黃雙根,吳 燕,胡建平,劉木華,吳瑞梅,范 苑,王曉彬
(1.江蘇大學(xué)現(xiàn)代農(nóng)業(yè)裝備與技術(shù)教育部重點(diǎn)實(shí)驗(yàn)室,鎮(zhèn)江212013;2.江西農(nóng)業(yè)大學(xué)工學(xué)院生物光電及應(yīng)用重點(diǎn)實(shí)驗(yàn)室,南昌330045)
大白菜中馬拉硫磷農(nóng)藥的表面增強(qiáng)拉曼光譜快速檢測(cè)
黃雙根1,2,吳 燕2,胡建平1※,劉木華2,吳瑞梅2,范 苑2,王曉彬2
(1.江蘇大學(xué)現(xiàn)代農(nóng)業(yè)裝備與技術(shù)教育部重點(diǎn)實(shí)驗(yàn)室,鎮(zhèn)江212013;2.江西農(nóng)業(yè)大學(xué)工學(xué)院生物光電及應(yīng)用重點(diǎn)實(shí)驗(yàn)室,南昌330045)
為了檢測(cè)大白菜中馬拉硫磷農(nóng)藥殘留,該文采用表面增強(qiáng)拉曼光譜技術(shù)結(jié)合化學(xué)計(jì)量學(xué)方法建立馬拉硫磷殘留的快速檢測(cè)模型。采用硫酸鎂、N-丙基乙二胺、石墨化炭黑和C18去除大白菜中蛋白質(zhì)、脂肪、碳水化合物等物質(zhì)的影響。利用不同預(yù)處理方法對(duì)原始光譜信號(hào)進(jìn)行預(yù)處理,建立大白菜中馬拉硫磷殘留的偏最小二乘模型。研究發(fā)現(xiàn),大白菜中馬拉硫磷的檢測(cè)濃度達(dá)到1.082 mg/L以下;歸一化預(yù)處理后建立的模型預(yù)測(cè)性能最好。配制5個(gè)未知濃度樣本驗(yàn)證模型的準(zhǔn)確度,預(yù)測(cè)值與真實(shí)值相對(duì)誤差的絕對(duì)值為0.70%~9.84%,預(yù)測(cè)回收率為99.30%~109.84%;配對(duì)t檢驗(yàn)的結(jié)果表明樣本的預(yù)測(cè)值與真實(shí)值之間無明顯差異,說明模型是準(zhǔn)確可靠的。結(jié)果表明,SERS(surface-enhanced Raman spectroscopy)方法可以實(shí)現(xiàn)大白菜中馬拉硫磷殘留的快速檢測(cè)。
光譜分析;農(nóng)藥;檢測(cè);表面增強(qiáng)拉曼光譜;大白菜;馬拉硫磷;偏最小二乘;快速檢測(cè)
黃雙根,吳 燕,胡建平,劉木華,吳瑞梅,范 苑,王曉彬.大白菜中馬拉硫磷農(nóng)藥的表面增強(qiáng)拉曼光譜快速檢測(cè)[J].農(nóng)業(yè)工程學(xué)報(bào),2016,32(6):296-301.doi:10.11975/j.issn.1002-6819.2016.06.041 http://www.tcsae.org
Huang Shuanggen,Wu Yan,Hu Jianping,Liu Muhua,Wu Ruimei,Fan Yuan,Wang Xiaobin.Rapid detection of malathion residues in Chinese cabbage by surface-enhanced Raman spectroscopy[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2016,32(6):296-301.(in Chinese with English abstract) doi:10.11975/j.issn.1002-6819.2016.06.041 http://www.tcsae.org
馬拉硫磷(malathion),化學(xué)名稱為O,O-二甲基-S-[1,2-二(乙氧基羰基)乙基]二硫代磷酸酯,屬低等毒性有機(jī)磷殺蟲劑,具有觸殺、胃毒和一定的熏蒸作用[1],適用于煙草、茶和蔬菜上的刺吸式口器和咀嚼式口器害蟲,如主要用于防治稻縱卷葉螟、稻飛虱、菜蚜、菜青蟲等害蟲。中毒癥狀為頭暈、無力、嘔吐、流涎、痙攣、昏迷等。目前,馬拉硫磷農(nóng)藥的常規(guī)檢測(cè)方法有液相色譜和液質(zhì)聯(lián)用的方法[2-3]、氣相色譜和質(zhì)譜聯(lián)用的方法[4-5]等,這些方法具有準(zhǔn)確、靈敏度高等特點(diǎn),但前處理復(fù)雜、成本高、檢測(cè)速度慢,不適合現(xiàn)場(chǎng)實(shí)時(shí)快速檢測(cè)篩選[6-7]。
