稳定同位素和元素指纹分析在白术产地溯源中的应用

为解决道地药材白术的产地溯源问题,本研究采用稳定同位素比质谱仪(EA-IRMS)和电感耦合等离子质谱仪(ICP-MS)测定了6个产地168种白术中δ¹³C、δ¹⁵N、δ²H、δ¹⁸O和δ³⁴S值及41种矿质元素含量,并结合化学计量学对不同产地白术进行鉴别。结果表明,线性判别分析(LDA)可以区分6个产地的白术。基于不同变量共建立7种不同判别模型,其平均交叉验证率分别为:稳定同位与素(88.17%)、矿质元素(98.67%)、常量矿物元素(41.83%)、 痕量矿质元素(99.33%)、稳定同位素与矿物质元素(100%)、稳定同位素与常量矿质元素(91.83%)以及稳定同位素与痕量矿物质(98.83%)。其中,δ¹⁸O、 δ³⁴S值以及Li、Dy、Sm、Er等稀土元素是影响判别的主要因素,主要由产地地理位置、气候条件和土壤类型决定。综上,基于稳定同位素、矿质元素和化学计量学的方法可对不同产地白术进行产地溯源。     

贵州名优茶与产地土壤矿质元素相关性及其溯源影响的研究

为探究贵州省名优茶产地不同深层土壤对茶叶矿质元素溯源效果的影响,以土壤-茶叶的多矿质元素法结合主成分分析(PCA)、反向传播(BP)神经网络法、逐步线性判别分析(SLDA)对茶叶产地进行溯源。结果显示,不同产地的茶叶和土壤具有独特的矿质元素指纹,茶叶中Fe、Mn、K、Ca、Mg、Cu的含量与土壤中对应元素含量显著相关(P<0.05),以这6种矿质元素含量结合PCA可有效区分茶叶的地理起源;不同深层土壤对茶叶产地的溯源有不同影响,通过SLDA法、BP神经网络法明确了以60~80 cm的土层进行产地溯源的效果最优,产地溯源验证判别率分别为98.5%(SLDA法)和100%(BP神经网络法),并基于SLDA法确定了Zn、Cu、P、Mn、Fe、Mg和K 7种元素构建的贵州名优茶产地溯源模型。此外,研究发现茶叶产地溯源几乎不受茶树品种的影响。本研究结果为贵州省名优茶矿质元素指纹图谱的建设及土壤-茶叶产地溯源的关系研究提供了理论依据。     

基于稳定同位素和矿质元素的决明子产地特征与溯源判别研究

为了更加精确高效地对中药决明子产区进行判别,本研究采用稳定同位素比质谱(IR-MS)和电感耦合等离子体质谱(ICP-MS)技术分别检测不同产地决明子稳定同位素比值和多种矿质元素含量特征,并结合化学计量学模型主成分分析(PCA)和人工神经网络(ANN)对决明子产地进行判别。方差统计结果表明,在优质产区(浙江新昌)决明子区别于普通产区决明子方面,19种矿质元素含量存在显著差异(P<0.05);PCA分析(同位素+矿物元素)显示,前2个主成分累计方差贡献率为45.23%,且该模型可显著区分道地产区决明子;ANN模型分析对浙江新昌产区的决明子判别正确率高达100%,且产地判别总体正确率高于90%。以上结果表明,稳定同位素比值和矿质元素含量结合化学计量学模型分析可有效判别决明子不同产区。本研究结果为决明子产地溯源提供了一种新的方法。     

茶叶产地溯源与鉴别检测技术研究进展

茶叶是我国重要的特色农产品,地域特色和品质特征明显,溯源与鉴别检测技术对保证其产地和品质真实性具有重要意义.本文系统介绍了国内外茶叶产地溯源和种类鉴别中稳定同位素、多矿物元素、近红外光谱和化学指纹图谱等检测技术的研究进展,分析茶叶产地溯源与鉴别检测技术面临的关键问题,提出茶叶产地溯源和鉴别的研究重点,对我国茶叶原产地保护具有积极的借鉴作用.     

