基于傅里叶变换红外光谱技术对木耳中镰刀菌的识别检测

Identification and Detection of Fusarium in Black Fungus Using Fourier Transform Infrared Spectroscopy

摘要:

该研究建立了一种基于傅里叶变换红外光谱的木耳中镰刀菌的定性定量检测方法：对经高压灭菌的木耳样品分别接种木耳中常见的五种镰刀菌（层出镰刀菌189975、串珠镰刀菌340687、尖孢镰刀菌120618、木贼镰刀菌124121、茄病镰刀菌121547），并于28 ℃，相对湿度80%的条件下进行储存培养，同时采集不同储存阶段的木耳样品在1 800~900 cm-1的红外光谱信息。分别运用主成分分析（PCA）、线性判别分析（LDA）以及偏最小二乘回归分析（PLSR）建立木耳中镰刀菌的快速识别检测模型。结果表明：LDA模型对受不同镰刀菌侵染的木耳样品的平均判别正确率达到87.50%，对受单一镰刀菌侵染的木耳样品霉变状态的平均判别正确率达到82.50%；PLSR模型对木耳样品中菌落总数的预测实现了较好的定量结果（R2 p=0.842 8，RMSEP=0.292 log CFU/g，RPD=2.81）；通过实际样品验证分析表明傅里叶变换红外光谱方法可以实现木耳中镰刀菌的快速识别检测。

Abstract:

A qualitative and quantitative detection method for Fusarium in black fungus based on Fourier transform infrared spectroscopy was established. Autoclaved black fungus samples were inoculated with five common Fusarium species (F. proliferatum 189975, F. moniliforme 340687, F. oxysporum 120618, F. equiseti 124121, F. solani 121547) at 28 ℃ and 80% relative humidity. The infrared spectra of the samples at different storage periods were obtained at 1 800~900 cm-1. The rapid identification and detection model of Fusarium in black fungus was established using principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares regression analysis (PLSR). The results showed that the LDA model correctly identified 87.50% of the black fungus samples infected with different Fusarium species and 82.50% of the black fungus samples infected with a single Fusarium species. The PLSR model achieved good quantitative results in predicting the total number of colonies in the samples (R2 p=0.842 8, RMSEP=0.292 log CFU/g, RPD=2.81). Validation analysis of the actual samples showed that Fourier transform infrared spectroscopy could achieve rapid identification and detection of Fusarium in black fungus.