Modeling and predicting second and third-order fluorescence spectroscopy data as a novel quality control strategy on mayonnaise

AZCARATE, S.M.; GOICOECHEA H.C.; CAMIÑA J.M.; DE ARAÚJO GOMES, ADRIANO

Halifax

Congreso; 1. XVII Chemometrics in analytical chemistry conference - CAC 2018; 2018

Over the last years, in multivariate classification setting, second-order classification methods have been applied as a need to solve a high complexity data structure [1]. Notwithstanding, their applications has not been motivated on the need to increase the data order as a strategy to solve a classification problem [2]. In this work, the possibility to increase order data from two to three ways to achieve a potential classification when a first-order classification is not possible is demonstrated. For that, first-and second-order fluorescence spectroscopy data sets -simulated and experimental- were acquired. Simulated two- and three-way data involving three target classes were generated. Later, experimental data was acquired from 80 mayonnaise samples to evaluate spoiled mayonnaise according to different five-storage time. On the one hand, samples were excited to 320 nm and the spectra were recorded from 300 to 500 nm. After, fluorescence excitation spectra were recorded between 230 and 400 nm, and emission wavelengths from 300 to 500 nm. PLS-DA and N-PLS-DA were employed for first and second-order data, respectively. Later, model predictive ability were evaluated through some figures of merit, e.g. sensitivity, specificity, precision and accuracy. The results showed a poor classification for first- order data with a high error rate of 78 %, which was awesomely improved when second-order data were applied.Certainly, it was possible to attain a significant improvement on results and a positive impact on analytical figures of merit when second-order data were analyzed. Thus, it can be demonstrated that the way in which the data are generated has a significant effect on the classification.