張文強(qiáng),李 容,許文濤
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農(nóng)藥殘留的表面增強(qiáng)拉曼光譜快速檢測(cè)技術(shù)研究現(xiàn)狀與展望
張文強(qiáng)1,李 容1,許文濤2
(1. 中國(guó)農(nóng)業(yè)大學(xué)工學(xué)院,北京 100083; 2. 中國(guó)農(nóng)業(yè)大學(xué)食品科學(xué)與營(yíng)養(yǎng)工程學(xué)院,北京 100083)
果蔬農(nóng)藥殘留危害人類健康,施藥后,農(nóng)藥分布于其表皮和內(nèi)部組織,果蔬表面農(nóng)藥絕對(duì)殘留量低、不均勻,直接光譜檢測(cè)表征難,而表面增強(qiáng)拉曼散射(surface-enhanced Raman scattering, SERS)技術(shù)具有分子級(jí)檢測(cè)精度,可以有效擴(kuò)增信號(hào),在實(shí)現(xiàn)微量物質(zhì)檢測(cè)方面優(yōu)勢(shì)明顯。為此,論文綜述了國(guó)內(nèi)外表面增強(qiáng)拉曼散射技術(shù)的研究現(xiàn)狀,特別是詳細(xì)介紹了通過設(shè)計(jì)合理的表面增強(qiáng)拉曼基底結(jié)構(gòu),實(shí)現(xiàn)農(nóng)藥殘留信號(hào)增強(qiáng)的主要技術(shù)手段和表面增強(qiáng)拉曼光譜信號(hào)分析方法。在此基礎(chǔ)上,指出農(nóng)藥殘留的表面增強(qiáng)拉曼檢測(cè)技術(shù)研究中的前沿?zé)狳c(diǎn)問題,探討并展望了表面增強(qiáng)拉曼技術(shù)在農(nóng)藥殘留快速檢測(cè)方面的發(fā)展趨勢(shì)?;诒砻嬖鰪?qiáng)拉曼的農(nóng)藥高靈敏度、快速檢測(cè)表征技術(shù),將在農(nóng)藥違禁使用和農(nóng)藥殘留超標(biāo)監(jiān)管中有廣闊應(yīng)用前景。
農(nóng)藥;光譜分析;無損檢測(cè);殘留;表面增強(qiáng)拉曼散射光譜
中國(guó)作為農(nóng)藥生產(chǎn)和施用大國(guó),已有農(nóng)藥制劑1 000多種,農(nóng)藥用量達(dá)到337萬t/a,防治面積達(dá)3 億 hm2以上[1];但農(nóng)藥只有20%~30%的利用率,不適時(shí)、不對(duì)癥和過量用藥,帶來了農(nóng)藥殘留毒性、病蟲抗(耐)藥性上升、環(huán)境污染等一系列問題[2]。農(nóng)藥在施用到果蔬表面后,不同種類的農(nóng)藥遷徙特性不同,在表皮、果漿、果肉的分布與遷徙規(guī)律也不盡相同,已有的農(nóng)藥殘留檢測(cè)方法,需要對(duì)果蔬進(jìn)行破碎后,采用化學(xué)萃取等前處理手段富集農(nóng)藥后再進(jìn)行表征[3],操作復(fù)雜,不能適應(yīng)現(xiàn)場(chǎng)快速、實(shí)時(shí)檢測(cè)的需求。光譜檢測(cè)法盡管具有對(duì)農(nóng)藥快速、無損的檢測(cè)特征,且研究表明,利用紅外、拉曼光譜等手段可以實(shí)現(xiàn)多種農(nóng)藥的一致檢測(cè)。但分布于果蔬表皮表面的農(nóng)藥殘留占果蔬整體農(nóng)藥殘留比例較低,光譜信號(hào)弱,需要采用大型高靈敏設(shè)備進(jìn)行探測(cè),不能滿足現(xiàn)場(chǎng)快檢的需求。近來研究發(fā)現(xiàn),采用表面增強(qiáng)拉曼的方法,不僅可以極大提升農(nóng)藥的特征拉曼光譜信號(hào)強(qiáng)度,還突破了液體中拉曼光譜信號(hào)弱、熒光干擾的問題,有可能實(shí)現(xiàn)無損條件下的現(xiàn)場(chǎng)微量甚至分子級(jí)農(nóng)藥的快速檢測(cè)。采用商品級(jí)納米金/銀球與農(nóng)藥溶液混合的方法進(jìn)行增強(qiáng)檢測(cè),由于團(tuán)聚、氧化、組分不穩(wěn)定的原因,難以使分子有效附著在增強(qiáng)基底表面,信號(hào)增強(qiáng)效果有限,定量分析表征難。為此,國(guó)內(nèi)外表面增強(qiáng)拉曼光譜領(lǐng)域的研究者通過研制多種功能材料的納米表面增強(qiáng)拉曼基底,優(yōu)化獲得各種農(nóng)藥最佳的特征拉曼信號(hào)增強(qiáng)單元或組合結(jié)構(gòu),力圖實(shí)現(xiàn)微量農(nóng)藥快速檢測(cè)并建立低濃度農(nóng)藥特征拉曼信號(hào)的表征模型。此外,為實(shí)現(xiàn)基于表面增強(qiáng)拉曼散射(surface-enhanced Raman scattering, SERS)技術(shù)的農(nóng)藥殘留現(xiàn)場(chǎng)快速檢測(cè),對(duì)農(nóng)藥拉曼光譜進(jìn)行分析必不可少。拉曼光譜分析技術(shù)是以拉曼效應(yīng)為基礎(chǔ)建立起來的分子結(jié)構(gòu)表征技術(shù),其信號(hào)來源于分子的振動(dòng)和轉(zhuǎn)動(dòng)[4]。