亚洲免费av电影一区二区三区,日韩爱爱视频,51精品视频一区二区三区,91视频爱爱,日韩欧美在线播放视频,中文字幕少妇AV,亚洲电影中文字幕,久久久久亚洲av成人网址,久久综合视频网站,国产在线不卡免费播放

        ?

        線掃描式拉曼高光譜成像技術(shù)無(wú)損檢測(cè)奶粉三聚氰胺

        2018-01-09 01:22:51楊桂燕王慶艷黃文倩王曉彬陳立平
        關(guān)鍵詞:三聚氰胺拉曼像素點(diǎn)

        劉 宸,楊桂燕,王慶艷,黃文倩,王曉彬,陳立平

        ?

        線掃描式拉曼高光譜成像技術(shù)無(wú)損檢測(cè)奶粉三聚氰胺

        劉 宸1,2,3,4,楊桂燕2,3,4,王慶艷2,3,4,黃文倩2,3,4,王曉彬2,3,4,陳立平1,2,3,4※

        (1. 西北農(nóng)林科技大學(xué)機(jī)械與電子工程學(xué)院,楊凌 712100;2. 國(guó)家農(nóng)業(yè)智能裝備工程技術(shù)研究中心,北京 100097; 3. 農(nóng)業(yè)部農(nóng)業(yè)信息技術(shù)重點(diǎn)實(shí)驗(yàn)室,北京 100097;4. 農(nóng)業(yè)智能裝備技術(shù)北京市重點(diǎn)實(shí)驗(yàn)室,北京 100097)

        為了實(shí)現(xiàn)顆粒狀樣本的大面積無(wú)損快速檢測(cè),該研究結(jié)合拉曼光譜和高光譜技術(shù)搭建了一套線掃描式拉曼高光譜檢測(cè)系統(tǒng),對(duì)奶粉和三聚氰胺顆?;旌蠘颖具M(jìn)行了檢測(cè)研究。研究通過(guò)高斯窗平滑法和airPLS基線校正方法分別消除了拉曼光譜中的噪聲信號(hào)和熒光背景,選取三聚氰胺主要特征峰(671.71 cm-1)處的單波段圖像作為是否含有三聚氰胺顆粒的判斷依據(jù)。研究首先對(duì)三聚氰胺產(chǎn)生的拉曼信號(hào)在奶粉顆粒中的穿透深度進(jìn)行了檢測(cè),隨后完成了10種不同濃度的三聚氰胺奶粉混合樣本的拉曼高光譜采集,對(duì)特征單波段圖像中各像素點(diǎn)的拉曼強(qiáng)度平均值進(jìn)行一元線性分析,并對(duì)單波段圖像進(jìn)行二值化處理。結(jié)果顯示,在三聚氰胺特征單波段圖像中,感興趣區(qū)域內(nèi)所有像素點(diǎn)的拉曼強(qiáng)度平均值與三聚氰胺濃度之間線性度較高,其決定系數(shù)2達(dá)到了0.995 4。在二值圖像中,三聚氰胺顆粒的位置信息能夠直觀的展現(xiàn)。研究結(jié)果表明,拉曼高光譜成像技術(shù)具有快速、無(wú)損和大面積檢測(cè)的特點(diǎn),在實(shí)際應(yīng)用中具有巨大潛力。

        無(wú)損檢測(cè);圖像處理;光譜分析;拉曼光譜;高光譜成像技術(shù);線掃描式;脫脂奶粉;三聚氰胺

        0 引 言

        三聚氰胺屬于化工原料,是食品非法添加劑的一種,用來(lái)虛擬提升奶粉或飼料中的蛋白質(zhì)含量指標(biāo)[1]。目前,國(guó)內(nèi)外學(xué)者應(yīng)用高效液相色譜法(HPLC)和表面增強(qiáng)拉曼光譜法(SERS)均實(shí)現(xiàn)了奶粉中的三聚氰胺的快速檢測(cè)[2-5]。但在這2種方法中,奶粉樣本都需要先局部取樣,再轉(zhuǎn)化成液態(tài)形式方能進(jìn)行下一步操作,檢測(cè)過(guò)程中還需借助一些化學(xué)分析純或增強(qiáng)試劑[6-8]。這2種常用的檢測(cè)方法均影響了奶粉顆粒的使用性能,屬于破壞性檢測(cè)。此外,經(jīng)此2種方法得出的檢測(cè)結(jié)果只能代表局部取樣的平均情況,無(wú)法反映整體樣本的濃度信息和三聚氰胺顆粒的具體分布。顆粒狀樣本與液態(tài)樣本不同,三聚氰胺顆粒在奶粉中的分布可能并不均勻,局部采樣的單點(diǎn)檢測(cè)方式結(jié)果并不準(zhǔn)確。拉曼光譜作為一種散射光譜在無(wú)損檢測(cè)方面具有一定優(yōu)勢(shì)[9-11]。高光譜成像技術(shù)不僅可以獲得樣本的圖像信息,圖像中每個(gè)像素點(diǎn)均包含了一條完整的光譜譜線。在食品的品質(zhì)安全檢測(cè)中,高光譜成像技術(shù)已經(jīng)應(yīng)用于果蔬、肉制品、乳制品等多個(gè)領(lǐng)域,常用來(lái)展示樣本中某一特定成分的空間分布信息[12-15]。因此,結(jié)合拉曼光譜和高光譜成像技術(shù)有望實(shí)現(xiàn)對(duì)奶粉樣本的大面積直接檢測(cè)。目前,Dhakal等對(duì)點(diǎn)掃描式拉曼高光譜檢測(cè)中顆粒狀樣本的最優(yōu)厚度進(jìn)行了研究,結(jié)果顯示三聚氰胺的拉曼信號(hào)能穿透至多3 mm厚度的淀粉顆粒,在面粉顆粒中的穿透深度最高為1 mm,在奶粉顆粒中的穿透深度尚不明確[16]。Qin等分別選取點(diǎn)掃描式和線掃描式,結(jié)合拉曼光譜和高光譜成像技術(shù)對(duì)奶粉中的幾種非法添加劑檢測(cè)進(jìn)行了相關(guān)研究[17-18]。結(jié)果表明采用點(diǎn)掃描式的二值圖像中,識(shí)別為添加劑的像素點(diǎn)的個(gè)數(shù)與添加劑的濃度呈明顯線性關(guān)系,而在線掃描方式中,二者之間的關(guān)系尚不明確。點(diǎn)掃描式的掃描方式需要花費(fèi)大量時(shí)間,無(wú)法實(shí)現(xiàn)樣本的現(xiàn)場(chǎng)快速檢測(cè)[19]。因此,本研究搭建了一套線掃描式拉曼高光譜成像系統(tǒng),針對(duì)奶粉中三聚氰胺顆粒的檢測(cè)優(yōu)化試驗(yàn)參數(shù),探索拉曼高光譜圖像與三聚氰胺顆粒之間的關(guān)系,用以實(shí)現(xiàn)大面積混合樣本的快速無(wú)損檢測(cè)。

