建立基于机器学习的果感官评估模型
Minghao Liu1, Minhua Liu1, Lin Bai2
1School of Artifical Intelligence, Beijing Technology and Business University, Beijing 100048, China.
Foods (Basel, Switzerland)
|September 28, 2023
概括
机器学习增强了蓝的质量评估. 一个粒子群优化支持向量回归模型客观地使用物理和化学数据预测感官得分,优于其他模型.
科学领域:
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
- 计算科学 计算科学
背景情况:
- 生活质量的提高增加了对蓝等高品质水果的需求.
- 感官评估对于蓝的质量至关重要,但是主观的.
- 需要客观,数据驱动的方法来补充传统的感官分析.
研究的目的:
- 开发一个客观的机器学习模型来评估蓝的质量.
- 将粒子群优化支持向量回归模型与其他机器学习方法的性能进行比较.
- 为了减少蓝感官评估中的人类主观性.
主要方法:
- 作为输入特征,利用了十个蓝的物理和化学指数 (例如,酶,黄类,可溶固体).
- 开发了一个支持向量回归模型,通过粒子群优化 (PSO-SVM) 进行优化.
- 将PSO-SVM与卷积神经网络 (CNN) 和长短期记忆 (LSTM) 网络进行比较,重复实验20次.
主要成果:
- PSO-SVM模型实现了较低的误差指标:根平均平方误差 (RMSE) 为0.45和平均绝对误差 (MAE) 为0.40.
- CNN和LSTM模型显示出更高的误差:RMSE (0.96,1.22) 和MAE (0.78,0.97),分别.
- 该PSO-SVM模型显示出卓越的预测准确性,特别是在有限的样本数据下.
结论:
- 粒子群集优化有效地增强了蓝质量预测的支向量回归.
- 拟议的PSO-SVM模型为传统的感官评估提供了更可靠和客观的替代方案.
- 这种方法在蓝样本数据有限的场景中尤其有利.
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