表面增強(qiáng)拉曼光譜(surface-enhanced Raman spec troscopy,SERS)技術(shù)是指分子吸附到某些粗糙金屬(如金、銀、銅)的表面或溶膠中,在激發(fā)區(qū)域內(nèi),金屬表面或近表面電磁場(chǎng)增強(qiáng),使吸附分子的拉曼信號(hào)強(qiáng)度增強(qiáng)104~106倍[8-10]。SERS技術(shù)能實(shí)現(xiàn)對(duì)微量樣品的快速檢測(cè),可以實(shí)現(xiàn)單分子檢測(cè)[11-12]。SERS技術(shù)具有樣品制備簡(jiǎn)單[13]、操作簡(jiǎn)便、靈敏度高等優(yōu)點(diǎn),已逐步應(yīng)用于食品和農(nóng)產(chǎn)品中農(nóng)藥殘留的快速檢測(cè)[14-16]。Shende等[17]采用固相萃取技術(shù)對(duì)橙汁進(jìn)行前處理,結(jié)合SERS技術(shù),檢測(cè)最低濃度為50 μg/L。Li等[18]以銀納米粒子作為增強(qiáng)基底,應(yīng)用SERS技術(shù)檢測(cè)了蘋果表皮的甲拌磷和倍硫磷農(nóng)藥殘留,最低檢測(cè)濃度分別為0.05和0.4 mg/L。張萍等[19]采用表面增強(qiáng)拉曼光譜檢測(cè)技術(shù)結(jié)合快速溶劑提取前處理方法建立了豆芽中6-BA殘留物質(zhì)的快速檢測(cè)方法。Kim等[20]以苯并咪唑類為研究對(duì)象,采集不同pH值下待測(cè)溶液的表面增強(qiáng)拉曼光譜,對(duì)拉曼譜峰進(jìn)行了歸屬。He等[21]利用SERS技術(shù)結(jié)合一個(gè)快速簡(jiǎn)單的方法實(shí)現(xiàn)了蘋果表面噻菌靈農(nóng)藥的檢測(cè),檢測(cè)時(shí)間大約10 min。目前利用表面增強(qiáng)拉曼光譜技術(shù)結(jié)合化學(xué)計(jì)量學(xué)方法檢測(cè)大白菜中農(nóng)藥殘留還沒有報(bào)道。
本文采用SERS技術(shù)結(jié)合化學(xué)計(jì)量學(xué)方法對(duì)大白菜中馬拉硫磷殘留進(jìn)行快速檢測(cè)。以大白菜為載體,馬拉硫磷農(nóng)藥為研究對(duì)象,模擬農(nóng)藥殘留狀態(tài),利用快速溶劑提取前處理方法對(duì)大白菜中馬拉硫磷農(nóng)藥進(jìn)行提取,采用無水無水硫酸鎂、N-丙基乙二胺(primary secondary amine,PSA)、石墨化炭黑和C18去除蛋白質(zhì)、脂肪、碳水化合物等物質(zhì)的影響,結(jié)合化學(xué)計(jì)量學(xué)方法,實(shí)現(xiàn)大白菜中馬拉硫磷農(nóng)藥的快速檢測(cè),為實(shí)現(xiàn)農(nóng)藥殘留檢測(cè)提供快速、簡(jiǎn)便、準(zhǔn)確的檢測(cè)方案。
1.1 儀器與試劑
拉曼光譜儀(RamTracer-200-HS,歐普?qǐng)D斯光學(xué)納米科技有限公司);天平(AR3202CN,精度為0.01 mg,奧豪斯電子天平);低速離心機(jī)(JW1024,安徽嘉文儀器設(shè)備有限公司);渦漩混合器(Vortex-Genie 2/2T,上海凌初環(huán)保儀器有限公司);氣相色譜串聯(lián)質(zhì)譜儀(Agilent GC 700,美國安捷倫科技有限公司);色譜柱(HP-5MS,5%Phenyl Methyl Silox,30 m×250 μm×0.25 μm,美國安捷倫科技有限公司)。
馬拉硫磷標(biāo)準(zhǔn)品(99.5%,中國標(biāo)準(zhǔn)物質(zhì)網(wǎng));乙腈,乙腈(色譜純,國藥集團(tuán)化學(xué)試劑北京有限公司);PSA、無水硫酸鎂、C18和石墨化炭黑(分析純,國藥集團(tuán)化學(xué)試劑北京有限公司);氯化鈉(分析純,國家標(biāo)準(zhǔn)物質(zhì)信息中心);表面增強(qiáng)試劑(OTR202、OTR103,歐普?qǐng)D斯光學(xué)納米科技有限公司);有機(jī)濾膜(0.22 μm,安捷倫科技有限公司);大白菜(江西農(nóng)業(yè)大學(xué)試驗(yàn)基地)。
1.2 試驗(yàn)方法
1.2.1 樣品制備
馬拉硫磷標(biāo)準(zhǔn)溶液配制:準(zhǔn)確量取標(biāo)準(zhǔn)品馬拉硫磷200 mg于200 mL容量瓶中,加入乙腈超聲溶解后,定容至刻度,得到濃度為1 000 mg/L的馬拉硫磷儲(chǔ)備溶液,放置于4℃避光環(huán)境中存放。再用乙腈將1 000 mg/L的馬拉硫磷儲(chǔ)備溶液分別稀釋為100、50、20、15、10、5、2、1和0.5 mg/L的標(biāo)準(zhǔn)工作液。
