光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
SPECTROSCOPY AND SPECTRAL ANALYSIS
2010年
4期
911-914
,共4页
双色牛肝菌%傅里叶变换红外光谱%主成分分析%鉴别分类
雙色牛肝菌%傅裏葉變換紅外光譜%主成分分析%鑒彆分類
쌍색우간균%부리협변환홍외광보%주성분분석%감별분류
Boletus bicolor%Fourier transform infrared spectroscopy%Principal component analysis%Identification
同一种蕈菌子实体,由于外观形貌相似,凭传统外观形貌特征难以鉴别产地来源.应用傅里叶变换红外光谱(FTIR)法测定了云南省5个不同地区58个野生双色牛肝菌子实体样品的红外光谱.借助于红外光谱具有的指纹特性,利用SPSS 13.0统计软件对1 350~750 cm~(-1)范围光谱数据进行主成分分析(PCA),根据前三个主成分累积贡献率已达到88.87%以及主成分载荷分析,表明前三个主成分能够反映样品在该段光谱的主要信息.对前三个主成分作投影显示并进行比较,发现以主成分1和主成分2作二维线形投影,对不同产地的双色牛肝菌有较好的聚类和鉴别作用,所有样品被划分为5个区域,98.3%的样品被正确归类.研究结果提示,傅里叶变换红外光谱结合主成分分析方法可以快速、方便地对不同产地的同一种野生双色牛肝菌进行鉴别分类.
同一種蕈菌子實體,由于外觀形貌相似,憑傳統外觀形貌特徵難以鑒彆產地來源.應用傅裏葉變換紅外光譜(FTIR)法測定瞭雲南省5箇不同地區58箇野生雙色牛肝菌子實體樣品的紅外光譜.藉助于紅外光譜具有的指紋特性,利用SPSS 13.0統計軟件對1 350~750 cm~(-1)範圍光譜數據進行主成分分析(PCA),根據前三箇主成分纍積貢獻率已達到88.87%以及主成分載荷分析,錶明前三箇主成分能夠反映樣品在該段光譜的主要信息.對前三箇主成分作投影顯示併進行比較,髮現以主成分1和主成分2作二維線形投影,對不同產地的雙色牛肝菌有較好的聚類和鑒彆作用,所有樣品被劃分為5箇區域,98.3%的樣品被正確歸類.研究結果提示,傅裏葉變換紅外光譜結閤主成分分析方法可以快速、方便地對不同產地的同一種野生雙色牛肝菌進行鑒彆分類.
동일충심균자실체,유우외관형모상사,빙전통외관형모특정난이감별산지래원.응용부리협변환홍외광보(FTIR)법측정료운남성5개불동지구58개야생쌍색우간균자실체양품적홍외광보.차조우홍외광보구유적지문특성,이용SPSS 13.0통계연건대1 350~750 cm~(-1)범위광보수거진행주성분분석(PCA),근거전삼개주성분루적공헌솔이체도88.87%이급주성분재하분석,표명전삼개주성분능구반영양품재해단광보적주요신식.대전삼개주성분작투영현시병진행비교,발현이주성분1화주성분2작이유선형투영,대불동산지적쌍색우간균유교호적취류화감별작용,소유양품피화분위5개구역,98.3%적양품피정학귀류.연구결과제시,부리협변환홍외광보결합주성분분석방법가이쾌속、방편지대불동산지적동일충야생쌍색우간균진행감별분류.
It is hard to differentiate the same species of wild growing mushrooms from different areas by macromorphological fea-tures. In this paper, Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis was used to identify 58 samples of boletus bicolor from five different areas. Based on the fingerprint infrared spectrum of boletus bicolor sam-ples, principal component analysis was conducted on 58 boletusbicolor spectra in the range of 1 350-750 cm~(-1) using the statistical software SPSS 13. 0. According to the result, the accumulated contributing ratio of the first three principal components accounts for 88. 87%. They included almost all the information of samples. The two-dimensional projection plot using first and second principal component is a satisfactory clustering effect for the classification and discrimination of boletus bicolor. All boletus bicol-or samples were divided into five groups with a classification accuracy of 98. 3 %. The study demonstrated that wild growing bo-letus bicolor at species level from different areas can he identified by FTIR spectra combined with principal components analysis.