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

被引：1

作者：

Liu Q. ^{[1
]}

Zhang T. ^{[1
]}

Zhou D. ^{[2
]}

Ding H. ^{[1
]}

Zhang B. ^{[3
]}

Chen M. ^{[3
]}

Ding C. ^{[1
]}

Pan L. ^{[3
]}

Tu K. ^{[3
]}

机构：

[1] Collaborative Innovation Center for Modern Grain Circulation and Safety, Jiangsu Key Laboratory of Quality Control and Further Processing of Cereals and Oil, College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing

[2] College of Light Industry and Food Engineering, Nanjing Forestry University, Nanjing

[3] College of Food Science and Technology, Nanjing Agricultural University, Nanjing

来源：

Shipin Kexue/Food Science | 2022年 / 43卷 / 12期

关键词：

Electronic nose; Gray mold; Non-destructive detection; Strawberry fruit;

D O I：

10.7506/spkx1002-6630-20210511-112

中图分类号：

学科分类号：

摘要：

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 3.95 μg/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(Rp2) 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. © 2022, China Food Publishing Company. All right reserved.

引用

页码：341 / 349

页数：8

共 26 条

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