杜昌文

（土壤與農(nóng)業(yè)可持續(xù)發(fā)展國(guó)家重點(diǎn)實(shí)驗(yàn)室/中國(guó)科學(xué)院南京土壤研究所，南京 210008）

現(xiàn)代光譜技術(shù)在植物營(yíng)養(yǎng)品質(zhì)分析中的應(yīng)用

杜昌文

（土壤與農(nóng)業(yè)可持續(xù)發(fā)展國(guó)家重點(diǎn)實(shí)驗(yàn)室/中國(guó)科學(xué)院南京土壤研究所，南京 210008）

目前，我國(guó)的糧食安全由總量保障優(yōu)先邁入到總量保障和營(yíng)養(yǎng)品質(zhì)安全并重的發(fā)展階段，因此植物營(yíng)養(yǎng)品質(zhì)分析是迫切的社會(huì)需求。常規(guī)的植物營(yíng)養(yǎng)品質(zhì)分析主要是基于實(shí)驗(yàn)室的化學(xué)分析，成本高、耗時(shí)長(zhǎng)和人力密集，與海量快速的植物營(yíng)養(yǎng)品質(zhì)信息需求不相適應(yīng)，而現(xiàn)代光譜技術(shù) (spectroscopic technology) 為海量植物營(yíng)養(yǎng)品質(zhì)信息的需求提供新的技術(shù)支撐。目前廣泛應(yīng)用的光譜技術(shù)主要是分子光譜 (molecular spectroscopy)，包括紫外可見光譜、紅外光譜、熒光光譜和拉曼光譜，這些光譜在糧食作物、蔬菜、水果以及中藥材等經(jīng)濟(jì)作物的營(yíng)養(yǎng)品質(zhì)分析中開始發(fā)揮越來越大的作用，并能將傳統(tǒng)的主觀性較強(qiáng)的感觀品質(zhì)常數(shù)客觀化。隨著光譜技術(shù)的發(fā)展，以光聲效應(yīng) (photoacoustic effect) 為理論基礎(chǔ)的紅外光聲光譜，以及以激光誘導(dǎo)擊穿 (laser induced breakdown) 為技術(shù)基礎(chǔ)的現(xiàn)代原子光譜 (atomic spectroscopy) 開始應(yīng)用于植物營(yíng)養(yǎng)品質(zhì)分析，并展現(xiàn)了廣闊的應(yīng)用潛力。以上光譜分析應(yīng)用依賴于現(xiàn)代多元校正的化學(xué)計(jì)量學(xué)方法 (chemometrics) 和計(jì)算機(jī)技術(shù)，結(jié)合植物營(yíng)養(yǎng)專業(yè)知識(shí)，利用現(xiàn)代的互聯(lián)網(wǎng)和云技術(shù)平臺(tái)以及智能終端，植物營(yíng)養(yǎng)品質(zhì)信息的適時(shí)快速常規(guī)化獲取將成為現(xiàn)代農(nóng)業(yè)的重要發(fā)展方向。

營(yíng)養(yǎng)品質(zhì)；紅外光譜；原子光譜；化學(xué)計(jì)量學(xué)；模型

植物營(yíng)養(yǎng)與我國(guó)的糧食安全密切相關(guān)，是關(guān)系到國(guó)家糧食安全和環(huán)境質(zhì)量的重大科技問題[1]，我國(guó)的糧食安全大致分為三個(gè)階段：第一階段為國(guó)民經(jīng)濟(jì)發(fā)展水平較低時(shí)期，大致為1980年以前，這一時(shí)期的基本特征是糧食還沒有滿足消費(fèi)需求，整個(gè)社會(huì)面臨的是溫飽問題，因此總量保障是這一時(shí)期糧食安全的重點(diǎn)；第二階段是國(guó)民經(jīng)濟(jì)快速發(fā)展時(shí)期，大致為1980年到2000年，這一時(shí)期的基本特征是糧食總量已能滿足社會(huì)需求，社會(huì)已經(jīng)擺脫了糧食短缺的困擾，人們的選擇性明顯加強(qiáng)，小康社會(huì)的種種特征日益明顯；第三階段是國(guó)民經(jīng)濟(jì)發(fā)展到工業(yè)化水平時(shí)期，大致為2000年以后，這一時(shí)期的基本特征是糧食生產(chǎn)的潛力得到了較充分的發(fā)揮，人們除了關(guān)注總量，更關(guān)心營(yíng)養(yǎng)品質(zhì)[2]，如硝酸鹽累積、重金屬超標(biāo)和農(nóng)藥殘留等，這與人體健康密切相關(guān)，因此國(guó)家也開始關(guān)注和重視農(nóng)業(yè)供給側(cè)改革[3]。

廣義的植物營(yíng)養(yǎng)品質(zhì)涉及到諸多方面，除了營(yíng)養(yǎng)成分外，還包括如外觀品質(zhì)和口感味道等，狹義的植物營(yíng)養(yǎng)品質(zhì)主要是指植物的營(yíng)養(yǎng)成分，實(shí)際上植物的營(yíng)養(yǎng)與成分也決定著外觀品質(zhì)和口感味道等，本文主要是針對(duì)狹義上的植物營(yíng)養(yǎng)品質(zhì)。不同的作物、不同的生長(zhǎng)環(huán)境以及不同的管理措施對(duì)植物營(yíng)養(yǎng)品質(zhì)的影響巨大，因此植物營(yíng)養(yǎng)品質(zhì)的表征成為現(xiàn)代社會(huì)中人們十分關(guān)注的問題。近60年以來，傳統(tǒng)的植物營(yíng)養(yǎng)品質(zhì)分析主要以化學(xué)分析為主，即通過各種提取和分離手段，進(jìn)行顯色和比色，這些化學(xué)分析方法為植物營(yíng)養(yǎng)品質(zhì)的表征做出了重大貢獻(xiàn)，并還將繼續(xù)發(fā)揮重要作用。但隨著社會(huì)的發(fā)展，人們對(duì)分析的時(shí)效性和成本提出了越來越高的要求，并推動(dòng)了現(xiàn)代儀器分析技術(shù)的發(fā)展，而現(xiàn)代光譜技術(shù)則是現(xiàn)代儀器分析技術(shù)最重要表現(xiàn)之一[4]。

1 現(xiàn)代光譜技術(shù)

光譜分析是基于物質(zhì)與電磁輻射相互作用，這種相互作用取決于該物質(zhì)的性質(zhì)，這種方法主要體現(xiàn)輻射能與物質(zhì)組成和結(jié)構(gòu)之間的關(guān)系。電磁輻射按頻率 (波長(zhǎng)) 可分為不同的區(qū)域 (圖1)，形成了不同光譜分析法，其中紅外光譜是迄今最為重要的分析方法之一[5]；該方法最大的特點(diǎn)在于幾乎所有形態(tài)的樣本均可以采用這種方法進(jìn)行研究，即不論是固態(tài)、液態(tài)和氣態(tài)樣本還是糊狀、塊狀和粉狀樣本，都可以采用不同的檢則附件進(jìn)行研究。隨著現(xiàn)代科學(xué)技術(shù)的發(fā)展和制造技術(shù)的進(jìn)步，很多新型的檢測(cè)附件不斷被開發(fā)和應(yīng)用，使得不同形態(tài)的樣本能得到更為有效的檢測(cè)[6]。