大白菜中馬拉硫磷農(nóng)藥殘留的模擬過程及提?。?)稱取50g大白菜放置于保鮮膜中,使用噴壺按比例噴灑濃度為100mg/L的馬拉硫磷標(biāo)準(zhǔn)儲(chǔ)備液。配置76種不同濃度大白菜樣本,每個(gè)濃度復(fù)制2份,編號(hào)1~76。晾干后,分別放入攪拌機(jī)將樣品加工成漿狀,備用。2)稱取10 g大白菜樣本于50 mL離心管中,依次加入10 mL乙腈、5 g氯化鈉和1 g無水乙酸鈉,搖勻后渦旋混合器上混合1 min,將離心管放入離心機(jī)以4200r/min的速度離心5min,上清液為黃色。3)取上述黃色上清液2 mL,放于裝有適量硫酸鎂、PSA、石墨化炭黑和C18的15mL離心管中,搖勻后在渦旋混合器上混合1 min,去除蛋白質(zhì)、脂肪、碳水化合物等物質(zhì)的影響,將此離心管放入離心機(jī)以4200r/min的速度離心5 min,得到無色上清液,上清液過0.22 μm有機(jī)濾膜,上機(jī)檢測(cè)拉曼光譜。4)取步驟3)的過濾液1 mL,加入10 mL離心管,氮吹。5)氮吹后,在10mL離心管中加入1mL乙酸乙酯,渦旋振蕩。6)取步驟5)的溶液100μL,和900μL乙腈混合,稀釋10倍,渦旋振蕩,過0.22μm有機(jī)濾膜,放置于進(jìn)樣瓶,上氣相質(zhì)譜儀檢測(cè)大白菜樣本中馬拉硫磷的真實(shí)殘留值。
1.2.2 拉曼光譜數(shù)據(jù)采集
拉曼光譜檢測(cè)參數(shù)如下:激發(fā)波長為785 nm,功率為200 mW,掃描范圍400~1 800 cm-1,分辨率為4 cm-1,積分時(shí)間為10 s,積分2次求平均,向2 mL進(jìn)樣瓶中依次加入500 μL OTR202、20 μL待測(cè)液、100 μL OTR103,混合均勻后其采集表面增強(qiáng)拉曼光譜。
1.2.3 氣相色譜串聯(lián)質(zhì)譜試驗(yàn)條件
色譜條件 色譜柱(HP-5MS,5%Phenyl Methyl Silox,30 m×250 μm×0.25 μm)進(jìn)樣口溫度:250℃;升溫程序:初始柱溫為50℃,保持2 min,以50℃/min升至150℃,以5℃/min升至200℃,以16℃/min升至280℃;升至300℃,保持2 min(后運(yùn)行);載氣為高純氦氣(純度≥99.999%),恒壓64.469 6 kPa;載氣流速為1.2 mL/min;進(jìn)樣量1 μL;進(jìn)樣方式:不分流進(jìn)樣[22]。
質(zhì)譜條件 EI源;接口溫度:230℃;四極桿溫度:150℃;傳輸線溫度:280℃;溶劑延遲0 min;碰撞氣為高純氮?dú)猓兌取?9.999%);采集模式:多反應(yīng)監(jiān)測(cè)模式(multireactions monitoring,MRM)。
1.2.4 數(shù)據(jù)處理
采用標(biāo)準(zhǔn)正態(tài)變量變換(standardnormalvariate,SNV)、多元散射校正(multiplicative scatter correction,MSC)、歸一化(normalization)3種預(yù)處理方法對(duì)原始光譜數(shù)據(jù)進(jìn)行預(yù)處理,消除基線偏移、隨機(jī)噪聲和背景的干擾,利用偏最小二乘回歸(partial least squares,PLS)方法建立大白菜中馬拉硫磷農(nóng)藥殘留的預(yù)測(cè)模型,以RMSECV、Rc、RMSEP、Rp對(duì)模型進(jìn)行綜合評(píng)價(jià)。采用5個(gè)未知濃度樣本評(píng)價(jià)模型準(zhǔn)確度,對(duì)模型的真實(shí)值與預(yù)測(cè)值進(jìn)行配對(duì)t檢驗(yàn),以驗(yàn)證模型的準(zhǔn)確度。所有數(shù)據(jù)分析基于MATAB R2010a和SPASS V17.0平臺(tái)完成。
2.1 馬拉硫磷的表面增強(qiáng)拉曼光譜
圖1為馬拉硫磷農(nóng)藥的表面增強(qiáng)拉曼光譜圖和背景信號(hào)拉曼光譜。從圖1可看出,馬拉硫磷農(nóng)藥分子結(jié)構(gòu)包含了P=S、C-C、P-S、C-H、C-O-C、P-O、C=O和C-O等基團(tuán)。圖1(a)為馬拉硫磷表面增強(qiáng)拉曼光譜圖(濃度為20mg/L),764、816、862、1 096、1 152、1 448和1 724 cm-1處強(qiáng)度較高,對(duì)這7處拉曼特征峰進(jìn)行歸屬[23-25]:764 cm-1處拉曼特征峰為有機(jī)磷化物中P=S和P-O鍵伸縮振動(dòng)引起的,816 cm-1歸屬于C-H面外彎曲振動(dòng),862 cm-1歸屬于CO-C對(duì)稱伸縮振動(dòng),1 096 cm-1歸屬于C-C伸縮振動(dòng),并伴有CH2面內(nèi)變形振動(dòng),1 152 cm-1歸屬于P-S伸縮振動(dòng),1 448 cm-1歸屬于C-O基團(tuán)對(duì)稱伸縮振動(dòng),1 724 cm-1歸屬于C=O伸縮振動(dòng)。這些特征峰可作為馬拉硫磷農(nóng)藥分子的定性判別依據(jù)。
圖1 馬拉硫磷標(biāo)準(zhǔn)溶液和背景信號(hào)的拉曼光譜Fig.1 Raman spectra of malathion solution and background signals
