Fourier Transform Infrared (FTIR) spectroscopy is one of the commonly used methods to characterise materials. To detect unknown compounds and product quality, it provides precise molecular information in seconds. But even the most groundbreaking products can deliver exceptional results. In this article, you will learn how researchers can get the most out of their FTIR systems and improve results.

1. Poor Sample Preparation

Dust or pollution can introduce distortion in the infrared wavelength, rendering your data inconsistent. Solids require clean and well-ground samples. The liquids in the channel should be bubble-free, and thin films must have uniform thickness. Additionally, accessories such as ATR (attenuated total reflectance) can be employed to help, but simple preparation is crucial for dependable results.

2. Incorrect Instrument Settings

The FTIR equipment is multipurpose, providing the optimal conditions for every specific analysis. Wrong choices of resolution, scan number and/or background correction often cause errors. For instance, low resolution in studying fine structures can lead to obfuscation of the most significant issues. Matching settings to the sample type will always lead to better clarity and cancel repetition of testing.

3. Ignoring Background Spectra

Remember, background correction is very important in FTIR analysis, but it is always ignored. The instrument is required to take a background spectrum before the measurements are taken in order to remove atmospheric interference, like water vapour or carbon dioxide. By missing this data duplication step or by not updating the database background often enough, corrupted information is obtainable.

4. Overlooking Instrument Maintenance

Performance can be affected if dirty optics, spent desiccants or failed detectors are part of the unit. Regular checks and maintenance will prevent these issues from negatively affecting your testing. Many of today’s systems are ruggedly built, Agilent for example, but they will appreciate routine attention. The cleanliness and calibration of the instruments affect their life as well as the reliability of data.

5. Misinterpreting Spectra

Lots of people ask “what is FTIR?” FTIR generates rich spectra, but overlapping peak shifts can confuse inexperienced users. Relying only on visual comparisons without proper software analysis increases the risk of mistakes. Using spectral libraries, advanced software tools, and proper training avoids incorrect conclusions. Having a clear interpretation is just as important as collecting accurate data.

6. Using Inadequate Reference Materials

A spectrum is only as useful as the data, but many errors occur when outdated or poor-quality libraries are used. Without reliable references, even a high-quality spectrum may lead to wrong identifications. Investment in new reference libraries and validation of reference data is used to achieve more reliable outcomes. As an example, Agilent offers extensive libraries for a large diversity of applications.

7. Lack of Operator Training

Most people tend to underestimate the value of training. A lack of experience with sample treatment, instrument parameters or data interpretation opens scope for users to obtain low-grade results. Regular training ensures researchers are up-to-date with best practice and get the most out of their instruments. An increasing number of suppliers provide training schemes for new and established users.

Preventing Mistakes with Modern Instruments

Although operator expertise is essential, contemporary FTIRs are engineered to reduce typical mistakes. An automatic background correction algorithm and easy-to-use user interfaces and workflows support the avoidance of errors. For example, Agilent FTIR systems feature common-sense hardware and software that enable both novice and expert users to minimise errors in day-to-day analysis.

Getting the Best from FTIR Technology

The FTIR spectroscopy technique is one of the most effective tools for materials analysis; however, slight mismanoeuvrings can compromise this effectiveness. Whether laboratories are preparing samples or the operator is being trained, these same attention-to-detail relationships mean they can trust their results. With advanced systems, much of this is avoidable via smart system design and enabling software.