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Position: Home > Articles > Nondestructive Detection of Apple Watercore Based on FT-NIR and Electronic Nose FOOD SCIENCE 2018 (16) 306-310

基于FT-NIR和电子鼻的苹果水心病无损检测

作  者：
袁鸿飞;胡馨木;杨军林;任亚梅;马惠玲;任小林
单  位：
西北农林科技大学食品科学与工程学院;西北农林科技大学园艺学院;西北农林科技大学生命科学学院
关键词：
苹果;水心病;近红外光谱;电子鼻;化学计量学
摘  要：
探讨傅里叶变换近红外光谱技术和电子鼻技术应用于苹果水心病检测的可行性。以277个"秦冠"水心病苹果和健康苹果为试材,分别采集每个样本在12 000~4 000 cm-1波数范围的近红外光谱和10个传感器的电子鼻信号,用不同预处理的近红外光谱方法提取主成分建立Fisher判别模型;同时电子鼻结合3种化学计量学的方法进行建模。结果表明,经一阶导数(9点平滑)预处理的近红外光谱,提取前20个主成分建立的Fisher判别模型效果最好,对未知样本的正确判别率达100%;电子鼻分别结合Fisher判别、多层感知器神经网络和径向基函数神经网络判别模型对未知样本的识别率为89.7%、89.5%和85.7%。故利用近红外光谱和电子鼻技术分别结合化学计量学的方法可快速、无损检测苹果的水心病。其中,近红外光谱技术结合Fisher判别对苹果水心病的识别率最高,是一种准确可靠的测定方法。
译  名：
Nondestructive Detection of Apple Watercore Based on FT-NIR and Electronic Nose
作  者：
YUAN Hongfei;HU Xinmu;YANG Junlin;REN Yamei;MA Huiling;REN Xiaolin;College of Food Science and Engineering, Northwest A&F University;College of Life Science, Northwest A&F University;College of Horticulture, Northwest A&F University;
单  位：
YUAN Hongfei%HU Xinmu%YANG Junlin%REN Yamei%MA Huiling%REN Xiaolin%College of Food Science and Engineering, Northwest A&F University%College of Life Science, Northwest A&F University%College of Horticulture, Northwest A&F University
关键词：
apple;;watercore;;NIR spectroscopy;;electronic nose;;chemometrics
摘  要：
This study aimed to explore the feasibility of applying near infrared spectroscopy(NIR) and electronic nose(E-nose) for detecting apple watercore. A total of 277 samples of "Qinguan" apples with watercore and healthy apples were tested. NIR spectra of each sample in the range of 12 000 to 4 000 cm-1 and E-nose signals from 10 sensors were collected, The Fisher discriminant model was established with the principal components extracted by different preprocessing methods. Meanwhile, the E-nose data were used for modeling by 3 different chemometric methods. The results indicated that the Fisher discriminant model developed based on the first twenty principal components from the NIR spectra subjected to the first derivative(9-point smoothing) pretreatment worked best with discrimination accuracy rates of 100% for unknown samples. The correct discrimination rates of the discriminant models developed by Fisher discriminant, multilayer perceptron(MLP) neural network and radial basis function(RBF) neural network for unknown samples were 89.7%, 89.5%and 85.7%, respectively. Thus, the combined application of NIR spectroscopy and E-nose with chemometrics can rapidly and nondestructively test watercore apples. NIR spectroscopy combined with Fisher discriminant analysis is an accurate and reliable method for detecting watercore apples with the highest correct recognition rate.

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