從圖1(a)和(b)可看出,馬拉硫磷標(biāo)準(zhǔn)溶液的普通拉曼光譜只出現(xiàn)了乙腈的拉曼峰,而未出現(xiàn)馬拉硫磷農(nóng)藥的拉曼特征峰。背景信號(hào)(c)和(d)比較微弱,而且出峰位置和馬拉硫磷的拉曼特征峰不一致。另外,對(duì)這4類數(shù)據(jù)進(jìn)行主成分分析(n=5)得到的結(jié)果見圖2。從圖2中看出,馬拉硫磷的表面增強(qiáng)拉曼光譜信號(hào)和背景信號(hào)(金膠和乙腈)有很好的分離。由此說明,SERS技術(shù)能夠用來檢測(cè)馬拉硫磷農(nóng)藥。
圖2 主成分分析結(jié)果Fig.2 Results of principal component analysis
2.2 馬拉硫磷標(biāo)準(zhǔn)溶液的表面增強(qiáng)拉曼光譜分析
圖3為不同濃度馬拉硫磷標(biāo)準(zhǔn)溶液的表面增強(qiáng)拉曼光譜。由圖3可看出,隨著馬拉硫磷標(biāo)準(zhǔn)溶液濃度的增加,其特征峰的強(qiáng)度不斷增強(qiáng),但各特征峰的峰強(qiáng)度變化速度不同:1 096、1 152、1 448和1 724 cm-1處隨濃度變化較快,816和862 cm-1處隨濃度變化較慢,764 cm-1處變化最慢,這可能是因?yàn)榧{米增強(qiáng)粒子與馬拉硫磷農(nóng)藥分子中各個(gè)基團(tuán)表面吸附力的大小和方向不同導(dǎo)致的。從圖3中可看出,隨著馬拉硫磷濃度的降低,拉曼特征峰的強(qiáng)度逐漸減弱,濃度為20、10、5 mg/L時(shí),馬拉硫磷的7處拉曼峰明顯,易識(shí)別;濃度為0.5 mg/L時(shí),764 cm-1處峰強(qiáng)十分微弱,但依然能識(shí)別出,其他的特征峰已不能識(shí)別。由此表明,利用SERS技術(shù)檢測(cè)馬拉硫磷標(biāo)準(zhǔn)溶液能夠達(dá)到0.5 mg/L以下。
圖3 不同濃度馬拉硫磷標(biāo)準(zhǔn)溶液的表面增強(qiáng)拉曼光譜Fig.3 SERS spectra of malathion with different concentrations
2.3 大白菜中馬拉硫磷農(nóng)藥殘留檢測(cè)結(jié)果分析
受蛋白質(zhì)、脂肪、碳水化合物等物質(zhì)的干擾,馬拉硫磷農(nóng)藥分子的拉曼信號(hào)被削弱。本文采用無水無水硫酸鎂、PSA、石墨化炭黑和C18對(duì)大白菜提取液進(jìn)行凈化處理,減弱大白菜提取液中蛋白質(zhì)、脂肪、碳水化合物等物質(zhì)的干擾,凈化后含馬拉硫磷農(nóng)藥的大白菜溶液的表面增強(qiáng)拉曼光譜如圖4所示。圖4(a)-(c)中,764、816、862和1724cm-1處特征峰明顯,易識(shí)別;濃度為2.256 mg/L時(shí),764、816和862 cm-1處的峰強(qiáng)度明顯降低,依然能識(shí)別,1 724 cm-1處的拉曼特征峰已無法識(shí)別;濃度為1.082 mg/L時(shí),764和816 cm-1處特征峰依然存在,峰強(qiáng)十分微弱,但依然能識(shí)別。因此,利用表面增強(qiáng)拉曼光譜方法檢測(cè)大白菜中馬拉硫磷農(nóng)藥的最低檢測(cè)濃度在1.082 mg/L以下。從圖4中看出,馬拉硫磷溶液的拉曼特征峰強(qiáng)度隨濃度的增大而增強(qiáng)。因此,可采用化學(xué)計(jì)量方法建立大白菜中馬拉硫磷農(nóng)藥殘留的預(yù)測(cè)模型,對(duì)馬拉硫磷農(nóng)藥進(jìn)行定量分析。
圖4 不同濃度的大白菜馬拉硫磷提取液的表面增強(qiáng)拉曼光譜Fig.4 SERS spectra of malathion solutions extracted from chinese cabbage with different concentrations
拉曼光譜采集時(shí),會(huì)受到隨機(jī)噪聲、基線漂移、外界雜散光和電荷耦合器件(charge-coupled device,CCD)熱穩(wěn)定噪聲等因素影響,直接影響模型的可靠性和穩(wěn)健性。因此,需要對(duì)原始光譜進(jìn)行預(yù)處理,增強(qiáng)特征信息,提高模型的預(yù)測(cè)能力。本文采用3種預(yù)處理方法,由各種預(yù)處理方法處理后所建偏最小二乘法模型的預(yù)測(cè)效果來優(yōu)化最佳預(yù)處理方法。根據(jù)76個(gè)樣本的測(cè)量值,采用2:1的分配方案,從每3個(gè)樣品中選擇2個(gè)作為校正集,剩余的1個(gè)作為預(yù)測(cè)集,所以校正集由51個(gè)樣品組成,預(yù)測(cè)集由25個(gè)樣品組成。表1為原始光譜經(jīng)不同預(yù)處理方法后所建模型結(jié)果。
表1 不同預(yù)處理方法下模型校正和預(yù)測(cè)的結(jié)果Table 1 Results for each of pre-processing method for calibration and prediction model
