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一个基于色度传感器阵列和化学测量的智能嗅觉可视化系统,用于识别米造

Muhammad Arslan¹, Muhammad Zareef¹, Mubrrah Afzal²

¹Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013 Zhenjiang, Jiangsu, China.

|July 23, 2025

概括

一个新的智能嗅觉可视化系统可以快速识别优质大米中的杂物. 这种色度传感器阵列提供了一种快速,简单和低成本的方法来检测劣质谷物.

科学领域:

分析化学分析化学
传感器技术传感器技术
食品科学食品科学食品科学

背景情况:

用低质量的谷物造大米对消费者和行业来说是一个重大问题.
准确和快速的检测方法对于确保大米的真实性和质量至关重要.
检测米造的传统方法可能耗时,需要专门的设备.

研究的目的:

开发和验证一个智能嗅觉可视化系统,用于快速识别超级印度Kainat 1121优质大米中的杂质.
评估系统在不同质量的新鲜收获和储存大米之间进行区分的能力.
评估化学测量算法在分析传感器数据以检测伪造的有效性.

主要方法:

使用色度传感器阵列制造智能嗅觉可视化系统.
在不同百分比 (10-75%) 的低质量储存大米中改优质大米.
使用头部空间固相微提取气体染色学-质谱学 (HS-SPME-GC/MS) 分析挥发性化合物.
应用主要组件分析 (PCA),层次组件分析 (HCA) 和k-近邻 (kNN) 算法进行数据分析.

主要成果:

嗅觉传感器阵列成功捕获了样品的特征性挥发性化合物概况.
根据大米样本的化学环境生成了不同的色度图.
在PCA和HCA模型中,基于不同程度的改,有效地对米样本进行了歧视.
kNN算法在识别伪造的米样本方面表现出高准确度.

结论:

开发的智能嗅觉可视化系统提供了一种快速,简单和低成本的方法来检测米造.
该系统有效地根据挥发性化合物分析来区分真正的和伪造的米饭.
这项技术有可能确保优质大米品种的质量和真实性.

关键词:

化学测量化学测量化学测量在HS-SPME-GC/MS分析中,嗅觉传感器阵列嗅觉传感器阵列米造是一种造方式.超级印度凯纳特 1121

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