本文選題：光譜學(xué) + 激光誘導(dǎo)擊穿光譜�。� 參考：《激光與光電子學(xué)進(jìn)展》2016年11期

【摘要】：為了探索利用激光誘導(dǎo)擊穿光譜(LIBS)對(duì)水田污染區(qū)稻殼中鉻(Cr)元素含量進(jìn)行綠色、快速檢測(cè)的可行性,采用LIBS結(jié)合聯(lián)合區(qū)間偏最小二乘法(SiPLS),對(duì)產(chǎn)自江西省某湖周邊24個(gè)水田污染區(qū)稻殼樣品中的Cr元素進(jìn)行了定量分析。利用原子吸收光譜法(AAS)測(cè)得樣品中Cr元素的真實(shí)濃度為32.51~510.33μg/g,利用LIBS光譜獲得的Cr元素三個(gè)特征譜線Cr I 425.43nm、Cr I 427.48nm和Cr I 428.97nm清晰明顯。對(duì)稻殼樣品在422~446nm波段的LIBS光譜數(shù)據(jù)進(jìn)行九點(diǎn)平滑處理后,在采用SiPLS獲得的最佳模型基礎(chǔ)上,得出模型交叉驗(yàn)證均方根誤差與預(yù)測(cè)均方根誤差分別為26.1μg/g和22.6μg/g,訓(xùn)練集相關(guān)系數(shù)與預(yù)測(cè)集相關(guān)系數(shù)分別為0.9714和0.9840。對(duì)預(yù)測(cè)集樣品進(jìn)行相對(duì)誤差及T檢驗(yàn)分析,結(jié)果顯示稻殼中Cr元素濃度的預(yù)測(cè)值與AAS法測(cè)量的真實(shí)值之間的平均相對(duì)誤差為6.17%,且無顯著性差異,表明模型具有較好的預(yù)測(cè)精度,可為自然條件下生長(zhǎng)的農(nóng)產(chǎn)品重金屬安全綠色分析提供參考依據(jù)。
[Abstract]:In order to explore the feasibility of using laser induced breakdown spectroscopy (LIBS) to detect the content of chromium (Cr) in rice husks in polluted paddy fields. A quantitative analysis of Cr in rice husk samples from 24 paddy fields around a lake in Jiangxi Province was carried out by using Libs combined with the combined interval partial least square (SiPLS) method. The true concentration of Cr in the sample was determined by atomic absorption spectrometry (AAS) to be 32.51 渭 g / g. The three characteristic lines of Cr I _ (425.43) nm ~ (-1) Cr I 427.48nm and Cr I 428.97nm obtained by Libs spectra were clear and obvious. After nine points smoothing the Libs spectral data of rice husk samples in 422~446nm band, the best model was obtained by using SiPLs. The results show that the root-mean-square error and the predicted RMS error of cross-validation are 26.1 渭 g / g and 22.6 渭 g / g, respectively, and the correlation coefficients of training set and prediction set are 0.9714 and 0.9840 respectively. The relative error and T test analysis of the predicted sample show that the average relative error between the predicted value of Cr concentration in rice husk and the true value measured by AAS method is 6.17, and there is no significant difference, which indicates that the model has good prediction accuracy. It can provide a reference for the safety and green analysis of heavy metals in agricultural products grown under natural conditions.
【作者單位】： 江西農(nóng)業(yè)大學(xué)工學(xué)院;江西省高校生物光電及應(yīng)用重點(diǎn)實(shí)驗(yàn)室;江西農(nóng)業(yè)大學(xué)生物科學(xué)與工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(31560482,31460419) 江西省自然科學(xué)基金重大科技項(xiàng)目(20143ACB21013) 2014年江西省遠(yuǎn)航工程計(jì)劃(20140142) 江西省水稻產(chǎn)業(yè)技術(shù)體系專家項(xiàng)目(JXARS-02)
【分類號(hào)】：X835;S38;TN249
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本文編號(hào)：2084562