由表可知,經(jīng)3種預(yù)處理方法后所建模型的預(yù)測(cè)結(jié)果均優(yōu)于原始光譜,原始光譜歸一化預(yù)處理后,當(dāng)主成分?jǐn)?shù)為13時(shí)所建模型的性能最好。模型對(duì)校正集樣本的相關(guān)系數(shù)(Rc)為0.983 2,交互驗(yàn)證均方根誤差(RMSECV)為1.78 mg/L,模型對(duì)預(yù)測(cè)集樣本的相關(guān)系數(shù)(Rp)為0.973 2,預(yù)測(cè)均方根誤差(RMSEP)為2.37 mg/L,較高的Rc和較低的RMSECV說明采用表面增強(qiáng)拉曼光譜方法預(yù)測(cè)大白菜中馬拉硫磷農(nóng)藥殘留是可行的。圖5為經(jīng)歸一化預(yù)處理后預(yù)測(cè)集樣本的預(yù)測(cè)值與測(cè)量值之間的散點(diǎn)圖。
圖5 歸一化預(yù)處理后預(yù)測(cè)集的散點(diǎn)圖Fig.5 Scatter diagram of prediction set by normalization
2.4 模型準(zhǔn)確度驗(yàn)證
2.4.1 預(yù)測(cè)相對(duì)誤差和回收率
為了驗(yàn)證方法的準(zhǔn)確度,對(duì)5個(gè)未知濃度大白菜樣本進(jìn)行前處理,用GC-MS方法測(cè)定5個(gè)未知濃度農(nóng)藥大白菜樣本的真實(shí)值。對(duì)5個(gè)未知濃度農(nóng)藥大白菜樣本分別采集SERS信號(hào),用上述方法建立的預(yù)測(cè)模型對(duì)5個(gè)未知濃度農(nóng)藥大白菜樣本進(jìn)行預(yù)測(cè),將真實(shí)值與預(yù)測(cè)值的進(jìn)行比較,結(jié)果見表2。由表2可知,本方法的預(yù)測(cè)結(jié)果與GC-MS方法結(jié)果基本一致,真實(shí)值與預(yù)測(cè)值相對(duì)誤差為0.38%~6.80%,回收率為96.1%~107.29%,表明利用表面增強(qiáng)拉曼光譜方法快速檢測(cè)大白菜中馬拉硫磷農(nóng)藥殘留是可行的。
表2 大白菜中馬拉硫磷農(nóng)藥的真實(shí)值與預(yù)測(cè)值對(duì)比Table 2 Predicted value and measured value of malathion in chinese cabbage
2.4.2 配對(duì)t檢驗(yàn)
表3為5個(gè)未知農(nóng)藥大白菜樣本的真實(shí)值與預(yù)測(cè)值配對(duì)t檢驗(yàn)結(jié)果,t=-1.589,其絕對(duì)值小于t0.05,4=2.776,表明真實(shí)值與預(yù)測(cè)值之間無明顯差異,說明利用表面增強(qiáng)拉曼光譜方法快速檢測(cè)大白菜中馬拉硫磷農(nóng)藥殘留的預(yù)測(cè)結(jié)果是準(zhǔn)確可靠的。
表3 真實(shí)值與預(yù)測(cè)值配對(duì)t檢驗(yàn)結(jié)果Table 3 t-test result between reference values and prediction values
1)采用表面增強(qiáng)拉曼光譜技術(shù)和快速溶劑提取前處理方法快速檢測(cè)大白菜中馬拉硫磷農(nóng)藥殘留,找到了馬拉硫磷農(nóng)藥分子的7個(gè)拉曼特征峰,這些特征峰可作為馬拉硫磷農(nóng)藥的定性定量判別依據(jù),該方法對(duì)大白菜中馬拉硫磷農(nóng)藥的檢測(cè)濃度達(dá)到1.082 mg/L以下。
2)采用標(biāo)準(zhǔn)正態(tài)變換、多元散射校正和歸一化對(duì)大白菜馬拉硫磷提取液的原始拉曼光譜進(jìn)行預(yù)處理,結(jié)果表明,經(jīng)歸一化預(yù)處理后所建PLS模型預(yù)測(cè)性能最好。
3)用5個(gè)未知濃度的大白菜樣本對(duì)模型的準(zhǔn)確性進(jìn)行驗(yàn)證,結(jié)果顯示本方法的預(yù)測(cè)結(jié)果與經(jīng)典化學(xué)法測(cè)量值基本一致;配對(duì)t檢驗(yàn)結(jié)果顯示樣本的預(yù)測(cè)值與實(shí)際測(cè)量值之間無顯著差異,說明采用該方法檢測(cè)大白菜中的馬拉硫磷農(nóng)藥殘留是準(zhǔn)確可靠的。研究結(jié)果表明SERS技術(shù)能夠?qū)崿F(xiàn)對(duì)大白菜中農(nóng)藥殘留的檢測(cè),研究方法和思路能為農(nóng)產(chǎn)品中其他農(nóng)藥的拉曼光譜快速檢測(cè)提供參考。