        1 材料與方法

        1.1 線掃描拉曼高光譜成像系統(tǒng)

        線掃描拉曼高光譜成像系統(tǒng)由拉曼成像光譜儀(ImSpector R10E,Specim,F(xiàn)inland)、線陣CCD相機(jī)(iKon-M 934,Andor Technology plc.,N. Ireland)、一字線激光器(innovative photonic solutions,USA)、成像鏡頭、二向色鏡及濾光片、樣品升降臺(tái)、移動(dòng)軌道、步進(jìn)電機(jī)、電源以及計(jì)算機(jī)組成,如圖1所示。拉曼成像光譜儀的采集范圍是770~980 nm(?261~2539 cm-1),光譜分辨率是0.6 nm,線陣CCD相機(jī)的分辨率是512× 1 024 pixels,系統(tǒng)空間分辨率是0.25 mm/pixel。線激光的波長(zhǎng)為785 nm,空間線寬2 mm,張角為31°[20]。線激光由半導(dǎo)體激光器產(chǎn)生,具有體積小、壽命長(zhǎng)的特點(diǎn),此外可以有效抑制奶粉產(chǎn)生的熒光背景。采集時(shí)線激光通過(guò)二向色鏡反射到樣本表面,線陣CCD相機(jī)采集狹縫范圍內(nèi)樣本的高光譜圖像,然后通過(guò)電動(dòng)位移臺(tái)水平移動(dòng)完成整個(gè)樣本的掃描。

        圖1 拉曼高光譜成像系統(tǒng)原理圖

        1.2 樣本制備

        試驗(yàn)用奶粉共3種,分為全脂奶粉(全脂奶粉,伊利)、低脂奶粉(學(xué)生高鋅高鈣奶粉,伊利)和脫脂奶粉(高蛋白脫脂高鈣奶粉,伊利),均購(gòu)買(mǎi)于北京超市發(fā)超市;三聚氰胺分析純(99%)來(lái)自上海晶純生化科技股份有限公司。

        在奶粉樣本最佳厚度的試驗(yàn)中,研究制備如圖2a所示的雙層樣本。雙層樣本上半部為1—5個(gè)鋁環(huán)厚度的奶粉層,高度從1.0 mm至5.0 mm可調(diào),其中鋁環(huán)尺寸為外徑40 mm,內(nèi)徑28 mm;下半部為裝滿三聚氰胺的培養(yǎng)皿,高度為5.0 mm,培養(yǎng)皿直徑35 mm。檢測(cè)時(shí)首先放置5個(gè)鋁環(huán)厚度的奶粉顆粒進(jìn)行高光譜采集,然后依次去除1個(gè)鋁環(huán)厚度的奶粉層,每次奶粉層的上表面沿鋁環(huán)外圍刮平,重復(fù)此步驟直至鋁環(huán)全部移除完成一組采集。試驗(yàn)依照此流程分別選取全脂奶粉、低脂奶粉和脫脂奶粉填充奶粉層,共完成了3組檢測(cè)。

