Yasin Nausheen, Naqvi Syed Mumtaz Danish, Akhter Syed Mamnoon
Department of Applied Chemistry and Chemical Technology, University of Karachi, Karachi, Pakistan.
Department of Applied Physics, University of Karachi, Karachi, Pakistan.
Heliyon. 2024 Feb 16;10(4):e26373. doi: 10.1016/j.heliyon.2024.e26373. eCollection 2024 Feb 29.
This study aims at the application of two chemometric techniques to visible spectra of acetic acid solutions of Co (II) and Co (III) for simultaneous determination thereof. Spectral data of 145 samples in the range of 400-700 nm were used to build the models. Partial least squares regression models were developed for which latent variables were determined using internal cross-validation with a leave-one-out strategy and 3 and 2 latent variables were selected for Co(II) and Co(III) based on root mean square error of cross-validation. For these models, root mean square errors of prediction were 1.16 and 0.536 mM and coefficients of determination were 0.975 and 0.892 for Co (II) and Co (III). As an alternate method, artificial neural networks consisting of three layers, with 10 neurons in hidden layer, were trained to model spectra and concentrations of cobalt species. Levenberg-Marquardt algorithm with feed-forward back-propagation learning resulted root mean square errors of prediction of 0.316 and 0.346 mM for Co (II) and Co (III) respectively and coefficients of determination were 0.996 and 0.988.
本研究旨在将两种化学计量技术应用于钴（II）和钴（III）乙酸溶液的可见光谱，以同时测定它们。使用400 - 700 nm范围内145个样品的光谱数据建立模型。开发了偏最小二乘回归模型，采用留一法内部交叉验证确定潜在变量，并根据交叉验证的均方根误差为钴（II）和钴（III）分别选择3个和2个潜在变量。对于这些模型，钴（II）和钴（III）的预测均方根误差分别为1.16和0.536 mM，决定系数分别为0.975和0.892。作为一种替代方法，训练了由三层组成、隐藏层有10个神经元的人工神经网络来模拟钴物种的光谱和浓度。采用前馈反向传播学习的Levenberg - Marquardt算法，钴（II）和钴（III）的预测均方根误差分别为0.316和0.346 mM，决定系数分别为0.996和0.988。
