情报检测油脂吸收法式条通过表面形状的情报检测
Simeng Wang1, Shuli Zhao1, Nan Wang1
1State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock Jointly Constructed by Ministry and Province, Inner Mongolia University, 24 Zhaojun Road, Hohhot, Inner Mongolia 010020, China; School of Life Sciences, Inner Mongolia University, 24 Zhaojun Road, Hohhot, Inner Mongolia 010020, China.
Food research international (Ottawa, Ont.)
|February 3, 2024
概括
这项研究开发了智能模型,通过使用表面特征来监测法式条的油脂吸收. 这些模型准确地预测了油含量,提供了一种快速的方法来控制炸食品的质量.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 数据科学数据科学数据科学
背景情况:
- 油脂吸收在油炸食品的影响质量和健康影响.
- 表面特征是石油吸收的关键指标.
- 精确监测油含量对于质量控制至关重要.
研究的目的:
- 建立智能模型来监测法式条中的油脂吸收.
- 为了研究表面特征和油含量之间的关系.
- 评估部分最小平方回归 (PLSR) 和反向传播人工神经网络 (BP-ANN) 模型的有效性.
主要方法:
- 研究了用油菜油 (RO) 和高油性花生油 (HOPO) 炸的法式炸的表面形态和质地.
- 开发并应用了部分最小平方回归 (PLSR) 和反向传播人工神经网络 (BP-ANN) 模型.
- 与油含量相关的表面参数 (地比,粗度,质地)
主要成果:
- 油含量随着油炸温度的增加而增加,达到37.7% (RO) 和41.4% (HOPO).
- 烤温度增加了皮层比率,粗度和特定的纹理参数 (Fm,Nwr,fwr,Wc),同时降低了均性.
- PLSR和BP-ANN模型实现了超过0.93.9的预测系数.
结论:
- 表面特征与化学测量相结合,提供了快速而精确的方法来确定油油含量.
- 智能模型 (PLSR,BP-ANN) 根据表面特征有效监测油吸收行为.
- 这项研究为炸食品行业的质量评估提供了宝贵的见解.
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