[1]謝鋒,孫海達(dá),李占彬,等.QuEChERS-表面增強(qiáng)拉曼光譜聯(lián)用快速測(cè)定豆類蔬菜中馬拉硫磷殘留[J].食品科技,2014,39 (8):286-290.Xie Feng,Sun Haida,Li Zhanbin,et al.QuEChERS sample preparation method for rapid screening of malathion in legume vegetables by surface-enhanced Raman spectroscopy[J].Food Science and Technology,2014,39(8):286-290.(in Chinese with English abstract)
[2]王岙,高茜,王曉麗,等.高效液相色譜法同時(shí)測(cè)定水體中馬拉硫磷和阿特拉津[J].吉林大學(xué)學(xué)報(bào)(理學(xué)版),2008,46(1):157-161.Wang Ao,Gao Qian,Wang Xiaoli,et al.Simultaneous determination of malathion and atrazine in water by high performance liquid chromatography[J].Journal of Jilin University (Science Edition),2008,46(1):157-161.(in Chinese with English abstract)
[3]Xu Zhenlin,Deng Hao,Deng Xingfei,et al.Monitoring of organophosphorus pesticides in vegetables using monoclonal antibody-based direct competitive ELISA followed by HPLC-MS/ MS[J].Food Chemistry,2012,131(4):1569-1576.
[4]王吉祥,向文娟,王亞琴,等.GPC-GC/MS測(cè)定火腿中多種有機(jī)磷農(nóng)藥的殘留[J].食品研究與開發(fā),2014,35(2):77-80.
[5]Alves A A R,Rodrigues A S,Barros E B P,et al.Determination of pesticides residues in brazilian grape juices using GC-MSSIM[J].Food Analytical Methods,2014,7(9):1834-1839.
[6]李曉舟,于壯,楊天月,等.SERS技術(shù)用于蘋果表面有機(jī)磷農(nóng)藥殘留的檢測(cè)[J].光譜學(xué)與光譜分析,2013,33(10):2711-2714. Li Xiaozhou,Yu Zhuang,Yang Tianyue,et al.Detection of organophosphorus pesticide residue on the surface of apples using SERS[J].Spectroscopy and Spectral Analysis,2013,33 (10):2711-2714.(in Chinese with English abstract)
[7]Müller C,David L,Chis V,et al.Detection of thiabendazole applied on citrus fruits and bananas using surface enhanced Raman scattering[J].Food Chemistry,2014,145:814-820.
[8] 歐陽雨.樂果涂膜表面增強(qiáng)拉曼光譜研究[J].分析測(cè)試學(xué)報(bào),2012,31(8):996-1000. OuyangYu.Surface-enhanced raman scattering study of dimethoate coating[J].Journal of Instrumental Analysis,2012, 31(8):996-1000.(in Chinese with English abstract)
[9]Huang S G,Hu J P,Guo P,et al.Rapid detection of chorpyriphos residues in rice by surface-enhanced Raman scattering[J].Analytical Methods,2015(7):4334-4339.
[10]Nguyen T H D,Zhang Z,Mustapha A,et al.Use of graphene and gold nanorods as substrates for the detection of pesticides by surface enhanced Raman spectroscopy[J].Journal of Agricultural and Food Chemistry,2014,62(43):10445-10451.
