This work presents a novel colorimetric sensor based on nanoscaled chemo dyes which can detect inert volatile organic compounds (VOCs) during the mildewing process of stored wheat. 1-Octen-3-ol and 3-octanone were selected as the marked compounds by gas chromatography mass spectrometry (GC-MS) analysis. In this work, poly(styrene-co-acrylic acid) microbeads were prepared by soap-free emulsion copolymerisation. Boron-dipyrromethene dyes with PSA were fabricated as a novel sensor to obtain digital data before and after exposure to VOCs, and the correlation coefficients (R²) between the digital data and the concentration of VOCs were 0.8078 and 0.8324, respectively. And root mean square errors (RMSEs) were 3.05 g L⁻¹ and 1.65 g L⁻¹, respectively. The data based on the identification of mouldy wheat samples were processed by principal component analysis (PCA) and linear discriminant analysis (LDA). The optimal performance obtained for the LDA model was 83.33% in the prediction set and 90% in the calibration set.

中文翻译：

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一种新型的基于纳米级化学染料的传感器，用于鉴定小麦贮藏过程中的挥发性有机化合物

这项工作提出了一种基于纳米级化学染料的新型比色传感器，该传感器可以在储存小麦的发霉过程中检测惰性挥发性有机化合物（VOC）。通过气相色谱质谱法（GC-MS）分析，选择1-Octen-3-ol和3-octanone作为标记的化合物。在这项工作中，通过无皂乳液共聚制备了聚（苯乙烯-共丙烯酸）微珠。制备了带有PSA的硼二吡咯亚甲基染料作为新型传感器，以获取暴露于VOCs之前和之后的数字数据，数字数据与VOCs浓度之间的相关系数（R ²）分别为0.8078和0.8324。均方根误差（RMSE）为3.05 g L ^-1和1.65 g L ^-1， 分别。通过主成分分析（PCA）和线性判别分析（LDA）处理基于发霉小麦样品鉴定的数据。LDA模型获得的最佳性能在预测集中为83.33％，在校准集中为90％。