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基于电子鼻的花生有害霉菌种类识别及侵染程度定量检测

花匠小妙招
2024-11-09 23:44

摘要: 针对花生霉变传统分析方法操作繁琐、时效性差等不足，该研究拟利用电子鼻气体传感技术建立起花生有害霉菌污染的快速检测方法。辐射灭菌花生籽粒分别接种5种谷物中常见有害霉菌（黄曲霉3.17、黄曲霉3.395 0、寄生曲霉3.395、寄生曲霉3.012 4和赭曲霉3.648 6），并于26 ℃、80%相对湿度条件下储藏9 d至严重霉变。利用电子鼻气体传感器获取不同储藏时期（0、3、6、9 d）花生样品的整体挥发性气味信息。最后，结合多元统计分析方法对电子鼻传感器响应信号进行特征提取，建立了花生中有害霉菌污染程度的定性定量分析模型。结果显示，主成分分析法（principal component analysis，PCA）可成功区分不同霉菌侵染程度的花生样品，线性判别分析（linear discriminant analysis，LDA）模型对样品不同储藏天数判别的准确率均达到或接近100%。花生中菌落总数的偏最小二乘回归分析（partial least squares regression，PLSR）模型的预测决定系数和预测相对均方根误差分别达到0.814 5和0.244 0 lg（CFU/g）。结果表明，应用电子鼻技术快速检测储藏期间花生霉变状况具有一定可行性，可为利用气味信息实现粮食霉菌污染的在线监测提供理论参考。

关键词: 农作物 / 模型 / 特征提取 / 电子鼻 / 花生 / 有害霉菌 / 快速检测

Abstract: Abstract: Current methods for fungi contamination determination in peanuts are usually labor-intensive and time-consuming. In this paper, a new method for rapid detection of the contamination by harmful fungi species in peanut kernels based on electronic nose (E-nose) technology was investigated. Peanut samples were firstly irradiated by Co-60 gamma radiation with a dose of 15 kGy to kill all fungi on or within kernels. After irradiation, clean and sterile peanuts were placed in moist chambers and inoculated with 5 different spore suspensions of aspergillus spp., which were A. flavus 3.17, A. flavus 3.395 0, A. parasiticus 3.395, A. parasiticus 3.012 4 and A. ochraceus 3.648 6, the former 3 of which were aflatoxin (AFT) producer, and the latter one was ochratoxin (OT) producer. Spore suspensions were prepared by blending the 7-day old colonies cultured on potato dextrose agar (PDA) with ultrapure sterilized water. Initial spore concentration was about 5 log (CFU/mL), and then 10 μL spore suspension was dropped onto individual peanut sample by a pipette. All infected samples were stored at 26 ℃ and 80% relative humidity (RH) for 9 d until all peanut samples were covered with a mass of fungi. Subsequently, the E-nose (Fox 3000, Alpha Mos) was used for the collection of volatile odor information from peanut samples stored for 0, 3, 6 and 9 d, respectively. Finally, response signals of 12 E-nose sensors were extracted by multivariate statistical analysis method. Qualitative and quantitative models for the determination of harmful fungi contamination in peanuts were established. The principal component analysis (PCA) results showed that peanut samples with different storage days could be successfully discriminated for different fungal infection levels. Loading analysis of E-nose sensors indicated that the sensors of T70/2, LY2/LG, P10/1, T30/1 were found to be more sensitive than other sensors. These sensors might play an important role in the discrimination of samples, which provided a reference for the development of special-purpose sensor systems for peanut samples in future. The changes in volatile compounds of infected peanut samples could be mainly attributed to oxynitride, hydrocarbon and aromatic compounds. For the classification of peanut samples with different infection levels, the correct rate of 100%(or approaching) was obtained by linear discriminant analysis (LDA) models. The results also verified the possibility of discriminating peanuts infection by different fungi species. In addition, good correlation between E-nose signals and colony forming units in peanut samples was obtained by partial least squares regression (PLSR) analysis models. The coefficient of determination for the prediction set (Rp2) and the root mean square error of prediction (RMSEP) for the prediction models were 0.814 5 and 0.244 0 lg (CFU/g), respectively. Both LDA and PLSR methods were proven to be effective in the discrimination/quantification of fungi contamination in peanuts. The results indicate that E-nose technology can be used as a feasible and reliable method for the determination of peanut quality during the storage, which can provide the theoretical reference for rapid detection of mold contamination during grain storage using volatile odor information.

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