CAJ | 학술논문

本文利用拉曼光谱和化学计量学方法,建立快速分类模型对大米进行区分。在使用最小二乘法对离散拉曼光谱进行多项式拟合去除荧光背景的前提下,利用在第一次迭代过程去除大型拉曼峰和计算噪声电平的方法,并且保留数据维数在原来的50%以下。获取精确的拉曼信号。再用主成分分析法( Principal component Analysis,PCA)对3种大米全波段的拉曼光谱进行降维分析,线性判别方法( Linear discrimination analysis,LDA)对样品进行分类,结果显示采用前两个主成分能达到93.8%的正确分类,采用前三个主成分能达到97.9%的正确分类。优化之后的模型对于大米的判别分析具有很好的效果。
본문이용랍만광보화화학계량학방법,건립쾌속분류모형대대미진행구분。재사용최소이승법대리산랍만광보진행다항식의합거제형광배경적전제하,이용재제일차질대과정거제대형랍만봉화계산조성전평적방법,병차보류수거유수재원래적50%이하。획취정학적랍만신호。재용주성분분석법( Principal component Analysis,PCA)대3충대미전파단적랍만광보진행강유분석,선성판별방법( Linear discrimination analysis,LDA)대양품진행분류,결과현시채용전량개주성분능체도93.8%적정학분류,채용전삼개주성분능체도97.9%적정학분류。우화지후적모형대우대미적판별분석구유흔호적효과。
A quick identification and classification model of rice was built based on Raman spectra and Chemometric methods. Firstly, least square method was used to remove fluorescence background of discrete Raman spectroscopy. Secondly, we acquired precise Raman signal by large Raman peak remove during the first iterative process and noise lev-el calculation. while keeping data dimension below 50%. Finally, Principal Component Analysis( PCA) was employed to reduce dimension analysis of the full-wave band of three kinds of rice and Linear Discrimination Analysis( LDA) was used to classify them. The result showed that accuracies of 93. 8% and 97. 9% were obtained by utilizing first two PCs and first three PCs separately. The optimized model had a good performance for rice classification.