圖1 電磁波譜Fig. 1 Electromagnetic wave spectrum

紅外光照射物質(zhì)后，其中某些波長(zhǎng)的光被物質(zhì)吸收，將通過物質(zhì)吸收后的紅外輻射強(qiáng)度按波長(zhǎng)逐一記錄下來即為該物質(zhì)的紅外光譜，測(cè)定這種光譜的儀器稱為紅外光譜儀。從上個(gè)世紀(jì)50年代以來，紅外光譜儀不斷得到改進(jìn)，已從色散型紅外光譜儀發(fā)展到了干涉型傅里葉轉(zhuǎn)換紅外光譜儀，這兩種光譜儀在不同的分析領(lǐng)域均得到廣泛的應(yīng)用，后者分辨率高、掃描速度快且波長(zhǎng)范圍寬，但價(jià)格較昂貴。分子是保持物質(zhì)理化性質(zhì)的基本微粒，在物質(zhì)的結(jié)構(gòu)中，分子由若干個(gè)原子組成。分子在不斷地運(yùn)動(dòng)，在外界條件的作用下，不同物質(zhì)的分子通過擴(kuò)散、碰撞、能量傳遞而發(fā)生理化反應(yīng)。在一定的條件下分子的運(yùn)動(dòng)會(huì)達(dá)到一種平衡狀態(tài)，并可以產(chǎn)生穩(wěn)定的光譜吸收。分子的光譜吸收除了包含有原子吸收的特征外，還具有自己的吸收特征，更多理論上的闡述參見相關(guān)參考文獻(xiàn)[5–7]。

現(xiàn)代紅外光譜分析技術(shù)是近十年來分析化學(xué)領(lǐng)域迅猛發(fā)展的高新分析技術(shù)，引起了國(guó)內(nèi)外分析專家的注目，在分析化學(xué)領(lǐng)域廣泛應(yīng)用，被譽(yù)為分析“巨人”，它的出現(xiàn)帶來了又一次分析技術(shù)的革命。本世紀(jì)以來，紅外光譜在各領(lǐng)域中的應(yīng)用全面展開，在植物營(yíng)養(yǎng)分析中，有關(guān)紅外光譜的研究及應(yīng)用文獻(xiàn)幾乎呈指數(shù)增 長(zhǎng)(圖2a)，成為發(fā)展最快、最引人注目的一門獨(dú)立的分析技術(shù)。我國(guó)的植物營(yíng)養(yǎng)品質(zhì)分析中，紅外光譜技術(shù)的研究及應(yīng)用起步較晚，但近10年以來發(fā)展迅速，將現(xiàn)代光譜測(cè)量技術(shù)、計(jì)算機(jī)技術(shù)、化學(xué)計(jì)量學(xué)技術(shù)與基礎(chǔ)測(cè)試技術(shù)有機(jī)結(jié)合，呈現(xiàn)出多學(xué)科的交叉與融合。相關(guān)研究論文已躍居世界第一 (圖2b)，取得了顯著的進(jìn)步，甚至明顯超過美國(guó)，但在原始創(chuàng)新、研究質(zhì)量和技術(shù)應(yīng)用等方面與美國(guó)等發(fā)達(dá)國(guó)家還存在相當(dāng)大距離。

紅外光譜反映樣品分子鍵的振動(dòng)信息，包括CH、O-H、N-H、S-H、C-C、N-O等化學(xué)鍵的信息，因此分析范圍幾乎可覆蓋所有含分子鍵的有機(jī)或無(wú)機(jī)樣本[5–6]。它是采用化學(xué)計(jì)量學(xué)方法建立校正模型，進(jìn)而預(yù)測(cè)未知樣品的一種分析方法，在植物營(yíng)養(yǎng)品質(zhì)分析中開始發(fā)揮越來越大的作用[8–9]。

2 紅外光譜在植物營(yíng)養(yǎng)品質(zhì)分析中的應(yīng)用

圖2 近40年來植物營(yíng)養(yǎng)品質(zhì)分析中與紅外光譜技術(shù)相關(guān)論文發(fā)表情況Fig. 2 Published papers using technique of infrared spectroscopy in plant nutritional analysis in last 40 years

表 1 紅外光譜在植物營(yíng)養(yǎng)品質(zhì)分析中的應(yīng)用Table 1 Application of infrared spectroscopy in plant nutritional analysis

紅外光譜已被廣泛應(yīng)用于各類植物品質(zhì)營(yíng)養(yǎng)分析，包括各類糧食作物、水果、蔬菜以及經(jīng)濟(jì)作物(表1)。在這些分析中，絕大部分采用的是近紅外光譜，同時(shí)，小部分分析也應(yīng)用了中紅外光譜，盡管中紅外光譜應(yīng)用明顯較少，但整體上，其效果等同或優(yōu)于近紅外。由于紅外光譜分析是多參數(shù)分析，往往需要采用多元校正的化學(xué)計(jì)量學(xué)方法，其中偏最小二乘法 (partial least square，PLS) 是應(yīng)用最廣泛的一種方法，這種方法表達(dá)的主要是一種線性關(guān)系，而對(duì)于非線性關(guān)系則可能出現(xiàn)較大預(yù)測(cè)誤差，因此非線性的方法也經(jīng)常被應(yīng)用，如支持向量機(jī)(support vector machine，SVM)、人工神經(jīng)網(wǎng)絡(luò)(artificial neural networks，ANN) 等。此外，還有很多其它算法以及不同算法的聯(lián)用，在實(shí)際應(yīng)用中可以進(jìn)行選擇和優(yōu)化。在植物營(yíng)養(yǎng)品質(zhì)參數(shù)上，紅外光譜分析幾乎涉及人們所關(guān)心的所有參數(shù)，如礦質(zhì)養(yǎng)分、氨基酸、蛋白質(zhì)、脂肪、有機(jī)酸、多糖、淀粉、微量元素、類胡蘿卜素、纖維、三聚氰胺、油酸、硬脂酸、維生素E、多酚、黃酮、花青素、植物甾醇、兒茶素、咖啡因、芥酸、硫甙等。不同的植物、不同的手段以及不同的算法在分析精度和準(zhǔn)確性上表現(xiàn)出顯著的差異，因此在實(shí)際應(yīng)用中需要結(jié)合需求進(jìn)行選擇使用。

除了以上常見的植物營(yíng)養(yǎng)參數(shù)，紅外光譜表達(dá)的是樣品的整體信息，因此，紅外光譜本身能在植物樣品品質(zhì)判別鑒定、植物樣品溯源和道地性上發(fā)揮獨(dú)特的作用，如蟲草真假的鑒定[78]、品種差異的鑒別[79]，中藥材的道地性等[80]。同時(shí)，融合光譜參數(shù)，可以對(duì)一些難以直接測(cè)定主觀性比較強(qiáng)的指標(biāo)進(jìn)行更客觀的分析，如茶葉、葡萄酒的口感和品味[56, 75, 81]。