拉曼光譜分析的研究方向一般分為定性分析(不同的物質(zhì)具有不同的特征光譜)、結(jié)構(gòu)分析(對(duì)光譜譜帶的分析)及定量分析(根據(jù)物質(zhì)對(duì)光譜的吸光度計(jì)算所得)。由于不同農(nóng)藥具有不同的拉曼特征峰,因此對(duì)被檢測(cè)物的分析主要運(yùn)用“特征峰位移”進(jìn)行定性、定量判定。農(nóng)藥的實(shí)際使用過程中多是幾種農(nóng)藥混合使用,有必要研究快速、準(zhǔn)確判定混合農(nóng)藥殘留的特征拉曼光譜計(jì)算模型。本文從表面增強(qiáng)活性基底的制備、農(nóng)藥殘留表面增強(qiáng)拉曼光譜分析兩方面綜述了農(nóng)藥殘留SERS檢測(cè)技術(shù)的研究現(xiàn)狀,展望農(nóng)藥殘留的SERS檢測(cè)方法發(fā)展趨勢(shì),以期為SERS技術(shù)在指導(dǎo)農(nóng)藥施用、農(nóng)產(chǎn)品生產(chǎn)收獲、控制食品安全等方面提供參考。
1928年Raman和Krishman首次在液體的散射光中發(fā)現(xiàn)了拉曼散色現(xiàn)象[5],對(duì)CCl4的檢測(cè)限一般只能達(dá)到10-3g/L,而當(dāng)激發(fā)光波長(zhǎng)落在分子的電子躍遷吸收波長(zhǎng)區(qū)間時(shí),拉曼散射強(qiáng)度就會(huì)提高103~104倍[6]。1974年Fleischmann等[7]發(fā)現(xiàn)吡啶分子吸附在粗糙金銀表面時(shí),拉曼信號(hào)強(qiáng)度提高顯著,同時(shí)信號(hào)強(qiáng)度隨著電極所加電位的變化而變化。直至1977年,Jeanmaire等[8]與Albrecht等[9]經(jīng)過系統(tǒng)的試驗(yàn)研究和理論計(jì)算后,將這種與金、銀、銅等粗糙表面相關(guān)的增強(qiáng)效應(yīng)稱為表面增強(qiáng)拉曼散射效應(yīng),對(duì)應(yīng)的光譜則稱為表面增強(qiáng)拉曼光譜。SERS效應(yīng)發(fā)現(xiàn)以來,研究者們長(zhǎng)時(shí)間運(yùn)用粗糙化的Au、Ag金屬作為增強(qiáng)基底,其中Frens法[10]至今仍是制備納米金粒子的經(jīng)典方法,此外還有機(jī)械研磨法、金屬蒸汽溶劑化法、還原含金/銀化合物法、晶種生長(zhǎng)法等。1985年張鵬翔等[11]根據(jù)Creighton描述的納米銀膠體制備法,用NaBH4還原AgNO3水溶液得到粒徑約20 nm的銀溶膠增強(qiáng)基底,將其運(yùn)用于苯甲酸飽和水溶液SERS檢測(cè)中,得到的拉曼信號(hào)與普通苯甲酸飽和水溶液譜圖相比,其譜峰強(qiáng)度約提高1倍,增強(qiáng)因子達(dá)2.3×104。Emory等[12]利用分布過濾的方法分離不同粒徑的銀粒子,并發(fā)現(xiàn)粒徑在80~100 nm之間SERS信號(hào)最強(qiáng)。王健等[13]通過檸檬酸三鈉還原HAuCl4制備了粒徑分別約為16、26、40、66 nm的金粒,直接獲取偶連層分子的SERS信號(hào),發(fā)現(xiàn)在所研究的粒徑范圍內(nèi),SERS增強(qiáng)因子隨粒徑的增大而增加。金銀溶膠的經(jīng)典制備方法雖然比較方便,但是直接使用納米金/銀膠體作為拉曼增強(qiáng)基底,受納米金屬的氧化和團(tuán)聚等因素的影響,便攜式激光拉曼光譜儀的檢測(cè)靈敏度會(huì)降低1~2個(gè)數(shù)量級(jí),不能達(dá)到農(nóng)藥殘留量標(biāo)準(zhǔn)檢測(cè)要求[14-15]。此外采用貴金屬作為增強(qiáng)基底材料,存在成本高、不可重復(fù)利用等缺點(diǎn),制約其在現(xiàn)場(chǎng)快速檢測(cè)中的應(yīng)用。因此拉曼增強(qiáng)基底從簡(jiǎn)單納米Au、Ag微粒,研究逐漸趨向于廉價(jià)金屬納米微粒、各向異性納米氧化物微粒、納米陣列結(jié)構(gòu)等方向發(fā)展[16]。
1.2.1 表面增強(qiáng)拉曼光譜活性基底制備研究
國(guó)內(nèi)外研究表明通過優(yōu)化設(shè)計(jì)、選擇合適的材料、完善制造工藝參數(shù)等手段可以獲得SERS性能顯著提升的活性基底,在果蔬表面農(nóng)藥殘留快速檢測(cè)中已經(jīng)取得一定的進(jìn)展。Zhao等[17]通過化學(xué)反應(yīng)將碳點(diǎn)(carbon dots, CDs)與Ag納米顆粒(Ag nanoparticles, Ag NPs)結(jié)合形成Ag NPs/CDs雜化物作為新的表面增強(qiáng)拉曼散射(SERS)基底,可以檢測(cè)濃度低至10-9mol/L的氨基苯硫酚(-aminothiophenol, PATP)。賓夕法尼亞州立大學(xué)的Feng團(tuán)隊(duì)[18]通過控制氮摻雜石墨烯的費(fèi)米能級(jí)(fermi level, EF)偏移使電荷轉(zhuǎn)移增強(qiáng),從而顯著擴(kuò)大分子的振動(dòng)拉曼模式。