线性判别分析
人工智能
支持向量机
直链淀粉
机器学习
算法
质量(理念)
偏最小二乘回归
品味
数学
糙米
随机森林
模式识别(心理学)
计算机科学
食品科学
化学
物理
量子力学
淀粉
作者
Edenio Olivares Díaz,Haruka Iino,Kento Koyama,Shuso Kawamura,Shigenobu Koseki,Suxing Lyu
出处
期刊:Food Chemistry
[Elsevier]
日期:2023-12-01
卷期号:429: 136907-136907
被引量:10
标识
DOI:10.1016/j.foodchem.2023.136907
摘要
The taste quality of rice is determined by protein and amylose percentages, with low levels indicating high-quality taste in Japan. However, accurate non-destructive screening remains a challenge for the industry. We explored the use of machine learning models and near-infrared spectra to classify rice taste quality. Three models were optimized using 796 brown rice samples from Hokkaido, Japan, produced between 2008 and 2016, and tested on 278 distinct samples from the same region produced between 2017 and 2019. Logistic regression and support vector machine models outperformed the partial least-squares discriminant analysis model, achieving high accuracy (94%), f1-score (90%), average precision (0.94), and low classification error (4%) and allowing accurate non-destructive classification of rice quality. These results not only improve rice quality, post-harvest technology, and producer output in Japan but also could enhance quality control processes and foster the production of high-quality products for other agricultural goods and food commodities worldwide.
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