[11]Guerrini L,Sanchez C S,Cruz V L,et al.Surface-enhanced Raman spectra of dimethoate and omethoate[J].Journal of Raman Spectroscopy,2011,42(5):980-985.
[12]Buyukgoz G G,Bozkurt A G,Akgul N B,et al.Spectroscopic detection of aspartame in soft drinks by surface-enhanced Ramanspectroscopy[J].EuropeanFoodResearchandTechnology, 2015,240(3):567-575.
[13]李水芳,張欣,李姣娟,等.拉曼光譜法無損檢測(cè)蜂蜜中的果糖和葡萄糖含量[J].農(nóng)業(yè)工程學(xué)報(bào),2014,30(6):249-255. Li Shuifang,Zhang Xin,Li Jiaojuan,et al.Non-destructive detecting fructose and glucose content of honey with Raman spectroscopy[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(6):249-255.in Chinese with English abstract)
[14]Dhakal S,Li Y Y,Peng Y K,et al.Prototype instrument development for non-destructive detection of pesticide residue in apple surface using Raman technologyc Journal of Food Engineering,2014,123:94-103.
[15]Wijaya W,Pang S,Labuza T P,et al.Rapid detection of acetamiprid in foods using surface-enhanced Raman spectroscopy(SERS)[J].Journal of Food Science,2014,79(4): T743-T747.
[16]Craig A P,Franca A S,Irudayaraj J.Surface-enhanced Raman spectroscopy applied to food safety[J].Annual Review of Food Science and Technology,2013,4(1):369-380.
[17]Shende C,Inscore F,Sengupta A,et al.Rapid extraction and detection of trace chlorpyrifos-methyl in orange juice by surfaceenhanced Raman spectroscopy[J].Sensing and Instrumentation for Food Quality and Safety,2010,4(3-4):101-107.
[18]Li X Z,Zhang S,Yu Z,et al.Surface-enhanced Raman spectroscopic analysis of phorate and fenthion pesticide in apple skin using silver nanoparticles[J].Applied Spectroscopy,2014, 68(4):483-487.
[19]張萍,鄭大威,劉晶,等.基于表面增強(qiáng)拉曼光譜技術(shù)的豆芽6-BA殘留快速檢測(cè)方法[J].光譜學(xué)與光譜分析,2012,32(5):1266-1269. Zhang Ping,Zheng Dawei,Liu Jing,et al.Rapid detection of 6-benzylaminopurine residues in sprout beans by surface-enhanced Raman spectroscopy[J].Spectroscopy and Spectral Analysis, 2012,32(5):1266-1269.(in Chinese with English abstract)
[20]Kim M S,Kim M K,Lee C J,et al.Surface-enhanced Raman spectroscopy of benzimidazolic fungicides:benzimidazole and thiabendazole[J].Bulletin of the Korean Chemical Society,2009, 30(12):2930-2934.
[21]He L L,Chen T,Labuza T P.Recovery and quantitative detection of thiabendazole on apples using a surface swab capture method followed by surface-enhanced Raman spectroscopy[J].Food Chemistry,2014,148:42-46.
[22]莫李桂,馬盛韜,李會(huì)茹,等.氣相色譜/三重四極桿串聯(lián)質(zhì)譜法檢測(cè)土壤中氯代多環(huán)芳烴和溴代多環(huán)芳烴[J].分析化學(xué),2013,41(12):1825-1830. Mo Ligui,Ma Shengtao,Li Huiru,et al.Determination of chlorinated and brominated polycyclic aromatic hydrocarbons in soil samples by gas chromatography coupled with triple quadrupole mass spectrometry[J].Chinese Journal of Analytical Chemistry,2013,41(12):1825-1830.(in Chinese with English abstract)
[23]孫旭東,郝勇,劉燕德.表面增強(qiáng)拉曼光譜法檢測(cè)農(nóng)藥殘留的研究進(jìn)展[J].食品安全質(zhì)量檢測(cè)學(xué)報(bào),2012,3(5):421-426.
[24]朱自瑩,顧仁敖,陸天虹.拉曼光譜在化學(xué)中的應(yīng)用[M].沈陽:東北大學(xué)出版社,1998.
[25]朱自瑩,譯.有機(jī)化合物的特征拉曼頻率[M].北京:中國化學(xué)會(huì)),1980.
