霉变玉米气体传感器阵列快速检测

玉米易受霉菌感染发生霉变,影响食用安全。快速测定玉米霉变程度是控制其危害的前提。本研究拟利用基于气体传感器阵列的电子鼻技术,获取不同霉变程度玉米的特征气味信息,建立玉米霉变程度快速检测方法。辐照灭菌玉米分别接种5种谷物中常见有害霉菌,并于28 ℃和85%相对湿度环境中储藏15 d直至严重霉变。在第0、6、9、12和15 d,采集样品的气味信息的电子鼻特征响应信号,建立了玉米霉变程度的定性定量模型。结果表明,主成分分析(PCA)法可成功区分不同霉变程度的玉米样品。线性判别分析(LDA)对受单一霉菌侵染的不同霉变程度玉米样品的平均识别率达93.3%以上,全部样品达76.7%。样品中菌落总数的偏最小二乘回归分析(PLSR)模型的预测决定系数(R_p2)达0.777,预测均方根误差和相对分析偏差(RPD)分别为0.981 log CFU/g和2.12。结果表明,应用电子鼻技术快速检测玉米霉变具有一定可行性,下一步需要不断扩大样本量以提高方法的精度和可靠性。

Rapid Detection of Moldy Maize Based on Gas Sensor Array

Maize is susceptible to mildew infection,which affects food safety.Rapid determination of moldy maize is the prerequisite for controlling its hazards.This study intends to use the electronic nose(E-nose)technology based on gas sensor array to obtain the characteristic odor information of maize with different mildew degrees,and to establish a rapid detection method for mildew degree detection of maize.Irradiated sterilized maize was inoculated with 5 common harmful fungal strains of grains,and stored in 28℃and 85%relative humidity for 15 days to serious mildew.On day 0,6,9,12 and 15,E-nose characteristic response signal of the sample’s odor information was collected,and qualitative and quantitative models towards maize mildew degree were established.The results showed that principal component analysis(PCA)can successfully distinguish maize samples with different mildew degrees.Linear discriminant analysis(LDA)has an average recognition rate of 93.3%for maize samples with different mildew degree infestation by single fungal,and 76.7%for all samples.The prediction coefficient(R^2 p)of the partial least squares regression analysis(PLSR)model of the colony count in maize samples reached 0.777,the root mean square error of prediction set and the residual predictive deviation(RPD)was 0.981 log CFU/g and 2.12,respectively.The results showed that it is feasible to use the E-nose to quickly detect moldy maize.The next step is to expand the sample set size to improve the accuracy and reliability of the method.