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使用机器学习的生物阻抗和直径测量对普通话 (Citrus reticulata L.) 的储存温度进行分类
Daesik Son1,2, Siun Lee1, Sehyeon Jeon1
1Department of Biosystems Engineering, Seoul National University, Seoul 08826, Republic of Korea.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究介绍了一种使用生物阻抗光谱 (BIS) 和机器学习来评估普通储存温度的非破坏性方法. 整合直径变化与BIS显著提高了预测水果新鲜度的准确性.
科学领域:
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
- 电气工程 电气工程
背景情况:
- 在不适当的储存温度下,普通话的质量下降,导致味道丧失和营销能力降低.
- 对普通新鲜度的视觉评估是不可靠的,因为储存引起的变化并不明显.
- 一种非破坏性,简单的方法对于评估普通的质量和储存条件至关重要.
研究的目的:
- 开发和评估一种使用生物阻抗光谱 (BIS) 评估普通储存温度的非破坏性方法.
- 研究机器学习 (ML) 模型在基于BIS数据的存储温度分类方面的有效性.
- 为了确定是否将果子直径和时间序列数据与BIS集成,可以提高ML模型的准确性.
主要方法:
- 非侵入性生物阻抗光谱学 (BIS) 用于在不同温度下储存的普通菜.
- 八个机器学习 (ML) 模型使用生物阻抗数据进行训练,以分类存储温度.
- 该研究探讨了直径和时间序列变化的整合,以及等效电路 (EC) 参数,与用于ML训练的BIS数据.
主要成果:
- 支持向量机 (SVM) 模型在与直径数据集成的生物阻抗变化进行训练时,获得了最高的精度 (0.92).
- 与仅使用原始生物阻抗数据 (0.76) 相比,整合直径和时间序列数据显著提高了ML模型的准确性.
- 相当电路 (EC) 参数提供了高效的数据维度减小,尽管略低于原始生物阻抗数据的准确性.
结论:
- 将生物阻抗变化与水果直径相结合,提供了一种新的,准确的,非破坏性的方法来评估的储存温度.
- 这种方法提高了监测水果新鲜度和优化储存条件的能力,减少收获后的损失.
- 使用BIS和ML的拟议方法可能适用于其他水果类型的质量评估.
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