浦江葡萄同位素与矿物元素特征及地理标志保护模型构建

为探讨稳定同位素与矿物元素在葡萄产地鉴别上的可行性,本研究分别从浦江、浙江其他产地(慈溪、温岭)、上海和安徽4个不同产地采集代表性葡萄样品;通过稳定同位素比率质谱(EA-IRMS)元素分析仪和电感耦合等离子体质谱(ICP-MS)测定其同位素比率与矿物质元素含量,结合化学计量学统计工具对浦江葡萄开展原产地判别研究.结果表...     

甘肃不同生态区域高原夏菜的矿质元素与抗氧化性分析

为探究甘肃不同生态区域高原夏菜的矿质元素和抗氧化活性，根据海拔和气候差异，选取沿黄灌区、河西戈壁绿洲以及过渡带不同生态区域的6个产地，进行西兰花、娃娃菜、红笋3个高原夏菜主导品种的铁、钙、钾含量以及FRAP值、DPPH清除率、POD活性、PPO活性4种抗氧化活性指标的测定。结果表明，西兰花的铁、钙、钾含量均高于娃娃菜和红笋，榆中2产地的3种蔬菜的铁、钙、钾含量均较高，反映了西兰花对3种矿质元素的高富集性和榆中区域性的土壤矿质元素富集特点，3种蔬菜的4种抗氧化性指标测定结果未表现出品种间抗氧化性强弱的规律性特点，需进一步丰富测定指标，进行综合评价。     

云南省不同产地核桃品质分析与综合评价

为揭示云南省石屏、大姚、景东、永平、昌宁等5个产地生产的代表性核桃品种的品质,本研究对5个产地核桃仁中的矿质元素、蛋白质、粗脂肪、脂肪酸等成分进行测定分析。结果表明,5个产地核桃仁中的磷（P）、钾（K）、钙（Ca）、镁（Mg）、铁（Fe）、锌（Zn）、铜（Cu）、锰（Mn）等8种矿质元素含量在14.26～6 780 mg/kg,其中P、 K、 Mg元素的含量较高,P和Zn含量变异系数较大;蛋白质含量为13%～19.87%,最高为永平核桃;粗脂肪含量为66.97%～72.44%,最高为景东核桃;饱和脂肪酸含量为7.47%～11.35%,不饱和脂肪酸含量为83.92%～92.53%; 17种营养成分符合正态分布,与产地无显著相关性;Ca元素与蛋白质含量存在极显著正相关,硬脂酸与油酸含量存在极显著正相关,3种不饱和脂肪酸（油酸、亚油酸、 α-亚麻酸）之间存在显著相关性;因子分析提取前3个公因子,方差累积贡献率达97.42%,对5个产地的核桃品质进行综合评价,排名依次为石屏、景东、大姚、永平、昌宁。本研究通过分析云南省5个产地主要代表性核桃品种的营养特征,为各地区核桃品种选育和深加工奠定基础。     

不同泌乳期牛奶矿物元素特征及产地溯源研究

为研究不同泌乳期及产地来源对牛奶中矿物元素含量的影响,并建立产地溯源模型,从我国4个地区(河北、宁夏、陕西和内蒙古)共采集120份牛奶样品,用电感耦合等离子体质谱法测定牛奶中12种矿物元素的含量,研究泌乳期不同阶段牛奶中矿物元素特征的变化并进行产地溯源。结果表明,泌乳期不同阶段样品的矿物元素含量差异不显著(P>0.05),同一泌乳阶段,含量最高的是钾,其次是钙,最低的是铬。除钼外,其余11种矿物元素的含量在4个地区间均存在显著差异(P<0.05)。偏最小二乘判别分析法(PLS-DA)和交叉验证的结果表明,矿物元素可以对不同产地来源的牛奶样品进行分类,交叉验证的准确率为88.3%,通过计算得出产地鉴别的关键因子是锶、铬、锌及锰。综上,多元矿物元素是鉴别牛奶地理来源的有效工具。本研究结果为保障乳制品安全及真实性提供了参考依据。     

