Next Article in Journal
Narrow-Banded UVB Affects the Stability of Secondary Plant Metabolites in Kale (Brassica oleracea var. sabellica) and Pea (Pisum sativum) Leaves Being Added to Lentil Flour Fortified Bread: A Novel Approach for Producing Functional Foods
Next Article in Special Issue
Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence
Previous Article in Journal
Advances in Food and Byproducts Processing towards a Sustainable Bioeconomy
 
Search for Articles:
Title / Keyword
Author / Affiliation
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
Journals
Foods
Volume 8
Issue 10
10.3390/foods8100426
foods-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share announcement question_answer thumb_up
...
textsms
...
Article

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

by
Thejani M. Gunaratne
1,
Claudia Gonzalez Viejo
1,
Nadeesha M. Gunaratne
1,
Damir D. Torrico
1,2,
Frank R. Dunshea
1 and
Sigfredo Fuentes
1,*
1
School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia
2
Department of Wine, Food and Molecular Biosciences, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7647, New Zealand
*
Author to whom correspondence should be addressed.
Foods 2019, 8(10), 426; https://doi.org/10.3390/foods8100426
Received: 1 September 2019 / Revised: 13 September 2019 / Accepted: 18 September 2019 / Published: 20 September 2019
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment)
View Full-Text Download PDF
Browse Figures

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 chocolate. The results show that the models developed had high accuracy, with R = 0.99 for Model 1 and R = 0.93 for Model 2. The thus-developed models can be used as an alternative to consumer panels to determine the sensory properties of chocolate more accurately with lower cost using the chemical parameters. View Full-Text
Keywords: sensory; physicochemical measurements; artificial neural networks; near infra-red spectroscopy sensory; physicochemical measurements; artificial neural networks; near infra-red spectroscopy
►▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
SciFeed

Share and Cite

MDPI and ACS Style

Gunaratne, T.M.; Gonzalez Viejo, C.; Gunaratne, N.M.; Torrico, D.D.; Dunshea, F.R.; Fuentes, S. Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling. Foods 2019, 8, 426. https://doi.org/10.3390/foods8100426

AMA Style

Gunaratne TM, Gonzalez Viejo C, Gunaratne NM, Torrico DD, Dunshea FR, Fuentes S. Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling. Foods. 2019; 8(10):426. https://doi.org/10.3390/foods8100426

Chicago/Turabian Style

Gunaratne, Thejani M., Claudia Gonzalez Viejo, Nadeesha M. Gunaratne, Damir D. Torrico, Frank R. Dunshea, and Sigfredo Fuentes. 2019. "Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling" Foods 8, no. 10: 426. https://doi.org/10.3390/foods8100426

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Map by Country/Region

1
Back to TopTop