基于无监督可能模糊学习矢量量化的近红外光谱生菜品种鉴别研究

The Identification of Lettuce Varieties by Using Unsupervised Possibilistic Fuzzy Learning Vector Quantization and Near Infrared Spectroscopy

为解决模糊学习矢量量化（FLVQ）对噪声数据敏感问题,在无监督可能模糊聚类（UPFC）基础上提出一种无监督可能模糊学习矢量量化（UPFLVQ）算法。UPFLVQ用UPFC的隶属度和典型值来更新学习矢量量化网络的学习速率,计算类中心矢量。UPFLVQ属于无监督机器学习算法,适用于无学习样本情况下的样本分类。研究了UPFLVQ用于近红外光谱生菜品种鉴别的可行性。采用FieldSpec@3型便携式光谱分析仪获取波长范围为350~2 500nm的三种生菜样本的短波近红外光谱和长波近红外光谱,然后采用主成分分析（PCA）进行近红外光谱的维数压缩,取前三个主成分,累计可信度达97.50%,将2151维的近红外光谱压缩为三维数据。再运行模糊C-均值聚类（FCM）至迭代终止,并以FCM的类中心作为UPFLVQ的初始聚类中心,最后运行UPFLVQ得到隶属度和典型值以实现近红外光谱的生菜品种鉴别。同时,运行UPFC进行近红外光谱的生菜品种鉴别。实验结果表明：UPFLVQ和近红外光谱技术相结合的模型具有检测速度快,鉴别准确率高,对生菜不造成损坏等优点,可实现不同品种生菜的鉴别。UPFLVQ是将UPFC和FLVQ相结合的聚类算法,利用UPFLVQ建立近红外光谱的生菜品种鉴别模型时无需学习样本,适用于线性可分的数据聚类,为快速和无损地鉴别生菜品种提供了一种新的方法。

To solve the noisy sensitivity problem of fuzzy learning vector quantization（FLVQ）,unsupervised possibilistic fuzzy learning vector quantization（UPFLVQ）was proposed based on unsupervised possibilistic fuzzy clustering（UPFC）.UPFLVQ aimed to use fuzzy membership values and typicality values of UPFC to update the learning rate of learning vector quantization network and cluster centers.UPFLVQ is an unsupervised machine learning algorithm and it can be applied to classify without learning samples.UPFLVQ was used in the identification of lettuce varieties by near infrared spectroscopy（NIS）.Short wave and long wave near infrared spectra of three types of lettuces were collected by FieldSpec@3portable spectrometer in the wavelength range of 350~2 500 nm.When the near infrared spectra were compressed by principal component analysis（PCA）,the first three principal components explained 97.50% of the total variance in near infrared spectra.After fuzzy c-means（FCM）clustering was performed for its cluster centers as the initial cluster centers of UPFLVQ,UPFLVQ could classify lettuce varieties with the terminal fuzzy membership values and typicality values.The experimental results showed that UPFLVQ together with NIS provided an effective method of identification of lettuce varieties with advantages such as fast testing,high accuracy rate and non-destructive characteristics.UPFLVQ is a clustering algorithm by combining UPFC and FLVQ,and it need not prepare any learning samples for the identification of lettuce varieties by NIS.UPFLVQ is suitable for linear separable data clustering and it provides a novel method for fast and nondestructive identification of lettuce varieties.