試驗(yàn)證實(shí)此基底可以實(shí)現(xiàn)濃度為10-11mol/L的羅丹明B、結(jié)晶紫和亞甲藍(lán)分子的檢測(cè)。Kim等[19]利用銀鏡為表面增強(qiáng)基底,采集不同pH值下的苯并咪唑類表面增強(qiáng)拉曼光譜,對(duì)拉曼譜峰進(jìn)行了歸屬,其試驗(yàn)結(jié)果與密度泛函理論計(jì)算值吻合較好。Carrillo-Carrión等[20]制備了具有CdSe/ZnS顆粒的Ag-QD納米結(jié)構(gòu),使其形成核-殼量子點(diǎn)SERS活性基底。使用微分散器與海綿狀SERS活性基底結(jié)合形成精確的液相色譜檢測(cè)系統(tǒng),對(duì)注射農(nóng)藥(綠麥隆、莠去津、敵敵畏和特丁津)進(jìn)行SERS檢測(cè),檢測(cè)限可達(dá)0.2~0.5 ng/L,精確度在10.2%~12.5%之間。Li等[21]和Kubackova等[22]分別研究了基于改性納米銀微粒和二氧化硅包覆納米金微粒做基底材料,對(duì)有機(jī)氯農(nóng)藥和對(duì)硫磷農(nóng)藥進(jìn)行快速檢測(cè),證實(shí)改性和控制微粒排布有利于檢測(cè)靈敏度提升,如圖1所示。
注:I新鮮柑橘的拉曼光譜曲線;II含有對(duì)硫磷的果皮拉曼光譜;III金/二氧化硅納米顆粒修飾的柑橘表面的拉曼光譜;IV固體甲基對(duì)硫磷拉曼光譜。(激光功率為0.5 mW,采集次數(shù)為30 s)
Dai等[23]采用三步法制備了多功能TiO2/Ag納米粒子(Ag NPs)復(fù)合基底,該基底對(duì)蘋果汁中福美雙的檢測(cè)限低于美國(guó)環(huán)境保護(hù)局(U.S. environmental protection agency, EPA)規(guī)定的水果中最大殘留限量(如圖2所示)7 ppm(2.9×10-5mol/L)。一般三維納米顆粒制備過程中都會(huì)引入十六烷基三甲基溴化銨(hexadecyl trimethyl ammonium bromide, CTMAB)等表面活性劑[24],而這些活性劑會(huì)抑制痕量樣品的SERS檢測(cè),因此Xu等[25]開發(fā)了一種無須表面活性劑的方法制備出爆米花狀金納米顆粒,并借助納米孔之間形成的“熱點(diǎn)”,對(duì)水果樣品表面毒死蜱殘留進(jìn)行檢測(cè),檢測(cè)濃度低至1mol/L,符合國(guó)家標(biāo)準(zhǔn)。Li等[26]通過加熱回流法獲得由硬脂酸(stearic acid, SA)和聚乙烯吡咯烷酮(polyvinyl pyrrolidone, PVP)混合物改性合成的Ag/ZnO納米復(fù)合材料。改性的Ag/ZnO納米結(jié)構(gòu)與普通親水性Ag/ZnO基底相比,具有3倍的增強(qiáng)效果。Li等[27]制備的Ag2O@Ag核-殼型納米結(jié)構(gòu)活性增強(qiáng)基底,可以避免環(huán)境中雜質(zhì)帶來的干擾,從而獲得更穩(wěn)定的信號(hào);同時(shí)超薄的內(nèi)殼結(jié)構(gòu)讓基底靈敏度更高、均勻性和重現(xiàn)性更好,對(duì)R6G的檢測(cè)濃度低至10-11mol/L,增強(qiáng)因子高達(dá)106。實(shí)際應(yīng)用中,Ag2O@ Ag/PMMA混合形成的柔性SERS基底對(duì)黃瓜和蘋果皮上的毒死蜱進(jìn)行實(shí)時(shí)原位檢測(cè),檢測(cè)濃度低至10-7mol/L。Li等[28]通過制備PS/Ag納米顆粒作為動(dòng)態(tài)SERS檢測(cè)活性基底,對(duì)有機(jī)磷殺蟲劑對(duì)氧磷和殺螟松進(jìn)行低濃度檢測(cè),檢測(cè)濃度低至10 nmol/L時(shí)仍有較好的檢測(cè)效果。Wang等[29]在2D PS模板上通過濺射Ag和SiO2材料交替地制備了Ag/SiO2多層的“柱帽”形陣列。在氬氣下退火加速了Ag和SiO2之間的界面擴(kuò)散,一些Ag顆粒通過SiO2孔隙進(jìn)入SiO2層間,根據(jù)退火溫度不同,Ag納米粒子的尺寸從2~5 nm不等,在Ag顆粒和底層Ag之間產(chǎn)生1~2 nm SiO2的分離,為SERS提供了大量的熱點(diǎn)位點(diǎn)。
注:圖b中a-f為在SERS基底上分別摻加10-3、10-4、10-5、10-6、10?7、0 mol×L-1福美雙。
1.2.2 表面沉積納米顆粒的陣列SERS基底研究
陣列SERS結(jié)構(gòu)可以有效提升快速檢測(cè)結(jié)果的均勻性、一致性、準(zhǔn)確性,如Zhu等[30]構(gòu)筑了具有強(qiáng)電磁場(chǎng)耦合效應(yīng)的銀納米棒簇有序陣列,該陣列的表面增強(qiáng)拉曼光譜增強(qiáng)因子高達(dá)108,并具有較好的信號(hào)均勻性和重現(xiàn)性,能夠同時(shí)檢測(cè)水果中多種痕量農(nóng)藥,如甲基對(duì)硫磷和2-4-二氯苯氧乙酸(如圖3所示)等。Kumar等[31]通過將Ag納米棒嵌入聚二甲基硅氧烷(PDMS)聚合物中來制造新型表面增強(qiáng)拉曼光譜陣列基底。在機(jī)械拉伸應(yīng)變條件下對(duì)這些柔性基底進(jìn)行原位表面增強(qiáng)拉曼光譜測(cè)量。研究結(jié)果表明,柔性表面增強(qiáng)拉曼光譜基板可以承受高達(dá)30%的拉伸應(yīng)變()值,而不會(huì)損失表面增強(qiáng)拉曼光譜性能。通過簡(jiǎn)單的“粘貼和剝離”方法從果皮中直接提取微量(約10-9g/cm2)的福美雙(thiram)殺蟲劑,證明了銀納米顆粒(Ag NRs)嵌入PDMS組成表面增強(qiáng)拉曼光譜基底的高靈敏檢測(cè)功能。
