisau, Switzerland.
Side reactions in Karl Fischer titration

Side reactions in Karl Fischer titration

Many chemists that utilize Karl Fischer titration are nervous about the presence of side reactions because they know that the water determination in their samples can only be specific without any side reactions. Other KFT users do not know what the possible side reactions are and therefore may obtain incorrect results.

What are side reactions?

These are reactions with substances in the sample that:

  • interfere with the stoichiometry of the KF reaction
  • change the pH value of the KF reagent
  • either produce or use up water themselves
  • oxidize on the anode of the generator electrode
  • reduce on the cathode of the generator electrode
  • react with the ingredients of the KF reagent

Recognizing side reactions

One of the worst things that can happen with KFT is not knowing that a side reaction is falsifying your results. Below are some characteristic signs of side reactions.

Titration time and titration curve

Some indications of side reactions include longer titration times compared to the titration of a water standard, slow endpoint detection, and a higher drift value after the titration finishes than at the titration start. Comparing the titration curves of the sample and a water standard with a similar water quantity makes it easier to evaluate the situation. Just plot a graph of the volume against time (or µg water against time, in the case of coulometry). If the graph exhibits a curve that increases steadily as illustrated in Figure 1 (in orange), this can indicate a side reaction.

Figure 1. Side reactions can often be identified from checking the titration time and the titration curve, as shown in this graph.

If you notice that the water content depends on the sample weight or the titrant consumption (µg water for coulometry), then you can check the slope of a regression line after plotting water content against titrant consumption (µg water).

Ideally, the slope (b) should be 0. Significantly positive or negative values can indicate a side reaction, as shown in Figure 2.

Figure 2. If the slope of the regression line for the water content / titrant consumption value pairs deviates significantly from 0, this indicates a side reaction.

If the water recovery value found after spiking the samples is not within 100 ± 3%, this can indicate a side reaction. Depending on the type and speed of the side reaction, the recovery may be too high or too low. For example, samples which contain DMSO (dimethyl sulfoxide) change the stoichiometry of the Karl Fischer reaction and therefore result in false low readings.

Please note that a recovery rate of almost 100% does not guarantee the absence of a side reaction. Side reactions that take place very rapidly will not be detected, since the side reaction is already complete when the spiking process begins. A spiking procedure is described in detail in chapter 2.5.12 of the European Pharmacopoeia.

Preliminary tests

The oxidation of iodide or reduction of iodine leads to incorrect results.

How can you check whether your sample is undergoing a side reaction with iodine or iodide? A simple preliminary test can clarify the situation. Dissolve the sample in a weakly acidic (alcoholic) solution and then add some drops of iodine or potassium iodide solution. Based on the coloring (a discoloration of iodine or the formation of brown iodine), a side reaction can be detected.

Evaluating redox potentials

Comparing the redox potentials of the redox pairs of sample substances with the redox potential of iodine/iodide can be helpful to assess whether an undesired redox reaction may occur.

If the standard potential is higher than that of iodine/iodide, as in the case of e.g., chlorine, the oxidation of the iodide may result in false low readings.

If it is lower (e.g., lead), the reduction of the iodine may result in values that are too high.

Element changing oxidation state oxidized form + x e → reduced form Standard electrode potential E°
Cl Cl2 + 2e ⇌ 2 Cl +1.36 V
I I2 + 2e ⇌ 2 I +0.54 V
Pb Pb2+ + 2e ⇌ Pb -0.13 V

Avoiding side reactions

Most side reactions can be suppressed by taking suitable measures, such as those listed here.

