多变量回归和人工神经网络建模的应用,用于预测葡萄皮堆肥的物理化学特性
Tea Sokač Cvetnić1, Korina Krog1, Davor Valinger1
1Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10 000 Zagreb, Croatia.
Bioengineering (Basel, Switzerland)
|March 27, 2024
概括
使用回归和人工神经网络模型预测葡萄皮堆肥特性有助于可持续的粮食生产. 这些模型为更绿色的堆肥过程和质量控制提供了宝贵的见解.
科学领域:
- 环境科学 环境科学
- 农业科学 农业科学
- 数据科学数据科学数据科学
背景情况:
- 食品行业副产品的可重复使用性符合可持续和绿色生产原则.
- 葡萄皮的有效堆肥需要对物理化学特性进行监测和预测.
研究的目的:
- 评估多重线性回归 (MLR),断片线性回归 (PLR) 和人工神经网络 (ANN) 模型的适用性.
- 在容器内堆肥过程中预测葡萄皮堆肥的物理化学特性.
- 为了将预测与初始堆肥条件 (空气流量,湿度,采样日) 相对应.
主要方法:
- 开发和验证MLR,PLR和ANN模型.
- 预测主要的堆肥特性:水分,干物质,有机物质,灰,碳,,C/N比率,颜色变化,pH,导电性和总溶解固体.
- 使用R平方,RMSEP,SEP,RPD和RER等指标评估模型性能.
主要成果:
- 所有开发的MLR和PLR模型都被认为是可接受的过程选.
- 该ANN模型在预测水分和干物质含量 (RER>11) 方面表现出高准确度,适用于质量控制.
- 多变量建模显示了分析堆肥物理化学性能的巨大潜力.
结论:
- 回归和ANN模型是预测葡萄皮堆肥特性的有效工具.
- 这些建模方法支持优化食品工业中更绿色的生产过程.
- 该研究证实了多变量建模在可持续废物管理和资源回收方面的高适用性.
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