圖3 痕量有機(jī)污染物表面增強(qiáng)拉曼檢測(cè)[30]
近年來,如何低成本、快速制造納米陣列表面增強(qiáng)拉曼光譜結(jié)構(gòu)備受關(guān)注。Wei等[32]通過使用Au納米顆粒/蜻蜓翼(Au NPs/DW)陣列作為SERS活性基底,通過簡(jiǎn)單的二步法在蜻蜓雙翼(DW)表面上沉積組裝金納米顆粒(Au NPs)作為增強(qiáng)檢測(cè)基底,對(duì)羅丹明6G(R6G)的檢出限可達(dá)10-8mol/L,對(duì)于三維Au NPs/DW的應(yīng)用,也可以定量檢測(cè)西維因和甲萘威的微量樣品,檢出限達(dá)到10-7mol/L。Ren等[33-34]、Kwon等[35]分別利用硅藻多孔及其吸附特性和透明介質(zhì)屬性,在其表面自組裝了銀、金納米顆粒(如圖4,圖5所示),檢測(cè)發(fā)現(xiàn)拉曼增強(qiáng)效果好于單純的納米顆粒、薄膜。
注:1納米顆粒置于平板玻璃上;2納米顆粒置于硅藻上;3未加修飾的硅藻;4 硅藻殼上的表面增強(qiáng)拉曼散射;5 玻璃基底上表面增強(qiáng)拉曼散射。
注:情況Ⅰ和情況Ⅲ, Ag NPs二聚體位于孔上方;情況ⅡAg NPs二聚體位于殼體上方;
Zhan等[36]推出了制造具有有序納米結(jié)構(gòu)且低成本的柔性SERS基底。將聚二甲基硅氧烷(polydimethylsiloxane, PDMS)滴加到自組裝聚苯乙烯(polystyrene, PS)納米顆粒(NP)模板上時(shí),PDMS溶液在固化過程中的流動(dòng)性可以更好地進(jìn)入PS納米球(NP)之間的間隙以形成更緊密的填充,使PDMS膜上形成有序納米結(jié)構(gòu)。該基底用于彎曲物體上測(cè)試分析物,增強(qiáng)因子約為107,重復(fù)性好,偏差小于13%。并有望擴(kuò)展到靈敏傳感器和執(zhí)行器的未來應(yīng)用中。劉紹根等[37]制備了具有柔韌性和透光性的Au NPs/PMMA表面增強(qiáng)拉曼基底,其對(duì)魚表面殘留孔雀石綠的原位檢測(cè)下限達(dá)到 0.1mol/L。Sivashanmugan等[38]運(yùn)用聚焦離子束與納米壓痕法制備出Au/Ag/Au納米棒陣列,以羅丹明6G作為探針分子進(jìn)行SERS檢測(cè),得到增強(qiáng)因子達(dá)2.15×108,并用該陣列基底對(duì)氯菊酯、氯氰菊酯、甲萘威和亞胺硫磷殺蟲劑進(jìn)行痕量檢測(cè),檢測(cè)濃度可低至10-8mol/L。
此外,本課題組系統(tǒng)地研究了基于硅藻的功能結(jié)構(gòu)制造技術(shù),已經(jīng)實(shí)現(xiàn)了基于硅藻的微納米功能單體、器件、微流體芯片的制造;如利用空氣液面自組裝方法實(shí)現(xiàn)硅藻的一致定向密排[39]。以硅藻為模板,在自組裝前提下,實(shí)現(xiàn)納米級(jí)金微柱陣列的制造,并用結(jié)晶紫作為檢測(cè)探針得到極強(qiáng)的拉曼增強(qiáng)效果(如圖6a所示)[40],其增強(qiáng)因子達(dá)到7.64×104;試驗(yàn)證實(shí)硅藻殼體的多級(jí)孔系結(jié)構(gòu),對(duì)蛋白分子、脂質(zhì)體分子、微米級(jí)的碳素微粒、神經(jīng)毒氣、喹硫磷均有吸附富集作用[41-42]。對(duì)比發(fā)現(xiàn),硅藻富集吸附后,利用生物分子馬達(dá)對(duì)農(nóng)藥喹硫磷的檢測(cè)限值從5g/mL下降到0.5g/mL。此外,通過硅藻的多級(jí)孔系結(jié)構(gòu)及其本身帶負(fù)電性質(zhì)成功吸附納米Au顆粒,以此制備的SERS基底呈單層穩(wěn)定狀態(tài),試驗(yàn)也證明了該基底對(duì)核酸的拉曼檢測(cè)有較強(qiáng)增強(qiáng)作用(如圖6b所示)。結(jié)合上述國(guó)內(nèi)外研究文獻(xiàn)資料,不難發(fā)現(xiàn),基于復(fù)合拉曼增強(qiáng)基底的低濃度、非均勻、便攜快速的高靈敏檢測(cè),以及低成本制造,多種結(jié)構(gòu)陣列化耦合技術(shù)等將成為未來一段時(shí)間內(nèi)的研究熱點(diǎn)。
注:基底Ⅰ,Au NPs置于修飾中心圓篩藻上;基底Ⅱ,Ag NPs和R6G置于修飾中心圓篩藻上;基底Ⅲ,Au NPs和R6G置于中心圓篩藻上。