  • For ketones and substances that react with the methanol present in the KF reagent: Use methanol-free reagents.
  • For samples that lower the pH range of the KF reagent: Add buffer solution for acids or a stoichiometric excess of imidazole.
  • For samples that increase the pH value (e.g., aminic bases): Add buffer solution for bases or a stoichiometric excess of salicylic acid / benzoic acid.
  • High drift after titration: Postdrift correction may help. This is done by stopping the titration at a defined time and recording the additional consumption over several minutes. This allows the calculation of the drift after the titration. This postdrift is then used to correct the water quantity found.
  • Samples that reduce iodine: Subtract the iodine consumption of the reductant in the sample from the overall iodine consumption of the sample.
  • Samples that oxidize iodide: Reduce the oxidant, e.g., Cl2, in advance with an excess of SO2, for example, by treating the sample with the solvent of a two-component reagent.
  • General: Carry out the titration in a thermostatically controlled cell connected to a circulation thermostat at, e.g., -20 °C in order to slow down the side reaction. Note that the titration parameters should be adjusted to the low temperatures.
  • General: Extract the water with the KF oven method if the interfering components are thermally stable at oven temperature.
  • General: Mask or eliminate the interfering component, e.g., by adding N-ethylmaleimide in the case of thiols.
Find out more about the Karl Fischer oven method in our blog article.


Side reactions can negatively influence and falsify your results. Recognizing and avoiding side reactions in KF titration is therefore crucial for the most accurate determinations.

For more information, check out our blog series about frequently asked questions in Karl Fischer titration.

Download our free monograph:

Water determination by Karl Fischer Titration
Post written by Michael Margreth, Sr. Product Specialist Titration (Karl Fischer Titration) at Metrohm International Headquarters, Herisau, Switzerland.
Staircase or linear scans: two options for reliable electrochemical experiments

Staircase or linear scans: two options for reliable electrochemical experiments

Electrochemical experiments are performed by delivering and controlling a potential or current signal to the electrochemical cell/device under test and measuring its response through a potentiostat/galvanostat (PGSTAT). Here, two different options for performing different electrochemical experiments are discussed: linear and staircase scans, as well as some applications where one may be preferred over another.

From analog to digital

Before modern digital electronics were widely available, PGSTATs worked with analog electronics, and therefore delivered analog signals. Analog boards were expensive and time-consuming to produce and test. Moreover, controlling the equipment from a computer is done via digital communication and requires digital electronics.

An analog signal is continuous, and it has virtually infinite resolution. On the other hand, a digital signal is written in discrete bits (0 and 1), so it is not continuous.

Linear scans

To better explain an analog signal, consider a linear sweep voltammetry (LSV) in potentiostatic mode, performed with an analog scan. Here, the applied voltage E versus time plot consists of a straight line between the initial and end potentials. The potential interval between two consecutive data points and the scan rate define the interval time—the slope of the E versus time plot (Figure 1).

Figure 1. A typical linear scan from an initial and an end potential. The interval time and the measurement time are also shown here.

The current resulting from the application of the LSV is measured at the end of the interval time. The measurement time defines the duration of the sampling. The current is composed of a capacitive part ic (given by the charging of the double layer), and a faradaic part if.

A double layer is formed when a potential is applied to an electrode. Then, current flows to the electrode, which becomes charged. Ions from the bulk solution migrate to the surface to balance this charge. Therefore, a layer of ions at the interface between the electrode and the electrolyte builds up, forming the equivalent of a capacitor.

Learn more about the principles and characterization of capacitors in our free Application Note.

The faradaic current is the result of the electrochemical reactions occurring at the working electrode | electrode and counter electrode | electrolyte interfaces, and it changes with the scan rate either linearly or with the square root of the scan rate, depending on the type of electron transfer.

The capacitive current resulting from a linear scan iC,ls is a constant value given by the product of the double layer capacitance Cdl and the scan rate.

Find out more about the differences between linear and staircase cyclic voltammetry on a commercial capacitor in our free Application Note.

Staircase scans

When modern digital electronics became more commercially available and economically feasible, PGSTAT manufacturers adopted them, together with personal computers, to control PGSTATs. This allowed researchers to create more complex procedures as well as to perform data analysis via software. Metrohm Autolab was the first company to deliver computer controlled PGSTATs to the market back in 1989.

