Question

If the measureable species, in ELISA, is fluorescent what are three things that increase (worsen) the limit of detection for this measurement?

Answer 1

SOLUTION:- If the measurable species, in ELISA, is fluorescent what are three things that increase (worsen) the limit of detection for this measurement are as follows:-

(1.) Background and Background Noise

We define as the terms signal, noise, background, and blank. The part of an instrument response that contains, any useful information is called signal. Any unwanted fluctuations in the instrument response, are noise. When the average noise level in absence of an input concentration is nonzero, it is called a background. These definitions are based on what is considered to be the useful (or wanted) information.

(2.) Peak Height

Often readings are made from an analog recording of a continuous output from an instrument. Because these results are usually stored as a trace on chart paper, I am using some simulated signal traces as examples. Figure 1 shows three such noisy, flat baselines with two peaks each: a narrow peak is centered at x= 75 and .a wide one at x= 200. The plot on the right contains 10 the same data as the one on the left, just the y-axis is magnified ten times. The peak heights are equivalent to 2a, 3a and 6u values of the background noise, It does not matter what the actual values for the background or peak height are, all I want to convey is a feeling for the signal-to-noise ratios. For instance, if the detection limit is set to 3u above the background noise, the middle trace shows what you're asking the analyst to measure.

(3.) Calibration Curve

Calibration checks should be performed regularly, and the whole calibration procedure has to be repeated when the check yields a result outside the confidence limit for the original regression line. A calibration check for a straight line requires two points. When it is known that the original calibration curve passes through zero, one calibration point is sometimes replaced by a blank. This combination of a one-point calibration check and a blank measurement produces satisfactory results most of the time but cannot catch all errors, A better procedure is to select one point in the lower third and one in the upper third of the calibration line.

Three factors influence the decision about how to establish a calibration curve:-

(a.) the spread of the data, (b.) the error of the measurement and (c.) the model used to fit the data. There are three ranges that have to be matched: the concentration range of the actual samples, the measurement range of the instrument and the range where the fitting function is valid. It makes a difference to the design of calibration measurements if it is known independently that the model is applicable. Once it has been shown that the model is appropriate, the number of calibration points can be cut down to a minimum.