衰减全反射-傅里叶变换红外光谱法对花生仁霉变的分析  

Analysis of Moldy Peanut Kernel by Attenuated Total Reflectance-Fourier Transform Infrared Infrared Spectroscopy

作　　者：蒋雪松[1] 刘鹏[1] 沈飞[2] 周宏平[1] 陈青[1] JIANG Xuesong;LIU Peng;SHEN Fei;ZHOU Hongping;CHEN Qing(College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China;College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210046, China)

机构地区：[1]南京林业大学机械电子工程学院,江苏南京210037 [2]南京财经大学食品科学与工程学院,江苏南京210046

出　　处：《食品科学》2017年第12期315-320,共6页Food Science

基　　金：南京林业大学青年科技创新基金项目(CX2015010);江苏省高校优秀中青年教师和校长境外研修资助项目(苏教办师﹝2015﹞7号);"十二五"国家科技支撑计划项目(2014BAD08B04);江苏高校优势学科建设工程资助项目(PAPD)

摘　　要：为快速检测贮藏花生的质量安全,对灭菌后的新鲜花生仁样品分别接种5种常见的有害霉菌,并于26℃、相对湿度80%条件下贮藏9 d。利用衰减全反射-傅里叶变换红外光谱(attenuated total reflectance-Fourier transform infrared spectroscopy,ATR-FTIR)采集不同贮藏阶段花生样品在4 000~600 cm^(-1)的光谱信息,通过权重分析阐述花生中侵染霉菌后光谱特征的变化,并结合偏最小二乘回归(partial least squares regression,PLSR)分析建立样品有害霉菌污染的定量分析模型。结果表明,不同贮藏阶段样品的峰谱出现明显波动,PLSR模型对单一菌株与多种菌株样品菌落总数的预测精度较高,其中对赭曲霉3.6486处理组样品预测模型相对偏优,有效决定系数(R_p^2)为0.915 7、交互验证均方根误差(root mean-square error of cross-validation,RMSECV)为0.208 0(lg(CFU/g))、剩余预测偏差(residual predictive deviation,RPD)为2.52;对多种菌株预测结果R_p^2、RMSECV、RPD分别为0.780 3、0.358 0(lg(CFU/g))与1.76。应用ATR-FTIR技术对花生受霉菌侵染的状况进行快速分析具有可行性。Peanut products are susceptible to changes in temperature and relative humidity(RH)during storage.Peanuts areeasily infected by hazardous fungal species,producing a variety of potent mycotoxins.This study aimed to develop a methodfor the rapid detection of moldy peanuts.Firstly,clean and fresh peanut kernels were sterilized and inoculated individuallywith five common hazardous fungal species.Then,the samples were stored at26℃and80%RH for9days.During thisperiod,spectral information of the peanut samples in the wave number range of4000to600cm-1were collected usingattenuated total reflectance-Fourier transform infrared spectroscopy(ATR-FTIR).The spectral changes of peanut samplesinfected with different fungal species were analyzed by loading analysis.A quantitative model to predict contamination levelsof hazardous fungi in peanut samples was developed by partial least squares regression(PLSR).The results showed that thespectral alterations for the samples were clearly fluctuated during different storage periods.The PLSR model could predictthe total number of colonies of single and multiple strains in fungus-infected peanut samples with good accuracy.Especially,the model provided better prediction of Aspergillus ochraceus3.6486infection with a coefficient of determination for theprediction set(Rp2)of0.9157,a root mean-square error of cross-validation(RMSECV)of0.2080(lg(CFU/g))and a residualpredictive deviation(RPD)of2.52.The Rp2,RMSECV and RPD values of the prediction model for total fungal species were0.7803,0.3580(lg(CFU/g))and1.76,respectively.These findings demonstrated that ATR-FTIR could be used as a reliableanalytical method for rapid determination of fungal contamination levels in peanuts during storage.

关 键 词：衰减全反射-傅里叶变换红外光谱 花生仁 有害霉菌 快速检测 

分 类 号：TS255.7[轻工技术与工程—农产品加工及贮藏工程] O657.33[轻工技术与工程—食品科学与工程]