If you have pure starting material and product, you can make a calibration table for your HPLC and then run a measurement on the reaction mixture. You can determine the ratio of starting compound and product. A variation from 100% suggests decomposition products or side reactions, which may appear as additional peaks.

I'm assuming there is some reason you can't simply weigh the amount of product you have.

I would measure the fluorescence of your sample and compare it against a standard curve of of dilutions of a known quantity of the fluorophore.

Mrs/Miss Spitalny,

The method of choice for the purpose of your research is the mass spectrometry (MS), in particular, looking at ESI-method; because of, it provides an exact, selective, sensitive, accurate and precise analytical quantitative information about the concentration of analytes in solution.

However, among known methods for quantifying MS observable phenomena (for instance, kinetics, thermodynamics and diffuion theories) and methods for data-processing of MS outcomes (statistical approaches, empirical relations accounting for experimental parameters, et cetera,) which are directly applicable to quantify analytes in solution, thus obtaining information about reaction yields, you could focus your attention on the following two methods:

(i) Cooks' kinetic method (reference [1]:)

[1] Cooks, R.; Wong, P. Accts. Chem. Res. 1998, 31, 379–386;

and

(ii) our own authored (mine and my co-author's innovative theory and model equations according to the authorship shown below) stochastic dynamic method and model formulas applicable directly to obtain analyte concentration in solution. (Consider references [2,3].) There has been obtained an absolute quantitative determination of mixtures of analytes in chemometric terms, /r/ = 1, at anlyte concentration level pg.(mL)-1.

[2] Stochastic Dynamic Mass Spectrometric Approach to Quantify Reserpine in Solution; Bojidarka Ivanova, Michael Spiteller Analytical Chemistry Letters, 10 (2020) 703-721; Received 13 Oct 2020, Accepted 16 Dec 2020, Published online: 28 Jan 2021 Download citation https://doi.org/10.1080/22297928.2020.1865834

[3] Steroids, 164 (2020) 108750 Stochastic dynamic mass spectrometric quantification of steroids in mixture — Part II; Bojidarka Ivanova, Michael Spiteller.

However, I would like to underline that, if you use the former method (point (i),) then you should bear in mind that the quantitative analysis has been carried out using the total average intensity values of analyte MS peaks over the whole time of the measurements. For this reason, references reporting application of the Cooks' kinetic method, show a reliability at about 98 % of the data. The deviation from the exact quantitative MS determination, cannot be associated with an inadequacy of the model equation, however. Rather, it is a result from error contribution to methods for data-processing of MS measurable variables. Because of, the employment of the total average intensity data does not account for perturbation of electronic structures of the analyte. It accounts for the 3D molecular conformation. On the contrary, our theory (references [2,3]) uses quantitative data obtained per span of short scan time, thus accounting exactly for fluctuations of experimental measurable variables, due to perturbation of the electronic structures of the analyte.

More detail on the context of the latter paragraph can be found in reference [4]. Therein, we detail on available and empirically testable model relations for accurate and exact data processing of MS results, making a parallel between our innovative theory and model equations and theories by other authors. A comprehensive chemometric treatment is presented, therein. Thus, error contributions to model equations; fluctuations of experimental measurable variables; and methods for data-processing of experimental results among different model formulas could be evaluated, I hope, precisely.

[4] B.Ivanova, M.Spiteller, Mass spectrometric and quantum chemical treatments of molecular and ionic interactions of flavonoid-glycosides - a stochastic dynamic approach, (2021) submitted.