摘要
新疆南疆是全国杏种植面积最大的地区,杏品种繁多。在杏果品市场中,不同品种杏的品质和价格差异较大,以次充好、品质参差不齐等现象严重制约了新疆杏果业的发展。为探究利用可见/近红外光谱快速检测杏品种的可行性,基于样品的可见/近红外光谱与化学计量学方法,对新疆南疆地区的6个品种杏进行定性判别分析,建立一种杏品种的无损鉴别方法。采用光谱仪采集6个品种杏(“黄杏”、“橄榄杏”、“小白杏”、“小米杏”、“库买提杏”、“小吊干杏”)在350~1 000 nm(VIS/NIR)和1 000~2 500 nm(NIR)两个范围内的光谱数据,去除原始光谱首端的噪声后,对保留的光谱使用Savitzky-Golay(SG)卷积平滑和多元散射校正(MSC)处理以消除光谱存在的干扰信息,采用主成分分析(PCA)、竞争性自适应重加权算法(CARS)、随机蛙跳(RF)、连续投影算法(SPA)对原始光谱降维,结合线性判别法(LDA)、朴素贝叶斯(NB)、 K最近邻(KNN)和支持向量机(SVM)对全光谱和降维后光谱建模对比。结果表明:基于全光谱数据建立的模型有较为准确的分类结果,在VIS/NIR范围,SVM模型分类正确率为95.7%, NIR范围内,LDA模型分类正确率为97.8%;采用PCA、 CARS-SPA、 RF-SPA与SPA方法对光谱数据降维后,模型仍能保持较高的分类精度,在VIS/NIR范围,PCA-LDA模型的分类正确率为97.8%, NIR范围内,RF-SPA-LDA模型的分类正确率高达95.7%。不同模型的结果表明,VIS/NIR范围内的模型分类效果优于NIR范围内模型;4种降维方法中,PCA方法降维效果最优;4种分类器中,LDA与SVM模型的正确率高于NB与KNN模型,更适用于杏品种的鉴别。结果表明,基于VIS/NIR范围光谱结合主成分分析和线性判别法可以实现杏品种的快速无损鉴别,为杏果实的在线分拣鉴定提供了新途径。
Southern Xinjiang is the region with the largest apricot planting area in the country,with a wide variety of apricots.In the apricot fruit market,the quality and price of different varieties of apricots ware vary greatly,and the phenomenon of shoddy and uneven quality has seriously restricted the development of the apricot industry in Xinjiang.To investigate the feasibility of rapid detection of apricot varieties using visible/near-infrared spectroscopy,a non-destructive identification method for apricot varieties is set up based on the qualitative discriminant analysis of six varieties of apricots in the southern Xinjiang region by visible/near-infrared spectroscopy of samples with chemometrics methods.The spectral data of six apricot varieties("Huang apricot","Ganlan apricot","Xiaobai apricot","Xiaomi apricot","Kumaiti"and"Xiaodiaogan apricot")were collected in the range of 350~1000 nm(VIS/NIR)and 1000~2500 nm(NIR)by the spectrometer.After deleting the obvious noise at the head of the original spectrum,the retained spectrum is processed using Savitzky-Golay(SG)convolution smoothing and multiple scatter correction(MSC)to eliminate the interference information in the spectrum.The original spectra are reduceddimension using principal component analysis(PCA),competitive adaptive re-weighted sampling(CARS),random frog(RF),successive projection algorithm(SPA),and linear discriminant analysis(LDA),naive Bayesian(NB),K-nearest neighbor(KNN),support vector machine(SVM)were combined with modeling the whole spectrum and the reduced spectrum.The results showed that the model based on full-spectral data has a comparatively accurate result,and the classification accuracy of the SVM model was 95.7%in the VIS/NIR range and 97.8%in the NIR range for the LDA model,which could achieve the discriminative analysis of different species of apricots.After the reduced-dimension of spectral data by PCA,CARS-SPA,RF-SPA and SPA,the model still maintained high classification accuracy,and the PCA-LDA model had 97.8%classification accuracy in the VIS/NIR range,and the RF-SPA-LDA model had 95.7%classification accuracy in the NIR range.The results of different models show that the classification effect of models in the VIS/NIR range was better than that in the NIR range;among the four dimensionality reduction methods,the PCA method has the best dimensionality reduction effect;among the four classifiers.The accuracy of LDA and SVM models is higher than that of NB and KNN models,which is more suitable for the identification of apricot varieties.The results show that the rapid and nondestructive identification of apricot varieties can be achieved based on the VIS/NIR range spectrum combined with principal component analysis and linear discriminant analysis method,which provides aninnovative way for online sorting and identifying apricot fruits.
作者
高峰
邢雅阁
罗华平
张远华
郭玲
GAO Feng;XING Ya-ge;LUO Hua-ping;ZHANG Yuan-hua;GUO Ling(College of Mechanical and Electrical Engineering,Tarim University,Alar 843300,China;Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region,Alar 843300,China;College of Horticulture and Forestry,Tarim University,Alar 843300,China;Key Laboratory of Biological Resources Conservation and Utilization of Tarim Basin,Xinjiang Production and Construction Corps,Alar 843300,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2024年第1期44-51,共8页
Spectroscopy and Spectral Analysis
基金
国家重点研发计划项目(2019YFD1000600)
国家自然科学基金项目(32160694,31760560,11964030)资助。
关键词
可见光光谱
近红外光谱
化学计量学
杏
品种鉴别
Visible spectrum
Near-infrared spectrum
Chemometrics
Apricot
Varieties discrimination
