Chemometric approach to evaluate trace metal concentrations in some spices and herbs

Herbs (mint, thyme and rosemary) and spices (black pepper, chili pepper, cinnamon, cumin, sweet red pepper and turmeric) were analysed using atomic spectrometry and then subjected to chemometric evaluation in an attempt to classify them using their trace metallic analyte concentrations (As, Ba, Ca, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Sr and Zn). Trace metals in acid digests of these materials were determined using both inductively coupled plasma-atomic emission spectrometry and inductively coupled plasma-mass spectrometry. The chemometric techniques of principal component analysis (PCA), linear discriminant analysis (LDA) and cluster analysis (CA) were used for the classification studies. These herbs and spices were classified into five groups by PCA and CA. When the results of these techniques were compared with those from LDA, it was found that all group members determined by PCA and CA are in the predicted group that 100.0% of original grouped cases correctly classified by LDA.     

Evaluation of trace metal concentrations in some herbs and herbal teas by principal component analysis

Sixteen trace metallic analytes (Ba, Ca, Ce, Co, Cr, Cu, Fe, K, La, Mg, Mn, Na, Ni, P, Sr and Zn) in acid digests of herbal teas were determined and the data subjected to chemometric evaluation in an attempt to classify the herbal tea samples. Nettle, Senna, Camomile, Peppermint, Lemon Balm, Sage, Hollyhock, Linden, Lavender, Blackberry, Ginger, Galangal, Cinnamon, Green tea, Black tea, Rosehip, Thyme and Rose were used as plant materials in this study. Trace metals in these plants were determined by using inductively coupled plasma-atomic emission spectrometry and inductively coupled plasma-mass spectrometry. Principal component analysis (PCA), linear discriminant analysis (LDA) and cluster analysis (CA) were used as classification techniques. About 18 plants were classified into 5 groups by PCA and all group members determined by PCA are in the predicted group that 100.0% of original grouped cases correctly classified by LDA. Very similar grouping was obtained using CA.     

Chemometric analysis of minerals and trace elements in raw cow milk from the community of Navarra,Spain

The concentrations of protein, fat, five minerals (Na, K, P, Ca and Mg) and nine trace elements (Fe, Zn, Cu, Mn, Se, Al, Cd, Cr and Pb) have been determined in 347 samples of raw cow milk from the community of Navarra, north Spain, using infrared analysis, atomic absorption spectrometry (flame and electrothermal atomisation) and inductively coupled plasma atomic emission spectroscopy. A preliminary chemometric study with the use of pattern recognition methods was carried out in order to characterise, classify and distinguish the different collected samples on the basis of their contents. Principal component analysis (PCA) has permitted the reduction of 16 variables to five principal components which interpret reasonably well the correlations of these studied variables. These variable associations may be attributed to intrinsic (lactogenesis) and other extrinsic factors, such as seasonal variation, animal feeding or geographical situation. Changes in these contents during different seasons were also assessed and consistently interpreted. Linear discriminant analysis (LDA) was used to explore cow milk samples, classifying according to season or geographical location, providing complementary information to PCA. This work shows that PCA and LDA are useful chemometric tools for the multivariate characterisation of raw cows’ milk.     

基于金属元素组成的新疆石榴产地识别分析

采用电感耦合等离子体原子发射光谱法,对新疆6 个主要产地（库车、吐鲁番、叶城、疏附、喀什、和田）的36 个石榴样品的可食部分（果肉）和籽中12 种金属元素的含量进行测定,采用主成分分析（principalcomponent analysis,PCA）和线性判别分析（linear discrimination analysis,LDA）对石榴可食部分和籽中金属元素进行综合评价。结果表明：PCA得出2 个三因子模型,分别解释了石榴可食部分和籽中金属元素数据的84.29%和60.33%;通过对石榴可食部分中金属元素组成进行PCA,PCA更好地将36 个石榴样品划分为6 类,与实际产地吻合。LDA得出新疆不同产地石榴可食部分和籽的总体验证判别率分别为100%和100%,交互验证判别率分别为100%和94.44%。说明提出的方法具有很好的产地识别作用,可作为石榴产地的一种鉴别方法。     

Multivariate Statistical Analysis of Data and ICP-MS Determination of Heavy Metals in Different Brands of Spices Consumed in Kayseri,Turkey

The concentrations of Cr, Mn, Fe, Co, Ni, As, Cd, Pb, and Zn in 19 different spices from 11 different brands (in total 69 samples) collected from Kayseri, Turkey, were determined by inductively coupled plasma mass spectrometry (ICP-MS) after microwave digestion. Multivariate and univariate statistical techniques such as principal component analysis (PCA), cluster analysis (CA), correlation analysis, and one way ANOVA were applied for the interpretation of the obtained data. Three principal components explain 79.6% of the total variance. They are as follows: PC1 with Cr, Fe and Pb; PC2 with Mn, As, and Cd; and PC3 with Ni and Co. The spices were classified into their different types and brands by PCA and CA. The certified reference material (GBW07605 Tea Leaves) was analyzed to confirm the accuracy of the method.     