        在三聚氰胺濃度檢測(cè)試驗(yàn)中,為了減少脂肪含量的影響,試驗(yàn)選取脫脂奶粉制備不同濃度的三聚氰胺奶粉混合樣本共10份,每份10 g,質(zhì)量濃度范圍從0.01%至2.00%。制備過(guò)程中先在電子天平上分別稱(chēng)量每份樣本所需質(zhì)量的脫脂奶粉顆粒和三聚氰胺顆粒,隨即倒入50 mL離心管中,將離心管放置于旋渦振蕩器上運(yùn)行20 min至二者充分混合,混合均勻后將離心管放置于試管架上。此外,脫脂奶粉和三聚氰胺的純物質(zhì)樣本也經(jīng)相同過(guò)程制備。在圖像采集時(shí),將混合樣本顆粒填滿于如圖2b中的鋁合金容器中,沿容器上邊面進(jìn)行刮平。該容器的外部尺寸為100 mm×55 mm×10 mm,內(nèi)部的凹陷部分尺寸為90 mm×45 mm×2 mm。試驗(yàn)中相同濃度的混合樣本重復(fù)取樣共采集3次。根據(jù)之前的試驗(yàn)結(jié)果,試驗(yàn)參數(shù)設(shè)置為激光強(qiáng)度100 mW,曝光時(shí)間1 000 ms。

        圖2 樣本制備示意圖

        1.3 高光譜圖像預(yù)處理

        在采集的高光譜圖像中,研究首先進(jìn)行感興趣區(qū)域選取。在奶粉顆粒樣本最佳厚度的試驗(yàn)中,研究選取鋁環(huán)中心點(diǎn)附近面積為15 mm×15 mm區(qū)域作為感興趣區(qū)域,該區(qū)域包含60 pixels×60 pixels共3 600個(gè)像素點(diǎn)。在三聚氰胺濃度檢測(cè)試驗(yàn)中,研究從鋁合金容器中心區(qū)域選取40 mm×80 mm面積范圍作為感興趣區(qū)域,該面積內(nèi)包含160 pixels×320 pixels共51 200個(gè)像素點(diǎn)。對(duì)于感興趣區(qū)域內(nèi)每個(gè)像素點(diǎn)的拉曼光譜,研究首先采用高斯窗平滑法消除噪聲信號(hào),然后采用airPLS基線校正法消除熒光背景[21-24]。預(yù)處理后,研究挑選三聚氰胺的特征單波段圖像,統(tǒng)計(jì)所有像素點(diǎn)的拉曼強(qiáng)度平均值并進(jìn)行一元線性分析,通過(guò)二值化法獲取了相應(yīng)的二值圖像[25-26]。高光譜圖像的預(yù)處理過(guò)程在ENVI 5.2(Exelis Visual Information Solutions,Boulder, CO, USA)軟件和MATLAB(R2014a, Math Works, Natick, MA, USA)軟件中完成。

        2 結(jié)果與分析

        2.1 三聚氰胺的拉曼光譜分析

        脫脂奶粉和三聚氰胺純物質(zhì)的平均拉曼光譜如圖3所示,圖中光譜均為純物質(zhì)樣本感興趣區(qū)域(40 mm× 80 mm)內(nèi)51 200個(gè)像素點(diǎn)的平均光譜。

        圖3 三聚氰胺純物質(zhì)和脫脂奶粉的平均拉曼光譜圖

        在預(yù)處理后的校正光譜中,三聚氰胺主要的拉曼特征峰分別在577.93、671.71、979.01、1 440.82和1 553.86 cm-1位移處。其中577.93和979.01 cm-1處的拉曼特征峰分別由C-N-C鍵的彎曲振動(dòng)和對(duì)稱(chēng)伸縮振動(dòng)引起[27];1 440.82和1 553.86 cm-1處拉曼特征峰的由C=N鍵的伸縮振動(dòng)以及N-H鍵的彎曲振動(dòng)引起;在671.71 cm-1處的為最強(qiáng)拉曼特征峰,形成原因是三嗪環(huán)的剪式振動(dòng)[28-29]。由于脫脂奶粉在671.71 cm-1處并沒(méi)有明顯的特征峰存在,因此研究選取671.71 cm-1位移處的特征單波段圖像作為是否能夠檢測(cè)到三聚氰胺顆粒的判斷依據(jù)。

        圖4以三聚氰胺1.00%濃度時(shí)混合樣本的采集結(jié)果為例,對(duì)特征單波段圖像的處理過(guò)程進(jìn)行說(shuō)明。在671.71 cm-1處的原始圖像中,由于受到熒光背景的干擾,每個(gè)像素點(diǎn)之間的拉曼強(qiáng)度值差異并不明顯。經(jīng)過(guò)光譜預(yù)處理后,校正圖像中部分像素點(diǎn)的亮度較高,說(shuō)明該像素點(diǎn)區(qū)域含有三聚氰胺顆粒。此時(shí)選取純奶粉樣本在671.71 cm-1處出現(xiàn)的最大值作為閾值,對(duì)圖像進(jìn)行二值化處理,所獲二值圖像中含有三聚氰胺顆粒的像素點(diǎn)能夠更清晰的展現(xiàn)。研究依照此方法對(duì)采集的每張拉曼高光譜圖像進(jìn)行圖像處理。

        圖4 三聚氰胺濃度為1.00%時(shí)特征單波段圖像的熒光校正和二值化處理結(jié)果

        2.2 三聚氰胺的拉曼信號(hào)在奶粉層中的穿透深度分析

        在顆粒狀樣本的高光譜圖像采集中,保證光能穿透整個(gè)樣本厚度是十分必要的[30]。上節(jié)已經(jīng)得出三聚氰胺的最強(qiáng)拉曼特征峰出現(xiàn)在671.71 cm-1處,為了降低奶粉中三聚氰胺顆粒的檢測(cè)限,在穿透深度試驗(yàn)中研究依然以671.71 cm-1處的拉曼信號(hào)強(qiáng)度作為是否能夠采集到底層三聚氰胺產(chǎn)生拉曼信號(hào)的判定依據(jù)。在穿透深度檢測(cè)結(jié)果中,不同奶粉層厚度下校正圖像內(nèi)的拉曼光譜平均值如圖5所示。

