本文選題：動物血液 + 熒光光譜��； 參考：《光譜學與光譜分析》2017年12期

【摘要】：利用光譜檢測和數(shù)據(jù)挖掘?qū)崿F(xiàn)不同種類動物血液光譜數(shù)據(jù)的精確識別與分類具有重要意義,目前尚未見到較為完善及普適的相關(guān)研究報道。實驗采集了鴿、雞、鼠、羊四種動物全血和紅細胞溶液(濃度為1%)的熒光光譜數(shù)據(jù);基于小波變換的軟閾值去噪方法,首先對原始光譜數(shù)據(jù)進行去噪處理,并確定了717個原始特征(包括熒光峰強度值、熒光峰連線斜率等4類特征);提出以"區(qū)分度統(tǒng)計量"為核心的特征提取方法,結(jié)合主成分分析法和平均影響值算法,實現(xiàn)了對717個原始特征到2個識別特征的高效篩選;進一步建立了徑向基核函數(shù)的支持向量機分類器,對四類不同動物的全血熒光光譜數(shù)據(jù)實現(xiàn)了準確率為100%的識別分類,對紅細胞熒光光譜數(shù)據(jù)實現(xiàn)了94.69%~99.12%的識別率;最后蒙特卡洛交叉驗證的結(jié)果表明所提出的思路和方法對于動物全血溶液的識別分類具有較好的泛化能力,能對熒光光譜數(shù)據(jù)進行準確的識別分類,因此能夠在進出口檢查、食品安全、醫(yī)藥等領(lǐng)域發(fā)揮重要作用。針對動物血液熒光光譜,提出的基于"區(qū)分度統(tǒng)計量"的特征提取方法,相比于傳統(tǒng)的人為特征選取方法,能夠從大量原始特征中自動提取少量且有效的識別特征,具有較強的普適性和高效性,為其他領(lǐng)域的光譜特征提取和識別分類提供了一種新的思路。
[Abstract]:It is of great significance to use spectral detection and data mining to accurately identify and classify the blood spectral data of different species of animals. The fluorescence spectrum data of whole blood and red blood cell solution (1%) of pigeon, chicken, mouse and sheep were collected, and the original spectral data were de-noised based on the soft threshold denoising method based on wavelet transform. 717 original features (including fluorescence peak intensity, fluorescence peak line slope, etc.) were determined, and a feature extraction method based on "discriminant statistics" was proposed, which combined principal component analysis (PCA) and average influence value (AIA) algorithm. A highly efficient selection of 717 original features to 2 recognition features is realized, and a support vector machine classifier based on radial basis function (RBF) kernel function is further established. The classification accuracy of 100% is achieved for the whole blood fluorescence spectrum data of four different kinds of animals. The recognition rate of 99.12% was achieved for the red blood cell fluorescence spectrum data. Finally, the results of Monte Carlo cross-validation showed that the proposed method had a good generalization ability for the recognition and classification of animal whole blood solution. Fluorescence spectrum data can be accurately identified and classified, so it can play an important role in import and export inspection, food safety, medicine and other fields. Compared with the traditional artificial feature selection method, the proposed feature extraction method based on discriminant statistics for animal blood fluorescence spectrum can automatically extract a small number of effective recognition features from a large number of original features. It has strong universality and high efficiency, which provides a new way for spectral feature extraction and recognition and classification in other fields.
【作者單位】： 長春理工大學理學院;中國農(nóng)業(yè)科學院長春獸醫(yī)研究所;西安交通大學數(shù)學與統(tǒng)計學院;
【基金】：國家自然科學基金項目(1120420,11426045)資助
【分類號】：O657.3;S852.2
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本文編號：2083031