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

IF 2.3 4区 化学 Q1 SOCIAL WORK Journal of Chemometrics Pub Date : 2023-10-03 DOI:10.1002/cem.3520
Dipshikha Tamili, Susovan Jana, Paramita Bhattacharjee
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Abstract

This 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.

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同时测定血清素、褪黑素和l -色氨酸的色谱方法发展:传质模型、色谱分离因素和人工神经网络方法预测
本工作旨在建立一种高效液相色谱(HPLC)方法,用于同时定量三种重要的生物治疗分子,即l -色氨酸、血清素和褪黑素,它们在农产品中含量较低。在第一种方法中,使用混合物中相同的纯化学标准品,由人为小组判断峰锐度、峰肩和峰分离等色谱分离参数,并使用模糊逻辑分析避免偏倚和决策不确定性。在第二种方法中,对峰分辨率和分离因子等分离参数进行了评估,以获得高分辨率的色谱图。所述分离过程的参数成功地通过柱中分析物的无因次传质数来建模;并对传质方程进行后拟合,得到了较高的R2,表明色谱工艺开发成功。各色谱分离参数值与其理想值之间的欧氏距离;去模糊化的分数;传质研究结果;并建立了以1%乙酸-HPLC水(A) -HPLC级甲醇(B)为流动相,在梯度洗脱条件下(90% A - 10% B),流速为1 mL/min,可同时定量蘑菇中3个分子(μg),且具有较好的分辨率。电喷雾离子联用质谱仪(MS)也证实了上述生物分子在蘑菇提取物中的存在。为色谱用户开发了一个人工神经网络模型(高R2),该模型可以通过改变流动相组成和流速来准确预测色谱参数。
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来源期刊
Journal of Chemometrics
Journal of Chemometrics 化学-分析化学
CiteScore
5.20
自引率
8.30%
发文量
78
审稿时长
2 months
期刊介绍: The Journal of Chemometrics is devoted to the rapid publication of original scientific papers, reviews and short communications on fundamental and applied aspects of chemometrics. It also provides a forum for the exchange of information on meetings and other news relevant to the growing community of scientists who are interested in chemometrics and its applications. Short, critical review papers are a particularly important feature of the journal, in view of the multidisciplinary readership at which it is aimed.
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