基于矿物元素指纹图谱的黑龙江黄豆产地溯源

该研究探讨了矿物元素指纹分析技术对黑龙江黄豆产地溯源的可行性,筛选出判别黑龙江黄豆产地溯源的有效指标。利用电感耦合等离子体质谱仪(inductively coupled plasma mass spectrometry,ICP-MS)测定来自齐齐哈尔和北安2个地域50份黄豆样品中52种矿物元素的含量,并对数据进行了方差分析、主成分分析和判别分析。研究表明,46种矿物元素含量在地域间存在显著差异,通过逐步判别分析筛选出8项元素指标建立黄豆产地判别模型,所建立的模型对黄豆产地整体交叉检验判别率为95.7%。As、Ru、Gd含量在黄豆与土壤间呈显著正相关(P0.05),Tb含量在黄豆与土壤间呈极显著正相关(P0.01),由4种元素建立的判别模型对产地判别准确。因此,上述元素是黄豆矿物元素产地鉴别较可靠的指纹信息指标。     

武夷岩茶中矿质养分的地域性差异及影响因素

武夷岩茶是一种传统名优乌龙茶,其品质差异具有明显的地域性。本研究旨在探索不同产地武夷岩茶茶青中矿质养分的差异规律,以期了解茶青养分对武夷岩茶品质的贡献。共采集武夷山112个茶园的茶青和土壤样品,主要测定了茶青中12种矿质元素和土壤中5项肥力指标（pH、碱解氮、有效磷、速效钾和有机质）。通过主成分分析发现,不同产地茶青矿质元素差异明显;利用随机森林分析,明确了不同岩区的茶青中磷、铜和氮元素的差异贡献最大,且总体上浓度由高到低依次为洲茶区、半岩区、正岩区,表明高品质的正岩茶中存在较低浓度的磷、铜和氮;皮尔森相关分析显示,土壤肥力5项指标与茶青中上述三种元素浓度相关性较弱,说明茶青养分受到综合环境因素的影响。综上所述,茶青中磷、铜和氮的水平与武夷岩茶的品质存在一定的负相关,可为茶园土壤养分管理提供参考依据。     

Inductively coupled plasma optical emission spectrometric determination of minerals in thyme honeys and their contribution to geographical discrimination

Twenty-four Spanish thyme honey samples were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES). Twenty-four minerals were quantified for each honey. The elements Al, As, Ba, Ca, Cr, Cu, Fe, K, Li, Mg, Mn, Na, P, Pb, S, Se, Si, Sr, and Zn were detected in all samples; seven elements are very abundant (Ca, K, Mg, Na, P, S, and Si), and six are not abundant (Al, Cu, Fe, Li, Mn, and Zn). Eleven of them are trace elements (As, Ba, Cd, Co, Cr, Ni, Mo, Pb, Se, Sr, and V) at <1 mg kg(-)(1). Classification of thyme honeys according to their origin (coast, mountains) was achieved by pattern recognition techniques on the mineral data. By means of principal component analysis, a good separation by geographical origin is obtained when scores for the two first principal components are plotted. Classification functions of 11 metals (Al, As, Cr, Cu, K, Li, Mg, Na, P, S, and V) were obtained using stepwise discriminant analysis and applied to classify correctly approximately 100% of the honey samples.     

Use of chemical profiling to differentiate geographic growing origin of raw pistachios

The objective of this study was to demonstrate the feasibility of chemical profiling methods combined with multivariate methods to differentiate the geographical growing regions of pistachios (Pistachia vera). Elemental analysis (Ba, Be, Ca, Cu, Cr, K, Mg, Mn, Na, V, Fe, Co, Ni, Cu, Zn, Sr, Ti, Cd, and P) of pistachios samples was performed using inductively coupled plasma atomic emission spectrometry. Analysis of inorganic anions and organic acids (selenite, bromate, fumarate, malate, selenate, pyruvate, acetate, phosphate, and ascorbate) of pistachio samples was performed using capillary electrophoresis. Bulk carbon and nitrogen isotope ratios were performed using stable isotope MS. There were nearly 400 pistachio samples analyzed from the three major pistachio growing regions: Turkey, Iran, and California (United States). A computational evaluation of the trace element data sets was carried out using statistical pattern recognition methods including principal component analysis, canonical discriminant analysis, discriminant analysis, and neural network modeling. Several linear discriminant function models classified the data sets with 95% or higher accuracy. We report the development of a method combining elemental analysis and classification techniques that may be widely applied to the determination of the geographical origin of foods.     

