Journal article

Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling

Thejani M Gunaratne, Claudia Gonzalez Viejo, Nadeesha M Gunaratne, Damir D Torrico, Frank R Dunshea, Sigfredo Fuentes

Foods | MDPI AG | Published : 2019

Abstract

Chocolates are the most common confectionery and most popular dessert and snack across the globe. The quality of chocolate plays a major role in sensory evaluation. In this study, a rapid and non-destructive method was developed to predict the quality of chocolate based on physicochemical data, and sensory properties, using the five basic tastes. Data for physicochemical analysis (pH, Brix, viscosity, and color), and sensory properties (basic taste intensities) of chocolate were recorded. These data and results obtained from near-infrared spectroscopy were used to develop two machine learning models to predict the physicochemical parameters (Model 1) and sensory descriptors (Model 2) of choc..

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Grants

Awarded by Australian Government through the Australian Research Council


Funding Acknowledgements

This research was partially funded by the Australian Government through the Australian Research Council (Grant number IH120100053) 'Unlocking the Food Value Chain: Australian industry transformation for ASEAN markets.'