多源异构光谱信息融合的食用牛肝菌鉴别方法

The Identification of Edible Boletus Based on Heterogeneous Multi-Spectral Information Fusion

牛肝菌营养丰富,味道鲜美,备受各国消费者青睐。因种间差异和环境因素的多层次影响,不同种类及产地牛肝菌品质参差不齐。目前,利益驱动导致商家在牛肝菌销售过程中以次充好、以假乱真的行为扰乱了食用菌市场,不仅给消费者带来健康风险,也制约了牛肝菌的国际化贸易。采用多源异构信息融合策略对牛肝菌种类与产地进行鉴别,以期为追溯食用菌来源以及正确评价其品质提供一种快速有效的解决方法。试验样品灰褐牛肝菌(Boletus griseus)、栗色牛肝菌(B. umbriniporus)、美味牛肝菌(B. edulis)、皱盖疣柄牛肝菌(Leccinum rugosicepes)和绒柄牛肝菌(B. tomentipes)五种牛肝菌科(Boletaceae)真菌子实体采于云南省保山市、昆明市、玉溪市与红河州。采用傅里叶变换红外光谱仪(FTIR)和紫外可见分光光度计(UV-Vis)采集样品信息。Kennard-Stone算法将样品原始数据分为校正集和验证集。校正集基于FTIR、UV-Vis、低级、中级与高级数据融合建立偏最小二乘判别分析(PLS-DA)模型,其中决定系数(R2cal)、预测能力Q2、校正均方根误差(RMSEE)和交叉验证均方差(RMSECV)用来评价模型鲁棒性。研究结果显示:(1)不同种类及产地牛肝菌FTIR和UV-Vis吸收峰的峰位置、峰形和峰数相似,而吸收强度存有差异,表明牛肝菌所含化学成分相似,但含量有一定差别;(2)PLS-DA模型二维散点图可以看出,中级融合比低级融合能更好的鉴别样品种类及产地;(3)各模型中,中级融合模型具有更大的Q2和最小RMSECV,模型鲁棒性最强;(4)验证集样本用来验证模型泛化能力,FTIR、UV-Vis、低级融合、中级融合及高级融合模型样品种类鉴别正确率分别为92. 86%,35. 71%,97. 62%,100%和95. 23%;产地鉴别正确率分别为71. 43%,61. 90%,61. 90%,97. 62%和76. 19%。表明多源异构信息融合在一定程度上优于独立模型,其中,中级数据融合种类鉴别正确率100%,产地鉴别正确率97. 62%,模型具有更优的鉴别效果和泛化能力。FTIR和UV-Vis结合中级数据融合策略能实现牛肝菌种类快速精确鉴别,产地快速有效鉴别,可作为食用菌来源追溯以及品质评价的一种新方法。

Boletus is rich in nutrition,which is favored b y consumers all over the world.Due to the differences of species and environmen tal factors,the quality of boletus of different species and origin vaires.At present,the shoddy,which undermines the sales of genuine boletus and the mushro om market,not only poses a health risks to consumers,but also restricts the in ternational trade of boletus.In this study,the data fusion stra tegy was used to identify the species and origin of boletus,in order to provide a rapid and effective solution for tracing the source of edible fungi and corr ectly evaluating their quality.The test samples Boletus griseus,B.umbrin iporus,B.edulis,Leccinum rugosicepes and B.tomentipes of five species of boletus fungi fruiting bodies collected from Baoshan,Kunming,Yuxi and Honghe Prefecture of Yunnan province.The chemical information was collected w ith Fourier transform infrared spectroscopy(FT-IR)and UV-Visible spectrophot om eter(UV-Vis).The Kennard-Stone algorithm was used to divide the raw data of samples into calibration sets and validation sets.The calibration set establi shed partial least squares discriminant analysis(PLS-DA)models based on FT-IR,UV-Vis,low-level,mid-level and high-level data fusion.The determin ation coefficients R2 cal,predictive ability Q2,root mean s quare error of estimation(RMSEE)and root mean square error of estimation(RMSE CV)were used to evaluate the robustness of the model.The results showed that:(1)The peak position,peak shape and number of peaks of FT-IR and UV-Vis a bsorption peaks of different species and origin were similar,and there were differences in absorption intensity.This showed that the chemical compositions of boletus were similar,but the content was different.(2)Two-dimensional sc atter plots of PLS-DA model.It can be seen that mid-level fusion is better than low-level fusion to identify sample species and origin.(3)In each model,the mid-level fusion model has a larger Q 2 and a minimum RMSECV,it sh owed that the model has the strongest robustness.(4)The test sets used to verify the model generalization ability,the correct rate of FT-IR,UV-Vis,low-level,mid-level and high-level data fusion model of samples kind identifi cation were 92.86%,35.71%,97.62%,100%,95.23%,respectively;the cor rect rate of o rigin identification were 71.43%,61.90%,61.90%,97.62%,76.19%.The results showed that the data fusion is better than the independent model to some extent.Among them,the correct rate of mid-level data fusion is 100%in species identification,and the accuracy in origin identification is 97.62%.Mid-level data fusion model has better identification effect and generalization ab ility.FT-IR and UV-Vis combined with mid-level data fusion strategy can achieve the rapid and accurate identification of the boletus species,the fast and effective identification of origin.It can be used as a new method for traceability and quality evaluation of edible fungi.