农业工程学报
農業工程學報
농업공정학보
2015年
12期
1-7
,共7页
土壤%光谱测定%模型%激光诱导击穿光谱%判别分析%特征谱线
土壤%光譜測定%模型%激光誘導擊穿光譜%判彆分析%特徵譜線
토양%광보측정%모형%격광유도격천광보%판별분석%특정보선
soils%spectrometry%models%laser-induced breakdown spectroscopy%discriminant analysis%characteristic spectral line
为了更加全面的建立中国土壤类型系统，了解中国土壤地域差异，从而提高土地资源的利用率，以及根据土壤类型指导农业科学生产。该研究利用激光诱导击穿光谱（laser-induced breakdown spectroscopy，LIBS）技术结合化学计量学方法对土壤类型进行判别分析研究。从6种标准土壤样品出发，分析所采集6种土壤的LIBS光谱谱线特征，结合其主要成分物质（SiO2，Al2O3，Fe2O3，FeO，MgO，CaO，Na2O，K2O）的含量，针对每种主要物质选取了Si I 390.55 nm、Al I 394.40 nm、Fe I 422.74 nm、Mg I 518.36 nm、Na I 588.96 nm、Ca II 393.37 nm、K I 766.49 nm为特征分析谱线。结合所选的7条特征谱线下的300个标准土壤样品的光谱（200个为训练集，100个为预测集），对训练集光谱进行主成分分析（principal component analysis，PCA），6种土壤有明显的聚类。然后根据训练集光谱值和预先赋予土壤类型的虚拟等级值分别建立最小二乘判别分析（partial least squares discriminant analysis，PLS-DA）和最小二乘支持向量机（least-squares support vector machine，LS-SVM）判别模型，分析预测结果二者总的判别准确率分别为98%和100%。用受试者工作特征曲线（receiver operating characteristic curve，ROC）评价这2个模型的性能，结果表明LS-SVM判别模型性能优于PLS-DA模型。基于以上结果，选取不同于标准土壤的另7种不同类型土壤进行试验验证所选特征谱线和判别模型，建立7种不同类型土壤的LS-SVM预测模型，其预测准确率达100%，ROC曲线对其评价的性能很好。研究证明，LIBS技术结合化学计量学方法能够实现对土壤类型的判别分析，这为土壤质量的正确评价，土壤的整治、规划和合理利用提供理论基础。
為瞭更加全麵的建立中國土壤類型繫統，瞭解中國土壤地域差異，從而提高土地資源的利用率，以及根據土壤類型指導農業科學生產。該研究利用激光誘導擊穿光譜（laser-induced breakdown spectroscopy，LIBS）技術結閤化學計量學方法對土壤類型進行判彆分析研究。從6種標準土壤樣品齣髮，分析所採集6種土壤的LIBS光譜譜線特徵，結閤其主要成分物質（SiO2，Al2O3，Fe2O3，FeO，MgO，CaO，Na2O，K2O）的含量，針對每種主要物質選取瞭Si I 390.55 nm、Al I 394.40 nm、Fe I 422.74 nm、Mg I 518.36 nm、Na I 588.96 nm、Ca II 393.37 nm、K I 766.49 nm為特徵分析譜線。結閤所選的7條特徵譜線下的300箇標準土壤樣品的光譜（200箇為訓練集，100箇為預測集），對訓練集光譜進行主成分分析（principal component analysis，PCA），6種土壤有明顯的聚類。然後根據訓練集光譜值和預先賦予土壤類型的虛擬等級值分彆建立最小二乘判彆分析（partial least squares discriminant analysis，PLS-DA）和最小二乘支持嚮量機（least-squares support vector machine，LS-SVM）判彆模型，分析預測結果二者總的判彆準確率分彆為98%和100%。用受試者工作特徵麯線（receiver operating characteristic curve，ROC）評價這2箇模型的性能，結果錶明LS-SVM判彆模型性能優于PLS-DA模型。基于以上結果，選取不同于標準土壤的另7種不同類型土壤進行試驗驗證所選特徵譜線和判彆模型，建立7種不同類型土壤的LS-SVM預測模型，其預測準確率達100%，ROC麯線對其評價的性能很好。研究證明，LIBS技術結閤化學計量學方法能夠實現對土壤類型的判彆分析，這為土壤質量的正確評價，土壤的整治、規劃和閤理利用提供理論基礎。
위료경가전면적건립중국토양류형계통，료해중국토양지역차이，종이제고토지자원적이용솔，이급근거토양류형지도농업과학생산。해연구이용격광유도격천광보（laser-induced breakdown spectroscopy，LIBS）기술결합화학계량학방법대토양류형진행판별분석연구。종6충표준토양양품출발，분석소채집6충토양적LIBS광보보선특정，결합기주요성분물질（SiO2，Al2O3，Fe2O3，FeO，MgO，CaO，Na2O，K2O）적함량，침대매충주요물질선취료Si I 390.55 nm、Al I 394.40 nm、Fe I 422.74 nm、Mg I 518.36 nm、Na I 588.96 nm、Ca II 393.37 nm、K I 766.49 nm위특정분석보선。결합소선적7조특정보선하적300개표준토양양품적광보（200개위훈련집，100개위예측집），대훈련집광보진행주성분분석（principal component analysis，PCA），6충토양유명현적취류。연후근거훈련집광보치화예선부여토양류형적허의등급치분별건립최소이승판별분석（partial least squares discriminant analysis，PLS-DA）화최소이승지지향량궤（least-squares support vector machine，LS-SVM）판별모형，분석예측결과이자총적판별준학솔분별위98%화100%。용수시자공작특정곡선（receiver operating characteristic curve，ROC）평개저2개모형적성능，결과표명LS-SVM판별모형성능우우PLS-DA모형。기우이상결과，선취불동우표준토양적령7충불동류형토양진행시험험증소선특정보선화판별모형，건립7충불동류형토양적LS-SVM예측모형，기예측준학솔체100%，ROC곡선대기평개적성능흔호。연구증명，LIBS기술결합화학계량학방법능구실현대토양류형적판별분석，저위토양질량적정학평개，토양적정치、규화화합리이용제공이론기출。
