Indications for the applicability of element signature analysis for the determination of the geographic origin of dried beef and poultry meat

In order to determine the geographic origin of poultry and dried beef, concentrations of a total of 72 different elements (occasionally represented with several isotopes) were analyzed by inductively coupled plasma high resolution mass spectrometry (ICP-HRMS). Additionally, gross chemical composition (GCC) was analyzed. The 25 poultry breast filets samples originated from Switzerland, France, Germany, Hungary, Brazil, and Thailand, and the 23 dried beef samples, made from M. biceps femoris and M. semitendinosus, were produced in Switzerland, Austria, Australia, United States, and Canada out of raw meat originating either from these or from other countries. A total of 66 and 46 of the elements and isotopes followed were detected in beef and poultry, respectively. For statistical analyses, only the most abundant isotopes per element were used. For both poultry meat and dried beef, a differentiation of the origins was possible using those elements, which were significantly different across countries (As, Na, Rb, and Tl in poultry; B, Ca, Cd, Cu, Dy, Eu, Ga, Li, Ni, Pd, Rb, Sr, Te, Tl, Tm, V, Yb, and Zn in beef). No sufficient differentiation between origins was possible with GCC. Further studies have to confirm the suitability of this approach for meat authentication with more samples.     

Is authentication of the geographic origin of poultry meat and dried beef improved by combining multiple trace element and oxygen isotope analysis?

Data available on contents of up to 72 different trace elements and the oxygen isotope ratio of 78 poultry breast and 74 dried beef samples were analysed to determine whether the accuracy of the prediction of the geographic origin is improved by combining promising methods. Validation was performed by determining the origin of a smaller sub-group using a statistical model established from the data of the second, larger, sub-group. As expected, the combined data proved useful for the determination of the geographic origin of meat samples. However, combining data did not clearly reduce the percentage of incorrectly classified individual samples compared to the two approaches applied separately. In poultry, cross-validation and validation resulted in 83% and 50% correct classifications, respectively. The corresponding values in dried beef were 73% and 43%. In conclusion, compared to element signature data alone, combining both methods did not improve predictions of origin.     

Tracing the geographic origin of poultry meat and dried beef with oxygen and strontium isotope ratios

Two promising isotope ratios, the δ¹⁸O of the water fraction, as extracted by azeotropic distillation, and the ⁸⁷Sr/⁸⁶Sr of the ash fraction were tested for their applicability to determine the geographic origin. In two sampling phases, in total 78 fresh poultry breast meat and 72 dried beef samples, independent from each other and originating from different countries, were analysed. The δ¹⁸O was measured with isotope ratio mass spectrometry and the isotope abundance ratio of ⁸⁷Sr/⁸⁶Sr with a multicollector inductively coupled plasma mass spectrometer. With δ¹⁸O it was possible to distinguish (p < 0.001) poultry and dried beef samples according to their country of origin. The beef data suggests that the procedure of processing is of only low additional influence on δ¹⁸O and, if so, it seems to reduce the initial between-country differences. The ⁸⁷Sr/⁸⁶Sr ratio did not give sufficient indications for differentiation by geographic origin in either poultry meat or dried beef in the smaller, first phase, data set and was therefore not further tested.     

内蒙古放牧和舍饲牛肉矿物质指纹特征

为系统评价不同饲养方式牛肉的矿物质特征,从内蒙古自东向西8个旗县采集放牧和舍饲牛股二头肌和背最长肌样品共80份,测定其中27种样品矿物质元素含量,并进行主成分分析.结果表明:各旗县牛肉按照放牧和舍饲2种饲养方式或地区聚类,饲养方式聚类较地区聚类更明显;地区聚类与饲养方式聚类趋势一致,放牧旗县与舍饲旗县分别聚类;进一步分...     

基于无机多元素分析鉴别我国主要进口大米产地

目的利用大米中无机多元素分析数据建立判别模型,实现对4个我国进口大米主要产地的鉴别。方法采用ICP-MS和ICP-OES分析技术,测定了泰国、巴基斯坦、柬埔寨、缅甸4个产地的76份大米样本中磷、钾、镁、钙、锌、钠、锰、铁、铷、铜、硼、铝、钼、镍、钡、锶、砷、钛、硒、铬、钴、铯、铅、镉、锂、钒、银、锑、铍、铊、镧、铈、镨、钕、钐、铕、钆、铽、镝、钬、铒、铥、镱、镥、钪、钇等46种无机元素含量。通过对检测数据进行单因素方差分析、Fisher判别分析,建立了产地判别模型。结果4个产地大米的部分元素存在显著性差异,产地判别模型的原始校验准确率100%,交叉验证准确率96.1%。结论所建方法和判别模型对泰国、巴基斯坦、柬埔寨、缅甸所产大米具有很好产地鉴别能力。     

