First, there are several problems in the standard curve sample test, you need to pay attention to the ELISA kit.
1. The concentration of the sample and other indicators are calculated according to the standard curve, so it is important to consider the standard curve as a more important thing than the formal experiment. Otherwise, the results of the latter experiment cannot be discussed.
2. Set the standard concentration range of the standard curve sample to have a relatively large span, and to cover the concentration of the test sample you want to test, that is, the concentration of the sample should be within the concentration range of the standard curve, and the human ELISA kit includes the upper limit and Lower limit. For the standard curve of S type, try to make the concentration of the experimental sample in the steepest section of the middle slope, that is, the curve is almost in a straight line.
3. It is better to prepare the standard sample concentration in the standard curve by the double dilution method, so as to ensure that the concentration of the standard sample does not deviate greatly.
4. When testing standard samples, they should be carried out in increasing order of concentration to reduce the effect of high concentration on low concentration and improve accuracy.
5. The number of samples in the standard curve is generally 7 points, but at least 5 points must be guaranteed.
6. The correlation coefficient of the standard curve made varies according to the experimental requirements, but in general, the correlation coefficient R is at least greater than 0.98. For some experiments, at least 0.99 or even 0.999.
Second, choose what equation to fit the ELISA kit
In the low-concentration section of the S-curve, the power equation can be well fitted. The middle-low concentration department can use the linear equation, the middle sector can use the logarithmic equation, and the middle and rear sections can use the four parameters. In the immunoassay, the scale point (may be diluted or not). If the concentration is the same as the corresponding absorbance (OD) value, it is ideal if it can show a linear relationship. At this time, it can be easily obtained by EXELL. The curve, in turn, derives the concentration value of the sample.
Regarding the fitting method of the scale curve, the straight line, the quadratic curve, the cubic curve, the index, the logarithm, etc. can be used for curve fitting in ELISA and other biological reactions, but they are only used in one part of the curve, and some Used in the first half, some are used in the second half, some are used in the middle, and the Logistic curve is good for all of the curves. Logistic should fit well over a long interval. If used for quantification, the middle section of the S-shaped curve (shorter) is better, and the calculation error of the two flat sectors is large, sometimes even large. The so-called "scale curve" used for immunoassay is actually called a fit curve. At present, the most popular immunological detection fitting method in the world is “four-parameter logic fittingâ€. This kind of fitting method can often accurately compare the relationship between concentration and absorbance, so as to obtain the test substance in the sample more accurately. Concentration value. However, we do not have the ideal condition for immunoassay. The concentration of the standard and the corresponding OD value are often the curve relationship of the "ELISA kit S" type. At this time, the straight line fitting method cannot be used. It is necessary to choose the fitting method. The hub is where the dimension curve of your S-shape is, which part of the curve you want to test. Human ELISA kits Of course, if used for quantification, it is still better in the middle. But building a model is one thing, and using it to quantify is another matter. Although none of these methods can be used universally, they can all be used. But it is not that it is omnipotent. In fact, not only the ELISA kit, but many other biological reactions are sigmoidal curves, and can also be fitted with Logistic curves.
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