苹果霉心病不同光谱检测方式对比研究

Comparison of Different Detection Modes of Visible/Near-Infrared Spectroscopy for Detecting Moldy Apple Core

苹果霉心病是一种对消费者健康产生威胁的水果内部病害,在苹果进入消费市场前实现霉心病的快速无损检测有助于提升苹果品质和保障消费者安全。近年来,可见/近红外光谱技术凭借其快速无损、操作简单、成本低和批量在线检测等优势,被广泛用于水果品质无损检测研究中,而根据实际检测需求进行光谱检测方式选择是开展水果光谱无损检测的关键步骤,为探寻苹果霉心病光谱无损检测中最佳的检测方式,首先基于实验室自主搭建的苹果漫反射、漫透射和透射光谱采集系统分别获取了243个苹果样本的三种光谱数据,然后采用一阶导数(FD)、归一化(NOR)、 S-G平滑(S-G)、多元散射校正(MSC)和标准正态变量变化(SNV)五种方法对光谱数据进行预处理,其次将局部线性嵌入(LLE)、多尺度分析(MDS)、分布邻域嵌入(SNE)和t分布邻域嵌入(t-SNE)四种流形学习方法用于光谱数据降维,并与传统的主成分分析(PCA)降维方法比较,最后基于降维后数据建立了最小二乘支持向量机(LS-SVM)算法的苹果霉心病分类模型。结果表明:在三种不同的光谱检测方式中,透射检测方式优于漫透射检测方式,漫透射检测优于漫反射检测方式。在五种不同的光谱降维方法中,基于SNE的降维方法在三种不同的光谱数据中都优于其他降维方法,最终以透射检测方式结合SNE降维方法构建了最优的苹果霉心病判别模型,其校正集和测试集的准确率分别为99.52%和97.14%。该研究对苹果霉心病光谱无损检测研究中的实验平台搭建和检测装备研发提供了指导。

Moldy apple core is a kind of internal fruit disease that threatens consumers health.Rapid,nondestructive detection of moldy apple core is helpful to improve the quality of the apple and ensure the safety of consumers before entering the consumer market.In recent years,Vis/NIR spectroscopy has been widely used in the nondestructive detection of fruit quality by its advantages of rapid,nondestructive,simple operation,low cost and batch online detection.The selection of spectral detection mode according to the actual detection requirement is an important prerequisite for developing fruit spectral nondestructive detection.Three kinds of spectral data from 243 samples were obtained based on diffuse reflection,diffuse transmission and transmission spectrum acquisition systems built by the laboratory.Five spectral pretreatment methods,including S-G smoothing(S-G),multiplicative scatter correction(MSC),standard normal variation(SNV),first derivative(FD),and normalize(NOR),were used for spectral data preprocessing.Four manifold learning algorithms,including locally linear embedding(LLE),multidimensional scaling(MDS),distributed neighbor embedding(SNE)and t-distributed neighbor embedding(t-SNE),were systematically used for spectral data dimensionality reduction.These were compared with the traditional principal component analysis(PCA)dimensionality reduction method.Finally,the least squares-support vector machine(LS-SVM)classification model was established based on the dimensionality-reduced data.The results show that the transmission detection mode is better than the diffuse transmission detection mode,and the diffuse transmission detection mode is better than the diffuse reflection detection mode in three different detection modes.The distributed neighborhood embedding algorithm is better than other dimension reduction algorithms.The model constructed by transmission detection mode combined with the distributed neighborhood embedding dimension reduction algorithm performs best.The accuracy of the calibration set and test set is 99.52%and 97.14%,respectively.The research provide a reference for establishing a spectral nondestructive detection platform and developing detection equipment for moldy apple core.