Multi-element analysis for determining the geographical origin of mutton from different regions of China

Elemental fingerprints were investigated for their potential to classify mutton samples according to their geographical origin. The concentration of 25 element contents in 99 mutton samples from three pastoral regions and two agricultural regions of China were analysed by ICP-MS. Multivariate statistical analysis including principal component analysis (PCA) and linear discriminate analysis (LDA) were used for this purpose. Twenty-one elements (Be, Na, Al, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Ag, Sb, Ba, Tl, Pb, Th and U) in de-fatted mutton showed significant differences (p < 0.05). LDA gave an overall correct classification rate of 93.9% and cross-validation rate of 88.9%. Furthermore, mutton samples from agricultural regions and pastoral regions were differentiated with 100% accuracy. These results demonstrate the usefulness of multi-element fingerprints as indicators for authenticating the geographical origin of mutton in China.     

Geographical traceability of “Arròs de Valencia” rice grain based on mineral element composition

The geographical traceability of rice grain samples involves the use of analytical methodologies that allow their differentiation on the basis of the rice composition, thus confirming their authenticity. In this paper, trace element determination of rice samples by inductively coupled plasma optical emission spectroscopy (ICP-OES) has been addressed to achieve a complete geographical origin classification. Samples (107) from Spain including Valencia, Tarragona, Murcia and Extremadura, Japan, Brazil and India were employed as training set whereas the validation set was formed by 46 samples of the aforementioned geographical origins. Data were processed by linear discriminant analysis (LDA), using as analytical variable the concentration of 32 elements (Al, As, Ba, Bi, Cd, Ca, Cr, Co, Cu, Fe, Pb, Li, Mg, Mn, Mo, Ni, K, Se, Na, Sr, Tl, Ti, Zn, La, Ce, Pr, Nd, Sm, Eu, Ho, Er and Yb), enabling a 91.30% of correct classification for the validation set.     

The use of advanced chemometric techniques and trace element levels for controlling the authenticity of organic coffee

This study examines the application of advanced data mining techniques associated with trace element concentrations for authenticity evaluation of organic coffee samples. Fifty-four coffee samples (20 organic and 34 non-organic) were evaluated according to their macro and microelement contents to establish reference ranges and identify differences in patterns of chemical elements in both crop procedures. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of thirty-eight chemical elements (Al, As, B, Ba, Be, Bi, Cd, Ce, Co, Cr, Cs, Cu, Dy, Eu, Er, Gd, Ge, Ho, La, Lu, Mn, Mo, Ni, Pb, Rb, Se, Sm, Sr, Yb, W, U, Ta, Tb, Th, Ti, Tl, Zr and Zn). In general, higher levels of chemical elements were found in conventional coffee compared with organic samples. The classification of samples was performed by using three well known data mining algorithms, named Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naïve Bayes (NB). When all analyzed variables were considered, a 96.3% accuracy level was obtained with the three algorithms. The level of accuracy remained at 96.3% for SVM and MLP and rose to 98.2 for NB when only eight of the investigated minerals – i.e. Zn, Se, Ba, U, Dy, Tl, Th, Mo – were used for chemometric analysis. This level of accuracy provides a strong foundation to propose the combination of trace element spectral fingerprints with data mining techniques as a valuable tool to evaluate the authenticity of organic coffee samples.     

微波消解-电感耦合等离子体质谱法测定虾皮中28种元素

目的建立电感耦合等离子体质谱法(inductively coupled plasma mass spectrometry,ICP-MS)测定虾皮中K、Na、Ca、Mg、Fe、Mn、Cu、Zn、P、Li、Al、V、Cr、Co、Ni、As、Rb、Sr、Se、Mo、Ag、Cd、Pb、Ba、Ga、Cs、Ti、Bi 28种元素含量的分析方法。方法采用硝酸-过氧化氢消解体系,通过在线加入Ge、Rh、Re内标进行基体效应校正,碰撞反应池模式(kinetic energy discrimination,KED)消除多原子离子干扰,以国家标准物质大虾(GBW 10050)为研究对象,验证方法准确度。结果方法检出限为0.001-0.681 mg/kg,相对标准偏差(relative standard deviations,RSD)为0.6%~8.2%,各元素标准曲线线性关系良好,相关系数r²大于0.999,样品回收率为87.4%~110.0%。虾皮中常量元素K、Na、Ca、Mg等含量较高,Pb、Cd、Cr等重金属元素含量较低,Al、As需根据其存在形态评估可能存在的健康安全风险。结论该方法准确高效,灵敏度高,可应用于虾皮样品分析。     