Rapid detection of malathion residues in Chinese cabbage by surfaceenhanced Raman spectroscopy
Huang Shuanggen1,2,Wu Yan2,Hu Jianping1※,Liu Muhua2,Wu Ruimei2,Fan Yuan2,Wang Xiaobin2
(1.Key Laboratory of Modern Agriculture Equipment and Technology,Ministry of Education,Jiangsu University,Zhenjiang 212013,China; 2.Optics-Electrics Application of Biomaterials Lab,College of Engineering,Jiangxi Agricultural University,Nanchang 330045,China)
The traditional pesticide residues detection methods had the disadvantages of complex sample preparation, expensive apparatus and high cost.For developing a rapid analysis detection method of pesticide residues,we investigated a surface-enhanced Raman spectroscopy (SERS)method coupled with colloidal gold for detection and characterization malathion residues in Chinese cabbage.Chemometric method was used to establish a rapid detection model of malathion pesticide residues in Chinese cabbage.A 200 mg/L standard solution was prepared by dissolving malathion power in acetonitrile.The standard solution was serially diluted with ultrapure water to prepare working solutions of 100,50,20,15, 10,5,2,1 and 0.5 mg/L.Fresh Chinese cabbages were collected from the agronomy experimental base of Jiangxi Agricultural University in June 2015.The Chinese cabbages were used to prepare samples as follows.50 g Chinese cabbages were weighed and transferred on a plastic wrap.76 Chinese cabbage samples were manufactured by spraying different concentration standard solution with a sprinkling can,and each concentration has two parallel samples.Then the 76 samples were homogenized separately by pulverizer.After that,the sample preparation steps were implemented for both SERS collection and GC-MS measurement as follows.1)10 g homogenized chinese cabbage sample,1 g anhydrous sodium acetate,5 g sodium chloride and 10 mL acetonitrile were blended in a centrifuge tube of 50 mL,and the centrifuge tube was vibrated for 1 min with a vortex mixer.A homogeneous solution was obtained and then separated for 5 min at a speed of 4 200 rpm on the centrifuge,and a yellow supernatant was acquired.2)2 mL of the supernatant was injected to a centrifuge tube of 15 mL containing anhydrous Magnesium sulfate,PSA,graphitized carbon and C18for removing the effect of protein, fat,carbohydrates and other substances in Chinese cabbage.The centrifuge tube was blended for 1 min and then centrifuged for 5 min at a speed of 4 200 r/min.Then,the colourless supernatant was filtered.The filtrate was used directly for SERS measurement in the Optics-Electrics Application of Biomaterials Lab.3)1 mL of the filtrate was transferred into a 10 mL centrifuge tube and condensed with a termovap sample concentrator at 60℃until the solvent absolutely evaporated. 4)The concentrated pesticide was diluted with 1 mL ethyl acetate and shaken for a moment.Then the eluted solution was transferred into a vial and used to measure its actual value by GC-MS in Jiangxi Entry-Exit Inspection and Quarantine technology center.Then three methods as SNV,MSC and Normalization were used to optimize the original Raman spectra signals,and the PLS models of malathion pesticide residues in Chinese cabbage were established.The limit of detection (LOD)can reach the level of 1.082 mg/L by SERS method,and the concentration can meet the tolerance levels for malathion pesticide residues in chinese cabbage.The model predictive performance used normalization preprocessing method was optimal.The correlation coefficient of the calibration samples model(Rc)was 0.983 2,RMSECV was 1.78 mg/L, the correlation coefficient of prediction model(Rp)was 0.973 2,and RMSEP was 2.37 mg/L.The model results of the higher Rp value and the lower RMSEP value indicated that the method of SERS could accurately predict the malathion pesticide residues in Chinese cabbage.The five unknown concentration samples were prepared to verify the accuracy of the prediction models.The absolute values of relative deviation were calculated to be between 0.70%-9.84%.The predict recoveries were calculated to be between 99.30%-109.84%.These indicated that the SERS method was receivable and credible for rapid detection of malathion pesticide residues in Chinese cabbage.The t value was 1.589,less than t0.05,4= 2.776.The results of t test demonstrated that the difference between SERS and GC-MS was not significant.This study demonstrates that SERS is capable of detecting and identifying malathion pesticide residues in Chinese cabbage quickly and accurately.
spectrum analysis;pesticides;measurements;surface-enhanced Raman spectroscopy;chinese cabbage; malathion;partial least squares(PLS);rapid detection
10.11975/j.issn.1002-6819.2016.06.041
S634.1
A
1002-6819(2016)-06-0296-06
2015-09-23
2016-01-18
國家自然科學(xué)基金項(xiàng)目(31271612)
黃雙根(1979-),男,江西新干人,博士生,江西農(nóng)業(yè)大學(xué)副教授,主要從事農(nóng)產(chǎn)品品質(zhì)無損檢測(cè)。鎮(zhèn)江 江蘇大學(xué)現(xiàn)代農(nóng)業(yè)裝備與技術(shù)教育部重點(diǎn)實(shí)驗(yàn)室,212013。Email:shuang19792@163.com
※通信作者:胡建平(1965-),男,江蘇吳縣人,教授,博導(dǎo),主要從事精細(xì)農(nóng)業(yè)研究。鎮(zhèn)江 江蘇大學(xué)現(xiàn)代農(nóng)業(yè)裝備與技術(shù)教育部重點(diǎn)實(shí)驗(yàn)室,212013。Email:hujp@ujs.edu.cn
中國農(nóng)業(yè)工程學(xué)會(huì)會(huì)員:胡建平(E041200154S)
農(nóng)業(yè)工程學(xué)報(bào)2016年6期