基于电子鼻气味信息和多元统计分析的枸杞子产地溯源研究

doi:10.3969/j.issn.1004-1524.2018.09.23

浙江农业学报 ›› 2018, Vol. 30 ›› Issue (9): 1604-1611.DOI: 10.3969/j.issn.1004-1524.2018.09.23

基于电子鼻气味信息和多元统计分析的枸杞子产地溯源研究

田晓静¹, 龙鸣¹, 王俊^{2, *}, 马忠仁¹, 韦真博², 陈士恩¹, 高丹丹¹, 丁波¹

1.西北民族大学生命科学与工程学院,甘肃兰州 730124;
2.浙江大学生物系统工程与食品科学学院,浙江杭州 310058

收稿日期:2018-01-10 出版日期:2018-09-25 发布日期:2018-10-15
作者简介:田晓静(1982—),女,河南许昌人,博士,副教授,从事食品、农产品品质检测研究。E-mail: smile_tian@yeah.net

Identification of geographical origin for wolfberry by an electronic nose in combination with multivariate analysis

TIAN Xiaojing¹, LONG Ming¹, WANG Jun^{2, *}, MA Zhongren¹, WEI Zhenbo², CHEN Shi’en¹, GAO Dandan¹, DING Bo¹

1. College of Life Science and Engineering, Northwest Minzu University, Lanzhou 730124, China;
2. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China

Received:2018-01-10 Online:2018-09-25 Published:2018-10-15
Contact: 王俊,E-mail: jwang@zju.edu.cn
Supported by:
国家自然科学基金(31560477); 科技部援助项目(KY201501005); 甘肃省科技计划(1504WKCA094,17YF1WA166)

摘要/Abstract

摘要： 为实现不同产地枸杞子的快速、客观判别,在优化电子鼻检测条件的基础上,利用电子鼻信号定性判别3种不同产地枸杞子间的差异,并定量预测其产地。方差分析发现:顶空体积对电子鼻10个传感器的响应影响极显著;样品质量对S7响应影响显著,对其余9个传感器响应影响极显著;除对S2、S7、S9和S10影响不显著外,顶空生成时间对其余6个传感器响应影响极显著。方差分析结合判别分析确定电子鼻检测枸杞子的较佳条件为:载气流速300 mL·min^-1,样品质量20 g,顶空体积500 mL,顶空生成时间30 min。在此条件下检测3种不同产地(甘肃瓜州、青海柴达木和宁夏中宁)枸杞子,发现主成分分析和典则判别分析均能将3种不同产地枸杞子区分开,且典则判别分析结果图中数据点的集聚性更好;采用BP神经网络建立产地的预测模型能有效预测枸杞子的产地(正确识别率为96%)。电子鼻在枸杞子产地判别时具有可行性,为枸杞产地追溯提供理论依据。

关键词: 枸杞子, 电子鼻, 多元统计分析, 产地溯源

Abstract: The aroma profiles of wolfberry were studied by the electronic nose (E-nose) for aim of subjective and fast discrimination of the geographical origin of wolfberry. The effects of sample weight, headspace-generated time, and headspace volume on sensor responses were studied by single-factor experiments. Results of one-way analysis of variance found that the responses of E-nose sensors were significantly affected by these factors. The sample weight showed significant effect on S7 while very significant effect on the other 9 sensors. The effects of headspace-generated time were very significant on the sensors except for S2, S7, S9 and S10. With the help of canonical discriminant analysis (CDA), the optimum experimental parameters were acquired: flow rate of 300 mL·min^-1, 20 g of sample sealed in 500 mL beaker for 30 min headspace-generated time. With the optimum experimental parameters, samples produced in three different regions (Guazhou Gansu, Chaidamu Qinghai, Zhongning Ningxia) were detected. With PCA and CDA, the wolfberries were grouped according to the geographical origin, with three samples from Zhongning overlapped with each other. BPNN were employed to build the predictive model for the geographical origin of the wolfberry fruit samples, with 96% samples correctly predicted. The E-nose was proved to be useful for the identification of geographical origin of the wolfberry samples for its efficiency and high accuracy, which laid solid foundation for the traceability of wolfberry geographical origin.

Key words: wolfberry, electronic nose, multivariate data analysis, geographical origin

中图分类号:

TS251

田晓静, 龙鸣, 王俊, 马忠仁, 韦真博, 陈士恩, 高丹丹, 丁波. 基于电子鼻气味信息和多元统计分析的枸杞子产地溯源研究[J]. 浙江农业学报, 2018, 30(9): 1604-1611.

TIAN Xiaojing, LONG Ming, WANG Jun, MA Zhongren, WEI Zhenbo, CHEN Shi en, GAO Dandan, DING Bo. Identification of geographical origin for wolfberry by an electronic nose in combination with multivariate analysis[J]. , 2018, 30(9): 1604-1611.

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基于电子鼻气味信息和多元统计分析的枸杞子产地溯源研究

Identification of geographical origin for wolfberry by an electronic nose in combination with multivariate analysis

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