Multi-element analysis of South African wines by ICP-MS and their classification according to geographical origin

Wines from three important wine-producing regions, Stellenbosch, Robertson, and Swartland, in the Western Cape Province of South Africa, were analyzed by ICP-MS and the elemental composition used in multivariate statistical analysis to classify the wines according to geographical origin. The method is based on the assumption that the provenance soil is an important contributor to the trace element composition of a wine. A total of 40 elements were determined in 40 wines. Of these, 20 elements: Li, B, Mg, Al, Si, Cl, Sc, Mn, Ni, Ga, Se, Rb, Sr, Nb, Cs, Ba, La, W, Tl, and U showed differences in their means across the three areas. In a stepwise discriminant analysis procedure, functions based on linear combinations of the log-transformed element concentrations of Al, Mn, Rb, Ba, W, and Tl were generated to correctly classify wines from each region. In an alternative approach, a pairwise discriminant analysis procedure, not previously used in wine provenance studies, was tested. In this procedure, the classification was done in three steps, with each step classifying a wine as coming from a certain region or not. The combination of elements characterizing wines from a particular region was different in each region. The discriminant functions were based on the following elements: Al, Mn, Rb, Ba, and W for Stellenbosch; Se, Rb, Cs, and Tl for Robertson; and Al, Mn, Rb, Sr, Ba, and Tl for Swartland. After this procedure, the classification of the wines into one of the groups was 100% successful.     

Multielement analysis of Canadian wines by inductively coupled plasma mass spectrometry (ICP-MS) and multivariate statistics

Trace element fingerprints were deciphered for wines from Canada's two major wine-producing regions, the Okanagan Valley and the Niagara Peninsula, for the purpose of examining differences in wine element composition with region of origin and identifying elements important to determining provenance. Analysis by ICP-MS allowed simultaneous determination of 34 trace elements in wine (Li, Be, Mg, Al, P, Cl, Ca, Ti, V, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Mo, Ag, Cd, Sb, I, Cs, Ba, La, Ce, Tl, Pb, Bi, Th, and U) at low levels of detection, and patterns in trace element concentrations were deciphered by multivariate statistical analysis. The two regions were discriminated with 100% accuracy using 10 of these elements. Differences in soil chemistry between the Niagara and Okanagan vineyards were evident, without a good correlation between soil and wine composition. The element Sr was found to be a good indicator of provenance and has been reported in fingerprinting studies of other regions.     

Determining the geographic origin of potatoes using mineral and trace element content

To characterize potatoes according to their geographic origin and variety, 10 mineral and trace elements (Mg, Cr, Mn, Fe, Ni, Cu, Zn, Sr, Cd, and Ba) were determined in Italian potato samples. The data collected were successively elaborated using statistical techniques, namely, linear discriminant analysis (LDA). LDA was able to classify and discriminate the potatoes from Fucino both from those of other areas of Italy and from those of the four provinces of Abruzzo. A net separation between the Fucino potatoes and those of the other areas of Abruzzo was observed. LDA discriminated also the three potato varieties cultivated in the Fucino basin. The presence of these 10 mineral and trace elements was a good means for establishing the geographical place of origin of Fucino potatoes.     

西瓜稳定同位素与多元素特征分析及其产地溯源技术研究

本研究旨在利用稳定同位素与多元素分析技术结合化学计量学统计方法,开发不同省份西瓜产地溯源新策略。通过元素分析仪-同位素比质谱和电感耦合等离子质谱测定新疆、山东、广西、海南等产地的73份西瓜样本的碳、氮、氢、氧稳定同位素比值(δ¹³C、δ¹⁵N、δ²H、δ¹⁸O)与16种矿物元素含量(Na、K、Ca、Sr、Ba、Pb等)。单因素方差统计分析表明,δ¹³C、δ¹⁵N、δ²H、δ¹⁸O值与Na、K、Ca、Cr、Mn等元素含量特征在各省份西瓜样本间具有显著差异(P<0.05),但单一变量不足以将4个产地的西瓜完全进行区分。通过典则判别分析进行多变量统计分析,提取特征变量,构建线性判别函数,可以实现4个省份西瓜样本的原产地区分。该方法对新疆、山东、海南样本判别准确度高达100%,而广西样本判别率为71.4%,误判为海南产地概率为28.6%。由此可知,该策略能够实现大尺度地理空间的西瓜产地溯源,为其产地标识确证和优质品牌保护提供了有效的技术手段。     

