Question

what are the merits of normal absorbance to concentration calibration curve and log of absorbance versus log of concentration curve. Also state the comparison of the results obtained by those methods. which results are better.

Answer 1

The most simple form of the Beer's law plot involves a

absorbance vs concentration plot. The model equation is A = slope * C + intercept. This is the most common and straightforward method, and it is the one to use if you know that your instrument response is linear. You need a minimum of two points on the calibration curve. The concentration of unknown samples is given by (A - intercept) / slope where A is the measured signal and slope and intercept from the linear fit. In this the absorbance value if used directly and concentration of the unknown sample obtained directly.

In the calibration plot involving log(absorbance) versus log (concentration)

The logarithm of the measured absorbance is plotted against the logarithm of concentration and the calibration data are fit to a linear or quadratic model. The concentration of unknown samples is obtained by taking the logarithm of the instrument readings, computing the corresponding logarithms of the concentrations from the calibration equation, then taking the anti-log to obtain the concentration.

Log-log calibration is well suited for data with very large range of values because it distributes the relative fitting error more evenly among the calibration points, preventing the larger calibration points to dominate and cause excessive errors in the low points. In some cases a nonlinear relationship between signal and concentration can be completely linearized by a log-log transformation. There is drawback that zero or negative vales cannot be used in such a plot as log of these values don't exist.

Both results are comparable np result is better than the other.

They have their merits and demerits as discussed above