Abstract：
This study developed a novel nanocomposite colorimetric sensor array (CSA) to distinguish between fresh and moldy maize. First, the headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) method was used to analyze volatile organic compounds (VOCs) in fresh and moldy maize samples. Then, principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to identify 2-methylbutyric acid and undecane as key VOCs associated with moldy maize. Furthermore, colorimetric sensitive dyes modified with different nanoparticles were employed to enhance the dye properties used in the nanocomposite CSA analysis of key VOCs. This study focused on synthesizing four types of nanoparticles: polystyrene acrylic (PSA), porous silica nanospheres (PSNs), zeolitic imidazolate framework-8 (ZIF-8), and ZIF-8 after etching. Additionally, three types of substrates, qualitative filter paper, polyvinylidene fluoride film, and thin-layer chromatography silica gel, were comparatively used to fabricate nanocomposite CSA combining with linear discriminant analysis (LDA) and K-nearest neighbor (KNN) models for real sample detection. All moldy maize samples were correctly identified and prepared to characterize the properties of the CSA. Through initial testing and nanoenhancement of the chosen dyes, four nanocomposite colorimetric sensitive dyes were confirmed. The accuracy rates for LDA and KNN models in this study reached 100%. This work shows great potential for grain quality control using CSA methods.
摘要：
这项研究开发了一种新型的纳米复合比色传感器阵列（CSA）来区分新鲜和发霉的玉米。首先,采用顶空固相微萃取气相色谱-质谱(HS-SPME-GC/MS)法对新鲜和发霉玉米样品中的挥发性有机物(VOCs)进行分析。然后，主成分分析和正交偏最小二乘判别分析（OPLS-DA）用于鉴定2-甲基丁酸和十一烷是与发霉玉米相关的关键VOCs。此外，使用不同纳米颗粒修饰的比色敏感染料来增强关键VOCs的纳米复合CSA分析中使用的染料性能。这项研究的重点是合成四种类型的纳米颗粒：聚苯乙烯丙烯酸（PSA），多孔二氧化硅纳米球（PSN），沸石咪唑酯骨架-8(ZIF-8)，和蚀刻后的ZIF-8。此外,三种类型的基材，定性滤纸，聚偏氟乙烯薄膜，和薄层色谱硅胶，比较用于结合线性判别分析（LDA）和K最近邻（KNN）模型制造纳米复合材料CSA，用于实际样品检测。正确鉴定并制备所有发霉的玉米样品以表征CSA的性质。通过对所选染料的初始测试和纳米增强，确认了四种纳米复合比色敏感染料。本研究中LDA和KNN模型的准确率达到100%。这项工作显示了使用CSA方法进行谷物质量控制的巨大潜力。
