基于近红外光谱的腐皮镰孢菌侵染香蕉过程表征研究

香蕉在采后贮运过程极易受致病菌侵染腐烂，实现香蕉果实(以下简称蕉果)致病菌侵染程度的判别有利于潜在染病果实的及时检出和采取科学的防控措施.以腐皮镰孢菌(Fusarium solani)侵染的蕉果为对象，通过采集致病菌侵染不同阶段的蕉果的近红外光谱(930～1 650 nm)数据，基于全波段数据，对比不同光谱预处理方法对模型的影响后，分别建立了基于原始光谱的主成分-支持向量机判别模型(Principal component analysis-support vector machine classification, PCA-SVM)与偏最小二乘判别(Partial least squares discriminant analysis, PLSDA)模型，均取得了较好的判别效果，其验证集的判别准确率分别为83.33%和76.67%.利用竞争自适应重加权采样(Competitive adaptive reweighted sampling, CARS)算法进一步筛选出10个特征波长变量(1 117.5、1 140.7、1 146.4、1 255.5、1 284.0、1 312.5、1 403.2、1 493.2、1 498.8和1 621.5nm),分别应用SVM与PLSDA建立了基于特征波长的致病菌侵染程度判别模型，CARS-SVM模型判别效果优于CARS-PLSDA模型，训练集与验证集判别准确率分别为84.78%和78.57%.结果表明，近红外光谱技术可较好地用于判别香蕉病菌侵染过程与程度.

Characterization of infection process of Fusarium solani in banana by NIR spectroscopy

Banana is easily infected by spoilage fungus during transportation and post-harvesting storage. Identifying process of fungus infecting banana is conducive for timely detection of potentially infected fruit and taking scientific measures for prevention and control. In this paper, the near-infrared spectra (930-1 650 nm) of banana fruit infected by Fusarium solani at different infective stages were collected. Based on the full range data, principal component analysis-support vector machine (PCA-SVM) discriminant model and partial least squares discriminant (PLSDA) model based on the original spectrum were established respectively after comparing effects of different preprocessing methods on the models. Both models achieved good results with discriminant accuracies of 83.33% and 76.67% for validation sets, respectively. Furthermore, ten characteristic wavelengths (1 117.5,1 140.7,1 146.4,1 255.5,1 284.0,1 312.5,1 403.2,1 493.2,1 498.8,1 621.5 nm) were screened out using competitive adaptive reweighted sampling (CARS) algorithm, and SVM and PLSDA models were established based on these characteristic wavelengths, respectively. The performance of CARS-SVM model was better than that of CARS-PLSDA model, with identification accuracies of 84.78% and 78.57% for training and validation sets, respectively. Results indicated that NIR spectra could be used to identity process and degree of Fusarium solani infecting banana fruit.