        圖5中可以看出,三聚氰胺純物質(zhì)在671.71 cm-1處的拉曼強(qiáng)度值均為最大,隨著奶粉層厚度的增加,可采集到的拉曼信號(hào)強(qiáng)度逐漸變小。當(dāng)奶粉層厚度為5 mm時(shí),3組中671.71 cm-1處仍存在微弱的拉曼特征峰,說(shuō)明三聚氰胺產(chǎn)生的拉曼信號(hào)均能穿透5 mm厚度的3種奶粉層顆粒。通過(guò)對(duì)比,3種類(lèi)型奶粉的檢測(cè)結(jié)果基本一致。進(jìn)一步,研究對(duì)671.71 cm-1處的校正圖像進(jìn)行了二值化處理,閾值選取為各組純奶粉樣本在該單波段圖像中出現(xiàn)的最大值。若有像素點(diǎn)的拉曼信號(hào)強(qiáng)度超過(guò)了閾值,說(shuō)明該點(diǎn)檢測(cè)到了三聚氰胺的拉曼信號(hào),該像素點(diǎn)被標(biāo)記為黑色;反之說(shuō)明該像素點(diǎn)只含有奶粉顆粒,像素點(diǎn)被標(biāo)記為白色,最終的二值圖像結(jié)果如圖6所示。結(jié)果顯示,當(dāng)奶粉層厚度在3 mm范圍內(nèi),3組二值圖像中所有像素點(diǎn)均可以采集到三聚氰胺產(chǎn)生的拉曼信號(hào),此時(shí)三聚氰胺像素點(diǎn)的占比達(dá)到了100 %。但當(dāng)奶粉層厚度達(dá)到4 mm時(shí),3組中均有部分像素點(diǎn)無(wú)法采集到三聚氰胺的拉曼信號(hào)。當(dāng)奶粉層達(dá)到5 mm時(shí),低脂奶粉組中三聚氰胺像素點(diǎn)的個(gè)數(shù)最少,占比僅為37 %??紤]到奶粉顆粒之間容易聚集成團(tuán),顆粒的密集程度具有一定的隨機(jī)性,實(shí)際中三聚氰胺顆粒的濃度比較低。因此,為了保證混合樣本底部的三聚氰胺顆粒能夠100 %的檢測(cè)出,試驗(yàn)制定混合樣本的厚度為2 mm。

        圖5 3種類(lèi)型奶粉在不同厚度時(shí)感興趣區(qū)域內(nèi)的拉曼光譜平均值對(duì)比

        圖6 不同奶粉層厚度時(shí)雙層樣本在特征波段處的二值圖像

        2.3 混合樣本中三聚氰胺濃度檢測(cè)

        研究根據(jù)10種混合樣本在671.71 cm-1位移處的校正圖像,計(jì)算出了同種濃度下3次采集結(jié)果中感興趣區(qū)域內(nèi)所有像素點(diǎn)的拉曼強(qiáng)度平均值,根據(jù)對(duì)應(yīng)的三聚氰胺濃度進(jìn)行了一元線性回歸分析,結(jié)果如圖7所示。由圖7可以看出,隨著三聚氰胺濃度的升高,校正圖像中各像素點(diǎn)的拉曼強(qiáng)度平均值呈線性增長(zhǎng)。擬合直線的決定系數(shù)達(dá)到了0.995 4,說(shuō)明回歸直線對(duì)三聚氰胺濃度的擬合程度較高。因此,根據(jù)校正圖像中各像素點(diǎn)的拉曼強(qiáng)度可以對(duì)所選區(qū)域內(nèi)三聚氰胺的濃度進(jìn)行預(yù)測(cè)。對(duì)于不均勻的混合樣本,應(yīng)用此方法可以獲得整體樣本在不同區(qū)域的三聚氰胺濃度含量。

        圖7 在671.71 cm-1處拉曼強(qiáng)度平均值與三聚氰胺濃度的關(guān)系

        進(jìn)一步,研究對(duì)每張校正圖像進(jìn)行二值化處理,閾值選取與上節(jié)相同,即脫脂奶粉樣本在671.71 cm-1處校正圖像中出現(xiàn)的拉曼強(qiáng)度最大值。當(dāng)某一像素點(diǎn)的拉曼強(qiáng)度值大于閾值時(shí),判定該像素點(diǎn)檢測(cè)到了三聚氰胺,此時(shí)稱(chēng)該像素點(diǎn)為三聚氰胺像素點(diǎn),顯示為黑色;反之,當(dāng)像素點(diǎn)的拉曼強(qiáng)度小于閾值時(shí),視該像素點(diǎn)內(nèi)只含有脫脂奶粉顆粒,稱(chēng)該點(diǎn)為奶粉像素點(diǎn),顯示為白色。試驗(yàn)中10種濃度混合樣本分3次重復(fù)取樣,圖8展示了幾種不同濃度混合樣本的二值圖像結(jié)果。當(dāng)三聚氰胺濃度為0.01%時(shí),3次取樣結(jié)果中均檢測(cè)到了若干三聚氰胺像素點(diǎn),說(shuō)明在該試驗(yàn)參數(shù)下,奶粉樣本中三聚氰胺濃度的檢測(cè)限達(dá)到了0.01%。隨著三聚氰胺顆粒的不斷增加,二值圖像中三聚氰胺像素點(diǎn)的個(gè)數(shù)逐漸變多。檢測(cè)結(jié)果表明應(yīng)用此方法獲取的二值圖像能夠直觀的顯示出奶粉樣本中三聚氰胺顆粒的多少和具體的位置分布。

