Fourier transform infrared spectrometer based on an electrothermal MEMS mirror
Applied Optics
|August 18, 2018
Summary
A novel micro Fourier transform infrared spectrometer (μFTIR) utilizes a microelectromechanical systems (MEMS) mirror for accurate soybean composition analysis. This device precisely measures moisture, protein, and fat content in grains.
Area of Science:
- Spectroscopy
- Microelectromechanical Systems (MEMS)
Background:
- Traditional Fourier transform infrared spectrometers can be bulky and expensive.
- Miniaturization of spectroscopic instruments is crucial for portable and field applications.
Purpose of the Study:
- To develop a compact and cost-effective micro Fourier transform infrared spectrometer (μFTIR).
- To demonstrate the μFTIR's capability for analyzing soybean composition.
Main Methods:
- An H-shaped electrothermal microelectromechanical systems (MEMS) mirror was designed and fabricated.
- A specialized driving method ensured linear motion of the MEMS mirror.
- A telecentric lens and a new phase interpolation algorithm were integrated to enhance spectral stability and accuracy.
Main Results:
- The μFTIR achieved a linear, uniform-speed motion of the MEMS mirror without complex closed-loop control.
- The system demonstrated reduced influence from MEMS mirror tilting.
- Accurate measurements of soybean moisture, protein, and fat content were achieved.
Conclusions:
- The developed μFTIR is a viable tool for rapid and accurate on-site analysis of agricultural products.
- The integration of MEMS technology and advanced algorithms offers a promising direction for portable spectroscopic devices.
Related Concept Videos
Fast Fourier Transform
952
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
The computational efficiency of the FFT becomes...
952
Properties of Fourier Transform I
671
The application of Fourier Transform properties in radio broadcasting is multifaceted, enabling significant advancements in the way signals are transmitted and received. Key areas where these properties are utilized include simultaneous multi-channel transmission, audio clip speed adjustments, live broadcast delays for different time zones, audio frequency adjustments, and signal demodulation.
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
671
Properties of Fourier Transform II
782
The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
782
Discrete Fourier Transform
913
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
913
Basic signals of Fourier Transform
955
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
955
Continuous -time Fourier Transform
918
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
918


