Signal detection, analytical model

In order to illustrate the quality of this analytical model, the parameters have been optimised for the detection of ALP in our microchannels. As can be deduced from Fig. 36.10, the currents obtained from these analytical expressions are in very good agreement with the experimental data, and revealed to be valid over a large range of analyte concentrations (here from 0.1 to 100 pM). This model confirms that the measured signals correspond very well to the currents that can be expected for ALP determination in such an amperometric microsensor, and it constitutes a very useful tool for the optimisation of both the microchip features and the parameters of the assay protocols. 

Equation (4.20) was proposed by Hoskuldsson [65] many years ago and has been adopted by the American Society for Testing and Materials (ASTM) [59]. It generalises the univariate expression to the multivariate context and concisely describes the error propagated from three uncertainty sources to the standard error of the predicted concentration calibration concentration errors, errors in calibration instrumental signals and errors in test sample signals. Equations (4.19) and (4.20) assume that calibrations standards are representative of the test or future samples. However, if the test or future (real) sample presents uncalibrated components or spectral artefacts, the residuals will be abnormally large. In this case, the sample should be classified as an outlier and the analyte concentration cannot be predicted by the current model. This constitutes the basis of the excellent outlier detection capabilities of first-order multivariate methodologies. 

CONTENTS 1. Chemometrics and the Analytical Process. 2. Precision and Accuracy. 3. Evaluation of Precision and Accuracy. Comparison of Two Procedures. 4. Evaluation of Sources of Variation in Data. Analysis of Variance. 5. Calibration. 6. Reliability and Drift. 7. Sensitivity and Limit of Detection. 8. Selectivity and Specificity. 9. Information. 10. Costs. 11. The Time Constant. 12. Signals and Data. 13. Regression Methods. 14. Correlation Methods. 15. Signal Processing. 16. Response Surfaces and Models. 17. Exploration of Response Surfaces. 18. Optimization of Analytical Chemical Methods. 19. Optimization of Chromatographic Methods. 20. The Multivariate Approach. 21. Principal Components and Factor Analysis. 22. Clustering Techniques. 23. Supervised Pattern Recognition. 24. Decisions in the Analytical Laboratory. 

An example of the immobilization of antibodies on channel surfaces was presented by Eteshola and Leckband [395]. A microfluidic sensor chip was developed to quantify a model analyte (sheep IgM) with sensitivities down to 17 nM. This was achieved by first immobilizing a layer of bovine serum albumine (BSA) onto the channel wall, followed by specific adsorption of protein A to which the primary antibody for IgM was coupled covalently. This antibody could capture IgM, which was detected with the secondary antibody, labeled with horseradish peroxidase (Scheme 4.91). This enzyme catalyzes the conversion of the fluorogenic substrate 3-(p-hydroxyphenyl)propioni c acid into a fluorophore, which was quantified off-chip with a spectrofluorometer. The measured fluorescence signal was proportional to the analyte concentration in the test sample. 

Before assessing the other validation parameters (trueness, recovery, precision, selectivity, specificity, detection capability, stability, and applicability/mgged-ness), the appropriateness of the calibration model should be evaluated. The correctness of the analytical determination of elements in food and food products depends indeed on the choice and the evaluation of the calibration model. The calibration model gives the mathematical relationship between the signal of the measuring system and the concentration in the sample. Several authors have published guidelines concerning calibration in analytical chemistry [5-7]. 

---