農(nóng)藥殘留分析不僅需要考慮其痕量檢測(cè)拉曼光譜信噪比低、微弱信號(hào)極易被熒光信號(hào)泯滅等問題,還需考慮農(nóng)藥混合使用復(fù)雜體系中其它未知組成成分的干擾因素。為此,國(guó)內(nèi)外開展了借助拉曼光譜檢測(cè)分析方法對(duì)SERS譜圖進(jìn)行定性、定量分析算法的研究。拉曼振動(dòng)峰的位置只與化學(xué)鍵的振動(dòng)頻率或轉(zhuǎn)動(dòng)頻率有關(guān),不同位置的拉曼峰代表了不同的化學(xué)鍵,反映了分子的結(jié)構(gòu)信息,所以根據(jù)拉曼光譜可以確定分子的結(jié)構(gòu),而分子的化學(xué)鍵等結(jié)構(gòu)信息同樣表現(xiàn)在拉曼光譜上。因此確定各種農(nóng)藥的特征拉曼位移是進(jìn)行農(nóng)藥殘留表面增強(qiáng)拉曼光譜檢測(cè)的前提條件[43]。黃雙根等[44]應(yīng)用密度泛函理論對(duì)3種有機(jī)磷類農(nóng)藥分子(樂果、敵百蟲和伏殺硫磷)進(jìn)行了幾何結(jié)構(gòu)優(yōu)化和頻率計(jì)算,并將試驗(yàn)拉曼光譜、理論計(jì)算拉曼光譜和表面增強(qiáng)拉曼光譜進(jìn)行比較,對(duì)這3種有機(jī)磷類農(nóng)藥分子在400~1 800 cm-1范圍內(nèi)的振動(dòng)頻率進(jìn)行了全面地歸屬。得出樂果明顯拉曼振動(dòng)峰位于494、646、764、908、1 164和1 136 cm-1處;敵百蟲的位于438、618、784、1 026和1 238 cm-1;伏殺硫磷的位于650、692、755、1 109、1 238和1 283 cm-1處。趙琦等[45]采用距離匹配和判別分析的方法對(duì)蘋果汁中馬拉硫磷和二嗪農(nóng)進(jìn)行定性分析,再結(jié)合偏最小二乘法(partial least squares, PLS)對(duì)2種農(nóng)藥的表面增強(qiáng)拉曼光譜分別進(jìn)行數(shù)學(xué)建模分析。結(jié)果表明表面增強(qiáng)拉曼散射技術(shù)對(duì)無損快速定性分析馬拉硫磷和二嗪農(nóng)具有較高的準(zhǔn)確性,而定量分析二者的含量也具有較高的可行性;其中馬拉硫磷定量模型相關(guān)系數(shù)為0.99,校正均方根誤差為0.02,校正樣本的擬合值與真實(shí)值的最大殘差為0.059 mg/kg;二嗪農(nóng)定量模型相關(guān)系數(shù)為0.99,校正均方根誤差為0.01,校正樣本的擬合值與真實(shí)值最大殘差為0.03 mg/kg。李曉舟等[46]通過對(duì)蘋果表面殘留農(nóng)藥進(jìn)行檢測(cè),從獲得的譜圖中選取了728、1 512 cm-1處的拉曼信號(hào)分別作為甲拌磷與倍硫磷定量分析特征峰,并采用內(nèi)定法分別建立了甲拌磷和倍硫磷的線性回歸模型。利用拉曼光譜的方法對(duì)物質(zhì)檢測(cè)時(shí),每種物質(zhì)特征峰的檢出數(shù)量,除了與增強(qiáng)基底有關(guān),還與物質(zhì)的濃度密切相關(guān),所以當(dāng)檢測(cè)低濃度農(nóng)藥時(shí)就不可能將其所有的特征峰找出并一一對(duì)應(yīng),必要研究并找到被測(cè)物的特征明顯且在不同濃度下均能穩(wěn)定檢出的特征峰。對(duì)此鄒明強(qiáng)等[47]對(duì)百草枯經(jīng)過多樣品試驗(yàn)得出其SERS強(qiáng)度與對(duì)應(yīng)含量進(jìn)行線性分析,明確百草枯最明顯特征峰在1 665 cm-1處。王曉彬等[48]檢測(cè)臍橙果肉提取液中三唑磷溶液最低檢測(cè)濃度為0.5 mg/L,選用2 257 cm-1處乙腈C≡N伸縮振動(dòng)峰為內(nèi)標(biāo)峰,以三唑磷1 409 cm-1特征峰與乙腈2 257 cm-1特征峰強(qiáng)度比值作為相對(duì)強(qiáng)度,得出其相對(duì)強(qiáng)度在三唑磷溶液濃度為0.5~20 mg/L范圍內(nèi)具有良好的線性關(guān)系,同時(shí),建立的模型具有較好的預(yù)測(cè)效果,其預(yù)測(cè)誤差在小于0.12 mg/L,預(yù)測(cè)回收率為98.1%~102.5%。He等[49]開發(fā)了一種快速(約10 min)簡(jiǎn)單的表面擦拭捕獲農(nóng)藥的方法,利用不同濃度的噻苯達(dá)唑(thiabendazole,TBZ)建立標(biāo)準(zhǔn)校準(zhǔn)模型,在計(jì)算得到釋放因子為66.6%基礎(chǔ)上對(duì)蘋果表面噻菌靈進(jìn)行回收率計(jì)算和定量檢測(cè),得到不同水平下噻菌靈回收率在59.4%~76.6%之間,擦拭-表面增強(qiáng)拉曼法最終計(jì)算準(zhǔn)確度可達(dá)89.2%~115.4%。上述文獻(xiàn)表明,對(duì)單一品種農(nóng)藥的拉曼光譜特征信號(hào)增強(qiáng)及拉曼光譜特征峰值曲線表征與計(jì)算方法可行,開展結(jié)合實(shí)際工況的復(fù)雜環(huán)境下(多種混合分子條件下)的拉曼光譜特征曲線表征研究,建立對(duì)應(yīng)的標(biāo)準(zhǔn)校準(zhǔn)模型,實(shí)現(xiàn)痕量農(nóng)藥的精準(zhǔn)定量表征尤為重要。
