Quality (philosophy)

Random forest

Philosophy

Chemometrics

Electronic tongue

Support vector machine

Electronic nose

Computer science

Food science

Sensor fusion

Pattern recognition (psychology)

Partial least squares regression

Epistemology

Artificial intelligence

Taste

Chemistry

Machine learning

Food Chemistry

Electronic nose (E-nose), electronic tongue (E-tongue) and electronic eye (E-eye) combined with chemometrics methods were applied for qualitative identification and quantitative prediction of tea quality. Main chemical components, such as amino acids, catechins, polyphenols and caffeine were measured by traditional methods. Feature-level fusion strategy for the integration of the signals was introduced to integrate the E-nose, E-tongue and E-eye signals, aiming at improving the performances of identification and prediction models. Perfect results with an accuracy of 100% were obtained for qualitative identification of tea quality grades, based on fusion signals by support vector machine and random forest. Quantitative models were established for predicting the contents of the chemical components based on independent electronic signals and fusion signals by partial least squares regression, support vector machine and random forest. Random forest based on the fusion signals achieved the best performance in predicting the concentration of those chemical components.

The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics