Multi-analyte Quantification
Multi-analyte Quantification - Related Videos
05:40
Fabricating Cotton Analytical Devices
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2016
To investigate simple fabrication approaches for multiple assay needs, we created a fluid-absorbing channel system made of cotton material. This device was used to establish a multiple detection platform, and solve contamination issues that commonly affect lateral flow-based biomedical devices, for clinical urinalysis of nitrite, total protein, and urobilinogen.
12:29
Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology
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Cited by 11 •
2017
Two image analysis algorithms, "Drosophila NMJ Morphometrics" and "Drosophila NMJ Bouton Morphometrics" were created, to automatically quantify nine morphological features of the Drosophila neuromuscular junction (NMJ).
09:51
Sample Preparation for Analytical Characterization
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2023
Source: Laboratory of Dr. B. Jill Venton - University of Virginia Sample preparation is the way in which a sample is treated to prepare for analysis. Careful sample preparation is critical in analytical chemistry to accurately generate either a standard or unknown sample for a chemical measurement. Errors in analytical chemistry methods are categorized as random or systematic. Random errors are errors due to change and are often due to noise in instrument. Systematic errors are due to...
08:03
Multi-analyte Biochip (MAB) Based on All-solid-state Ion-selective Electrodes (ASSISE) for Physiological Research
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Cited by 3 •
2013
All-solid-state ion-selective electrodes (ASSISEs) constructed from a conductive polymer (CP) transducer provide several months of functional lifetime in liquid media. Here, we describe the fabrication and calibration process of ASSISEs in a lab-on-a-chip format. The ASSISE is demonstrated to have maintained a near-Nernstian slope profile after prolonged storage in complex biological media.
02:02
Detection of a Target Analyte Using a Bacterial Biosensor
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2025
Source: Barber, A. E., et al. Preparation and Application of a New Bacterial Biosensor for the Presumptive Detection of Gunshot Residue. J. Vis. Exp. (2019).This video demonstrates the detection of specific analytes using a bacterial biosensor. A bacterial culture carrying a plasmid with a red fluorescent protein (RFP) gene under an analyte-sensitive promoter is taken. After thoroughly wiping the test surface, a small section of the wipe is immersed in the culture and incubated. The analyte...
FAQs
The analytical platform separates or otherwise distinguishes the signals associated with individual targets, allowing each measurement to be interpreted within the same sample. This signal discrimination is essential because overlapping or interacting measurements can obscure the contribution of a particular analyte. Reliable separation or distinction therefore determines whether the resulting concentration profile accurately represents the sample’s physiological state.
Calibration standards provide reference points for relating measured analytical signals to analyte concentrations. By comparing signals from the sample with these known standards, the platform can calculate the amount of each target rather than merely reporting signal intensity. Applying calibration across the analytes supports quantitative results that can be compared within a coordinated concentration profile.
Interference occurs when measurements for one analyte influence or obscure the signal assigned to another. Because several targets are assessed together, the analytical approach must account for these interactions during signal interpretation and concentration calculation. Controlling interference is important for avoiding misleading values, particularly when the resulting measurements are used to characterize complex physiological processes or support clinical decisions.
Single-analyte testing focuses on an individual concentration, whereas a multi-analyte strategy produces coordinated data for several substances from the same sample. That broader dataset can reveal relationships among measurements and provide a more comprehensive view of physiological state. It also helps make use of limited sample volumes, which is valuable when biological material is restricted.
A typical workflow begins with analyzing the sample on a platform capable of separating or distinguishing the target signals. The measured signals are then compared with calibration standards, and the concentration of each analyte is calculated. During interpretation, the analysis must account for interference between measurements so that the final coordinated dataset supports meaningful physiological or clinical assessment.
In medicine, multi-analyte measurements support multiplexed biomarker testing, clinical diagnosis, therapeutic drug monitoring, and disease profiling. Assessing several concentrations together can help researchers and clinicians examine complex biological processes rather than relying on an isolated measurement. The resulting profiles may inform more precise research questions and clinical decisions while conserving limited sample volume.