3 化學(xué)計(jì)量學(xué)方法

在自然科學(xué)研究中經(jīng)常要獲取各種數(shù)據(jù)，尤其是實(shí)驗(yàn)科學(xué)，需要根據(jù)研究目的采用各種方法獲取或測(cè)定相關(guān)數(shù)據(jù)，然后在所獲取的數(shù)據(jù)基礎(chǔ)上進(jìn)行分析和總結(jié)，提出、證明、修正或推翻某一個(gè)結(jié)論、假說或理論。在一些實(shí)驗(yàn)科學(xué)中，如土壤學(xué)和生物學(xué)，經(jīng)常會(huì)處理海量的數(shù)據(jù)，因此在數(shù)據(jù)處理時(shí)必須借助計(jì)算機(jī)通過相關(guān)分析軟件進(jìn)行處理，從數(shù)據(jù)中挖掘目標(biāo)信息[82]。

3.1 數(shù)據(jù)預(yù)處理

在紅外光譜分析中，由于系統(tǒng)或環(huán)境干擾，原始光譜中含有與樣品組成和結(jié)構(gòu)無(wú)關(guān)的信息，即噪聲，使得近紅外光譜變得不穩(wěn)定，并可能發(fā)生漂移或重疊，所以首先有必要對(duì)光譜進(jìn)行預(yù)處理，以消除噪聲干擾，優(yōu)化光譜信號(hào)，提高光譜分辨率和校正模型的分析精度和穩(wěn)定性。光譜預(yù)處理方法有很多種，比如光譜平滑，其基本思想是在平滑點(diǎn)的前后各取若干點(diǎn)進(jìn)行平均或擬合，求得平滑點(diǎn)的最佳估計(jì)值，消除隨機(jī)噪聲，這一方法的前提是隨機(jī)噪聲的增均值為零。常用的平滑方法有Savitzky-Golay卷積平滑法、傅里葉變換濾波以及小波變換濾波[83–84]。平滑處理帶有一定的經(jīng)驗(yàn)性，如果平滑處理不合適有可能導(dǎo)致有用信息的丟失，紅外光譜多采用小波濾波進(jìn)行平滑化處理，本文以此為例簡(jiǎn)要介紹小波分析的方法和原理。

小波理論是上個(gè)世紀(jì)80年代后期發(fā)展起來的應(yīng)用數(shù)學(xué)分支，其思想來源于伸縮與平移，既保持了傅里葉變換的優(yōu)點(diǎn)又具有多分辨率、方向選擇性和自動(dòng)調(diào)焦的特點(diǎn)，被稱為數(shù)學(xué)上的顯微鏡[83]。數(shù)據(jù)的標(biāo)準(zhǔn)化是將數(shù)據(jù)按一定比例縮放，使之落入一個(gè)特定的區(qū)間。由于指標(biāo)體系的各個(gè)指標(biāo)度量單位不同，為了平衡選擇指標(biāo)權(quán)重，通過函數(shù)變換將其數(shù)值映射到某個(gè)數(shù)值區(qū)間，使得基本度量單位能統(tǒng)一起來，從而有利于進(jìn)一步的定性與定量分析。常見的標(biāo)準(zhǔn)化方法包括線性標(biāo)準(zhǔn)化法 (linear normalization algorithm) 和非線性標(biāo)準(zhǔn)化法 (nonlinear normalization algorithm) 兩大類。

對(duì)于相對(duì)復(fù)雜樣本如植物樣本的紅外分析，其紅外光譜是很多種不同組分吸收的疊加，因此不同組分之間的相互干擾很強(qiáng)，而將光譜進(jìn)行微分能提高光譜分析的分辨率和靈敏度，但隨著導(dǎo)數(shù)階數(shù)的增加，信噪比變低，預(yù)測(cè)誤差增加。微分處理不僅成為解析光譜的強(qiáng)有力工具，而且在相當(dāng)程度上改善了多重共線性，使校正模型的性能有了顯著的改善，但微分處理對(duì)微小的噪聲具有強(qiáng)調(diào)作用，因此在實(shí)際應(yīng)用中，一般采用一階和二階微分光譜，三階或三階以上的微分光譜則很少采用。此外，反卷積 (deconvolution) 也可以起到分離光譜信號(hào)的作用，但反卷積過程中，隨機(jī)高頻干擾信號(hào)可能會(huì)被放大，因此需要適當(dāng)抑制噪聲[85]。

3.2 模型構(gòu)建

光譜分析因涉及多元校正，因此要依賴于化學(xué)計(jì)量學(xué) (chemometrics)?；瘜W(xué)計(jì)量學(xué)是應(yīng)用數(shù)學(xué)、統(tǒng)計(jì)學(xué)與計(jì)算機(jī)科學(xué)的工具和手段，設(shè)計(jì)或選擇最優(yōu)化學(xué)量測(cè)方法，并通過解析化學(xué)量測(cè)數(shù)據(jù)以最大限度地獲取化學(xué)及其相關(guān)信息[86]?；瘜W(xué)計(jì)量學(xué)是化學(xué)、分析化學(xué)與數(shù)學(xué)、統(tǒng)計(jì)學(xué)及計(jì)算機(jī)科學(xué)之間的“接口”，是多學(xué)科融合的產(chǎn)物?；瘜W(xué)計(jì)量學(xué)之所以得以迅速發(fā)展的主要原因是計(jì)算機(jī)科學(xué)的發(fā)展不僅使大量化學(xué)測(cè)量?jī)x器操作實(shí)現(xiàn)了自動(dòng)化，并使得大量數(shù)據(jù)的自動(dòng)采集和傳輸成為事實(shí)。計(jì)算機(jī)科學(xué)的迅猛發(fā)展，對(duì)近代數(shù)學(xué)也產(chǎn)生了巨大影響，過去難以適用的復(fù)雜數(shù)學(xué)方法可在計(jì)算機(jī)上實(shí)現(xiàn)，為解決復(fù)雜的數(shù)據(jù)處理與目標(biāo)信息提取提供了可能。正是有了現(xiàn)代化學(xué)測(cè)量手段的進(jìn)步和數(shù)學(xué)解析手段的發(fā)展，多元校正的分析方法成了化學(xué)計(jì)量學(xué)中最活躍、最有生氣的一個(gè)分支。

植物樣品的紅外光譜包含了組成與結(jié)構(gòu)的信息，在樣品的紅外光譜和其理化性質(zhì)參數(shù)間也必然存在著內(nèi)在的關(guān)系。使用化學(xué)計(jì)量學(xué)這種數(shù)學(xué)方法對(duì)光譜和理化性質(zhì)進(jìn)行關(guān)聯(lián)，可確立這兩者間的定量或定性關(guān)系，即校正模型。進(jìn)而通過測(cè)量未知樣品的近紅外光譜，選擇正確模型或者構(gòu)建自適應(yīng)模型[87]，就可以預(yù)測(cè)樣品的理化性質(zhì)參數(shù)。因此，紅外光譜分析方法包括了預(yù)處理、校正和預(yù)測(cè)三個(gè)過程。由于校正模型的復(fù)雜性和經(jīng)驗(yàn)性，紅外光譜分析又稱“黑匣子”分析技術(shù)，即間接測(cè)量技術(shù)。

