Rapid Detection of Harmful Mold Infection in Rice by Near Infrared Spectroscopy
SHEN Fei, WEI Ying-qi, ZHANG Bin, SHAO Xiao-long, SONG Wei, YANG Hui-ping
College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics, Nanjing 210023, China
摘要： 稻谷是我国主要储粮品种。为快速、准确鉴定稻谷霉变状态，建立了一种基于近红外光谱的稻谷霉菌污染定性、定量分析方法。首先，将四种谷物中常见有害霉菌(黄曲霉3.17、黄曲霉3.3950、寄生曲霉3.3950、灰绿曲霉3.0100)分别接种在灭菌稻谷样品上。其次，将接种霉菌样品进行人工模拟储藏(28 ℃、RH 80%)，并采集不同储藏时间(0，2，4，7和10 d)稻谷的近红外漫反射光谱信号。最后，利用主成分分析(PCA)、判别分析(DA)和偏最小二乘回归(PLSR)方法建立稻谷霉菌污染的快速分析模型。结果显示，近红外光谱可有效区分感染不同霉菌的稻谷样品，平均判别正确率达87.5%。稻谷霉变随储藏时间逐渐加深，近红外光谱对感染单一霉菌稻谷样品霉变状态的判别正确率达92.5%，多种霉菌的判别正确率达87.5%。稻谷中的菌落总数的PLSR模型定量结果为：有效决定系数(R2P)为0.882 3、验证均方根误差(RMSEP)为0.339 Lg CFU·g-1，相对标准偏差(RPD)为2.93。结果表明，近红外光谱法可以作为一种快速、无损的分析方法来判定稻谷霉菌侵染状况，确保稻谷储运安全。
关键词：近红外光谱；稻谷；霉菌侵染；快速检测
Abstract：China has huge rice reserves. In order to develop a rapid and accurate method for harmful mold infection detection in rice, near infrared (NIR) spectroscopy was applied for qualitative and quantitative analysis of the process of rice mildew in this study. Sterilized rice samples were firstly inoculated with four mold Aspergillus spp. species (A. flavus 3.17, A. flavus 3.3950, A. parastiticus 3.3950, A. glaucus 3.0100), respectively. Then the rice samples were stored under appropriate conditions (28 ℃, 80% RH) for mould growth. NIR spectra of samples were collected during the storage on different days (0, 2, 4, 7 and 10 d). Analysis models of mold infection in rice were developed by principal component analysis (PCA), discriminant analysis (DA) and partial least squares regression (PLSR), respectively. The results indicated that rice samples infected by different mold species could be effectively distinguished by NIR spectroscopy, and the average classification accuracy was 87.5%. The degree of mildew intensified during storage. The average correct classification accuracy of storage time (mildew degree) was found to be 92.5% for samples infected by one mold species, and 87.5% for samples infected by the four mold species. The PLSR prediction results of mould cell concentration in samples was: R2P=0.882 3, root mean square error of prediction (RMSEP)=0.339 Log (CFU·g-1) and residual predictive deviation (RPD)=2.93. Overall, the results demonstrated that the NIRS can be used as a rapid and non-destructive method for harmful mold infection detection in rice, ensuring the safety of grain storage and transportation.
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