In the case of a digital scan, the applied potential versus time plot between an initial and end potential has the typical «staircase» shape of a digitized signal. The interval time tint defines the duration of each step, while the step potential Estep defines the potential difference between two consecutive steps (Figure 2).

Figure 2. A typical staircase scan profile. The step potential Estep is shown along with the interval time and the measurement time.

The resulting current is measured at the end of the step. The measurement time defines the sampling time.

In a digital scan, the step potential  Estep results in a capacitive current iC,ss which rises almost immediately up to the maximum value allowed by the current range, lim(CR), and then it decays exponentially as time constant t = RC. After the decay of the capacitive current, the faradaic current if is predominant.

The current is measured at the end of the step in order to remove the capacitive current and collect only the faradaic one (Figure 3).

Figure 3. Potential step (in blue) and current profile for a staircase scan. The current profile is divided into capacitive current iC,ss (black line) and faradaic current if (purple line). Total current itot is shown in dark red. The measurement (sampling) time is also shown here. For clarity, the decay of the capacitive current and the decay of the faradaic current are not in scale.

Application examples: staircase or linear scan?


Some electrochemical processes can result in a capacitive current, having a characteristic time comparable to the charging of the double layer. In such cases a digital scan would neglect such capacitive currents and all of the information contained within them.

This is the case of highly capacitive cells, such as capacitors and supercapacitors.

In Figure 4, cyclic voltammograms at different scan rates of a commercial 1 µF capacitor are shown. The diagram on the left shows the results from the digital scan, and on the right are the results from the analog scan [1].

Figure 4. Cyclic voltammograms of a 1 µF capacitor at different scan rates. Left: the cyclic voltammogram resulting from a digital scan. Right: the cyclic voltammogram resulting from an analog scan.

It is pretty clear that the digital scan results do not contain any information about the charge/discharge of the capacitor, while the analog scan results have the expected shape of a capacitor’s cyclic voltammograms.

Adsorption/desorption processes

Another application example includes cells in which fast adsorption/desorption of species at the electrode surface occurs in a short time, like the adsorption/desorption of hydrogen as part of the electrochemical behavior of platinum submerged in an aqueous solution of sulfuric acid (Figure 5).

Figure 5. The cyclic voltammogram of a Pt working electrode immersed in a 0.5 mol/L H2SO4 aqueous solution. The reference is a Ag/AgCl 3M KCl electrode, while the counter electrode is a Pt electrode.

Here, the fast adsorption/desorption of hydrogen occurs at time scales that are similar to the charging of the double layer in the capacitor example. Therefore, the linear scan is the preferred option, compared to the digital staircase scan which is unable to capture the hydrogen adsorption/desorption (Figure 6) [2].

Figure 6Top: linear cyclic voltammograms of a Pt working electrode immersed in a 0.5 mol/L H2SO4 aqueous solution at different scan rates. Bottom: staircase cyclic voltammograms of the same setup at the same scan rates.

Redox reactions

Another example of experiments in which the capacitive current should not be neglected are redox reactions in which the electron transfer is very fast. In these situations, the charge transfer resistance is very small and the cyclic voltammogram results in redox peaks which are symmetrical over the potential axes. Examples include redox reactions on species adsorbed on the working electrode surface [3].

VIONIC: the future of electrochemistry

The most recent generation of PGSTATs, such as VIONIC, are equipped with electronics that allow to users perform analog scans without the drawbacks mentioned earlier. This gives researchers the opportunity to choose the type of scan according to the type of systems being studied, the materials, and the importance of the capacitive current for the outcomes of the research.  

Learn more about VIONIC

Maximum experimental possibilities, intelligent software, and the most complete data.


[1] Locati, C. Comparison between linear and staircase cyclic voltammetry on a commercial capacitor, Metrohm AG: Herisau, Switzerland, 2021. AN-EC-026

[2] Locati, C. Study of the hydrogen region at platinum electrodes with linear scan cyclic voltammetry – How VIONIC powered by INTELLO can be used to characterize processes at the Pt-electrolyte interface, Metrohm AG: Herisau, Switzerland, 2021. AN-EC-025

[3] Chi, Q.; Zhang, J.; Andersen, J. E. T.; Ulstrup, J. Ordered Assembly and Controlled Electron Transfer of the Blue Copper Protein Azurin at Gold (111) Single-Crystal Substrates. J. Phys. Chem. B 2001, 105 (20), 4669–4679.

