探索深度学习以使用FT-NIR和微NIR光谱来预测子奶造假
Agustami Sitorus1, Ravipat Lapcharoensuk1
1Department of Agricultural Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
深度学习模型使用近红外光谱学 (NIR) 准确识别了子奶中的改水平. 便携式微型NIR设备显示出快速,现场的食品欺诈检测的希望.
科学领域:
- 食品科学与技术 食品科学与技术
- 分析化学 分析化学
- 人工智能在光谱学中的应用
背景情况:
- 食品改带来了严重的食品安全和经济风险.
- 目前用于检测诸如子奶中的玉米面粉和塔皮奥卡粉等杂质的方法往往缓慢且缺乏稳定性.
- 开发快速,准确和非破坏性的分析技术对于有效的伪造检测至关重要.
研究的目的:
- 探索深度学习算法的有效性,以量化子牛奶造.
- 为了比较桌面FT-NIR和便携式Micro-NIR光谱仪用于 adulterant检测的性能.
- 开发一个可靠的预测模型,使用近红外 (NIR) 光谱数据识别伪造水平.
主要方法:
- 子牛奶样本被故意改为玉米面粉和塔皮奥卡粉 (1-50%).
- 使用FT-NIR和便携式Micro-NIR光谱仪获得近红外 (NIR) 光谱.
- 四个修改后的深度学习架构 (CNN,S-AlexNET,ResNET,GoogleNET) 应用于NIR数据集进行定量分析.
主要成果:
- 深度学习模型在预测伪造水平方面表现可靠 (R2: 0.886-0.999,RMSE: 0.370-6.108%).
- 百分比偏差比 (RPD) 值表明大多数模型和仪器具有出色的定量预测能力.
- 便携式Micro-NIR在检测固体 adulterants方面显示出比FT-NIR更有希望的结果,这表明在现场应用的潜力.
结论:
- 深度学习算法与NIR光谱数据相结合,提供了一种快速,准确和非破坏性的方法来评估子奶造物质.
- 该研究验证了使用先进的计算技术用于食品质量控制和欺诈预防的可行性.
- 便携式NIR技术,特别是Micro-NIR,是现场实时监测食品改的一个有前途的工具.
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