        圖8 不同濃度脫脂奶粉樣本的二值圖像結(jié)果

        研究進(jìn)一步統(tǒng)計(jì)了同濃度3張二值圖像中三聚氰胺像素點(diǎn)的總數(shù),它們與三聚氰胺濃度的關(guān)系如圖9所示。

        圖9 二值圖像中三聚氰胺像素點(diǎn)總數(shù)與三聚氰胺濃度的關(guān)系

        在圖9中,當(dāng)三聚氰胺濃度增加時(shí),三聚氰胺像素點(diǎn)的總數(shù)呈非線性增長(zhǎng)。也就是說(shuō),三聚氰胺像素點(diǎn)在感興趣區(qū)域內(nèi)(3次采集共153 600 pixels)的占比與三聚氰胺的濃度并不一致。造成這種結(jié)果的原因可能與拉曼信號(hào)在奶粉層中的穿透深度有關(guān)。在上節(jié)的試驗(yàn)結(jié)果中,當(dāng)奶粉層厚度為2 mm時(shí),三聚氰胺產(chǎn)生的拉曼信號(hào)能夠100%的穿透奶粉層被系統(tǒng)采集到。由此可見(jiàn),本章節(jié)中混合樣本的二值圖像代表的是多層樣本的采集結(jié)果。在低濃度時(shí),檢測(cè)結(jié)果以表層三聚氰胺顆粒為主,深層三聚氰胺顆粒產(chǎn)生的拉曼信號(hào)較弱無(wú)法被采集到。隨著三聚氰胺濃度的升高,越來(lái)越多的處在深層的三聚氰胺顆粒被采集到,此時(shí)對(duì)應(yīng)的二值圖像中三聚氰胺像素點(diǎn)個(gè)數(shù)會(huì)成倍的增加??梢灶A(yù)見(jiàn)的是,當(dāng)三聚氰胺像素點(diǎn)幾乎占滿整個(gè)感興趣區(qū)域時(shí),其增長(zhǎng)速率必定會(huì)放緩。綜上所述,基于線掃描式拉曼高光譜技術(shù)對(duì)奶粉中三聚氰胺濃度進(jìn)行檢測(cè)時(shí),可以根據(jù)671.71 cm-1處的校正圖像中所選區(qū)域內(nèi)拉曼強(qiáng)度平均值對(duì)該區(qū)域三聚氰胺濃度進(jìn)行預(yù)測(cè),對(duì)應(yīng)的二值圖像中可以直觀地觀測(cè)到三聚氰胺顆粒的多少和位置分布信息。

        3 結(jié) 論

        本研究結(jié)合拉曼光譜與高光譜成像技術(shù),搭建了一套線掃描式拉曼高光譜檢測(cè)系統(tǒng),對(duì)大面積奶粉樣本中的三聚氰胺濃度進(jìn)行了無(wú)損檢測(cè)研究。研究結(jié)果表明:1)奶粉和三聚氰胺混合樣本的厚度不宜超過(guò)2 mm,以此確保混合樣本底部的三聚氰胺顆粒能夠被檢出。2)校正圖像中671.71 cm-1處各像素點(diǎn)的拉曼強(qiáng)度平均值與三聚氰胺濃度呈明顯線性關(guān)系,擬合結(jié)果中決定系數(shù)達(dá)到了0.995 4。3)對(duì)應(yīng)的二值圖像中,三聚氰胺像素點(diǎn)的總數(shù)呈非線性增長(zhǎng),三聚氰胺顆粒的位置分布可以直觀的展現(xiàn)。4)在該試驗(yàn)參數(shù)下,奶粉樣本中三聚氰胺的檢測(cè)限可達(dá)0.01%,單次檢測(cè)總面積達(dá)到40 mm×80 mm。與傳統(tǒng)的檢測(cè)方法相比,該系統(tǒng)可直接對(duì)顆粒狀樣本進(jìn)行檢測(cè),無(wú)需轉(zhuǎn)化成液態(tài)形式,也不必借助任何化學(xué)試劑,具有更高的時(shí)效性和更簡(jiǎn)單的操作,在實(shí)際應(yīng)用中具有巨大潛力。

        [1] 任東升,周志俊. 三聚氰胺毒理學(xué)研究進(jìn)展[J]. 環(huán)境與職業(yè)醫(yī)學(xué),2008,25(6):595-598.

        Ren Dongsheng, Zhou Zhijun. Updated information on toxicology of melamine[J]. Journal of Environmental and Occupational Medicine, 2008, 25(6): 595-598. (in Chinese with English abstract)

        [2] Venkatasami G, Jr J R S. A rapid, acetonitrile-free, HPLC method for determination of melamine in infant formula[J]. Analytica Chimica Acta, 2010, 665(2): 227-230.

