本文選題：激光誘導(dǎo)擊穿光譜 + 土壤分類��； 參考：《光譜學(xué)與光譜分析》2017年01期

【摘要】：為實(shí)現(xiàn)不同種類土壤的快速分類鑒別,實(shí)驗(yàn)研究了基于激光誘導(dǎo)擊穿光譜技術(shù)的土壤快速分類方法。由于不同類型的土壤在元素組成上會(huì)存在較大差異,所以利用激光誘導(dǎo)擊穿光譜技術(shù)進(jìn)行土壤分類具有可行性。不同土壤在相同實(shí)驗(yàn)條件下產(chǎn)生的等離子體溫度會(huì)存在較大差異,可以作為分類的重要依據(jù),所選擇的7類土壤中,赤紅壤的等離子體溫度最高。選取土壤中6種常量元素Si,Fe,Al,Mg,Ca和Ti的光譜強(qiáng)度作為分類指標(biāo),利用主成分分析(principal component analysis,PCA)對(duì)7種土類的25個(gè)樣品進(jìn)行了分類,其中磚紅壤和赤紅壤分類出現(xiàn)了交疊,而不同高山草甸土樣品之間元素差異較大,并沒(méi)有實(shí)現(xiàn)較好的聚類。利用反向傳播神經(jīng)網(wǎng)絡(luò)(back-propagation artificial neural network)結(jié)合土壤的LIBS光譜對(duì)土壤進(jìn)行了分類,分類結(jié)果與PCA結(jié)果相近,赤紅壤與磚紅壤出現(xiàn)了識(shí)別錯(cuò)誤。當(dāng)用PCA分析獲得三個(gè)主成分值作為BP神經(jīng)網(wǎng)絡(luò)的輸入量時(shí),獲得了較好的分類結(jié)果,因?yàn)楹?jiǎn)化了輸入量,降低了BP神經(jīng)網(wǎng)絡(luò)的誤差,此時(shí)只有一個(gè)高山草甸土被識(shí)別成褐土,而高山草甸土的等離子體溫度顯著低于褐土,所以結(jié)合不同土壤類型的等離子體溫度差異,能夠?qū)崿F(xiàn)不同土壤的分類識(shí)別。實(shí)驗(yàn)證明激光誘導(dǎo)擊穿光譜技術(shù)可以應(yīng)用于土壤分類,為土壤普查和合理利用提高了一種新的技術(shù)。
[Abstract]:In order to realize the rapid classification and identification of different kinds of soils, a rapid classification method based on laser induced breakdown spectroscopy was studied. Because there are great differences in element composition among different types of soil, it is feasible to classify soil by laser induced breakdown spectroscopy. The temperature of plasma produced by different soils under the same experimental conditions will be quite different, which can be used as an important basis for classification. The plasma temperature of lateritic soil is the highest among the selected seven types of soils. The spectral intensities of six kinds of constant elements, Si-FeFeAlMg-Ca and Ti, were selected as classification indexes. 25 samples of 7 soil groups were classified by principal component analysis (principal component analysis), in which the classification of latosol and lateritic red soil appeared overlapping. However, the elements of different alpine meadow soil samples are different, and there is no good clustering. Soil classification was carried out by back-propagation artificial neural network) and LIBS spectrum of soil. The classification results were similar to those of PCA, and there were some errors in identification between lateritic soil and latosol. When using PCA analysis to get three principal component values as the input of BP neural network, a better classification result is obtained, because the input quantity is simplified and the error of BP neural network is reduced, only one alpine meadow soil is recognized as cinnamon soil. The plasma temperature of alpine meadow soil is significantly lower than that of cinnamon soil, so combining the plasma temperature difference of different soil types, the classification and recognition of different soils can be realized. The experimental results show that laser induced breakdown spectroscopy can be applied to soil classification, which improves a new technique for soil survey and rational utilization.
【作者單位】： 中國(guó)科學(xué)院安徽光學(xué)精密機(jī)械研究所環(huán)境光學(xué)重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家(863計(jì)劃)項(xiàng)目(2014AA06A513,2013AA065502) 安徽省杰出青年科學(xué)基金項(xiàng)目(1508085JGD02) 國(guó)家自然科學(xué)基金項(xiàng)目(61378041) 中國(guó)科學(xué)院STS項(xiàng)目(KFJ-EW-STS-083);中國(guó)科學(xué)院合肥研究院院長(zhǎng)基金項(xiàng)目(YZJJ201502)資助
【分類號(hào)】：S155

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本文編號(hào)：1973209