摘要
水果新鲜度是反映水果是否新鲜、饱满的重要品质指标,为了探讨水果不同货架期的预测和判别方法,以酥梨为研究对象,利用高光谱成像技术,结合偏最小二乘判别法(PLS-DA)和偏最小二乘支持向量机(LS-SVM)算法对酥梨货架期进行判别。由光源、成像光谱仪、电控位移平台和计算机等构成的高光谱成像装置采集样品光谱,装置光源采用额定功率为200 W四个溴钨灯泡成梯形结构设计,光谱范围为 1 000 ~2 500 nm,分别率为10 nm。选取优质酥梨30个,货架期设置为1, 5和10 d,对30个样品完成3次光谱图像的采集,并矫正原始图像。实验结果表明:基于图像的酥梨货架期定性分析时,对不同货架期样品的原始图像进行PCA压缩,得到三种不同货架期的权重系数数据, PC1图像提取特征波长点为 1 280 , 1 390 , 1 800 , 1 880和2 300 nm,以特征图像的平均灰度值作为自变量且以货架期作为因变量建立定性判别模型,建模集68个,预测集22个。最小二乘支持向量机以RBF为核函数时,预测集中样品的误判个数为1,误判率为4.5%。而当采用lin核函数时,样品的误判个数为0,误判率为0。 PLS-DA定性分析时RMSEC为1.24, R c为0.93。 RMSEP为1, R p为0.96,预测集误判率为0。特征图像对酥梨货架期判别LS-SVM中的lin核函数所建立的模型结果较好,优于RBF核函数的建模效果,也优于PLS-DA判别模型。 ENVI软件提取实验样品光谱后建立LS-SVM和PLS-DA判别模型, LS-SVM利用RBF和lin核函数误判率分别为4.5%和0。与RBF核函数相比, lin核函数所建立的模型预测酥梨货架期的效果更好。 PLS-DA方法主成分因子数为12, RMSEC和RMSEP分别为0.48和0.78, R c和 R p分别为0.99和0.97,建模集与预测集的误判率均为零。 LS-SVM中的lin核函数所建立的模型结果较好,依然优于PLS所建立的检测模型。酥梨的光谱信息结合LS-SVM可以实现对酥梨货架期的检测和判别。基于图像建�
Fruit freshness is an important quality index reflecting whether the fruit is fresh and full.In order to explore the prediction and discrimination methods of different shelf life of fruits,this paper takes the pear as the research object,and uses hyperspectral imaging technology combined with partial least squares discrimination (PLS).DA and partial least squares support vector machine (LS-SVM) algorithm to distinguish the shelf life of pears.The spectrum of the sample is collected by a high-spectrum imaging device consisting of a light source,an imaging spectrometer,an electronically controlled displacement platform,and a computer.The device light source is designed with a ladder power of 200 W four bromine tungsten bulbs,and the spectral range is 1 000~2 500 nm.10 nm.The material was selected from 30 high-quality pears,and the shelf life was set to 1 day,5 days and 10 days.Three spectral images were acquired for 30 samples and the original image was corrected.The experimental results show that the image-based analysis of the shelf life of the pears is carried out by PCA compression of the original images of different shelf life samples,and the weight coefficient data of three different shelf periods are obtained.The wavelength points of PC1 image extraction are 1 280,1 390 and 1 800 nm.1 880 and 2 300 nm,with the average gray value of the feature image as the independent variable and the shelf life as the dependent variable to establish a qualitative discriminant model,68 modeling sets and 22 prediction sets.When the least squares support vector machine uses RBF as the kernel function,the number of misjudgments in the predicted concentrated samples is 1,and the false positive rate is 4.5%.When the lin kernel function is used,the number of misjudgments of the sample is 0,and the false positive rate is 0.The RMSEC for PLS-DA qualitative analysis was 1.24,which was 0.93.The RMSEP is 1,which is 0.96,and the prediction set false positive rate is zero.The characteristic image has better model for the lin kernel funct
作者
李雄
刘燕德
欧阳爱国
孙旭东
姜小刚
胡军
欧阳玉平
LI Xiong;LIU Yan-de;OUYANG Ai-guo;SUN Xu-dong;JIANG Xiao-gang;HU Jun;OUYANG Yu-ping(School of Mechatronics Engineering,East China Jiaotong University,Nanchang 330013,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2019年第8期2578-2583,共6页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(31760344)
南方山地果园智能化管理技术与装备协同创新中心(赣教高字[2014]60号)
江西省优势科技创新团队(20153BCB24002)资助
关键词
高光谱成像
货架期
特征图像
最小二乘判别
偏最小二乘支持向量机
Hyperspectral imaging technique
Shelf life
Feature image
Least squares discriminant
Partial least squares support vector machine