Multispectral image analysis approach to detect adulteration of beef and pork in raw meats

The aim of this study was to investigate the potential of multispectral imaging supported by multivariate data analysis for the detection of minced beef fraudulently substituted with pork and vice versa. Multispectral images in 18 different wavelengths of 220 meat samples in total from four independent experiments (55 samples per experiment) were acquired for this work. The appropriate amount of beef and pork-minced meat was mixed in order to achieve nine different proportions of adulteration and two categories of pure pork and beef. After an image processing step, data from the first three experiments were used for partial least squares-discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) so as to discriminate among all adulteration classes, as well as among adulterated, pure beef and pure pork samples. Results showed very good discrimination between pure and adulterated samples, for PLS-DA and LDA, yielding 98.48% overall correct classification. Additionally, 98.48% and 96.97% of the samples were classified within a ± 10% category of adulteration for LDA and PLS-DA respectively. Lastly, the models were further validated using the data of the fourth experiment for independent testing, where all pure and adulterated samples were classified correctly in the case of PLS-DA, while LDA was proved to be less accurate.     

产地、品种和年际对大豆矿物元素含量的影响

研究产地、品种、年份及其交互作用对大豆中矿物元素的影响,在2014—2016年黑龙江嫩江和北安两个大豆主产区连续三年种植10个不同品种的大豆,得到90份大豆样本作为实验研究目标。利用电感耦合等离子体质谱仪(Inductively Coupled Plasma-Mass Spectrometry,ICP-MS)测定大豆中52种矿物元素含量,结合多元方差分析探寻产地、品种、年份及其交互作用对大豆矿物元素含量的影响,筛选大豆产地特征元素。结果表明,大豆中受产地影响较大的元素有Mg、K、V、Mn、Co、Cu、Rb、Sr、Pd、La、Pr、Nd、Sm、Eu、Gd、Tb、Ir、Ti、Dy、Er和Hf;受年份影响较大的元素As、Se、Ag、Sb、Te、Tb、Er、V、Pd、Ir、Lu、Hf、Pt、Au和Pb;受品种影响较大的元素Cr、Dy、Ho和Pb。对大豆中与产地直接相关的矿物元素进行主成分分析和判别分析,最终元素Mg、Mn、Sr、La、Gd、Tb、Hf和Ti先后进入了判别模型,对大豆产地的整体正确判别率为93.2%,可见该模型对嫩江和北安大豆样品具有有效的判别力。     

Minor and trace elements in different honey types produced in Siena County (Italy)

The concentrations of 23 chemical elements (Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Na, Ni, Pb, Sb, Se, Sr, Th, Tl, U, Zn) were determined in 51 honey samples of different botanical origin produced in Siena County (Italy). K, Ca, Na and Mg were the most abundant elements, with mean contents of 1195, 257, 96.6 and 56.7 mg/kg, respectively. The Fe, Zn and Sr contents generally ranged from 1 to 5 mg/kg. Except for Ba, Cu, Mn and Ni, the trace element contents were below 100 μg/kg. The analytical data indicated a good level of quality of the honeys, especially with regard to the concentrations of toxic trace elements, such as As, Cd, Pb and Sb, and suggested a significant influence of the botanical origin on the element composition. Some local geological and geochemical features also seemed to affect the chemistry of the honey.     

Multiangle discrimination of geographical origin of rice based on analysis of mineral elements and characteristic volatile components

The aim of the study was to develop a multiangle discrimination method which combines mineral elements with volatile compounds to identify the geographical origin of rice. The levels of twenty‐four elements and characteristic volatile compositions of rice samples from different growing area (Panjin, Wuchang and Sheyang city of China) were measured by inductively coupled plasma mass spectrometer (ICP‐MS) and electronic nose (E‐nose), respectively. The obtained data were subject to the analysis of variance (anova ), principal component analysis (PCA) and linear discriminant analysis (LDA). The twenty elements including B, Mg, Al, Ti, V, Cr, Mn, Fe, Ni, Zn, Ga, As, Sr, Cd, Sn, Sb, Ba, Pb, Bi and Tl and eleven sensors including LY2/LG,LY2/G, LY2/AA, LY2/GH, LY2/gCTL, LY2/gCT, P10/1, T30/1,T70/2, P10/2 and PA/2 were mainly contributed to discriminate the origin of the rice, and the average classification accuracy was 93.5%. Compared with one‐dimensional discrimination method, the multiangle analytical method of combining mineral elements with volatile compounds significantly improved the discrimination accuracy of origin of rice.     

