不同储藏年限绒柄牛肝菌紫外&红外光谱数据融合鉴别研究

Research on Identification of Boletus tomentipes with Different Storage Period by UV and FT-IR Combined with Data Fusion

野生食用菌干品长时间储藏会引起微生物增殖、物理及化学变化,影响其商品品质,为保证其质量安全,亟需建立快速有效的方法,鉴别不同储藏年限野生食用菌。本研究采集5个储藏年限,77个绒柄牛肝菌子实体的紫外(UV)与傅里叶变换红外(FT-IR)光谱,采用卷积平滑(SG)、二阶导数(2-D)、标准正态变量(SNV)等方法对光谱进行预处理,结合偏最小二乘判别分析(PLS-DA),建立UV、FT-IR、低级和中级数据融合模型。结果显示:UV与FT-IR光谱最佳预处理分别为SG+2-D和SG+2-D+SNV;UV、FT-IR、低级和中级数据融合模型,总样品分类错误数分别为10、6、4、3,且中级数据融合的R2cal平均值最接近于1、RMSECV平均值最小,表明中级数据融合分类效果,优于UV、FT-IR和低级数据融合。采用UV与FT-IR中级数据融合策略结合PLS-DA,能够准确鉴别不同储藏年限牛肝菌样品,为野生食用菌品质评价提供一种新思路。

Long term storage of wild edible mushrooms would cause microbial proliferation and physico-chemical changes, affecting the quality. In order to ensure the security and quality, it was essential to establish a quick and efficient method to identify wild edible mushrooms with different storage periods. Ultraviolet （UV） and fourier transform infrared （FT-IR） spectra of 77 fruit bodies of B. tomentipes （5 years of storage） were preprocessed by using Savitzky-Golay （SG） smoothing, second derivative （2-D） and standard normal variate （SNV）, and UV, FT-IR, low-level and mid-level data fusion models were established with partial least squares discriminant analysis （PLS-DA）. The results showed that the optimal pretreatment of UV and FT-IR spectra were SG＋2-D and SG＋2-D＋SNV, respectively, and the classified individual errors were 10, 6, 4 and 3 in UV, FT-IR, low-level and mid-level data fusion models.Theaverage of R2cal in mid-level data fusion model was closest to 1, and the average of RMSECV was minimum, which indicated that the effects of mid-level data fusion model were better than those of other three models. The UV and FT-IR mid-level data fusion strategy combined with PLS-DA could accurately identify the B. tomentipes with different storage periods, which provided a novel reference for quality evaluation of wild edible mushrooms..