Solving the spectroscopy interference effects of beta-carotene and lycopene by neural networks

In this study a new computerized approach and linear models (LMs) to solve the UV/vis spectroscopy interference effects of beta-carotene with lycopene analysis by neural networks (NNs) are considered. The data collected (absorbance values) obtained by UV/vis spectrophotometry were transferred into an NN-trained computer for modeling and prediction of output. Such an integrated NN/UV/vis spectroscopy approach is capable of estimating beta-carotene and lycopene concentrations with a mean prediction error 50 times lower than that calculated by the LM/UV/vis spectroscopy approach (without any previous physicochemical knowledge of the process to be modeled).     

Quantification of phenolic compounds in olive oil mill wastewater by artificial neural network/laccase biosensor

In this paper is considered a new computerized approach to the determination of concentrations of phenolic compounds (caffeic acid and catechol). An integrated artificial neural network (ANN)/laccase biosensor is designed. The data collected (current signals) from amperometric detection of the laccase biosensor were transferred into an ANN trained computer for modeling and prediction of output. Such an integrated ANN/laccase biosensor system is capable of the prediction of caffeic acid and catechol concentrations of olive oil mill wastewater, based on the created models and patterns, without any previous knowledge of this phenomenon. The predicted results using the ANN were compared with the amperometric detection of phenolic compounds obtained at a laccase biosensor in olive oil wastewater of the 2004-2005 harvest season. The difference between the real and the predicted values was <0.5%. biosensor; olive oil mill wastewater; chemical analysis; phenolic compounds.