Chromatographic method development for simultaneous determination of serotonin, melatonin, and L‐tryptophan: Mass transfer modeling, chromatographic separation factors, and method prediction by artificial neural network

Author:

Tamili Dipshikha1,Jana Susovan2,Bhattacharjee Paramita1ORCID

Affiliation:

1. Department of Food Technology and Biochemical Engineering Jadavpur University Kolkata India

2. Department of Computer Science and Engineering (IoT) Institute of Engineering & Management Kolkata India

Abstract

AbstractThis work endeavored to develop a high performance liquid chromatography (HPLC) method for simultaneous quantification of three important biotherapeutic molecules namely, L‐tryptophan, serotonin, and melatonin, present in low amounts in agro‐commodities. In the first approach, using pure chemical standards of the same in a mixture, chromatogram separation parameters such as peak sharpness, peak shouldering, and peak separation were judged by a human panel and biasness‐cum‐decision uncertainties were averted using fuzzy logic analysis. In the second approach, the separation parameters such as peak resolution and separation factors were evaluated to obtain a well‐resolved chromatogram. The parameters of the said separation process were successfully modeled by dimensionless numbers of mass transfer of the analytes in the column; and post‐fitting of mass transfer equations, high R2 was obtained suggesting successful development of the chromatographic process. Euclidean distances between the values of each chromatographic separation parameter and their respective ideal values; the defuzzified scores; findings of mass transfer study; and separation factors concomitantly established that the mobile phase of composition 1% acetic acid in HPLC water (A)–HPLC grade methanol (B) in gradient elution conditions (90% A–10% B) at a flow rate of 1 mL/min could simultaneously quantify the three molecules (μg) in button mushrooms with good resolution. The presence of the said biomolecules in the extract of button mushrooms was also confirmed by electrospray ionization (ESI)‐mass spectrometer (MS). An artificial neural network model (high R2) was also developed for chromatographic users, which would allow accurate prediction of the chromatographic parameters by varying mobile phase composition and flow rates.

Publisher

Wiley

Subject

Applied Mathematics,Analytical Chemistry

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