發(fā)布時間：2018-06-10 11:21

  本文選題：激光誘導擊穿光譜 + 支持向量機��； 參考：《分析化學》2017年03期

【摘要】：為提高激光誘導擊穿光譜技術(Laser-induced breakdown spectroscopy,LIBS)對鮮肉品種的識別率,采用支持向量機結合主成分分析算法輔助LIBS技術對鮮肉品種進行識別。對鮮肉切片用載玻片壓平,采用LIBS技術對鮮肉組織(豬肉、牛肉和雞肉)表面進行光譜數(shù)據(jù)的采集,每種鮮肉采集150幅光譜并進行隨機排列,取前75幅光譜作為訓練集建立模型,后75幅作為測試集測試建模結果。研究選取K、Ca、Na、Mg、Al、H、O等元素的49條歸一化譜線數(shù)據(jù)進行主成分分析,并用所得數(shù)據(jù)建立支持向量機分類模型。結果表明,通過主成分分析降維,輸入變量從49個優(yōu)化減少到18個,模型建模速度從88.91 s降至55.52 s,提高了支持向量機的建模效率;并使預測集的平均識別率提高到89.11%。本研究為激光誘導擊穿光譜技術在鮮肉品種快速分類領域提供了方法和數(shù)據(jù)參考。
[Abstract]:In order to improve the recognition rate of fresh meat varieties by Laser-induced breakdown spectroscopy (LIBS), support vector machine (SVM) combined with principal component analysis (PCA) algorithm was used to identify fresh meat varieties. The surface of fresh meat tissue (pork, beef and chicken) was collected by Libs technique. 150 spectra of each meat were collected and arranged randomly. The first 75 spectra were used as the training set to establish the model, and the latter 75 as the test set test modeling results. The principal component analysis (PCA) of 49 normalized spectral line data of elements such as KKCa-Ca-NaMg-Mg-AL-HZO was carried out, and the classification model of support vector machine was established with the obtained data. The results show that the input variables are reduced from 49 optimizations to 18 and the modeling speed is reduced from 88.91 s to 55.52 s through principal component analysis (PCA), which improves the modeling efficiency of SVM, and increases the average recognition rate of prediction set to 89.11 s. This study provides a method and data reference for fast classification of fresh meat varieties by laser induced breakdown spectroscopy.
【作者單位】： 華中科技大學武漢光電國家實驗室(籌)激光與太赫茲技術功能實驗室;
【基金】：國家重大科學儀器設備開發(fā)專項(No.2011YQ160017) 國家自然科學基金項目(No.6157031235)資助~~
【分類號】：TS251.7;O657.3

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