文章基本信息

标题：Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies
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作者：Aimi Aznan ; Claudia Gonzalez Viejo ; Alexis Pang 等
期刊名称：Foods
电子版ISSN：2304-8158
出版年度：2022
卷号：11
期号：9
DOI：10.3390/foods11091181
语种：English
出版社：MDPI Publishing
摘要：Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice types using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture, and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R = 0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable, and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain.
关键词：enrice aromanear-infraredelectronic noseartificial neural networksmachine learning