Abstract This study described the rapid determination of potential aflatoxigenic fungi contamination on peanut kernels based on headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) coupled to fluorescence spectroscopy. Data-level and feature-level fusion strategies were introduced to integrate HS-GC-IMS and fluorescence spectra, aiming at improving the performances of identification and prediction models. The application of feature-level data fusion using first 10 PCs coupled with orthogonal partial least squares discriminant analysis (OPLS-DA) offered more accurate characterization (96.7 %) for aflatoxigenic and non-aflatoxigenic fungal infection on peanut samples. Regression models were established for predicting colony counts of peanuts infected with aflatoxigenic fungi based on independent and fused signals by partial least squares regression (PLSR). Feature-level data fusion using first 10 PCs achieved the best performances in colony counts predictions for A. flavus (R2 = 0.950) and A. parasiticus (R2 = 0.971). These results demonstrated that the combination of HS-GC-IMS and fluorescence spectra might offer the feasibility for early detection of potential aflatoxigenic risk in peanuts.

中文翻译：

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HS-GC-IMS和荧光光谱数据融合快速测定贮藏期间花生仁潜在的黄曲霉毒素真菌污染

摘要 本研究描述了基于顶空-气相色谱-离子迁移谱 (HS-GC-IMS) 结合荧光光谱法快速测定花生仁中潜在的黄曲霉毒素真菌污染。引入数据级和特征级融合策略以整合HS-GC-IMS和荧光光谱，旨在提高识别和预测模型的性能。使用前 10 个 PC 结合正交偏最小二乘判别分析 (OPLS-DA) 应用特征级数据融合，为花生样品上的黄曲霉毒素和非黄曲霉毒素真菌感染提供了更准确的表征 (96.7%)。基于偏最小二乘回归（PLSR）的独立和融合信号，建立回归模型用于预测感染黄曲霉毒素真菌的花生的菌落数。使用前 10 台 PC 的特征级数据融合在 A. flavus (R2 = 0.950) 和 A. parasiticus (R2 = 0.971) 的菌落计数预测中取得了最佳性能。这些结果表明 HS-GC-IMS 和荧光光谱的结合可能为早期检测花生中潜在的黄曲霉毒素风险提供了可行性。