Post written by Dr. Corrado Locati, Application Specialist at Metrohm Autolab, Utrecht, The Netherlands.

From corn to ethanol: improving the fermentation process with NIRS

From corn to ethanol: improving the fermentation process with NIRS

The production of biofuels from renewable feedstock has grown immensely in the past several years. Bioethanol is one of the most interesting alternatives for fossil fuels, since it can be produced from (renewable) raw materials rich in sugars and starch.

Fermenting corn starch to produce ethanol for fuel is a complex biochemical process that requires monitoring of several parameters to ensure optimal production. Measuring these parameters via traditional laboratory techniques takes about an hour to complete and is a limiting step for increasing plant capacity and efficiency. Near-infrared spectroscopy (NIRS) can replace routine laboratory analysis, decreasing operating costs and increasing plant efficiency and capacity.

Learn more about this fast, non-destructive analysis technique in our different series of blog posts, including the benefits of using NIRS and some frequently asked questions.

Producing high quality ethanol as a fuel additive

Ethanol is an increasingly important component in the global fuel market, with countries looking to secure domestic fuel supplies and reduce their greenhouse gas emissions relative to fossil fuels. The United States and Brazil lead world bioethanol production, accounting for 83% of the supply.

According to the Renewable Fuels Association, approximately 26 billion gallons (nearly 100 billion liters) of ethanol were produced globally in 2020 [1], slightly reduced from a 2019 peak due to the global pandemic crushing demand for gasoline and ethanol as well. Demand for corn to transform into ethanol is still likely to rise as the United States increases adoption of E15 blends (15% ethanol in gasoline) [2]. Ethanol for export is also likely to increase in demand, with countries such as China implementing a E10 fuel standard for motor vehicles.

One of the primary ways to meet increasing product demand while maintaining price competitiveness is to increase plant capacity. However, the standard laboratory analytical workflow for monitoring the different parts of the fermentation process can be a limiting factor for growing a production site or improving its efficiency. Another consideration is the seasonal, and even regional variation of feedstock quality, requiring ethanol producers to closely monitor the fermentation process to ensure the same quality product is achieved.

A report from the National Renewable Energy Laboratory estimated that nearly 40% of the production cost of fuel ethanol from corn comes from labor, supplies, overhead, and variable operating costs [3]. Optimization of these costs, which include routine quality checks of the fermentation broth, regular maintenance of the fermenters and distillation towers, and triaging process upsets in a timely manner, leads to higher profitability of the ethanol production facility.

To maximize bioethanol production and profitability, laboratory limitations must be overcome. Near-infrared (NIR) spectroscopy is a proven economical, rapid, and operator friendly way to overcome common laboratory limitations. First, a bit of background information about the production of bioethanol is needed before jumping into how to optimize the process.

Ethanol process: wet vs. dry milling

There are two main production processes when it comes to creating ethanol from sugars and starches from starting materials such as corn: the wet milling process and the dry milling process (shown in Figure 1). Nearly all ethanol produced for fuel in the U.S. (the largest bioethanol manufacturer in the world) is made using the dry mill process [2].

Figure 1. Schematic representation of the dry mill ethanol process.

Grains are first ground into smaller, more homogenous particles in the dry milling process, which allows the husk or shell to be more easily penetrated. Water and enzymes are then added to create a slurry called a «mash». To facilitate the conversion of starches to sugars, the mash is heated to specific temperatures, then cooled before yeast is added. The yeast performs the work of creating ethanol from the converted sugars via the process of fermentation. However, the percentage of ethanol is still quite low, and therefore the solution must be distilled and dehydrated to obtain the concentration and purity necessary for fuel additives.

