文章基本信息

标题：Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling
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作者：Thejani M. Gunaratne ; Claudia Gonzalez Viejo ; Nadeesha M. Gunaratne 等
期刊名称：Foods
电子版ISSN：2304-8158
出版年度：2019
卷号：8
期号：10
页码：426-436
DOI：10.3390/foods8100426
出版社：MDPI Publishing
摘要：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.
关键词：sensory; physicochemical measurements; artificial neural networks; near infra-red spectroscopy sensory ; physicochemical measurements ; artificial neural networks ; near infra-red spectroscopy