TRFES Combined with Chemometrics for Geographical Identification of Camellia Oils
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Abstract:
An accurate and rapid method for geographical identification of camellia oil was established based on the combination of time-resolved fluorescence emission spectrum (TRFES) and chemometrics. Totally 180 samples from Zhejiang, Jiangxi and Hunan were collected and their TRFES were compared from the dimensions of steady-state fluorescence emission and fluorescence decay; Parallel factor analysis (PARAFAC) was performed for the dimensional reduction and the optimization of characteristics based on training set; two factors were selected and then their factor scores were utilized as the input of artificial neural network (ANN), and finally the geographical identification model of camellia oil was established. The result showed that fluorescence decay was less influenced by the concentration of fluorophores than steady-state fluorescence emission. Hence, TRFES displayed stronger fingerprint characteristics and could be good for the geographical identification of camellia oil. The cross-validation coefficient was 98.7% and prediction coefficient was 96.1% for the geographical identification model of camellia oil, indicating the strong robustness and high accuracy of the model, which is considered to be suitable for the geographical identification of camellia oil. This study proved that geographical identification of camellia oil could be completed by the combination of TRFES and chemometrics.
