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Study of the aging and oxidation processes of vinegar samples from different origins during storage by near-infrared spectroscopy [An article from: Analytica Chimica Acta]
Study of the aging and oxidation processes of vinegar samples from different origins during storage by near-infrared spectroscopy [An article from: Analytica Chimica Acta]
This digital document is a journal article from Analytica Chimica Acta, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Near infrared spectroscopy combined with several chemometrical techniques has been used to control the aging process in vinegar and the changes produced during storage. Several considerations regarding NIR instrument changes, which can add noise to the determinations, were made and a strategy based on the correction of the spectra was used to eliminate the differences between spectra due to instrument variation, in particular to lamp change. Ninety-five different vinegars were measured using a near-infrared spectrometer. The spectrum of each vinegar sample was collected twice: the first time as soon as the bottle was opened and the vinegar had not undergone any oxidation process and the second measurement was taken after a certain period of time. Then, in order to quantify the discrimination between the two groups of spectra recorded in the two occasions and so to estimate the aging effect in the vinegar samples, Linear Discriminant Analysis was used as a classification method. To improve the results, bearing in mind that many of the measured variables could be useless and that the elimination of these wavelengths can help in interpretation, a feature selection technique was applied. For chemical interpretation of the retained wavelengths, the spectral zones correlated with the aging process were isolated and the species and compounds that can absorb in these NIR spectra bands were studied. -
Hydroxyl and acid number prediction in polyester resins by near infrared spectroscopy and artificial neural networks [An article from: Analytica Chimica Acta]
Hydroxyl and acid number prediction in polyester resins by near infrared spectroscopy and artificial neural networks [An article from: Analytica Chimica Acta]
This digital document is a journal article from Analytica Chimica Acta, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Back-propagation artificial neural networks (BP-ANN) are applied for modeling hydroxyl number and acid value of a set of 62 samples of polyester resins from their near infrared (NIR) spectra. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR) and partial least squares (PLS). The set of available samples is split into: (i) a training set, for models calculation; (ii) a test set, for setting the correct number of latent variables in PCR and PLS and for selecting the end point of the training phase of BP-ANN; (iii) a ''production set'' of samples, which are predicted to evaluate the models predictive ability. This approach guarantees that the predictive ability of the models is evaluated by genuine predictions. BP-ANN resulted always better than the classical PCR and PLS, from the point of view of the predictive ability. The study of the breakdown number of experiments to include in the training set showed instead that this factor does influence PCR and PLS at a lesser degree than what happens for BP-ANN. The latter approach requires a larger number of experiments for obtaining good results. The choice of optimal training sets is efficiently performed by Kohonen self-organizing maps (SOMs). It can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for monitoring the polyesterification of dicarboxylic acids with diols by predicting the acid and hydroxyl numbers directly along the process line.
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