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Spectral analysis is an important research tool for deciphering information in various fields of science and technology. Spectral analysis is based on the Fourier theorem which states that any waveform can be decomposed into a sum of sine waves at different frequencies with different amplitudes and different phase relationships.
When summed up, these waves reconstitute the original waveform. This process can convert the data domain to the spectral domain. Spectral analysis studies the spectral frequency in discrete and uniformly sampled data.
The following are some of the research outcomes where spectral analysis played a vital role.
Spectral analysis is one method of identifying the cyclical components of time-series data. Cycles are periodic events that are typically represented as a waveform of trigonometric sine or cosine functions, or sinusoid, which are interpreted graphically and mathematically.
High-Precision Spectral Analysis Techniques
High-precision spectral analysis techniques have proved to be an important means to carry out research on rotor system stability. After adopting this technique, the identification accuracy of the spectral feature parameter for the speeding-change process improved significantly and the maximum amplitude error was controlled at less than 15%.
In this research, spectral analysis techniques were linked with several other methods, namely, proportional interpolation, time-space domain transformation, and time-domain refinement into the sampling method.
Arc Atomic Emission Spectral Analysis Method
Arc atomic emission spectral analysis method is a novel method for the determination of macro and micro contents of human bio-substrates. This analysis is based on the complex physical and chemical studies for preparing hair (one of the many human bio substrates).
Following this technique, analysis was carried out on the hair samples of a group of patients in order to diagnose and also to restore the element balance in the body. The research revealed that by comparing the elemental content in the human hair with reference values, it is possible to assess the degree of element imbalance in the body.
Spectral analysis also offers a rapid, accurate, versatile, and reliable method of measuring the quality of both fresh and frozen fish by identifying and quantifying specific contaminants and determining physical/chemical processes that indicate spoilage. Spectrophotometric instrumentation has been recently used to monitor a number of key parameters for quality checks, such as oxidative rancidity, dimethylamine, ammonia, hypoxanthine, thiobarbituric acid, and formaldehyde levels.
Researchers have developed a novel colorimetric method, i.e., analysis of tri-methyl amine using microvolume UV-Vis spectrophotometry in combination with headspace-single-drop microextraction. This method has increased sensitivity, stability, simplicity, and rapidity which provides the detection of spoilage at an earlier stage across a larger number of species. This spectral analysis technique is an economical method for quality assurance and thus has a huge positive impact on the fish industry.
Entropy Spectral Analysis Methods
Entropy spectral analysis methods are applied for the forecasting of streamflow that is vital for reservoir operation, flood control, power generation, river ecological restoration, irrigation, and navigation. This method is used to study the monthly streamflow for five hydrological stations in northwest China and is based on using maximum Burg entropy, maximum configurational entropy, and minimum relative entropy.
Similarly, spectral analysis acts as an important tool for deciphering information from the paleoclimatic time series in the frequency domain. Thus, it is utilized to detect the presence of harmonic signal components in a time series or to obtain phase relations between harmonic signal components being present in two different time series (cross-spectral analysis). The spectral analysis of surface waves (SASW) method is a nondestructive method that determines the moduli and thicknesses of pavement systems.
Conclusion
Spectral analysis provides a spectrum of the kinetic components which are involved in the regional uptake and partitioning of tracer from the blood to the tissue. This technique involves minimal modeling assumptions for tissue impulse response function.
Spectral analysis can be applied to the various dynamic data acquired by planar scintigraphy, single-photon emission computed tomography (SPECT) or positron emission tomography (PET) as an alternative approach to compartment analysis. This analysis appears to be clinically useful because it not only facilitates the interpretation of dynamic scintigraphy, SPECT or PET data but also simplifies comparisons between regions and between subjects.
This article depicts how spectral analysis is extensively used in various fields of research. It is considered a dynamic tool in research methodology for its high sensitivity, accuracy, and efficiency in data interpretation.
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