從農(nóng)藥使用現(xiàn)狀來看,經(jīng)營(yíng)者多只看到高產(chǎn)量帶來的高經(jīng)濟(jì)收益而忽略消費(fèi)者健康與生態(tài)環(huán)境保護(hù),不合理配藥、施藥使作物抗性增強(qiáng)。因此市場(chǎng)監(jiān)管部門需要對(duì)農(nóng)藥毒性劃分并能給予消費(fèi)者直觀的判斷;如何運(yùn)用SERS檢測(cè)技術(shù)對(duì)農(nóng)藥毒性進(jìn)行快速區(qū)分尤為重要,其難點(diǎn)不在于濃度的高低而是如何快速劃分并直觀顯示檢測(cè)結(jié)果。從果蔬購(gòu)買者來說,由于種植者為提高果蔬產(chǎn)量會(huì)在實(shí)際種植中使用殺蟲劑、除草劑等農(nóng)藥,使得最終果蔬表面殘留多種混合農(nóng)藥,而能否即檢即測(cè)農(nóng)藥殘留是否超標(biāo),是果蔬購(gòu)買者最為關(guān)心的問題;在未知農(nóng)藥類型和微量無損條件下,農(nóng)藥殘留的有效、快速低成本感知技術(shù)研究十分必要,相對(duì)其他快速檢測(cè)方法來說SERS檢測(cè)技術(shù)對(duì)農(nóng)藥的檢測(cè)限雖低,但是其難點(diǎn)在于僅僅通過特征峰判別種類對(duì)于混合農(nóng)藥的檢測(cè)存在困難,要解決該難點(diǎn)就需要針對(duì)常用農(nóng)藥在低濃度狀態(tài)下提取每種農(nóng)藥特征峰并建立農(nóng)藥殘留拉曼圖譜庫(kù)。考慮到在實(shí)際運(yùn)用中農(nóng)藥殘留的SERS檢測(cè)分析譜圖并非與所有待測(cè)農(nóng)藥之間存在線性關(guān)系,必要借助建立多局部線性模型[50]或者使用人工神經(jīng)網(wǎng)絡(luò)方法(artificial neural network, ANN)[51]等非線性建模手段對(duì)待測(cè)物質(zhì)進(jìn)行定量分析。
金屬表面產(chǎn)生SERS增強(qiáng)通常被解釋為表面等離子體共振(surface plasmon resonance,SPR)引起的[52]。同時(shí),表面等離子體共振激發(fā)主要集中在納米顆粒之間的空隙(稱為熱點(diǎn))[53]。而SERS檢測(cè)均勻性、重現(xiàn)性不好的主要因素就是因?yàn)橹苽涞慕饘倌z體穩(wěn)定性差,容易聚集,大大減少SERS檢測(cè)熱點(diǎn);使得信號(hào)強(qiáng)度提升小、測(cè)試結(jié)果不穩(wěn)定。本文綜述的文獻(xiàn)表明,制備三維有序陣列的增強(qiáng)基底,由于3D納米結(jié)構(gòu)的比表面積較大、結(jié)構(gòu)穩(wěn)定有序,在進(jìn)行SERS檢測(cè)時(shí),吸附待測(cè)分子的能力顯著,獲取的SERS信號(hào)穩(wěn)定性和均勻性更好。同時(shí),通過精確控制增強(qiáng)基底陣列納米顆粒之間形成的間隙大小可以產(chǎn)生更高密度的檢測(cè)“熱點(diǎn)”,從而具有更好的重現(xiàn)性。所以未來農(nóng)藥殘留檢測(cè)的SERS基底應(yīng)考慮農(nóng)藥富集濃縮結(jié)構(gòu)和圖形化表面增強(qiáng)拉曼結(jié)構(gòu)在微流控芯片上的集成制造。該結(jié)構(gòu)一方面能最大程度增加單位面積上的農(nóng)藥含量,提升信號(hào)強(qiáng)度;另一方面,讓微量農(nóng)藥與“帶有指紋”功能的圖形化多種表面增強(qiáng)拉曼復(fù)合結(jié)構(gòu)充分接觸,提升特征信號(hào)增強(qiáng)效果,實(shí)現(xiàn)混合農(nóng)藥的定性定量分析。此外,通過設(shè)計(jì)制造一種具有陣列結(jié)構(gòu)的柔性透明表面增強(qiáng)拉曼貼片,在施藥后,立即將功能貼片貼敷在果蔬表皮,通過拉曼光譜儀照射貼片,可以快速、直觀獲取果蔬表面的農(nóng)藥分布和含量,為合理施藥提供指導(dǎo)。
然而,就目前的現(xiàn)狀來看,上述柔性透明陣列基底距離實(shí)際應(yīng)用仍有一段距離。首先,從活性基底的制造工藝來看,有序陣列基底的制備工藝流程復(fù)雜,成本較高;此外,柔性基底對(duì)材料的要求高,目前集中在柔性碳材料及部分高分子聚合物上。其中柔性碳材料的制作工藝?yán)щy,而高分子聚合物本身的拉曼信號(hào)干擾又會(huì)增加光譜分析難度。因此在果蔬農(nóng)藥殘留實(shí)際檢測(cè)過程中,仍需努力突破以上瓶頸;進(jìn)一步,結(jié)合各類農(nóng)藥的遷徙和實(shí)效模型,建立果蔬農(nóng)藥殘留狀態(tài)的精準(zhǔn)預(yù)測(cè)模型,才能最終實(shí)現(xiàn)服務(wù)互聯(lián)網(wǎng)+農(nóng)業(yè)和安全消費(fèi)的目標(biāo)。
