Peak Detection
Peak Detection - Related Videos
01:08
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations
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2024
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single stretching vibration...
01:20
IR Spectrum Peak Intensity: Dipole Moment
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2024
The dipole moment of a bond is the product of the partial charge on either atom and the distance between them. Dipole moments influence the efficiency of IR absorption and the peak intensity. When a bond with a dipole moment is placed in an electric field, the direction of the field determines if the bond is compressed or stretched. Electromagnetic radiation consists of an electric field component that rapidly reverses direction. It follows that polar bonds are alternately stretched and...
00:55
IR Spectrum Peak Intensity: Amount of IR-Active Bonds
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2024
When infrared radiation is passed through a molecule, absorption occurs if the molecule's vibration leads to a substantial change in its bond dipole moment. Transitions between vibrational energy levels, typically corresponding to infrared frequencies (4000–400 cm−1), allow absorption if the vibration significantly alters the dipole moment, making the molecule infrared active. The molecular bonds have different stretching and bending vibrations, resulting in various peaks with varying...
01:23
IR Spectrum Peak Broadening: Hydrogen Bonding
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2024
The vibrational frequency of a bond is directly proportional to its bond strength. As a result, stronger bonds vibrate at higher frequencies, while weaker bonds vibrate at lower frequencies. The stretching vibration of the strong O–H bond in alcohols and phenols (very dilute solution or gas phase) appears as a sharp peak at 3600–3650 cm−1. However, the extent of hydrogen bonding influences the observed stretching frequency and band broadening. Intermolecular or intramolecular hydrogen bonding...
06:01
Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
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Cited by 1 •
2022
This article describes the semi-automated measurement of the amplitudes and latencies of the first five peaks and troughs in the auditory brainstem response waveform. An additional routine compiles and annotates the data into a spreadsheet for experimenter analysis. These free computer routines are executed using the open-source statistical package R.
FAQs
The method compares each data point with nearby values to determine whether it forms a local maximum. It then applies selection criteria such as height, threshold, prominence, width, or minimum spacing. Using several criteria together helps separate prominent signal features from smaller background fluctuations, improving the consistency of event or feature identification in biological measurements.
These criteria describe different properties of a candidate peak. Height or threshold limits how large a signal must be, prominence emphasizes how clearly it rises above surrounding values, width characterizes its extent, and minimum spacing prevents closely adjacent detections from being treated as separate events. Selecting appropriate criteria determines which features remain in the analysis.
Prominence helps evaluate whether a maximum stands out from the surrounding signal rather than merely exceeding an immediate neighboring point. This distinction is useful when biological data contain background fluctuations, because a sharp but poorly distinguished variation may be less meaningful than a feature that rises clearly above its local surroundings. The resulting detections better support pattern recognition and comparison.
The same comparison-based approach can be applied to measurements organized by time or by signal intensity. In fluorescence traces and electrophysiological recordings, detected positions can support event timing, whereas chromatograms and mass spectra can use peak locations and characteristics for feature or compound comparisons. Criteria such as width and spacing help reflect differences in signal shape and distribution.
A practical workflow begins with the measured signal, identifies candidate local maxima by comparing neighboring data points, and applies criteria for height, threshold, prominence, width, or minimum spacing. The retained peaks can then be examined for timing, signal characteristics, or pattern relationships. This sequence provides a consistent way to convert complex measurements into analyzable features.
Applications include chromatograms, mass spectra, fluorescence traces, and electrophysiological recordings, along with other time- or intensity-dependent measurements. In each case, the method can highlight prominent features that might otherwise be difficult to compare across a complex dataset. The relevant interpretation depends on the measurement, such as compound characterization in spectra or event timing in recordings.
Detected peaks can provide locations and signal characteristics that support quantitative comparisons, event timing, compound characterization, and pattern recognition. For example, fluorescence or electrophysiological data may be examined for when events occur, while chromatograms and mass spectra may be assessed for features associated with compounds. Reliable selection makes these downstream comparisons more consistent.