Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/8/10/426 |
| _version_ | 1819021654628499456 |
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| author | Thejani M. Gunaratne Claudia Gonzalez Viejo Nadeesha M. Gunaratne Damir D. Torrico Frank R. Dunshea Sigfredo Fuentes |
| author_facet | Thejani M. Gunaratne Claudia Gonzalez Viejo Nadeesha M. Gunaratne Damir D. Torrico Frank R. Dunshea Sigfredo Fuentes |
| author_sort | Thejani M. Gunaratne |
| collection | DOAJ |
| description | 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 <i>R</i> = 0.99 for Model 1 and <i>R</i> = 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. |
| first_indexed | 2024-12-21T04:10:32Z |
| format | Article |
| id | doaj.art-5d6851b620ca47558f9435a4606eefe1 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-12-21T04:10:32Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-5d6851b620ca47558f9435a4606eefe12022-12-21T19:16:28ZengMDPI AGFoods2304-81582019-09-0181042610.3390/foods8100426foods8100426Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning ModelingThejani M. Gunaratne0Claudia Gonzalez Viejo1Nadeesha M. Gunaratne2Damir D. Torrico3Frank R. Dunshea4Sigfredo Fuentes5School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, AustraliaSchool of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, AustraliaSchool of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, AustraliaSchool of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, AustraliaSchool of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, AustraliaSchool of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, AustraliaChocolates 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 <i>R</i> = 0.99 for Model 1 and <i>R</i> = 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.https://www.mdpi.com/2304-8158/8/10/426sensoryphysicochemical measurementsartificial neural networksnear infra-red spectroscopy |
| spellingShingle | Thejani M. Gunaratne Claudia Gonzalez Viejo Nadeesha M. Gunaratne Damir D. Torrico Frank R. Dunshea Sigfredo Fuentes Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling Foods sensory physicochemical measurements artificial neural networks near infra-red spectroscopy |
| title | Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling |
| title_full | Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling |
| title_fullStr | Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling |
| title_full_unstemmed | Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling |
| title_short | Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling |
| title_sort | chocolate quality assessment based on chemical fingerprinting using near infra red and machine learning modeling |
| topic | sensory physicochemical measurements artificial neural networks near infra-red spectroscopy |
| url | https://www.mdpi.com/2304-8158/8/10/426 |
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