Wet milling differs from this process by first soaking the grains before grinding and separating out the various components. The starches are then converted to sugars which are used for the fermentation process, just as with dry milling.

If you want to know more about the fermentation process, read our blog post about optimization of beer brewing.

Lab analysis shortfalls

The lab serves many functions, but one of the key ones is to monitor the progress of the fermentation in each fermentation tank. This typically requires many different technologies, because several parameters must be checked to ensure that a fermentation is on track. Tight monitoring and control over the various sugars present (e.g., glucose, maltose, DP3, etc.) throughout the fermentation process is necessary to understand the breakdown pathway of the starch (glucose generation) present in the mash and optimize ethanol production. Understanding this pathway enables the proper dosage of enzymes and yeast to the mash in the slurry tanks (Figure 1) to accelerate breakdown. Therefore, optimizing the enzyme and yeast blend is crucial for this process. These are the highest consumable costs for ethanol production and significantly affect the rate of production and final yield of ethanol.

Some of the most common analytical instruments and their use cases are listed in Table 1.

Table 1. Typical instruments and parameters that are measured during fermentation of corn to ethanol.
Parameter Measurement technique Analysis time (min) incl. sample prep.
Dissolved solids (°Bx) Refractometer 3–5
pH pH meter 3–5
Solids (non-volatiles) Infrared balance 15–20
Ethanol HPLC 30–45

Sugar profile 
(DP2, DP3, DP4+, glucose, total sugar)

HPLC 30–45
Glycerol HPLC 30–45
Lactic acid Ion chromatography 30–45
Acetic acid Ion chromatography 30–45
Water content Karl Fischer titration 5–10

If all the properties in Table 1 are to be measured, it can easily take an hour using six different pieces of equipment. Factor in conditioning steps and reference scans to ensure proper calibration, and the time for a routine fermentation analysis increases. For a single corn fermentation, this can take upwards of 55 hours—one hour to perform the analysis and six hours between each measurement. However, increasing the number of concurrent fermentations to four or six means that measurements from the different tanks will begin to overlap.

Overlapping instrument demand combined with long analysis times results in a number of different challenges for bioethanol producers. First, if scheduled sampling times overlap, then sampling must either be delayed or samples must age while waiting for analysis. Second, the long analysis time means that data is no longer current, but minimally one hour or older by the time it has been communicated to the plant control center, which decreases the ability to deal with deviations. Neither of these situations is ideal for manufacturers—time is money, after all.

Long laboratory analysis times and infrequent measurements reduce the ability to perform interventions or to adjust other critical parameters (e.g., enzyme addition rate or process temperature). Additionally, such long wait times can impede the decision to end a fermentation early and begin anew if the batch is judged to be beyond recovery.

Faster measurements equal higher profits

The most obvious way to overcome measurement time challenges is to increase the number of tools in the lab and/or to add automation. However, this approach has costs in time; twice the sample preparation increases operating expenses and still fails to give high-speed feedback to the plant operations team.

A better way to overcome measurement time delays is to deploy near-infrared spectroscopy (NIRS), which can make all of the traditional laboratory measurements with one piece of equipment, at the same time, in less than five minutes.

Figure 2 displays the average ethanol concentration from HPLC measurements during several fermentations from one plant. The data shows apparent discontinuities in the first 12 hours, with spikes in glucose and dissolved solids. It is also apparent that the total solids measurement at 48 hours is erroneous. However, because the lab data requires so much time to collect, this spike is ignored instead of retested.

Figure 2. Key parameters measured for corn fermentation to ethanol as reported by the primary analysis methods listed in Table 1.

The NIRS alternative to traditional measurements shown in Figure 3 is of a single fermentation monitored in near real time. This high-speed analysis is possible because sample preparation is trivial for NIRS. Compared to the combination of HPLC and other analytical methods that consume about 60 minutes of operator time per sample, NIRS measures the same parameters and produces a quality result in about a minute. The ability to collect many NIR spectra in the early stages of the fermentation process provides a higher fidelity picture, enabling more timely interventions to maximize ethanol production.