化學(xué)計(jì)量學(xué)是綜合使用數(shù)學(xué)、統(tǒng)計(jì)學(xué)和計(jì)算機(jī)科學(xué)等方法，并結(jié)合應(yīng)用領(lǐng)域?qū)I(yè)知識(shí)，從測(cè)量數(shù)據(jù)中提取信息的一門新興的應(yīng)用交叉學(xué)科。大量化學(xué)計(jì)量學(xué)方法被寫成軟件，并成為分析儀器 (尤其是紅外光譜儀) 的重要組成部分，這些軟件的出現(xiàn)使得應(yīng)用化學(xué)計(jì)量學(xué)方法解決實(shí)際復(fù)雜體系的分析問題成為現(xiàn)實(shí)。這些方法的基本原理、算法和功能可參考有關(guān)文獻(xiàn)[86, 88]。在常規(guī)植物營(yíng)養(yǎng)品質(zhì)分析中，從每個(gè)樣本中所獲取的數(shù)據(jù)多是具體的和點(diǎn)式的，如蛋白質(zhì)含量，僅僅就是單個(gè)數(shù)據(jù)點(diǎn)，而每一個(gè)植物樣本的紅外光譜其數(shù)據(jù)是二維的，即含波長(zhǎng)和吸收強(qiáng)度，一條數(shù)據(jù)往往含有數(shù)百乃至數(shù)千個(gè)帶式數(shù)據(jù)點(diǎn)，攜帶著十分豐富的樣本信息，但是很多信息是蘊(yùn)藏在眾多的信息之中，相互干擾和遮蓋，因此需要進(jìn)行信息提取或數(shù)據(jù)挖掘 (data mining)。光譜數(shù)據(jù)挖掘涉及到大量運(yùn)算，其復(fù)雜程度遠(yuǎn)高于點(diǎn)式數(shù)據(jù)運(yùn)算，現(xiàn)代計(jì)算機(jī)技術(shù)和化學(xué)計(jì)量學(xué)的發(fā)展為復(fù)雜的數(shù)據(jù)挖掘提供了可能的手段。

4 展望

4.1 新型光譜技術(shù)

目前，可見光、紫外光和紅外光在植物營(yíng)養(yǎng)品質(zhì)分析中得到廣泛的應(yīng)用，相應(yīng)的儀器設(shè)備也在分析能力、分辨率、分析精度以及便攜性上都不斷發(fā)展。從單樣品分析到多樣品自動(dòng)分析，從模擬信號(hào)到高精度數(shù)字信號(hào)，從單通道到多通道，從臺(tái)式機(jī)到手持式機(jī)等。同時(shí)，隨著技術(shù)和檢測(cè)手段的進(jìn)步，不斷產(chǎn)生新型光譜技術(shù)，其中開始應(yīng)用的包括紅外光聲光譜、拉曼光譜和激光誘導(dǎo)擊穿光譜。

Bell于1880年在研究光纖通訊時(shí)發(fā)現(xiàn)了光聲效應(yīng)[89–90]，但直到20世紀(jì)80年代，由于傅利葉紅外光譜儀、低噪音高靈敏度微音器以及計(jì)算機(jī)技術(shù)的發(fā)展，光聲光譜開始成為非常有價(jià)值的分析方法[91]。一束紅外光入射到光聲附件，通過KBr窗口照射到光聲池中的樣品，樣品受到紅外光照射后產(chǎn)生熱效應(yīng)，并將熱傳導(dǎo)給樣品池中的氣體 (通常為氦氣)，氣體受熱后會(huì)膨脹與收縮，從而產(chǎn)生熱波，熱波可被敏感的麥克風(fēng) (微音器) 檢測(cè)，即為光聲信號(hào)，光聲信號(hào)轉(zhuǎn)化成電信號(hào)，經(jīng)過放大后就得到紅外光聲光譜 (圖3)。熱波在樣品光激發(fā)處產(chǎn)生并開始傳遞，但衰減很快，這個(gè)衰減過程也決定了探測(cè)深度，因此不同的調(diào)制頻率就可以探測(cè)到不同深度的樣品，當(dāng)調(diào)制頻率增加時(shí)，樣品探測(cè)深度減小，反之增大[92]。紅外光聲光譜分析不需要或者需要很少的樣品前處理，而且能直接獲取樣品不同深度的信息，這種直接快速的分析方法適用于具有很弱和很強(qiáng)吸收的樣品，同時(shí)也適用于不同形態(tài)植物樣品的分析。

圖3 紅外光聲光譜原理示意圖Fig. 3 Schematic description of the photoacoustic spectroscopy setup

1928年印度物理學(xué)家拉曼在實(shí)驗(yàn)中發(fā)現(xiàn)，當(dāng)用波長(zhǎng)比試樣粒徑小得多的單色光照射氣體、液體或透明試樣時(shí)，發(fā)現(xiàn)還有一系列對(duì)稱分布的若干條譜線，強(qiáng)度很弱且與入射光頻率發(fā)生位移，這種現(xiàn)象稱為拉曼效應(yīng)，拉曼也因此獲得了1930年的諾貝爾物理學(xué)獎(jiǎng)。由于拉曼譜線的數(shù)目、位移的大小、譜線的長(zhǎng)度直接與試樣分子振動(dòng)或轉(zhuǎn)動(dòng)能級(jí)有關(guān)，與紅外吸收光譜類似，拉曼光譜可以得到有關(guān)分子振動(dòng)或轉(zhuǎn)動(dòng)的信息，已廣泛應(yīng)用于物質(zhì)的鑒定以及分子結(jié)構(gòu)的解析[93]。目前的拉曼技術(shù)包括單道檢測(cè)的拉曼光譜分析技術(shù)、以CCD為代表的多通道探測(cè)器的拉曼光譜分析技術(shù)、采用傅立葉變換技術(shù)的拉曼光譜分析技術(shù)、共振拉曼光譜分析技術(shù)和表面增強(qiáng)拉曼效應(yīng)分析技術(shù)。拉曼光譜的分析方法不需要樣品前處理，也沒有樣品的制備過程，并且具有分析過程操作簡(jiǎn)便、測(cè)定時(shí)間短和靈敏度高等優(yōu)點(diǎn)。

原子光譜是利用基于不同原子的特征光譜進(jìn)行元素分析，傳統(tǒng)的原子光譜分析需要進(jìn)行樣品前處理，通常包括濕消解和干消解，因而無(wú)法獲得樣品本身的原子光譜，而要獲得樣品本身的原子光譜就要求不能進(jìn)行復(fù)雜的樣品前處理，包括消解或提取。激光誘導(dǎo)擊穿光譜 (laser-induced breakdown spectroscopy，LIBS) 是一種利用高能量脈沖激光燒蝕樣品材料，使材料表面的微量樣品瞬間氣化形成高溫、高密度的等離子體，測(cè)量等離子體中原子發(fā)射光譜的譜線波長(zhǎng)和強(qiáng)度，進(jìn)而完成樣品材料所含化學(xué)元素的定性和定量分析的光譜檢測(cè)技術(shù) (圖4)。近10年來LIBS的研究得到了快速發(fā)展，相關(guān)研究論文逐年增多，應(yīng)用領(lǐng)域也逐漸擴(kuò)大[94]。LIBS具備許多獨(dú)特的優(yōu)點(diǎn)，如樣品預(yù)處理簡(jiǎn)單或無(wú)需預(yù)處理，適合于各種形態(tài) (氣態(tài)、液態(tài)、固態(tài)、顆粒) 物質(zhì)的分析，激光激發(fā)樣品無(wú)二次污染，近似于無(wú)損檢測(cè)，類似于激光探針，可進(jìn)行快速實(shí)時(shí)現(xiàn)場(chǎng)分析，能夠完成表面和逐層原位檢測(cè)，可以實(shí)現(xiàn)非接觸式遠(yuǎn)距離探測(cè)，能夠應(yīng)對(duì)惡劣環(huán)境下的在線分析，儀器操作簡(jiǎn)單方便。但LIBS在植物營(yíng)養(yǎng)品質(zhì)分析中的應(yīng)用還非常有限[95]，因此具有廣闊的發(fā)展前景。

