神经网络增强日记产品的老化时间测量,剩余的红外光谱学
Tobias Beck1, Bernhard Gatternig1, Antonio Delgado1
1Lehrstuhl für Strömungsmechanik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.
Heliyon
|November 30, 2023
概括
使用近红外光谱学和神经网络确定食品残留干燥提供了快速清洁见解. 卷积神经网络显著提高了各种食物类型的湿度预测准确度.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学是一种分析化学.
- 机器学习 机器学习
背景情况:
- 食品残留物的有效清洁需要对其干燥状态进行准确的评估.
- 对于快速评估食品残留老化的非侵入性技术是可取的.
- 近红外光谱 (NIRS) 是一种快速分析食品成分的方法.
研究的目的:
- 引入一种快速,非侵入性的方法来确定食品残留物的干燥程度.
- 为了比较部分最小平方 (PLS) 回归和神经网络 (NN) 分析NIRS数据的有效性.
- 评估不同神经网络架构的预测准确度,以估计含水量和干燥时间.
主要方法:
- 使用近红外光谱 (NIRS) 来测量干燥食品残留物 (粉制品,酸奶) 的光谱数据.
- 分析了NIRS数据,使用部分最小平方 (PLS) 回归与预处理.
- 采用了三种类型的神经网络 (NN),包括具有卷积层的神经网络,用于数据分析.
- 与食品残留老化时间相关联的水分含量预测.
主要成果:
- 简单的前神经网络显示了与PLS回归相似的准确性.
- 带有卷积层的神经网络显著优于PLS回归和其他NN类型.
- 卷积神经网络方法在不同类型的食品中表现出稳健性,并减少了对广泛数据预处理的需求.
结论:
- 卷积神经网络提供了一种优越的方法,可以从NIRS数据中预测食品残留中的水分含量.
- 这种方法可以准确地确定干燥时间,这对于有效的清洁方案至关重要.
- 开发的技术提供了一个快速的,非侵入性的解决方案,用于评估食品残留的老化和优化清洁过程.
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