表面增強(qiáng)拉曼光譜技術(shù)作為一種快速檢測(cè)技術(shù),在農(nóng)藥痕量檢測(cè),尤其是農(nóng)藥殘留快速篩選方面有較大的應(yīng)用潛力。由于表面增強(qiáng)拉曼光譜信號(hào)的穩(wěn)定性和重現(xiàn)性主要取決于于表面增強(qiáng)基底的納米材料選擇、陣列結(jié)構(gòu)等,研制方便易用的表面增強(qiáng)基底并不斷提升其靈敏度,建立復(fù)雜條件下農(nóng)藥特征拉曼光譜表征模型,將有效拓寬表面增強(qiáng)拉曼光譜的應(yīng)用領(lǐng)域,為安全合理施藥提供技術(shù)支持,保障食品安全。
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Research status and prospect of rapid detection technology of pesticide residues based on surface-enhanced Raman scattering
Zhang Wenqiang1, Li Rong1, Xu Wentao2
(1.,,100083,; 2.,,100083,)
Pesticide residues in fruits or vegetables are detrimental to human health seriously. After spraying pesticides, residual pesticides existed on the surface and internal tissues of fruits or vegetables. The surface pesticide residues were few and uneven, being difficult to distinguish quantitatively losslessly and rapidly with simple spectral detection. In order to prevent acute or chronic toxicity to human health through their residues in agricultural products and foodstuffs, monitoring pesticide residues was extremely crucial to ensure that pesticides in agricultural products were in permitted levels. Therefore, it was important to choose rapid and convenient technique with various advantages, like high sensitivity and selectivity, efficiency, and easy to operate for rapid detection of pesticide residues. The surface-enhanced Raman scattering (SERS) spectroscopy is a high sensitive analytical technology in the detection of trace material, and has been widely used in the field of public security, environmental monitoring and food safety, owing to the advantages of sharp bandwidth, effective signal amplification, rich molecular information and molecular level detection accuracy. Developing an SERS substrate which could be applied in rapid efficient sampling and direct detection in on-spot assay has been one of the keys of SERS research. Hence, the research status of SERS technology was summarized in this paper. In particular, the main technical methods to realize the pesticide residues signal enhancement by designing a reasonable surface-enhanced Raman substrate, and the surface-enhanced Raman spectral signal analysis methods were described in detail. By fabricating an ordered array with so-called “hot spot” of reinforced substrates, the SERS detection signal strength was greatly enhanced compared to the original SERS detection technique