Classification of geographical origins and prediction of δ13C and δ15N values of lamb meat by near infrared reflectance spectroscopy

Near infrared spectroscopy (NIRS) combined with chemometric analysis was investigated for its potential to classify the geographical origin and predict δ(13)C and δ(15)N values of lamb meat samples (n=99) from three pastoral regions and two agricultural regions of China. Principal component analysis (PCA), discriminant partial least squares analysis (D-PLS), linear discriminant analysis (LDA) and partial least squares regression (PLSR) were used for data analysis. D-PLS and LDA correctly classified 100% of the both pastoral and agricultural region samples, and gave a total correct classification of 88.9% and 75% to the five individual region samples, respectively. The best PLSR calibration models for predicting δ(13)C and δ(15)N of lamb meat were obtained with the determination coefficient (R(2)) 0.76 and 0.87, respectively. These results show that NIRS combined with chemometrics can be used as a rapid and effective method to discriminate the geographical origin and estimate the δ(13)C and δ(15)N of lamb meat.     

基于矿质元素指纹分析的陈皮产地溯源研究

通过分析不同产地陈皮中矿质元素含量的差异,结合多元统计分析,筛选基于矿质元素指纹分析技术的有效产地溯源指标,构建陈皮不同产地鉴别的判别模型。该研究采集了广东、福建、重庆3个不同产地的206份陈皮样品,利用电感耦合等离子体质谱仪（ICP-MS）和电感耦合等离子体发射光谱仪（ICP-AES）测定了32种矿质元素含量,结合方差分析、主成分分析和线性判别分析、正交-偏最小二乘法判别分析方法建立了陈皮产地判别模型。结果表明,陈皮样品的32种矿质元素中有26种元素含量在广东与其他两个产地间存在显著差异,而其中11种元素在3个不同产地间存在显著差异。经过主成分分析,从32种矿质元素可提取出4个主成分,代表了总指标70.0%的信息。基于主成分分析,陈皮样品可根据其来源进行初步聚类。其中前2个主成分的主要变量为Dy、Sm、Gd、Pr、Nd、Y、La、Fe、Be、V、Ce、Sc、Co、P、Mo、As、Pb、B这18种元素。通过线性判别分析确定了K、P、Ca、Co、Cu、Mn、Mo、V、Ni、B、Li、Pb、As、Sr、Ti、Th、Gd、Sc、Nd、Pr、Y这21种矿质元素为陈皮的有效溯源指标,基于正交-偏最小二乘法判别分析方法建立的判别模型确定了Sc、B、Y、Co、Nd、La、Pr、Be、Gd、Dy、Sm、Mo、Fe这13种元素的重要性。2种判别分析方法构建的判别模型的交叉验证和外部样品验证的整体正确判别率均达到100%,基本实现了陈皮的产地判别,研究证明矿质元素指纹分析技术可用于陈皮的产地溯源判别。     

基于产地、品种和年份影响矿物元素含量的大米判别

通过分析品种、年份与产地及其交互作用对大米矿物元素含量造成的差异,筛选有效特征指标,结合统计分析进行产地判别。以连续3?a（2016—2018年）在查哈阳、五常和建三江地区种植9?个品种的90?份田间试验样本为研究目标,采用电感耦合等离子体质谱仪测定样品的52?种元素。结果表明：Mg、Ca、Cr、Mn、Zn、As、Rb、Sr、Ag、Cd、Ba、La、Sm、Dy、Ho、Er、Pb、U受产地影响较大;Na、Mg、Al、Ca、Pb、U、V受年份影响较大;Na、Cr、Co、Ni、Tl、U、Mg、Al、La、Ho受品种影响较大。实验对筛选到与产地直接相关的18?种元素进行主成分分析和判别分析。6?个主成分累计贡献率80.333%。建立的判别模型对3?个产地的判别正确率均为100%,交叉验证率为100%。说明由这些元素组成的模型可以对样本实现正确判别。     