Laser-induced breakdown spectroscopy (LIBS), as a kind of atomic emission spectroscopy (AES), has been considered to be a future “Superstar” in the field of chemical analysis and green analytical techniques due to its unique features, like little or no sample preparation, stand-off or remote analysis, fast and multi-element analysis, wide application in various aspects. To establish the soil type system in China and more comprehensively understand the type of elements in the soil, soil types were studied to improve the utilization of land resources and offer a theoretical guide for agricultural scientific production. This research focused on investigating the soil types using LIBS coupled with chemometrics methods. A laboratorial LIBS device working in air was employed to obtain the 300 (every 50 LIBS spectra acquired from one type of soil) LIBS spectra of 6 soil samples. Based on the contents of main materials (SiO2, Al2O3, Fe2O3, FeO, MgO, CaO, Na2O, K2O) of 6 kinds of standard soil samples, their corresponding LIBS curve characteristics were analyzed. Then 7 characteristic spectral lines at SiI390.55 nm, AlI394.40 nm, FeI422.74 nm, MgI518.36 nm, NaI588.96 nm, Ca II 393.36 nm and KI766.49 nm (I represented atomic spectral line, II meant ionic spectral lines) were identified. Based on 300 spectra at 7 characteristic spectral lines from 6 standard reference soils, of which 200 were in training set and 100 in prediction set divided by sample set partitioning based on joint x-y distances (SPXY) method, the principal component analysis (PCA) was carried out on the training set and an obvious cluster was observed from the score plot of the first 2 principal components (PCs). Meanwhile, partial least squares discriminant analysis (PLS-DA) and least-squares support vector machine (LS-SVM) models were introduced to establish the discriminant models and the correct rates of discrimination were 98% and 100%, respectively. Then, the performances of PLS-DA and LS-SVM models were evaluated using receiver operating characteristic (ROC) curve. The results demonstrated that the LS-SVM discriminant model with the parameter area of 1 was superior to the model of PLS-DA with the area of 0.99569, which illustrated that the LS-SVM discriminant model was robust. Based on this, 7 types of soils from different places were used to conduct the same experiments to acquire 385 (every 55 LIBS spectra acquired from one type of soil) and then to verify the selected seven characteristic spectral lines and discriminant model. PCA on the training set of 255 LIBS spectra from 7 types of soil samples also displayed apparent cluster. Then, the LS-SVM model based on the training set from 7 types of soils was built to predict the prediction set of 130 LIBS spectra and the prediction accuracy was 100%. The performance of the model was also evaluated using ROC and it exhibited an excellent result. The research reveals that LIBS technology coupled with chemometrics methods can achieve the discriminant analysis of different types of soils, which provides a theoretical guidance for soil quality assessment, management, planning and reasonable use.