in controlling excellent uniformity, reproducibility and stability. The qualitative identification method of pesticide residue by SERS was mainly based on the mathematical model of pesticide characteristic peak shift. Then partial least squares were used to build the quantitative model, and the results showed that the qualitative identification based on SERS to detect trace levels of pesticide residues had high accuracy, but the quantitative analysis still needed further efforts. By the way, the frontier hotspots in the research of SERS detection technology for pesticide residues were pointed out in this paper. According to the actual situation of pesticide residues in fruits and vegetables, much attention has been paid to the flexible SERS substrate due to its powerful properties that met the food or vegetable demand. At the same time, it was proposed that SERS detection instruments should be more miniaturized and integrated, possess multi-channel detection, and have wireless communication and higher stability and repeatability in the development of future pesticide residue detection. In addition, the development trends of SERS technology in rapid detection of pesticide residues were discussed and forecasted. The rapid detection of pesticide residues and characterization techniques with high sensitivity, pollution-free and lossless nature based on SERS technology would have broad application prospects for the supervision on pesticides using.
pesticide; spectrum analysis; non-destructive detection; residues; surface-enhanced Raman scattering (SERS) spectroscopy
10.11975/j.issn.1002-6819.2017.24.035
S126
A
1002-6819(2017)-24-0269-08
2017-07-26
2017-10-23
國(guó)家自然科學(xué)基金(51305446)
張文強(qiáng),男,博士,副教授,主要從事農(nóng)業(yè)機(jī)器人、智能農(nóng)業(yè)裝備、生物制造方面研究。Email:zhangwq@cau.edu.cn
張文強(qiáng),李 容,許文濤. 農(nóng)藥殘留的表面增強(qiáng)拉曼光譜快速檢測(cè)技術(shù)研究現(xiàn)狀與展望[J]. 農(nóng)業(yè)工程學(xué)報(bào),2017,33(24):269-276. doi:10.11975/j.issn.1002-6819.2017.24.035 http://www.tcsae.org
Zhang Wenqiang, Li Rong, Xu Wentao. Research status and prospect of rapid detection technology of pesticide residues based on surface-enhanced Raman scattering[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(24): 269-276. (in Chinese with English abstract) doi:10.11975/j.issn.1002-6819.2017.24.035 http://www.tcsae.org