除了以上光譜外，還有同步輻射、各類核磁共振 (nuclear magnetic resonance，NMR)、質(zhì)譜 (mass spectroscopy，MS)、CT等，但這些光譜學(xué)方法設(shè)備昂貴，難以在常規(guī)植物營(yíng)養(yǎng)品質(zhì)分析中應(yīng)用，本文就不作介紹，進(jìn)一步了解可參閱有關(guān)文獻(xiàn)[96–98]。

4.2 光譜融合與現(xiàn)代信息技術(shù)

光譜根據(jù)波長(zhǎng)可分為X光、可見近紅外光譜、中紅外光譜等；根據(jù)光的種類可分為紅外、拉曼、熒光等；根據(jù)振動(dòng)的類型可為原子光譜、分子光譜等；根據(jù)信號(hào)獲取方法可分為吸收光譜、光聲光譜等。以上各種光譜具有各自的特點(diǎn)和優(yōu)勢(shì)，如紅外光譜主要是響應(yīng)極性分子振動(dòng)，而拉曼光譜則響應(yīng)非極性分子振動(dòng)，無(wú)疑兩種光譜數(shù)據(jù)的融合能獲得更多樣品的信息，從而為樣品的表征提供更好的技術(shù)支撐[4]。又如，分子光譜反映的是分子鍵的振動(dòng)，主要表現(xiàn)為結(jié)構(gòu)組成特征，而原子光譜反映的是原子的特征吸收，主要表現(xiàn)元素組成特征，兩者的結(jié)合也是信息的互補(bǔ)，因此光譜融合具有重要應(yīng)用前景[93]。但光譜數(shù)據(jù)的融合涉及數(shù)據(jù)的權(quán)重、數(shù)據(jù)的連接、信息的冗余以及干擾或噪音的引入，因此需要選擇或優(yōu)化不同的光譜數(shù)據(jù)處理方法和模型[81]，否則，光譜的融合不但不能發(fā)揮作用，反而可能使分析的精度和準(zhǔn)確度降低。

圖4 激光誘導(dǎo)擊穿光譜工作原理圖Fig. 4 Principle of laser induced breakdown spectroscopy (LIBS)

當(dāng)前信息技術(shù)發(fā)展迅猛，智能手機(jī)廣泛使用，互聯(lián)網(wǎng)和云技術(shù)開始應(yīng)用于諸多行業(yè)，如工業(yè)、商業(yè)和服務(wù)業(yè)等，在農(nóng)業(yè)中已開始運(yùn)用，但還相當(dāng)薄弱[99]?，F(xiàn)代光譜技術(shù)以互聯(lián)網(wǎng)和云技術(shù) (云貯存和云計(jì)算) 為平臺(tái)，通過智能終端 (如手機(jī))，結(jié)合植物營(yíng)養(yǎng)專業(yè)知識(shí)，將植物營(yíng)養(yǎng)品質(zhì)分析與鑒定常規(guī)化(圖5)，在滿足人們對(duì)植物營(yíng)養(yǎng)品質(zhì)信息需求的同時(shí)，也能進(jìn)一步規(guī)范市場(chǎng)，促進(jìn)植物營(yíng)養(yǎng)品質(zhì)的提升。

圖5 植物營(yíng)養(yǎng)品質(zhì)信息的適時(shí)快速獲取示意圖Fig. 5 In situ rapid obtaining of plant nutrition quality parameters combining information technology

[1]朱兆良, 金繼運(yùn). 保障我國(guó)糧食安全的肥料問題[J]. 植物營(yíng)養(yǎng)與肥料學(xué)報(bào), 2013, 19(2): 259–273.Zhu Z L, Jin J Y. Fertilizer use and food security in China[J]. Journal of Plant Nutrition and Fertilizer , 2013, 19(2): 259–273.

[2]張士康, 山麗杰, 吳林海. 中國(guó)農(nóng)產(chǎn)品消費(fèi)的形態(tài)特征、關(guān)注度與農(nóng)產(chǎn)品品質(zhì)的安全供給分析[J]. 世界農(nóng)業(yè), 2010, 376: 49–52.Zhang S K, Shan L J, Wu H L. Food quality and security in China regarding a new consumption perspective[J]. World Agriculture,2010, 376: 49–52.

[3]Luo B L. The key points, difficulties and direction of agricultural supply side reforms[J]. Agricultural Economics, 2017, 1: 1–10.

[4]Santosh L, Sangdae L, Hoonsoo L, et al. A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration[J]. Trends in Food Science and Technology, 2015, 46:85–98.

[5]Stuart B. Infrared spectroscopy: Fundamentals and applications [M].New Jersey: John Wiley & Sons, 2004.

[6]Gunzler H, Gremlich H U. IR spectroscopy: an introduction [M].Weinheim: Wiley-VCH, 2002.

[7]杜昌文. 土壤紅外光聲光譜原理與應(yīng)用[M]. 北京: 科學(xué)出版社,2012.Du C W. Soil infrared photoacoustic spectroscopy: principal and application[M]. Beijing: Science Press, 2012.

[8]Dixon R, Coates D. Near infrared spectroscopy of faeces to evaluate the nutrition and physiology of herbivores[J]. Journal of Near Infrared Spectroscopy, 2009, 17(1): 1–31.

[9]Lu X N, Rasco B A. Determination of antioxidant content and antioxidant activity in foods using infrared spectroscopy and chemometrics: A review[J]. Critical Reviews in Food Science and Nutrition, 2012, 52(10): 853–875.

[10]張玉森, 姚霞, 田永超, 等. 應(yīng)用近紅外光譜預(yù)測(cè)水稻葉片氮含量[J]. 植物生態(tài)學(xué)報(bào), 2010, 34(6): 704–712.Zhang Y S, Yao X, Tian Y C, et al. Estimating leaf nitrogen content with near infrared reflectance spectroscopy in rice[J]. Chinese Journal of Plant Ecology, 2010, 34(6): 704–712.

[11]Shen F, Niu X Y, Yang D T, et al. Determination of amino acids in Chinese rice wine by Fourier transform near-infrared spectroscopy[J].Journal of Agricultural and Food Chemistry, 2010, 58: 9809–9816.

[12]Escuredo O, Martín M I G, Moncada G W, et al. Amino acid profile of the quinoa (Chenopodium quinoa Willd.) using near infrared spectroscopy and chemometric techniques[J]. Journal of Cereal Science, 2014, 60: 67–74.

[13]Torit B B, Srigopal S, Krishnendu C. Development of NIRS models to predict protein and amylose content of brown rice and proximate compositions of rice bran[J]. Food Chemistry, 2016, 191: 21–27.

[14]Zhang B, Rong Z. Prediction of the amino acid composition in brown rice using different sample status by near-infrared reflectance spectroscopy[J]. Food Chemistry, 2011, 127: 275–281.