        [3] Domingo E, Tirelli A A, Nunes C A, et al. Melamine detection in milk using vibrational spectroscopy and chemometrics analysis: A review[J]. Food Research International, 2014, 60(6): 131-139.

        [4] 楊洋,徐春祥,車(chē)文軍. 高效液相色譜法測(cè)定奶粉中的三聚氰胺及其不確定度分析[J]. 食品科學(xué),2010,31(4):250-253.

        Yang Yang, Xu Chunxiang, Che Wenjun. Uncertainty evaluation of HPLC determination of melamine in milk powder chromatography[J]. Food Science, 2010, 31(4): 250-253. (in Chinese with English abstract)

        [5] 曾甜,陳錢(qián),江茜,等. PVDF微孔濾膜負(fù)載金納米粒子用于牛奶中三聚氰胺的SERS快速檢測(cè)[J]. 光散射學(xué)報(bào),2016,28(3):209-213.

        Zeng Tian, Chen Qian, Jiang Qian, et al. Rapid detection of melamine in milk by surface-enhanced Raman spectroscopy with PODF membranes as supports[J]. The Journal of Light Scattering, 2016, 28(3): 209-213. (in Chinese with English abstract)

        [6] Craig A P, Franca A S, Irudayaraj J. Surface-enhanced Raman spectroscopy applied to food safety[J]. Annual Review of Food Science & Technology, 2013, 4(1): 369-380.

        [7] 湯俊琪,田超,曾崇毅,等. 堿性銀膠的表面增強(qiáng)拉曼效應(yīng)及對(duì)牛奶中三聚氰胺的檢測(cè)[J]. 光譜學(xué)與光譜分析,2013,33(3):709-713.

        Tang Junqi, Tian Chao, Zeng Chongyi, et al. Alkaline silver colloid for surface enhanced Raman scattering and application to detection of melamine doped milk[J]. Spectroscopy and Spectral Analysis, 2013, 33(3): 709-713. (in Chinese with English abstract)

        [8] Wen Y, Liu H, Han P, et al. Determination of melamine in milk powder, milk and fish feed by capillary electrophoresis: A good alternative to HPLC[J]. Journal of the Science of Food & Agriculture, 2010, 90(13): 2178-2182.

        [9] 劉燕德,靳曇曇. 拉曼光譜技術(shù)在農(nóng)產(chǎn)品質(zhì)量安全檢測(cè)中的應(yīng)用[J]. 光譜學(xué)與光譜分析,2015, 35(9):2567-2572.

        Liu Yande, Jin Tantan. Application of raman spectroscopy technique to agricultural products quality and safety determination[J]. Spectroscopy and Spectral Analysis, 2015, 35(9): 2567-2572. (in Chinese with English abstract)

        [10] 劉宸,黃文倩,王慶艷,等. 拉曼光譜在食品無(wú)損檢測(cè)中的應(yīng)用[J]. 食品安全質(zhì)量檢測(cè)學(xué)報(bào),2015, 6(8):2981-2987.

        Liu Chen, Huang Wenqian, Wang Qinyan, et al. Application of Raman spectroscopy technique in food non-destructive determination[J]. Journal of Food Safety and Quality, 2015, 6(8): 2981-2987. (in Chinese with English abstract)

        [11] Yang Danting, Ying Yibin. Applications of Raman spectroscopy in agricultural products and food analysis: A review[J]. Applied Spectroscopy Reviews, 2011, 46(7): 539-560.

        [12] 金瑞,李小昱,顏伊蕓,等. 基于高光譜圖像和光譜信息融合的馬鈴薯多指標(biāo)檢測(cè)方法[J]. 農(nóng)業(yè)工程學(xué)報(bào),2015,31(16):258-263.

        Jin Rui, Li Xiaoyu, Yan Yiyun, et al. Detection method of multi-target recognition of potato based on fusion of hyperspectral imaging and spectral information[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(16): 258-263. (in Chinese with English abstract)

        [13] 李江波,饒秀勤,應(yīng)義斌,等. 基于高光譜成像技術(shù)檢測(cè)臍橙潰瘍[J]. 農(nóng)業(yè)工程學(xué)報(bào),2010,26(8):222-228.

        Li Jiangbo, Rao Xiuqin, Ying Yibin, et al. Detection of navel oranges canker based on hyperspectral imaging technology[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(8): 222-228. (in Chinese with English abstract)

        [14] Wang N N, Sun D W, Yang Y C, et al. Recent advances in the application of hyperspectral imaging for evaluating fruit quality[J]. Food Analytical Methods, 2015, 9(1): 1-14.

        [15] 黎靜,韓魯佳,楊增玲. 豆粕中三聚氰胺顯微近紅外成像檢測(cè)的掃描條件優(yōu)化[J]. 農(nóng)業(yè)工程學(xué)報(bào),2013,29(13):244-254.

        Li Jing, Han Lujia, Yang Zengling. Optimization of scanning conditions on near-infrared microscopic imaging for melamine detection in soybean meal[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2013, 29(13): 244-254. (in Chinese with English abstract)

        [16] Dhakal S, Chao K, Qin J, et al. Parameter selection for Raman spectroscopy-based detection of chemical contaminants in food powders[J]. Transactions of the Asabe, 2016, 59(2): 751-763.

