使用人工神经网络建模不同巧克力品种的纹理属性:构成的影响
1Department of Food Engineering, Istanbul Technical University, Istanbul, Turkiye.
Journal of texture studies
|February 14, 2026
概括
人工神经网络 (ANN) 建模准确地预测巧克力硬度和表面粘度从其组成. 这种非破坏性方法有助于质量控制和食品行业的新产品开发.
科学领域:
- 食品科学与技术 食品科学与技术
- 材料科学 材料科学 材料科学
- 计算建模 计算建模
背景情况:
- 巧克力的质地和质性质对于消费者接受和产品质量至关重要.
- 精确监测这些特性对于质量控制和巧克力制造过程优化至关重要.
研究的目的:
- 开发一种快速,非破坏性的方法来预测巧克力的质地和质性质.
- 利用人工神经网络 (ANN) 建模,将巧克力成分与硬度和表面粘度相关联.
主要方法:
- 开发了两个人工神经网络 (ANN) 模型:ANN-1用于硬度预测和ANN-2用于明显粘度预测.
- 使用巧克力组成作为输入变量,硬度和明显粘度作为输出变量.
- 使用R平方 (R2) 和平均平方误差 (MSE) 度量来评估模型性能.
主要成果:
- 对硬度 (R2 = 0.95) 和表面粘度 (R2 = 0.97) 的预测准确度都很高.
- 这些模型实现了低平均误差 (MSE),表明可靠的预测.
- 这项研究证实了ANN建模在基于成分预测巧克力特性方面的有效性.
结论:
- 人工神经网络建模为预测巧克力的关键性质提供了一种有效的,非破坏性的方法.
- 这种预测工具可以帮助巧克力制造商进行质量控制,工艺选择和新产品开发.
- 开发的模型提高了生产率,并通过准确的财产预测来确保产品的一致性.
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