[15]Chuang Y K, Hua Y P, Yang I C. Integration of independent component analysis with near infrared spectroscopy for evaluation of rice freshness[J]. Journal of Cereal Science, 2014, 60: 238–242.

[16]Chen K J, Huang M. Prediction of milled rice grades using Fourier transform near-infrared spectroscopy and artificial neural networks[J]. Journal of Cereal Science, 2010, 52: 221–226.

[17]Pornarree S, Kazuhiro N, Sirichai K, et al. Eating quality evaluation of Khao Dawk Mali 105 rice using near infrared spectroscopy[J].LWT - Food Science and Technology, 2017, 79: 70–77.

[18]Xie L H, Tang S Q, Chen N, et al. Optimization of near-infrared reflectance model in measuring protein and amylose content of rice flour[J]. Food Chemistry, 2014, 142: 92–100.

[19]Xu S X, Shi X Z, Wang M Y, et al. Determination of rice root density at the field level using visible and near-infrared reflectance spectroscopy[J]. Geoderma, 2016, 267: 174–184.

[20]Fan D M, Ma W R, Wang L Y, et al. Determination of structural changes in microwaved rice starch using Fourier transform infrared and Raman spectroscopy[J]. Starch, 2012, 64: 598–606.

[21]Shao Y N, Cen Y L, He Y, et al. Infrared spectroscopy and chemometrics for the starch and protein prediction in irradiated rice[J]. Food Chemistry, 2011, 126: 1856–1861.

[22]Shen F, Ying Y B, Li B B, et al. Prediction of sugars and acids in Chinese rice wine by mid-infrared spectroscopy[J]. Food Research International, 2011, 44: 1521–1527.

[23]Shao, Y N, He Y. Visible/near infrared spectroscopy and chemometrics for the prediction of trace element (Fe and Zn) levels in rice leaf[J]. Sensors, 2013, 13: 1872–1883.

[24]Cozzolino D. An overview of the use of infrared spectroscopy and chemometrics in authenticity and traceability of cereals[J]. Food Research International, 2014, 60: 262–265.

[25]Ingrid V, Martin E, Nicolas V, et al. Monitoring nitrogen leaf resorption kinetics by near-infrared spectroscopy during grain filling in durum wheat in different nitrogen availability conditions[J]. Crop Science, 2013, 53: 284–296.

[26]Ayse C M, Ismail H B, Huseyin E G, et al. Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks[J]. Europe Food Research and Technology, 2011,233: 267–274.

[27]Johannes H, Michael P, Lukas D, et al. A comparison between nearinfrared (NIR) and mid-infrared (ATR-FTIR) spectroscopy for the multivariate determination of compositional properties in wheat bran samples[J]. Food Control, 2016, 60: 365–369.

[28]Fontaine J, Schirmer B, Horr J. Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino acid contents. 2. Results for wheat, barley, corn,triticale, wheat bran/middlings, rice bran, and sorghum[J]. Journal of Agricultural and Food Chemistry, 2002, 50(14): 3902–3911.

[29]姚霞, 王雪, 黃宇, 等. 應(yīng)用近紅外光譜法估測(cè)小麥葉片糖氮比[J].應(yīng)用生態(tài)學(xué)報(bào), 2015, 26(8): 2371–2378.Yao X, Wang X, Huang Y, et al. Estimation of sugar to nitrogen ratio in wheat leaves with near infrared spectrometry[J]. Chinese Journal of Applied Ecology, 2015, 26(8): 2371–2378.

[30]姚霞, 湯守鵬, 曹衛(wèi)星, 等. 應(yīng)用近紅外光譜預(yù)測(cè)小麥葉片氮含量[J]. 植物生態(tài)學(xué)報(bào), 2011, 35(8): 844–852.Yao X, Tang S P, Cao W X, et al. Estimating the nitrogen content in wheat leaves by near-infrared reflectance spectroscopy[J]. Chinese Journal of Plant Ecology, 2011, 35(8): 844–852.

[31]Lidia E A, David D E, Susan D, et al. Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels[J]. Journal of Cereal Science, 2012, 55:160–165.

[32]Jiang H Y, Zhu Y J, Wei L M, et al. Analysis of protein, starch and oil content of single intact kernels by near infrared reflectance spectroscopy (NIRS) in maize (Zea mays L.)[J]. Plant Breeding,2007, 126: 492–497.

[33]Oreste B, Nicola B. Application of near-infrared reflectance spectroscopy (NIRS) to the evaluation of carotenoids content in maize[J]. Journal of Agricultural and Food Chemistry, 2004, 52:5577–5582.

[34]Tesfaye M B, Tom C P, Settles A M. Development of a calibration to predict maize seed composition using single kernel near infrared spectroscopy[J]. Journal of Cereal Science, 2006, 43: 236–243.

[35]Nicola B, Vincenza P, Paola B, et al. Rapid detection of kernel rots and mycotoxins in maize by near-infrared reflectance spectroscopy[J]. Journal of Agricultural and Food Chemistry, 2005,53: 8128–8134.

[36]Jasper G T, Natalia P, Paul R A. Prediction of maize seed attributes using a rapid single kernel near infrared instrument[J]. Journal of Cereal Science, 2009, 50: 381–387.

[37]魏良明, 嚴(yán)衍祿, 戴景瑞. 近紅外反射光譜測(cè)定玉米完整籽粒蛋白質(zhì)和淀粉含量的研究[J]. 中國(guó)農(nóng)業(yè)科學(xué), 2004, 37(5): 630–633.Wei L M, Yan Y L, Dai J R. Determining protein and starch contents of whole maize kernel by near infrared reflectance spectroscopy(NIRS)[J]. Scientia Agricultura Sinica, 2004, 37(5): 630–633.

[38]Sylwester M, Roman S, Agnieszka K. Application of infrared reflection and Raman spectroscopy for quantitative determination of fat in potato chips[J]. Journal of Molecular Structure, 2016, 1126:213–218.

[39]Oluwatosin E A, Suzanne D J, Van-Den T, et al. Development and validation of a near-infrared spectroscopy method for the prediction of acrylamide content in french-fried potato[J]. Journal of Agricultural and Food Chemistry, 2016, 64: 1850–1860.

[40]Timm B, Bernd T, Wolfgang F, et al. Development of near-infrared reflection spectroscopy calibrations for crude protein and dry matter content in fresh and dried potato tuber samples[J]. Potato Research,2016, 59: 149–165.

[41]Ainara L, Silvia A, Ignacio G, et al. Review of the application of near-infrared spectroscopy for the analysis of potatoes[J]. Journal of Agricultural and Food Chemistry, 2013, 61: 5413–5424.

[42]Xu S X, Zhao C, Shi X Z, et al. Rapid determination of carbon,nitrogen, and phosphorus contents of field crops in China using visible and near-infrared reflectance spectroscopy[J]. Crop Science,2017, 57: 475–489.

[43]Daniela S F, Juliana A L P, Ronei J P. Fourier transform nearinfrared spectroscopy (FT-NIRS) application to estimate Brazilian soybean (Glycine max (L.) Merril) composition[J]. Food Research International, 2013, 51: 53–58.

[44]Simon A H, Stewart F G, Emmanuelle C, et al. The application of near-infrared reflectance spectroscopy (NIRS) to detect melamine adulteration of soya bean meal[J]. Food Chemistry, 2013, 136:1557–1561.