Figure 3. Corn fermentation to ethanol as measured by near-infrared spectroscopy.

The higher speed NIRS analysis can be used to increase total plant throughput by growing the number of batches and revenue, as shown in Table 2. With the traditional analysis, the fermentation is allowed to run 62–65 hours, depending on the final laboratory results (Figure 2).

With NIRS analysis, this fermentation is shown to be complete in around 56 hours (Figure 3). Reducing fermentation time by six hours expands the potential number of batches by 13 over the course of a year, representing a potential plant capacity increase of 10%.

Table 2. Comparison of the apparent fermentation time based on primary lab analyses vs NIRS analysis.
Traditional Lab Analysis NIRS Analysis

Total measurement time

12 hours

5 hours

Number of measurements



Fermentation end point

~62 hours

56 hours

Batch capacity

37,850 L

37,850 L

Batches per year



Download our free White Paper to learn more.

Near-infrared spectroscopic solutions for ethanol producers

Metrohm offers several NIRS solutions for ethanol producers to make analysis easier and optimize production. The DS2500 Solid Analyzer (Figure 4) is ideal for rapid laboratory analysis of several critical quality parameters in the fermentation process.

Download our free Application Note below to learn more about how Metrohm NIRS laboratory instruments perform quality control measurements for the fermentation process.

Figure 4. The Metrohm DS2500 Solid Analyzer.

Additionally, Metrohm also manufactures NIRS instruments for measurements directly in the process, eliminating the need for removing samples and transporting them to the laboratory. Measurements taken in this way are the most representative of actual process conditions and therefore provide the highest quality data to operators. Learn more here about our different ranges of NIRS process analyzers and accessories.

Data communication between the process analyzer and the control room allows a direct overview of current conditions without delays and offers the possibility of integrating warnings when readings are out of specification or informing operators when the fermentation process is deemed to be complete.

For more information about the usage of NIRS for process analysis in bioethanol production, download our free Application Note.


Near-infrared analysis decreases measurement time for in-process fermentation samples by approximately 90%, from one hour to five minutes. Faster measurements allow the fermentation process to be followed much more closely, saving operator time to reduce costs and to optimize process conditions and plant operations. Capacity improvements of 10% are possible by being able to stop the fermentations based on rapid determination of the different parameters in the fermenter with NIRS rather than by slower traditional laboratory methods.

NIR methodology can provide benefits across the ethanol plant beyond fermentation monitoring to measure the performance of other plant components such as a centrifuge or dryer, making it a valuable tool to improve operations across the facility.

For more information about utilizing NIRS analysis in the bioethanol process as well as the available precalibrations for various quality parameters, download our free White Paper.

Free White Paper

Improving the corn to ethanol fermentation process with near-infrared spectroscopy (NIRS)


[1]  Annual Fuel Ethanol Production U.S. and World Ethanol Production. Renewable Fuels Association: Washington, DC, 2021.

[2]  Essential Energy: 2021 Ethanol Industry Outlook. Renewable Fuels Association: Washington, DC, 2021.

[3]  Determining the Cost of Producing Ethanol from Corn Starch and Lignocellulosic Feedstocks. National Renewable Energy Laboratory (NREL): Golden, Colorado, USA, 2000.

Post written by Dr. Adam J. Hopkins (PM Spectroscopy at Metrohm USA, Riverview, FL) and Dr. Alyson Lanciki (Scientific Editor at Metrohm International Headquarters, Herisau, Switzerland).

Fast and fundamental: influences on reliable electrochemical measurements

Fast and fundamental: influences on reliable electrochemical measurements

The ultimate goal of any researcher is to contribute to the progress of society by pioneering exploration beyond the known limits. Depending on the research type and application field, one way to fulfill this is to collect reliable experimental data on rapidly occurring processes (less than 1 ms).

