Multianalyte analysis

The problem of multianalyte analysis is of high importance in different methods of analytical chemistry. The measurements with sensor arrays are not an exception they are always complicated by multicomponent calibrations, as the number of these calibrations, even if defined in accordance with the modem principles of experimental design, increases exponentially with the increase in the number of analytes. 

Muldoon, M.T., G.F. Fries, and J.O. Nelson (1993). Evaluation of ELISA for the multianalyte analysis of s-triazines in pesticide waste and rinsate. J. Agric. Food Chem., 41 322-328. 

M.T. Muldoon, G.F. Fries, 1.0. Nelson, Evaluation of an ELISA for the Multianalyte Analysis of s-Triazines in Pesticide Waste and Rinsate , /. Agric. Food Chem., 41, 322-328 (1993). 

In both of these examples, PKU and fatty acid oxidation disorders, the methods utilized at this time are characterized by their comprehensiveness, cost-benefit ratios, and their ability to be used as part of a diagnosis of a particular disorder. In fact, these methods demonstrate that the future of clinical chemistry is multianalyte analysis that enables low cost even with relatively expensive instrumentation. Multianalyte analysis is not new to clinical chemists as the chemistry analyzers in hospitals and labs are used to analyze several dozens of compounds. A close look at these assays, however, will demonstrate that they are not true multiplexed analyses but rather a large robotic system performing 24 individual assays with 24 different chemistries and standards. MS/ MS enables a single analysis, a single chemistry, and many results. 

Although GC-MS provides a specific method of detection, the sample cleanup plays an important role in the final result. Often the procedures are used for multianalyte analysis destined to detect a number of chemically and/or structurally related analytes in one run. Especially with anabolic steroids, this implies the coextraction of many endogenous steroids and steroid metabolites that may be present at levels far higher than the target levels of the analytes (0.5 to 50 pig/L or kg). Furthermore, other sample constituents can interfere with the analysis and influence the final result both in a qualitative way (false positive or false negative result) as well in a quantitative way. Therefore, it is important that a particular method, if possible, is compared with other methods slightly differing in the extraction procedure [10]. 

Perform multivariate spectral analysis to obtain selective multianalyte response... 

J Caslavska, D Allemann, W Thormann. Analysis of urinary drugs of abuse by a multianalyte capillary electrophoretic immunoassay. J Chromatogr A 838 197-211, 1999. 

The utilization of lAC in analytical methods has received increasing retention in recent years [23,24], Of particular interest is the use of immobilized antibody columns in performing immunoassays, a technique known as a chromatographic immunoassay or flow-injection immunoassay. This approach has already been reported in a number of formats such as those involving simple analyte adsorption/desorption, sandwich immunoassays, competitive binding immunoassays, and multianalyte methods (see Figure 13,9) [23,24,73,74], Typical advantages of these methods include decreased analysis times and improved precision versus manual immunoassays. 

Tlie demand for reliable, sensitive, automated, fast, low-cost methods for residue analysis that are also applicable to a wide range of drugs and matrices is growing fast, especially in the field of food inspection. A universal analytical scheme that could simultaneously quantify all compounds of interest in the edible animal products, correctly identify the molecular structure of the analytes, and, at the same time, produce very few false-negative and -positive results to protect the consumer, producer and international trade, would provide the most desirable approach. A unified procedure would eliminate the need for using separate multiresidue methods to screen food commodities for potential drug residues, and combinations of suitable single- or multianalyte methods to identify and quantify residues of individual analytes. 

Tin is usually determined as the total metal, but it may also be measured as specific organotin compounds. Flame atomic absorption analysis is the most widely used and straightforward method for determining tin furnace atomic absorption analysis is used for very low analyte levels and inductively coupled plasma atomic emission analysis is used for multianalyte analyses that include tin. 

Matrix effects variable, depending on biorecognition principle and transduction element Direct analysis of the sample. Minimal sample preparation Limited multianalyte determination Possible automatization of the system Direct analysis after sampling is possible. 

Analytical methods for the determination of one antidepressant and/or its metabolite(s) were usually performed in isocratic mode, with total run times from seconds to a few minutes. However, as previously mentioned, multianalyte procedures are preferable, particularly if the method is intended for clinical or forensic analysis. Gradient separation was usually applied when the most common antidepressants were included in the methodology however, total chromatographic run times varied widely, from 5 to 40 min [57, 76], depending on column length, extraction technique (offline vs. online techniques), biological matrix or the specific application of the method. 

Taking advantage of the ability to detect multiple group II and group III metals at mercury film electrodes using stripping analysis, a multianalyte DNA sensor was developed using different semiconductor nanocrystals (ZnS, CdS, and PbS) for the... 

Figure 7 Multianalyte CE-IA of four drugs of abuse. Left panel (A) shows elec-tropherograms of samples with different dilutions right panel (B) shows control analysis in real urine samples. Peak identification M, methadone C, cocaine metabolite benzoylecgonine A, amphetamine/methamphetamine IS, internal standard. (Reprinted from Ref. 42.)...

An important extension of our large validation studies involves the use of data bases from field studies in the development of improved statistical methods for a variety of problems in quantitative applications of immunoassays. These problems include the preparation and analysis of calibration curves, treatment of "outliers" and values below detection limits, and the optimization of resource allocation in the analytical procedure. This last area is a difficult one because of the multiple level nested designs frequently used in large studies such as ours (22.). We have developed collaborations with David Rocke and Davis Bunch (statisticians and numerical analysts at Davis) in order to address these problems within the context of working assays. Hopefully we also can address the mathematical basis of using multiple immunoassays as biochemical "tasters" to approach multianalyte situations. 

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