新疆红枣营养品质与稳定同位素及矿物元素特征产地溯源比较

为开发基于营养品质、稳定同位素及矿物元素分析的不同产地新疆红枣地理标志产品溯源新方法,本研究采集了2016年新疆不同产区(若羌、和田、阿克苏、麦盖提等15个县市)97个红枣种植地块的50份灰枣和47份骏枣样本,测定其果肉中13类营养品质指标(蛋白质、还原糖、自由氨基酸、总黄酮等),稳定碳、氮、氢、氧同位素比率(δ¹³C、δ¹⁵N、δ²H和δ¹⁸O)及21种微量矿物元素含量(Na、Mg、K、Fe和Zn等)用于其产地溯源分析与建模。采用单因素方差(one-way ANOVA)法比较不同产区间营养品质、稳定同位素比率及矿物元素特征的差异,利用偏最小二乘判别分析(PLS-DA)分别对两类红枣内在品质特征建立产地判别模型,对比其溯源准确度。结果表明,红枣稳定同位素及矿物元素产地特征差异比营养品质特征更明显,溯源判别准确度得到明显提升。灰枣和骏枣营养品质参数建模总体判别准确度分别为86.00%和85.11%,稳定同位素及矿物元素参数判别准确度分别为94.12%和95.74%,且两种地理标志保护的红枣品牌(若羌灰枣和和田玉枣)产地判别准确度高达100%,而其他产地误判为这两个产地红枣的概率为0%。因此,该策略有望作为新疆红枣产地溯源与地理标志农产品保护的可靠技术。     

基于矿物元素指纹的羊肉产地溯源技术

为探讨矿物元素指纹对羊肉产地溯源的有效性,找寻表征地域特性的元素指标,利用电感耦合等离子体质谱仪分析中国内蒙古自治区阿拉善盟、锡林郭勒盟和呼伦贝尔市3个牧区,及重庆市和山东省菏泽市2个农区脱脂羊肉和土壤样品中4种常量元素(K、Ca、Na、Mg)和13种微量元素(V、Mn、Fe、Cr、Ni、Cu、As、Be、Sb、Zn、Se、Ba、Tl)的含量,对其进行方差分析、相关分析和判别分析。结果表明,羊肉中矿物元素含量组成在地域间有明显差异,农区元素含量普遍高于牧区。脱脂羊肉中Ca、Zn、Be、Ni、Fe、Ba、Sb、Mn和Se9种元素含量的地域差异与土壤相关,它们对农牧区的正确判别率达90%以上,对5个地域的正确判别率在70%～100%之间。利用羊肉中矿物元素指纹分析技术可鉴别和追溯羊肉的产地来源,为羊肉及其他食品的产地溯源研究提供方法参考。     

Depth Profile Of Trace Elements In a Sediment Core Of a High-Altitude Lake Deposit At The Pyrenees, Spain

Depth concentration profiles of 26 elements, i.e. trace elements (As, Ba, Cd, Co, Cr, Cu, Hg, Ni, Pb, Rb, Sr, V and Zn), lithophiles (Si, Al, Fe, Mn, Na, K, Mg, Ca and Ti) and nutrients (C, P, N, S) have been established in a sediment core collected at the Respomuso Lake (Pyrenees, Spain). This high altitude lake of glacier origin was transformed into reservoir in the fifties of last century. Correlations among the depth profiles of the above elements were established. HCl-extractable elements, which are better related to bioavailability, followed the profiles of total contents. Principal component analysis was applied to establish the binding behaviour of trace elements in the sediment matrix and, in turn, to search for their anthropogenic or natural sources. It was seen that Cu, Ni, Pb, Sr and Zn clustered together, and with exception of Sr, they were not clearly associated with any major component of the sediment. Their depth profiles correlate well with each other, hence indicating their presence as a result of atmospheric pollution. Redox-sensitive elements such as Fe and Mn follow a similar trend along the vertical profile. Depth profiles of As an Co correlate well with those of Fe and Mn. Principal component analysis showed that As, Co, Fe and Mn clustered together, which confirms the close association of As and Co with Fe-Mn oxyhydroxides. As concentrations in many strata exceed the ERM value, and therefore, significant biological effects are expected. Dissolution of minerals such as pyrite, chalcopyrite and galena taking part in the mineralogy of this area accounts for the As concentration found. Ba, Cr, Rb and V were associated with the sediment matrix (aluminosilicates), hence showing low mobility.