        [17] Qin J, Chao K, Kim M S. Raman chemical imaging system for food safety and quality inspection[J]. Transactions of the ASABE, 2010, 53(6): 1873-1882.

        [18] Qin J, Chao K, Kim M S. Simultaneous detection of multiple adulterants in dry milk using macro-scale Raman chemical imaging[J]. Food Chemistry, 2013, 138(2/3): 998-1007.

        [19] Zhang B, Huang W, Li J, et al. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review[J]. Food Research International, 2014, 62(62): 326-343.

        [20] Qin J, Chao K, Kim M S, et al. Line-scan macro-scale Raman chemical imaging for authentication of powdered foods and ingredients[J]. Food & Bioprocess Technology, 2015, 9(1): 1-11.

        [21] 鄭詠梅,張鐵強(qiáng),張軍,等. 平滑、導(dǎo)數(shù)、基線校正對(duì)近紅外光譜PLS定量分析的影響研究[J]. 光譜學(xué)與光譜分析,2004,24(12):1546-1548.

        Zheng Yongmei, Zhang Tieqiang, Zhang Jun, et al. Influence of smooth, 1st derivative and baseline correction on the near-infrared spectrum analysis with PLS[J]. Spectroscopy and Spectral Analysis, 2004, 24(12): 1546-1548. (in Chinese with English abstract)

        [22] Marquez C, Lopez M I, Ruisanchez I, et al. FT-Raman and NIR spectroscopy data fusion strategy for multivariate qualitative analysis of food fraud[J]. Talanta, 2016, 161:80-86.

        [23] Xu M L, Gao Y, Han X X, et al. Detection of pesticide residues in food using surface-enhanced Raman spectroscopy: A review[J]. Journal of Agricultural & Food Chemistry, 2017, 65(32): 6719-6726.

        [24] Zhang Z M, Chen S, Liang Y Z. Baseline correction using adaptive iteratively reweighted penalized least squares[J]. Analyst, 2010, 135(5): 1138-1146.

        [25] Zhang C, Zhao C, Huang W, et al. Automatic detection of defective apples using NIR coded structured light and fast lightness correction[J]. Journal of Food Engineering, 2017, 203: 69-82.

        [26] Guo Z, Huang W, Peng Y, et al. Color compensation and comparison of shortwave near infrared and long wave near infrared spectroscopy for determination of soluble solids content of ‘Fuji’ apple[J]. Postharvest Biology & Technology, 2016, 115: 81-90.

        [27] Prabhaharan M, Prabakaran A R, Gunasekaran S, et al. Molecular structure and vibrational spectroscopic investigation of melamine using DFT theory calculations[J]. Spectrochim Acta A Mol Biomol Spectrosc, 2014, 123(7): 392-401.

        [28] Mircescu N E, Oltean M, Chi? V, et al. FTIR, FT-Raman, SERS and DFT study on melamine[J]. Vibrational Spectroscopy, 2012, 62(9): 165-171.

        [29] 郭沫然,任玉,張?zhí)炷?,? 基于密度泛函理論的三聚氰胺結(jié)構(gòu)及振動(dòng)光譜研究[J]. 光譜學(xué)與光譜分析,2012,32(3):703-707.

        Guo Moran, Ren Yu, Zhang Tianmu, et al. Optimization of melamine structure using density functional theory and vibrational spectra studies[J]. Spectroscopy and Spectral Analysis, 2012, 32(3): 703-707. (in Chinese with English abstract)

        [30] Huang M, Kim M S, Chao K, et al. Penetration depth measurement of near-infrared hyperspectral imaging light for milk powder[J]. Sensors, 2016, 16(4): 441-451.

        Non-destructive detection of melamine in milk powder using Raman hyperspectral imaging technology combined with line-scanning

        Liu Chen1,2,3,4, Yang Guiyan2,3,4, Wang Qingyan2,3,4, Huang Wenqian2,3,4, Wang Xiaobin2,3,4, Chen Liping1,2,3,4※

        (1.712100,; 2.100097,; 3.100097,4.100097,)

        As a scattering spectrum, Raman spectroscopy has some advantages in non-invasive detecting. The hyperspectral data contain not only conventional image but also spectral information in each pixel. In this study, a line-scanning Raman hyperspectral imaging system was built to detect and quantify the melamine mixed in the milk powder with large sample areas in a fast and nondestructive way. The Gaussian filter smoothing and an adaptive iteratively reweighted penalized least squares (air PLS) method were used to remove noise signal and fluorescence interference. The corrected images at 671.71 cm-1waveband were extracted for detecting the melamine in the milk powder. Firstly, the penetration depth of Raman signal produced by melamine in the milk powder was measured. A designed two-layer sample was applied to measure the Raman signals after passing through milk layers of different thicknesses. According to the results, the optimum thickness of mixed samples was set to be 2 mm. Then, melamine-milk mixtures with 10 different concentrations were prepared for the experiment. Each mixture was collected by a designed aluminium alloy container with a sample thickness of 2 mm. In this case, the melamine particles at the bottom of mixed sample could be collected. After data preprocessing, a linear analysis of the averaged Raman intensity of each pixel was performed, and the concentration and distribution information of the melamine particles were finally obtained using a simple binarization arithmetic in the single-band image of mixtures at 671.71 cm-1waveband. The results showed that there was a linear relationship between the melamine concentration and the average Raman intensity of all pixels in the region of interest of the corrected image at 671.71 cm-1waveband, and the coefficient of determination was 0.995 4. In the binary images, the number and spatial location information of melamine particles could be visually displayed. Meanwhile, the total number of the additive pixels increased nonlinearly. It meant that the binary images from this research represented the accumulation of multiple layers in sample. At low concentrations, the Raman signal generated from the additive particles at the sub-surface is too weak to detect. When the additive concentration increases to a certain degree, the Raman signal generated from the additive particles at the sub-surface can be collected. In these areas, the pixels are identified as additive pixels even if there is no additive particle at corresponding surface. This situation led to a significant increase in the number of additive pixels. The research demonstrates that the Raman intensity in single-band corrected images can be used for quantitative analysis of melamine, and the binary images can reveal the identification and the distribution of melamine particles in the skim milk powder. More Raman active additives in powdered food could be detected in the same way. In our research, the milk powder samples can be scanned directly without any chemical reagents. The process of converting to liquid is dispensable. The limit of detection for melamine concentration was estimated as 0.01% with a total detection area of 40 mm × 80 mm each time. The results show that the line-scanning Raman hyperspectral imaging system has shown a great potential for rapid and non-invasive measurement of samples with large areas.

