高光谱图像对灰葡萄孢霉、匍枝根霉、炭疽菌的生长拟合及区分

Growth Simulation and Discrimination of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum by Using Hyperspectral Refl ectance Imaging Technique

利用高光谱成像系统获取真菌在马铃薯葡萄糖琼脂板上培养期间的高光谱图像,采用400~1 000 nm全波段光谱响应值,并计算全波段的平均值、波峰7 16 nm处的光谱值和全波段内光谱值第1主成分的得分值,利用这3种参数计算方法构建真菌生长模拟模型。结果表明,3种方法建立的模型测试集的决定系数（R2）为0.722 3~0.991 4,均方误差和均方根误差分别为2.03×10-4~5.34×10-3、0.011~0.756。建立的生长模型与传统菌落计数法建立的生长模型之间的相关系数为0.887~0.957。另外,主成分分析和偏最小二乘法判别分析可以区分3种不同菌种。其中,偏最小二乘法判别分析模型对培养36 h的3种真菌及对照组的区分准确率为97.5%。高光谱图像技术能够用来对真菌生长进行模拟和真菌的种类区分。

This study used a hyperspectral imaging system（HIS） to measure the spectral response of fungi inoculated on potato dextrose agar plates. In this work, three methods for calculating HIS parameters, including the mean of whole spectral response values covering the range of 400–1 000 nm, the spectral response value of the wave peak at 716 nm, and the score of the fi rs t principal component in the whole spectral range of 400–1 000 nm using principal component analysis（PCA）, were used to simulate the growth of fungi. The results showed that the coefficients of determination（R2） of the simulation models for test datasets of three fungi, Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum, were 0.722 3–0.991 4, and the sum square error（SSE） and root mean square error（RMSE） were in a range of 2.03 × 10-4–5.34 × 10-3 and 0.011–0.756, respectively, based on the three methods. The correlation coeffi cients between HIS parameters and colony forming units of fungi were high ranging from 0.887 to 0.957. In addition, fungal species can be discriminated by PCA and partial least squares-discrimination analysis（PLS-DA） based on the spectral information in the full wavelength range. The classifi cation accuracy of the test dataset by PLS-DA models for fungi cultured for 36 h was 97.5% among Botrytis cinerea, Rhizopus stolonifer, Colletotrichum acutatum, and the control. This paper offers a new technique and useful information for further study into modeling the growth of fungi and detecting fruit spoilage caused by fungi based on HIS.