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IR Spectrometers

IR Spectrometers

There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

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The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...

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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...

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ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

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Application-Specific Optimization of Integrated Spectral Sensors.

D M J van Elst¹, A van Klinken¹, M S Cano-Velázquez¹

¹Department of Applied Physics and Science Education, Eindhoven Hendrik Casimir Institute, Eindhoven University of Technology, NL 5600 MB, Eindhoven, The Netherlands.

|August 27, 2025

Summary

This summary is machine-generated.

This study presents an algorithm for optimizing near-infrared spectral sensors. The new method achieves high accuracy with fewer pixels, enabling cost-effective, tailored sensing solutions.

Keywords:

integration near-infrared optical sensors particle swarm optimization spectral sensing spectrometry

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Area of Science:

Spectroscopy
Optical Sensing
Material Science

Background:

Near-infrared (NIR) spectral sensing is vital for nondestructive material analysis.
Conventional sensors use fixed spectral bands, limiting application-specific optimization.

Purpose of the Study:

To develop an algorithm for tailoring NIR spectral sensors to specific applications.
To optimize sensor design by exploring all possible combinations of spectral bands.

Main Methods:

An algorithm was developed to optimize spectral band selection for NIR sensors.
Sensor performance was evaluated against manually selected designs and general-purpose sensors.
Experiments were conducted using fabricated four-pixel devices.

Main Results:

The algorithm-optimized sensors demonstrated superior performance compared to manually designed ones.
High sensing accuracy was achieved even with a minimal number of pixels (e.g., four pixels).
Fabricated devices exceeded the accuracy of general-purpose sensors in a practical application.

Conclusions:

Algorithm-driven spectral band optimization enables highly effective, application-specific NIR sensors.
This approach facilitates the creation of cost-effective spectral sensors with simplified read-out.
Potential applications span industrial and consumer electronics.