Having insight into the fundamentals of these reaction mechanisms can ultimately lead to the discovery of new materials or the improvement of current solutions. In electrochemical research, reaction mechanisms and intermediates are investigated by measuring the kinetics and dynamics of the electrochemical processes happening at the surface of the electrode on a sub-ms timescale.

This article provides a short overview of the factors that have a direct influence on fast and ultra-fast electrochemical measurements from an experimental setup perspective.

Considering the following factors in the experimental design and execution is the first condition to obtain reliable experimental results for such measurements.

Additional challenges which researchers must be aware of when experimenting with «transient electrochemistry», i.e. doing electrochemical measurements at very low time scales, is presented in the featured article from E. Maisonhaute et al. [1].

Main factors that influence the reliability of fast electrochemical experimental results

The primary components of an electrochemical experimental setup are:

  • The electrochemical cell including the electrodes and electrolyte
  • The electrochemical instrument, i.e., the potentiostat/galvanostat (PGSTAT)

To perform reliable electrochemical experiments in general, and fast electrochemical measurements in particular, the specifications of the complete work system must be considered and the optimal settings must be used for all of the individual parts of the experimental setup.

Time constant of the electrochemical cell

The electrochemical cell and its specifications must be taken into account as it is an important element of the experimental setup.

Transient electrochemical experiments are not meaningful unless the cell time constant is small relative to the timescale of the measurement, regardless of the high-frequency characteristics of the control circuitry.

The cell time constant RuCdl (s) depends directly on the uncompensated resistance Ru (Ω) (i.e. the resistance of the electrolyte between the reference and the working electrode) and the double-layer capacitance Cdl (F) of the electrode [2].

As a consequence, when the potential is stepped or scanned rapidly, the true measured potential Etrue (V) lags behind the applied potential Eappl (V), according to the following equation:

Where RuCdl (s) is the time constant of the cell and t (s) is the time at which the measurement is taken.

Figure 1. Theoretical and true waveform applied to a real electrochemical cell [1].

For fast scan rates (i.e. when 𝑡 is much smaller than RuCdl ), the exponential term approaches 1 and significant errors in 𝐸true with respect to 𝐸appl can arise. For slow scan rates (i.e. when 𝑡 is much larger than RuCdl), the exponential approaches 0 and the errors become negligible.

The time constant of the cell can be reduced in three ways:

  • Reduce Ru via increasing the conductivity of the electrolyte by either increasing concentration of supporting electrolyte or decreasing viscosity
  • Reduce the size of the working electrode (e.g., by using microelectrodes) so that Cdl will be minimized
  • Move the reference electrode as close as possible to the working electrode (e.g., by using a Luggin capillary) so that Ru will be minimized

The electrochemical instrument: potentiostat/galvanostat (PGSTAT)

The potentiostat/galvanostat (PGSTAT) is used to accurately control the applied signal (potential or current) and measure the response (current or potential, respectively) from the electrochemical cell. The accurate control of the applied signals is achieved by using a control loop (or feedback loop) circuit.

When fast electrochemical measurements are executed, the following specifications will have a direct influence on the results and must be considered.

Bandwidth of the control loop of the PGSTAT

In general terms, bandwidth can be described as the parameter that defines how fast the instrument is able to react to any changes in the signal.

In electrochemical terms, the bandwidth is the frequency beyond which the performance of the system is degraded.

The bandwidth of the control loop of the PGSTAT (i.e. bandwidth of the instrument) indicates how fast the applied signal is controlled through the feedback loop.

Higher bandwidth means that the instrument uses a faster control loop (faster feedback). As a result, the applied signal will reach the desired set point faster, and in ideal circumstances the output signal will be identical to the theoretical waveform. However, depending on the properties of the electrochemical cell connected to the instrument, the applied signal might overshoot. In extreme cases, the instrument feedback loop might get out of control causing the potentiostat to oscillate. This is more likely when high-capacitance electrochemical cells are connected to the PGSTAT.

