CAJ | 학술논문

为探讨快速、实时藻类检测方法，实验通过荧光光谱成像技术结合模式识别方法对不同藻类进行鉴别研究。发现藻类样本存在着显著的荧光特性，通过采集40个藻类样品的荧光光谱图像，对图像进行去噪、二值化处理，确定有效像素后，根据光谱立方体绘制每个样本的光谱曲线，将所得400～720 nm区段范围内的光谱数据作鉴别分析，再利用系统聚类分析及主成分分析两种不同的模式识别法对光谱数据进行处理。系统聚类分析结果表明：采用欧氏距离法及平均加权法计算样本间的聚类距离，在距离L＝2．452以上水平处可将样本正确分类，准确率为100％；主成分分析结果表明：通过对原始光谱数据进行一阶微分、二阶微分、多元散射校正、变量标准化等预处理后，再对数据进行主成分分析，其中二阶微分预处理后鉴别效果最佳，八种藻类样品在主成分特征空间中独立分布。因此，利用荧光光谱成像技术结合聚类分析法及主成分分析法对藻类进行鉴别是可行的，操作简便、快速、无损。
위탐토쾌속、실시조류검측방법，실험통과형광광보성상기술결합모식식별방법대불동조류진행감별연구。발현조류양본존재착현저적형광특성，통과채집40개조류양품적형광광보도상，대도상진행거조、이치화처리，학정유효상소후，근거광보립방체회제매개양본적광보곡선，장소득400～720 nm구단범위내적광보수거작감별분석，재이용계통취류분석급주성분분석량충불동적모식식별법대광보수거진행처리。계통취류분석결과표명：채용구씨거리법급평균가권법계산양본간적취류거리，재거리L＝2．452이상수평처가장양본정학분류，준학솔위100％；주성분분석결과표명：통과대원시광보수거진행일계미분、이계미분、다원산사교정、변량표준화등예처리후，재대수거진행주성분분석，기중이계미분예처리후감별효과최가，팔충조류양품재주성분특정공간중독립분포。인차，이용형광광보성상기술결합취류분석법급주성분분석법대조류진행감별시가행적，조작간편、쾌속、무손。
In order to explore rapid real-time algae detection methods,in the present study experiments were carried out to use fluorescence spectral imaging technology combined with a pattern recognition method for identification research of different types of algae.The fluorescence effect of algae samples is obvious during the detection.The fluorescence spectral imaging system was adopted to collect spectral images of 40 algal samples.Through image denoising,binarization processing and making sure the effective pixels,the spectral curves of each sample were drawn according to the spectral cube .The spectra in the 400~720 nm wavelength range were obtained.Then,two pattern recognition methods,i.e.hierarchical cluster analysis and principal compo-nent analysis,were used to process the spectral data.The hierarchical cluster analysis results showed that the Euclidean distance method and average weighted method were used to calculate the cluster distance between samples,and the samples could be cor-rectly classified at a level of the distance L= 2. 452 or above,with an accuracy of 100%.The principal component analysis re-sults showed that first-order derivative,second-order derivative,multiplicative scatter correction,standard normal variate and other pretreatments were carried out on raw spectral data,then principal component analysis was conducted,among which the identification effect after the second-order derivative pretreatment was shown to be the most effective,and eight types of algae samples were independently distributed in the principal component eigenspace.It was thus shown that it was feasible to use fluo-rescence spectral imaging technology combined with cluster analysis and principal component analysis for algae identification. The method had the characteristics of being easy to operate,fast and nondestructive.