番茄贮藏气氛3D荧光特征波长小波包选择及腐败预警方法

Feature Wavelength Selection of Three-Dimensional Fluorescence Data of Tomato Storage Room Gas Based on Wavelet Packet Decomposition for Early Warning of Its Spoilage

为实现基于贮藏气氛3D荧光信息的番茄贮藏过程中品质监控及腐败预警,本研究提出一种小波包分解的特征波长选择策略。首先,对原始数据进行去除瑞利散射及Savitzky-Golay平滑处理,消除噪声并剔除无荧光信息发射光谱;其次,对各激发波长下发射光谱进行小波包分解,用小波包分解最低频段的能量作为激发波长的表征量,并结合其在总能量中的占比选择特征激发波长;然后,对特征激发波长下发射光谱划分波段并进行小波包分解,以分解最低频段的能量作为波段表征量初选特征发射波长。考虑到番茄腐败前后荧光信息不同,故以特征激发波长总能量变化确定腐败基准;同时,对特征发射波长光谱值向量进行聚类分析也可得同样基准。通过腐败基准优选特征发射波长以减少波长个数,简化分析。最后,以腐败基准日的特征发射波长光谱表征能量值为基准信息向量,计算腐败基准日前各贮藏日特征发射波长光谱表征能量值到基准信息向量的马氏距离。结果表明,随着贮藏时间的延长,对应马氏距离变小,进而有效表征了番茄贮藏过程中的品质变化,并实现了腐败预警。结论:运用小波包分解根据特征激发波长下发射光谱的总能量变化及其聚类分析两种方法均可确定准确、可靠的腐败基准,所建立的马氏距离预警方法具有可行性,研究可为番茄品质监控提供新思路。

In order to realize quality monitoring and spoilage early warning of tomatoes during storage based on threedimensional(3D)fluorescence information of the storage room gas,a strategy for selecting feature wavelengths based on wavelet packet decomposition was proposed.Firstly,Rayleigh scattering removal and Savitzky-Golay(SG)smoothing were carried out on the original data to eliminate the noise interference and the background emission spectrum without fluorescence information.Secondly,the emission spectrum corresponding to each excitation wavelength was decomposed by the wavelet packet method and the feature excitation wavelengths were selected by the energy of the lowest frequency band combined with its proportion in the total energy.Thirdly,the emission spectrum corresponding to each selected feature excitation wavelength was divided into different bands,each band was decomposed by the wavelet packet method,and the feature emission wavelengths were selected by the lowest wavelet packet frequency band energy.Fourthly,considering the different fluorescence information of tomatoes before and after spoilage,the benchmark of tomato spoilage could be determined by the total energy change of the feature excitation wavelengths;at the same time,the same benchmark was obtained by cluster analysis of spectral vectors at the feature emission wavelengths.Fifthly,the feature emission wavelengths were optimized according to the spoilage benchmark to reduce the number of the feature wavelengths and simplify the analysis.Finally,the feature emission wavelengths of the spoilage benchmark date were taken as the benchmark vector to calculate the Mahalanobis distance between it and fluorescence feature vectors before the benchmark date.The results showed that with the advance of storage, the Mahalanobis distance decreased, effectively describing the quality change of tomatoes during storage and ultimately realizing spoilage early warning.