[45]Wang L, Wang Q, Liu H Z, et al. Determining the contents of protein and amino acids in peanuts using near-infrared reflectance spectroscopy[J]. Journal of the Science of Food Agriculture, 2013,93: 118–124.

[46]Zhang G Y, Li P H, Zhang W F, et al. Analysis of multiple soybean phytonutrients by near-infrared reflectance spectroscopy[J].Analytical and Bioanalytical Chemistry, 2017, 409: 3515–3525.

[47]Ferreira D S, Gal?o O F, Pallone J A L, et al. Comparison and application of near-infrared (NIR) and mid-infrared (MIR)spectroscopy for determination of quality parameters in soybean samples[J]. Food Control, 2014, 35: 227–232.

[48]Audrey P, Juan A F P, Vincent B, et al. Non-destructive measurement of vitamin C, total polyphenol and sugar content in apples using near-infrared spectroscopy[J]. Journal of Agricultural and Food Chemistry, 2013, 93: 238–244.

[49]Qi S Y, Seiichi O, Yoshio M, et al. Influence of sampling component on determination of soluble solids content of Fuji apple using nearinfrared spectroscopy[J]. Applied Spectroscopy, 2017, 71(5):856–865.

[50]Mark W D, Wouter S, Ellen H, et al. Application of visible and nearinfrared reflectance spectroscopy (Vis/NIRS) to determine carotenoid contents in banana (Musa spp.) fruit pulp[J]. Journal of Agricultural and Food Chemistry, 2009, 57: 1742–1751.

[51]Nicoletta S, Anna S, Valentina D E, et al. Evaluation of quality and nutraceutical content of blueberries (Vaccinium corymbosum L.) by near and mid-infrared spectroscopy[J]. Postharvest Biology and Technology, 2008, 50: 31–36.

[52]Bei D R, Fuentes S, Sullivan W, et al. Rapid measurement of total non-structural carbohydrate concentration in grapevine trunk and leaf tissues using near infrared spectroscopy[J]. Computers and Electronics in Agriculture, 2017, 136: 176–183.

[53]Cortés V, Ortiz C, Aleixos N, et al. A new internal quality index for mango and its prediction by external visible and near-infrared reflection spectroscopy[J]. Postharvest Biology and Technology,2016, 118: 148–158.

[54]Wang J, Zhao H B, Shen C W, et al. Determination of nitrogen concentration in fresh pear leaves by visible/near-infrared reflectance spectroscopy[J]. Agronomy Journal, 2014, 106(5): 1867–1872.

[55]Jiang H, Zhu W X. Determination of pear internal quality attributes by Fourier transform near infrared (FT-NIR) spectroscopy and multivariate analysis[J]. Food Analytical Methods, 2013, 6: 569–577.

[56]Cozzolino D. The role of visible and infrared spectroscopy combined with chemometrics to measure phenolic compounds in grape and wine samples[J]. Molecules, 2015, 20: 726–737.

[57]Li C Y, Du C W, Zeng Y, et al. Two-dimensional visualization of nitrogen distribution in leaves of Chinese cabbage (Brassica rapa subsp. chinensis) by the Fourier transform infrared photoacoustic spectroscopy technique[J]. Journal of Agricultural and Food Chemistry, 2016, 64: 7696–7701.

[58]Katherine F, María-Teresa S, Dolores P, et al. Feasibility in NIRS instruments for predicting internal quality in intact tomato[J]. Journal of Food Engineering, 2009, 91: 311–318.

[59]Steven V, Katrien B, Peter M, et al. Sequential injection ATR-FTIR spectroscopy for taste analysis in tomato[J]. Sensors and Actuators B,2009, 137: 715–721.

[60]Huseyin A, Andrea S, Didem P. Monitoring multicomponent quality traits in tomato juice using portable mid-infrared (MIR) spectroscopy and multivariate analysis[J]. Food Control, 2016, 66: 79–86.

[61]Iwona S C, Maryse R, Sylvie B, et al. Mid-infrared spectroscopy as a tool for rapid determination of internal quality parameters in tomato[J]. Food Chemistry, 2015, 125: 1390–1397.

[62]Pérez-Vicha B, Velascob L, Fernández-Martíneza J M. Composition in sunflower through the analysis of intact seeds, husked seeds, meal and oil by near-infrared reflectance spectroscopy[J]. JAOCS, 1998,75: 547–555.

[63]Sato T, Takahata Y, Noda T, et al. Nondestructive determination of fatty acid composition of husked sunflower (Helianthus annuus L.)seeds by near-infrared spectroscopy[J]. Journal of American Oil Chemistry Society, 1995, 72: 1177–1183.

[64]Chen G L, Zhang B, Wu J G, et al. Nondestructive assessment of amino acid composition in rapeseed meal based on intact seeds by near-infrared reflectance spectroscopy[J]. Animal Feed Science and Technology, 2011, 165: 111–119.

[65]Rafael F, Mercedes D R, Elena C, et al. Quantification of glucosinolates in leaves of leaf rape (Brassica napus ssp. pabularia)by near-infrared spectroscopy[J]. Phytochemistry, 2005, 66: 175–185.

[66]Tkachuk R. Oil and protein analysis of whole rapeseed kernels by near infrared reflectance spectroscopy[J]. JAOCS, 1981, 8: 819–822.

[67]陸宇振, 杜昌文, 余常兵, 等. 紅外光譜在油菜籽快速無(wú)損檢測(cè)中的應(yīng)用[J]. 植物營(yíng)養(yǎng)與肥料學(xué)報(bào), 2013, 19(5): 1257–1263.Lu Y Z, Du C W, Yu C B, et al. Advances in the application of infrared spectroscopy in the rapid and non-destructive characterization of rapeseeds[J]. Journal of Plant Nutrition and Fertilizer, 2013, 19(5): 1257–1263.

[68]Lu Y Z, Du C W , Yu C B, et al. Use of FTIR-PAS combined with chemometrics to quantify nutritional information in rapeseeds(Brassica napus)[J]. Journal of Plant Nutrition and Soil Science,2014, 177(6): 927–933.

[69]Lu Y Z, Du C W, Yu C B, et al. Fourier transform mid-infrared photoacoustic spectroscopy (FTIR-PAS) coupled with chemometrics for non-destructive determination of oil content in rapeseed[J].Transactions of the ASABE, 2015, 58(5): 1403–1407.

[70]Gotor A A, Farkas E, Berger M, et al. Determination of tocopherols and phytosterols in sunflower seeds by NIR spectrometry[J].European Journal of Lipid Science and Technology, 2007, 109(5):525–530.

[71]Ruoff K, Luginbuhl W, Bogdanov S, et al. Quantitative determination of physical and chemical measurands in honey by near-infrared spectrometry[J]. European Food Research and Technology,2007, 225: 415–423.

[72]Escuredo O, Seijo M C, Salvador J, et al. Near infrared spectroscopy for prediction of antioxidant compounds in the honey[J]. Food Chemistry, 2013, 141: 3409–3414.

[73]Ren G X, Wang S P, Ning J M, et al. Quantitative analysis and geographical traceability of black tea using Fourier transform nearinfrared spectroscopy (FT-NIRS)[J]. Food Research International,2013, 53: 822–826.

