为了能够快速、 无损地评价花生的质量, 确保储藏与食用安全, 开发了一种基于近红外光谱技术的花生产毒霉菌污染程度的定性定量分析方法。 首先对经过Co-60强辐射杀菌后的新鲜花生样品分别接种谷物中五种常见产毒霉菌(黄曲霉3.17、 黄曲霉3.3950、 寄生曲霉3.395、 寄生曲霉3.0124、 赭曲霉3.6486), 并于适宜条件下(26 ℃、 RH 80%)储藏9 d。 其次, 利用近红外光谱仪采集了不同时期花生样品在12 000～4 000 cm-1波段范围内的漫反射光谱, 运用主成分分析(PCA)、 判别分析(DA)和偏最小二乘回归(PLSR)建立了分析模型。 结果显示, 接种不同霉菌的样品随着储藏时间的延长均能得到有效区分, DA模型对储藏0, 3, 6与9 d花生的感染单一霉菌和多种霉菌的总体判别正确率分别达到100%和99.17%, PLSR模型对样品中的菌落总数的预测结果为: 有效决定系数(R2P)为0.874 1、 交互验证均方根误差(RMSECV)为0.276 Log CFU·g-1, 剩余预测偏差(RPD)为1.92。 结果表明, 近红外光谱技术可以作为一种可靠的分析方法对花生受霉菌侵染的状况进行快速分析, 从而确保贮藏期间花生的质量安全。

The quality of peanut products were rapidly and non-destructively assessed for storage and edibility safety. Near infrared spectroscopy (NIRS) was applied to develop qualitative and quantitative methods to determine the toxigenic fungal contamination levels in peanuts. Firstly, clean and fresh peanuts were sterilized with Co-60 and inoculated individually with five common toxigenic fungal species in grains, namely A. flavus 3.17, A. flavus 3.3950, A. parastiticus 3.395 0, A. parastiticus 3.012 4, and A. ochraceus 3.648 6. The samples were then incubated for 9 days under suitable conditions (26 ℃, RH 80%). Secondly, diffuse reflectance spectra were collected from peanut samples in the wavenumber range 12 000 to 4 000 cm-1 at different time during the inoculation. Analysis models were developed with principal component analysis (PCA), discriminant analysis (DA) and partial least squares analysis (PLSR), respectively. The results showed that the inoculated different fungal species of peanuts can be effectively distinguished during different storage periods. After peanuts samples were incubated for 0, 3, 6 and 9 days, the overall classification accuracy would be 100% and 99.17% for the treatment of individual fungal and total fungal species by using DA analysis models. PLSR models were also developed to predict the number of colonies of peanut samples with the coefficient of determination of the validation set (R2P) of 0.874 1, root mean square error of cross-validation (RMSECV) of 0.276 Log CFU·g-1 and residual predictive deviation (RPD) of 1.92. The results indicated that the NIR technology could be used as a reliable and rapid analytical method for determination of fungal contamination in peanuts, which could realize quality and safety control in the process of storing of peanuts.