A procedure based on a D-optimal design coupled with PARAFAC was proposed to optimise signals from molecular fluorescence spectroscopy to obtain the best experimental conditions for the achievement of the best fluorescence signal of green tea samples. Excitation-emission signals (EEMs) were used to analyse the liquid samples (tea infusions), whereas front-face fluorescence excitation-emission matrices (FFEEMs) were recorded for the solid samples (raw or powder tea leaves). The experimental effort was reduced considerably in both cases thanks to the D-optimal design. Once the optimal conditions have been found, the characterisation of green tea was carried out and the sensitivity and specificity were evaluated. The projection of the principal component analysis (PCA) scores enabled to differentiate among the types of liquid green tea (Chinese tea, Chinese tea with lemon and Indian tea with and without theine). The discrimination of solid green tea according to its geographical origin (Chinese, Indian and Japanese) was also carried out through PCA. In addition, the discrimination between the most expensive Japanese tea and the cheapest one was possible. The sensitivity of the models built with SIMCA was 100% and the specificity of the models for the Chinese tea with respect to the Japanese tea was also high.

D-Optimal Design and PARAFAC as Useful Tools for the Optimisation of Signals from Fluorescence Spectroscopy Prior to the Characterisation of Green Tea Samples

Hooshyari M.;Casale M.;Turrini F.;
2019

Abstract

A procedure based on a D-optimal design coupled with PARAFAC was proposed to optimise signals from molecular fluorescence spectroscopy to obtain the best experimental conditions for the achievement of the best fluorescence signal of green tea samples. Excitation-emission signals (EEMs) were used to analyse the liquid samples (tea infusions), whereas front-face fluorescence excitation-emission matrices (FFEEMs) were recorded for the solid samples (raw or powder tea leaves). The experimental effort was reduced considerably in both cases thanks to the D-optimal design. Once the optimal conditions have been found, the characterisation of green tea was carried out and the sensitivity and specificity were evaluated. The projection of the principal component analysis (PCA) scores enabled to differentiate among the types of liquid green tea (Chinese tea, Chinese tea with lemon and Indian tea with and without theine). The discrimination of solid green tea according to its geographical origin (Chinese, Indian and Japanese) was also carried out through PCA. In addition, the discrimination between the most expensive Japanese tea and the cheapest one was possible. The sensitivity of the models built with SIMCA was 100% and the specificity of the models for the Chinese tea with respect to the Japanese tea was also high.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/950512
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