[74]Chen Q S, Zhao J W, Sumpun C, et al. Simultaneous analysis of main catechins contents in green tea (Camellia sinensis L.) by Fourier transform near infrared reflectance (FT-NIR) spectroscopy[J]. Food Chemistry, 2009, 113: 1272–1277.

[75]Jiang H, Chen Q S. Chemometric models for the quantitative descriptive sensory properties of green tea (Camellia sinensis L.)using Fourier transform near infrared (FT-NIR) spectroscopy[J].Food Analytical Methods, 2015, 8: 954–962.

[76]Liu Y L. Recent progress in Fourier transform infrared (FTIR)spectroscopy study of compositional, structural and physical attributes of developmental cotton fibers[J]. Materials, 2013, 6:299–313.

[77]Huang Z R, Sha S, Rong Z Q, et al. Feasibility study of near infrared spectroscopy with variable selection for non-destructive determination of quality parameters in shell-intact cottonseed[J].Industrial Crops and Products, 2013, 43: 654–660.

[78]Du C W, Zhou J M, Liu J F. Identification of Chinese medicinal fungus Cordyceps sinensis by depth-profiling mid-infrared photoacoustic spectroscopy[J]. Spectrochimica Acta Part A,Molecular and Biomolecular Spectroscopy, 2017, 173: 489–494.

[79]Andreia M S, Mawsheng C, Jeemeng L, et al. Combining multivariate analysis and monosaccharide composition modeling to identify plant cell wall variations by Fourier Transform Near Infrared spectroscopy[J]. Plant Methods, 2011, 7: 26.

[80]Sun S Q, Chen J B, Zhou Q, et al. Application of mid-Infrared spectroscopy in the quality control of traditional Chinese medicines[J]. Planta Medica, 2010, 76: 1987–1996.

[81]Eva B, Joan F, Ricard B, et al. Data fusion methodologies for food and beverage authentication and quality assessment-A review[J].Analytica Chimica Acta, 2015, 891: 1–14.

[82]Du C W, Zhou J M. Application of infrared photoacoustic spectroscopy in soil analysis[J]. Applied Spectroscopy Reviews,2011, 46: 405–422.

[83]Michel M, Yves M, Georges O, et al. Wavelet toolbox for use with Matlab?[M]. The MathWorks, Inc., 2002.

[84]Pascal C, Serge W, Michel U. Combined wavelet transform-artificial neural network use in tablet active content determination by nearinfrared spectroscopy[J]. Analytica Chimica Acta, 2007, 591:219–224.

[85]鄒謀炎. 反卷積和信號(hào)復(fù)原[M]. 北京: 國(guó)防工業(yè)出版社, 2001.Zhou M Y. Deconvolution and signal recovery [M]. Beijing: National Defense Industry Press, 2001.

[86]Richard G B. Applied chemometrics for scientist[M]. Chichester,UK: John Wiley & Sons, Ltd, 2007.

[87]Ma F, Du C W, Zhou J M, et al. A self-adaptive model for the prediction of soil organic matter using mid-infrared photoacoustic spectroscopy[J]. Soil Science Society of America Journal, 2016,80(1): 238–246.

[88]Howard M, Jerry W. Chemometrics in spectroscopy [M]. London,UK: Academic Press, Elsevier Ltd., 2007.

[89]Bell A G. On the production and re-production of sound by light[J].American Journal of Science, 1880, 20: 305–324.

[90]Brunn J, Grosse P, Wynands R. Quantitative analysis of photoacoustic IR spectra[J]. Applied Physics B, 1988, 47: 343–348.

[91]Rosencwaig A, Gersho A. Theory of photoacosutic effect with solids[J]. Journal of Applied Physics, 1976, 47: 64–69.

[92]Du C W, Zhou J M, Wang H Y, et al. Determination of soil properties using Fourier transform mid-infrared photoacoustic spectroscopy[J]. Vibrational Spectroscopy, 2009, 49: 32–37.

[93]Xing Z, Du C W, Zeng Y, et al. Characterizing typical farmland soils in China using Raman spectroscopy[J]. Geoderma, 2016, 268:147–155.

[94]Motea M, James R, Simon F, et al. Evaluation of elemental profiling methods, including laser-induced breakdown spectroscopy (LIBS),for the differentiation of Cannabis plant material grown in different nutrient solutions[J]. Forensic Science International, 2015, 251:95–106.

[95]Banu S, Gonca B, Ismail H B. Laser-induced breakdown spectroscopy based protein assay for cereal samples[J]. Journal of Agricultural and Food Chemistry, 2016, 64: 9459–9463.

[96]Kovrlija R, Rondeau-Mouro C. Multi-scale NMR and MRI approaches to characterize starchy products[J]. Food Chemistry,2017, 236: 2–14.

[97]Lim S, Lee J G, Lee E J. Comparison of fruit quality and GC-MS-based metabolite profiling of kiwifruit 'Jecy green': Natural and exogenous ethylene-induced ripening[J]. Food Chemistry, 2017, 234:81–92.

[98]Mohoric A, Vergeldt F, Gerkema E, et al. The effect of rice kernel microstructure on cooking behaviour: A combined mu-CT and MRI study[J]. Food Chemistry, 2009, 115: 1491–1499.

[99]Ojha T, Misra S, Raghuwanshi N S. Sensing-cloud: Leveraging the benefits for agricultural applications[J]. Computers and Electronics in Agriculture, 2017, 135: 96–107.

Application of modern spectroscopic technologies in the analysis of plant nutritional quality

DU Chang-wen
( State Key Laboratory of Soil and Agricultural Sustainability/Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China )

In current stage food security has been considering both the total food amount and the food quality instead of formally mainly considering the total food amount, therefore, the society pay more attention to the analysis of food quality. The conventional plant nutritional analysis was laboratory-based chemical analysis,which was time- and cost-consuming as well as high labor intensity. Thus, the routine methods are difficult to meet the requirement of mass plant nutritional information deprived from food quality. Correspondingly,spectroscopic methods, including ultraviolet, visible, infrared, fluorescence and Raman etc., have been widely used for in situ rapid obtaining of food quality parameters in a large variety of plants including crops, vegetable and fruit as well as some Chinese medicine plants. Furthermore, some subjective sensory properties can be characterized by spectroscopic methods in a more objective way. Infrared photoacoustic spectroscopy and laser induced breakdown spectroscopy have been applied in various fields, and demonstrated great potential in plant nutritional analysis. Multivariate calibration and validation were needed since spectral analysis involved numerous data points, which were well supported by chemometrical methods and computer technology, and highlights will focused on the in situ rapid obtaining of food quality parameters based on plant nutrition knowledge combining the platforms of internet and cloud technology as well as intelligent terminals.

nutritional quality; infrared spectroscopy; atomic spectroscopy; chemometrics; model

2017–07–24 接受日期：2017–08–25

國(guó)家自然科學(xué)基金項(xiàng)目（41671238）；江蘇省現(xiàn)代農(nóng)業(yè)研發(fā)專項(xiàng)（BE2017388）；江蘇省農(nóng)業(yè)科技自主創(chuàng)新資金[CX（17）3010]資助。

杜昌文（1974—），男，湖北鄂州人，博士，研究員，主要從事土壤肥料與植物營(yíng)養(yǎng)研究工作。Tel：025-86881565 E-mail：chwdu@issas.ac.cn