When a Lower bandwidth is used, the overall stability of the PGSTAT increases by reducing the speed of the control loop. In this case, the consequence is that at very high measurement speeds, the output of the applied signal may be slightly less accurate due to a slower slew rate. Nevertheless, when measuring fast transients is not within the scope of the experiment, using the instrument with a lower bandwidth setting is recommended for highly accurate experimental results.

Figure 2. Schematic representation of the applied signal when Low bandwidth (Low speed) and High bandwidth (High speed) settings are used compared with the theoretical response.

Therefore, it is important to choose the control loop bandwidth settings according to the type of the measurement. For ultra-high speed measurements, a higher bandwidth setting must be used with the following considerations:

  • The higher the bandwidth, the higher the noise and the probability that the control loop will go out of control and oscillate.
  • When working with a High bandwidth setting, it is necessary to pay special attention and use adequate cell shielding and electrode connectors. The use of a Faraday cage is recommended in these cases.
  • The use of a high impedance reference electrode (RE) (e.g., double junction reference electrode, a salt bridge with frit) in combination with a High bandwidth of the control loop might lead to instability of the PGSTAT and even to oscillations.
Bandwidth of the current sensor (current range)

The measurement of the current response of an electrochemical cell (in potentiostatic mode) and the control of the applied current value (in galvanostatic mode) is executed with specially designed current sensors. In order to achieve the best sensitivity and resolution for the measurement, individual current sensors are used depending on the magnitude of the measured (or applied) current.

Each current sensor circuit (which corresponds to a current range) has a specific bandwidth or response time. Therefore for the most accurate results (especially important for fast, time resolved experiments), the current range must be selected so that the bandwidth of the current sensor will not be the limiting factor for the time response (speed) of the measurement.

In general, the lower the measured currents, the lower the bandwidth of the current sensor.

Data sampling interval vs the timescale of the investigated transient signal

The measured electrochemical response can have a complex shape with components at many frequencies. The highest frequency component of the measured or applied signal determines the bandwidth of that signal. The bandwidth of the signal should not be higher than the bandwidth of the measuring device.

If the highest frequency component of the signal is fSIGNAL, then according to the Nyquist Theorem [3] the sampling rate fSAMPLE must be at least 2 fSIGNAL (i.e. two times higher than the highest frequency component of the signal).

Figure 3. Effect of the sampling frequency of an ideal sinusoidal signal [3]. Shown here are the theoretical signal (dashed line), sample points, and resulting measured signal (orange line).

In other words, the data sampling interval must be lower than the timescale in which the time resolved (transient) measurement from the investigated electrochemical process is expected to occur. There is a practical correlation between the sampling interval and instrument bandwidth. When the sampling interval is:

  • higher than 100 μs: the 10 kHz (High Stability) bandwidth should be selected.
  • between 10–100 μs: the 100 kHz (Fast) bandwidth should be selected.
  • smaller than 10 μs: the 1 MHz bandwidth (Ultra-Fast) should be selected.


To measure reliable experimental data, all elements of the experimental setup must be considered with their own specifications and limitations. The overview above highlights the main factors and parameters which can have a direct influence on fast electrochemical measurements.

Fast measurements start here!

Visit our website to learn more about the variety of potentiostats/galvanostats from Metrohm Autolab.


[1] Maisonhaute, E.; et al. Transient electrochemistry: beyond simply temporal resolution, Chem.Commun., 2016, 52, 251—263. doi:10.1039/C5CC07953E

[2] Bard, A.J.; Faulkner, L.R. Electrochemical Methods: Fundamentals and Applications, New York: Wiley, 2001, 2nd ed. Russian Journal of Electrochemistry, 2002, 38, 1364–1365. doi:10.1023/A:1021637209564

[3] Keim, R. The Nyquist–Shannon Theorem: Understanding Sampled Systems. All About Circuits, May 26, 2020. 

Post written by Dr. Iosif Fromondi, Product Manager and Head of Marketing and Sales Support at Metrohm Autolab, Utrecht, The Netherlands.