食品科学 ›› 2022, Vol. 43 ›› Issue (12): 341-349.doi: 10.7506/spkx1002-6630-20210511-112

• 安全检测 • 上一篇    

基于电子鼻技术对草莓采后灰霉病的分析与早期诊断

刘强,张婷婷,周丹丹,丁海臻,张斌,陈敏,丁超,潘磊庆,屠康   

  1. (1.南京财经大学食品科学与工程学院,江苏省现代粮食流通与安全协同创新中心,江苏高校粮油质量安全控制及深加工重点实验室,江苏 南京 210023;2.南京林业大学轻工与食品学院,江苏 南京 210037;3.南京农业大学食品科学技术学院,江苏 南京 210095)
  • 发布日期:2022-07-01
  • 基金资助:
    江苏省高等学校自然科学研究面上项目(20KIB550005);南京财经大学青年学者支持计划项目; 江苏高校优势学科建设工程项目(PAPD)

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

LIU Qiang, ZHANG Tingting, ZHOU Dandan, DING Haizhen, ZHANG Bin, CHEN Min, DING Chao, PAN Leiqing, TU Kang   

  1. (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 210023, China; 2. College of Light Industry and Food Engineering, Nanjing Forestry University, Nanjing 210037, China;3. College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China)
  • Published:2022-07-01

摘要: 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0.85 μg/g快速上升至3.95 μg/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(Rp2)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。

关键词: 草莓果实;电子鼻技术;灰霉病;无损检测

Abstract: 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.

Key words: strawberry fruit; electronic nose; gray mold; non-destructive detection

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