基于电子鼻技术对草莓采后灰霉病的分析与早期诊断

Quantitative Analysis and Early Detection of Postharvest Gray Mold in Strawberry Fruit Using Electronic Nose

构建基于气味信息的草莓灰霉病无损检测的方法,对草莓果实灰霉病过程进行动态分析。以健康草莓果实作为对照,每隔24 h采用便携式电子鼻获取样品气味信息,并结合顶空固相微萃取-气相色谱-质谱联用技术对样本挥发性组分进行定量检测,最后采用偏最小二乘回归构建基于电子鼻技术的草莓果实菌落总数预测模型。结果表明:草莓果实接种灰霉病后120 h内,酯类、醛类和醇类含量变化明显,以乙醇为代表的醇类含量(以湿质量计算)从初始0.85μg/g快速上升至3.95μg/g;主成分分析表明基于电子鼻气味传感阵列对应的稳定值与微生物含量密切相关,结合偏最小二乘法回归的草莓果实微生物含量预测的相对最佳模型对应的决定系数(R_(p)^(2))为0.815,相对分析误差为2.270,基于电子鼻传感器稳定信号的无损预测可实现早期病害果实92.9%的准确区分。研究结果可以为实现草莓采后病害无损监控与早期诊断提供参考。

A non-destructive method for the detection of gray mold in strawberry fruit based on odor information was proposed in order to monitor the decay process of strawberry fruit.A portable electoral nose(E-nose)was utilized to collect the odor information of samples every 24 h.Healthy strawberry fruit were taken as the control group.The volatile compounds of samples were then quantitatively detected by headspace solid phase micro-extraction(HS-SPME)combined with gas chromatography-mass spectrometry(GC-MS).Finally,a regression model for predicting the microbial load in artificially infected strawberry fruit was established based on E-nose datasets by partial least squares regression(PLSR).The results showed that after 120 h storage,the contents of esters,aldehydes and alcohols in infected strawberry fruit were significantly changed,and the content of alcohol(mainly ethanol)increased rapidly from 0.85 to 3.95μg/g.Principal component analysis(PCA)showed a high correlation between the microbial load and the stable response of E-nose sensors.The optimal PLSR model for the microbial load showed a coefficient of determination for prediction(R_(p)^(2))of 0.815,and a relative percent deviation(RPD)of 2.270.Furthermore,the non-destructive detection method based on stable signals of E-nose sensors could identify early diseased strawberry fruit with an accuracy of 92.9%.These results can provide a reference for non-destructive monitoring and early detection of strawberry postharvest diseases.