Yao Siyu, Miyagusuku-Cruzado Gonzalo, West Megan, Nwosu Victor, Dowd Eric, Fountain Jake, Giusti M Monica, Rodriguez-Saona Luis E
Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, Nanjing 210009, China.
Department of Food Science and Technology, The Ohio State University, Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH 43210, USA.
Foods. 2024 Jan 2;13(1):157. doi: 10.3390/foods13010157.
A nondestructive and rapid classification approach was developed for identifying aflatoxin-contaminated single peanut kernels using field-portable vibrational spectroscopy instruments (FT-IR and Raman). Single peanut kernels were either spiked with an aflatoxin solution (30 ppb-400 ppb) or hexane (control), and their spectra were collected via Raman and FT-IR. An uHPLC-MS/MS approach was used to verify the spiking accuracy via determining actual aflatoxin content on the surface of randomly selected peanut samples. Supervised classification using soft independent modeling of class analogies (SIMCA) showed better discrimination between aflatoxin-contaminated (30 ppb-400 ppb) and control peanuts with FT-IR compared with Raman, predicting the external validation samples with 100% accuracy. The accuracy, sensitivity, and specificity of SIMCA models generated with the portable FT-IR device outperformed the methods in other destructive studies reported in the literature, using a variety of vibrational spectroscopy benchtop systems. The discriminating power analysis showed that the bands corresponded to the C=C stretching vibrations of the ring structures of aflatoxins were most significant in explaining the variance in the model, which were also reported for -infected brown rice samples. Field-deployable vibrational spectroscopy devices can enable in identification of aflatoxin-contaminated peanuts to assure regulatory compliance as well as cost savings in the production of peanut products.
开发了一种无损且快速的分类方法，用于使用现场便携式振动光谱仪器（傅里叶变换红外光谱仪和拉曼光谱仪）识别受黄曲霉毒素污染的单个花生仁。单个花生仁要么用黄曲霉毒素溶液（30 ppb - 400 ppb）加标，要么用己烷（对照）加标，然后通过拉曼光谱和傅里叶变换红外光谱收集它们的光谱。采用超高效液相色谱 - 串联质谱法通过测定随机选择的花生样品表面的实际黄曲霉毒素含量来验证加标准确性。使用类模拟软独立建模（SIMCA）的监督分类表明，与拉曼光谱相比，傅里叶变换红外光谱在区分受黄曲霉毒素污染（30 ppb - 400 ppb）的花生和对照花生方面表现更好，对外部验证样品的预测准确率为100%。使用便携式傅里叶变换红外光谱设备生成的SIMCA模型的准确性、灵敏度和特异性优于文献中报道的其他使用各种振动光谱台式系统的破坏性研究方法。判别力分析表明，对应于黄曲霉毒素环结构中C = C伸缩振动的谱带在解释模型方差方面最为显著，这在受感染的糙米样品中也有报道。现场可部署的振动光谱设备能够实现对受黄曲霉毒素污染的花生的识别，以确保符合监管要求，并在花生产品生产中节省成本。