基于矿物元素指纹分析中国地理标志猪肉的产地溯源

本研究初步探讨了矿物元素指纹分析对我国地理标志猪肉产地溯源的可行性。采用电感耦合等离子体质谱对四川巴山的青峪黑猪肉,山东莱芜黑猪肉和北京黑六猪肉三种不同地域来源的地理标志猪肉中33种矿物元素含量进行测定。通过元素含量筛选,排除猪肉样品中含量低于或接近检出限的元素,筛选出13个元素进行研究。结合主成分分析、多重比较分析和判别分析对数据进行统计分析。其中Na、Fe、Co、Cu、Zn、Se、Rb、Sr共8种元素在地域之间差异显著(p<0.05)。通过对比偏最小二乘法判别分析(PLS-DA)、基于正交信号校正的偏最小二乘判别分析(OPLS-DA)、支持向量机、朴素贝叶斯、决策树和神经网络六种分类模型,得出OPLS-DA和决策树分类模型较适合基于矿物元素指纹分析的特色猪肉产地溯源。矿物元素指纹分析在我国地理标志猪肉产地溯源领域具有长远的应用前景。     

有机成分与元素分析相结合鉴别6 种中国名茶

利用有机成分与元素分析相结合,建立6 种中国名茶的鉴别方法。通过原产地购买67 份不同产地的茶叶,包括绿茶（杭州龙井、苏州碧螺春、黄山毛峰、庐山云雾）和乌龙茶（武夷岩茶、安溪铁观音）,利用电感耦合等离子体-原子发射光谱测定茶叶中K、Ca、Mg、Al、Mn、Fe、Zn、Rb、Ti 9 种元素,利用电感耦合等离子体-质谱法测定Pb、As、Cd、Cr、V、Ni、Co、Cu、Sr、Cs、Ba、Se 12 种元素,利用超高效液相色谱测定茶叶中10 种有机成分。通过偏最小二乘变量筛选法分别建立6 种中国名茶的元素判别模型和有机成分判别模型,并设定已知茶叶样本的确证及未知茶叶样本的溯源流程。实际样品检测结果表明,6 种中国名茶分类效果较好,通过2 种模型交叉验证可有效减少结果误判。     

内蒙古牛肉和绵羊肉硒含量测定及评价

为研究内蒙古牛肉和绵羊肉硒水平,从内蒙古东部到西部12 个旗县采集放牧、半舍饲和舍饲牛肉124 份以及放牧绵羊肉49 份,利用原子荧光光谱法测定硒含量并进行描述性统计和比较。结果表明：内蒙古牛肉和绵羊肉硒含量地域特征显著,自东向西呈显著上升趋势,中东部差异不明显。乌审旗牛肉硒含量为（12.04±0.84） μg/100g,约为新巴尔虎左旗（（3.01±1.34） μg/100g）的4倍,杭锦后旗绵羊肉硒含量为（4.23±0.82） μg/100g,约为鄂温克旗（（1.41±0.57） μg/100g）的3倍;放牧绵羊肉硒含量数据较集中,地域特征更明显;牛肉硒含量数据较离散,提示饲养方式对硒水平有显著影响;牛肉硒含量（（6.23±2.63） μg/100g）约为绵羊肉（（2.24±1.14） μg/100g）的2.8倍;整体舍饲（（8.71±2.90） μg/100g）、半舍饲（（6.41±2.17） μg/100g）牛肉硒含量高于放牧牛肉（（5.10±1.80） μg/100g）,同地区比较结果一致,舍饲牛肉硒含量通常高于半舍饲和放牧牛肉。牛肉和绵羊肉硒含量地域性差异与地壳硒丰度和饲养因素均有关。     

Arsenic and other trace elements in wines of eastern Croatia

Levels of arsenic and other trace elements (Al, Ba, Be, Bi, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb, Sb, Se, Sn, Sr, Ti, Tl, U, V and Zn) in nine brands of wine from Slavonia (eastern Croatia) were investigated. Wines from two other viticulture areas, Istria (western Croatia) and Vojvodina (northern Serbia), were used for comparison. Elemental concentrations were determined by high resolution ICP-MS. Statistical methods were applied for the interpretation of obtained data. Regional differences were identified based on a set of geogenic elements (Al, Li, Be, Ti). Elements As and Pb were found to be of predominantly natural origin. Significant differences between the obtained values for As in wines from Slavonia and Vojvodina, compared to wines from Istria indicate that the geochemical arsenic anomalies present in these regions contribute to some extent to higher As content in analysed wines. The Cd, Ni, Zn, Fe and Cr in wine were found to be primarily of anthropogenic origin.