        nondestructive detection; image processing; spectrum analysis; Raman spectroscopy; hyperspectral imaging technology; line scanning; skimmed milk powder; melamine

        10.11975/j.issn.1002-6819.2017.24.036

        O657.37

        A

        1002-6819(2017)-24-0277-06

        2017-08-22

        2017-12-11

        國(guó)家自然科學(xué)基金項(xiàng)目(61605009)

        劉 宸,男,黑龍江哈爾濱人,博士研究生,研究方向是農(nóng)產(chǎn)品品質(zhì)安全無(wú)損檢測(cè)。Email:xmyliuchen@126.com

        陳立平,女,研究員,研究方向?yàn)檗r(nóng)業(yè)信息技術(shù)和農(nóng)業(yè)智能裝備研究開(kāi)發(fā)和示范推廣。Email:chenlp@nercita.org.cn

        劉 宸,楊桂燕,王慶艷,黃文倩,王曉彬,陳立平. 線掃描式拉曼高光譜成像技術(shù)無(wú)損檢測(cè)奶粉三聚氰胺[J]. 農(nóng)業(yè)工程學(xué)報(bào),2017,33(24):277-282. doi:10.11975/j.issn.1002-6819.2017.24.036 http://www.tcsae.org

        Liu Chen, Yang Guiyan, Wang Qingyan, Huang Wenqian, Wang Xiaobin, Chen Liping. Non-destructive detection of melamine in milk powder using Raman hyperspectral imaging technology combined with line-scanning[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(24): 277-282. (in Chinese with English abstract) doi:10.11975/j.issn.1002-6819.2017.24.036 http://www.tcsae.org

        猜你喜歡
        三聚氰胺拉曼像素點(diǎn)
        賊都找不到的地方
        三聚氰胺價(jià)格兩個(gè)月腰斬
        三聚氰胺:上半年走勢(shì)偏弱 下半年能否反彈?
        基于單光子探測(cè)技術(shù)的拉曼光譜測(cè)量
        三聚氰胺:上半年機(jī)會(huì)大于下半年
        基于canvas的前端數(shù)據(jù)加密
        基于逐像素點(diǎn)深度卷積網(wǎng)絡(luò)分割模型的上皮和間質(zhì)組織分割
        基于相干反斯托克斯拉曼散射的二維溫度場(chǎng)掃描測(cè)量
        三聚氰胺價(jià)格上躥下跳為哪般
        基于Node-Cell結(jié)構(gòu)的HEVC幀內(nèi)編碼
        日韩国产有码在线观看视频| 免费成人在线电影| 亚洲av无码日韩精品影片| 91性视频| 国产激情小视频在线观看的| 高黄暴h日本在线观看| 精品av天堂毛片久久久| 欧美成人a在线网站| 在线一区二区三区免费视频观看| 青青草亚洲视频社区在线播放观看| 亚洲av无码精品蜜桃| 国内精品一区视频在线播放| 成人精品国产亚洲av久久| 日韩不卡的av二三四区| 国产深夜男女无套内射| 亚洲自拍另类欧美综合| 人妻精品久久久一区二区 | 国产精品无码一区二区三级| 午夜成人理论无码电影在线播放| 欧美韩国精品另类综合| 日本视频一区二区三区| 无码a级毛片免费视频内谢| 又硬又粗又大一区二区三区视频| 无码啪啪熟妇人妻区| 日韩精品视频在线观看无| 日韩人妻一区二区三区蜜桃视频 | 国产人在线成免费视频麻豆| 男女啦啦啦视频在线观看| 18禁裸体动漫美女无遮挡网站 | 亚洲中文久久精品字幕| 久久不见久久见免费影院www| 久久99热精品免费观看欧美| 国产一区二区三区男人吃奶| 精品亚洲国产成人| 国产精品毛片无遮挡高清| 日本久久一级二级三级| 久久天天躁夜夜躁狠狠85麻豆| 国产高清乱理伦片| 丝袜人妻无码中文字幕综合网| 午夜一区二区视频在线观看| 亚洲av天天做在线观看|