Evaluation of trace and toxic element concentrations in Paris polyphylla from China with empirical and chemometric approaches

Trace and toxic elements in Paris polyphylla samples were determined by flame and graphite furnace atomic absorption spectrometry following microwave-assisted acid digestion, based on a mixture of nitric acid and hydrogen peroxide. The whole procedure, including sample preparation, digestion and measurements, was successfully validated against CRM GBW07603 (bush twigs and leaves). In order to get a better insight into the elemental patterns, common chemometric approaches to data evaluation, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA), were used as classification techniques. Five significant groups classified by PCA were attributed partly to significant influential sources. HCA revealed five groups of P. polyphylla samples based on their trace and toxic element concentrations.     

电感耦合等离子体原子发射光谱法测定不同产地山银花金属元素主成分及其聚类分析

采用电感耦合等离子体原子发射光谱仪,对全国7 个主要产地的26 个山银花样品中的10 种金属元素的 含量进行定量分析,应用主成分分析和聚类分析对山银花资源中金属元素进行评价。结果表明：Pb、As、Fe、 Ca、Zn、Mg、Mn、Cu、Cd、Cr的平均含量分别为0.25、0.065、77.9、3 277.1、31.0、2 244.7、319.9、14.6、 0.31、0.217 mg/kg;主成分分析得出3因子模型,可解释实验数据的88.58%;主成分分析将26 个山银花样品划分 为4 类,聚类分析显示了相似的结果。研究得出山银花中金属元素的含量分布特征,为山银花资源的品质评价和 质量控制提供实验依据。     

主成分分析技术对葡萄酒产地进行溯源

目的采用主成分分析技术对法国和山东产区的葡萄酒进行产地溯源。方法利用电感耦合等离子体质谱法(inductively coupled plasma mass spectroscopy,ICP-MS)、电感耦合等离子体发射光谱法(inductively coupled plasma emission spectrometry,ICP-OES)测定葡萄酒中30种无机元素及部分元素同位素含量,获取其中的元素成分信息,结合化学计量学中主成分分析技术(principal component analysis,PCA),分析不同地域样品的特征元素变量,研究筛选元素特征指纹。结果 PCA分析筛选出2个主成分因子,能对中国山东、法国波尔多产地的葡萄酒进行良好区分。利用PCA分析方法对中国山东、法国波尔多产区的葡萄酒样品的14种组分进行分析,筛选出~6Li、~7Li、~(10)B、~(11)B、Mg (280.270 nm)、P(213.618 nm)、Zn(213.857 nm)7种特征元素。结论该方法有望被应用于葡萄酒的产地溯源,可以为食品产地溯源技术的发展完善及相关部门的反欺诈监管提供技术积累。     

Identification of the Geographical Origins of Pomelos Using Multielement Fingerprinting

Eighty pomelo samples and 80 soil samples were examined using a multielement component test to predict the geographical origins of pomelos produced in 4 regions (Sichuan, Chongqing, Fujian, and Guangxi Provinces) of China. The concentrations of 8 elements were determined by atomic absorption spectrometry. Ca, K, and Na were the most abundant elements. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to reduce the dimensionality of the multielement data from 8 to 2 while retaining the highest possible variance. Using PCA and LDA, 69.66% and 91.30%, respectively, of the pomelo origins were classified correctly using multielement variables, along with 67.06% and 83.40% for soil multielement analysis. Results indicated that the LDA method was more effective for geographical origin classification than PCA. The results of the multielement component test demonstrated its capability to screen pomelo origins rapidly.     

基于多元素和稳定同位素的特基拉酒原产地鉴定

为探索特基拉酒中多元素和稳定同位素的地域特色及其产地溯源的可能性,研究采用电感耦合等离子体质谱法(ICP-MS)和稳定同位素比质谱法(IRMS)测定特基拉酒中多元素含量和稳定同位素比值,并结合化学计量学中主成分分析(PCA)和偏最小二乘法-判别分析法(PLS-DA)建立模型,对墨西哥特基拉酒进行产地溯源判定。结果表明, 4个产区的特基拉酒中22种元素含量和稳定同位素δ13C和δ18O数值范围不同,具有一定地域特征。采用PCA和PLS-DA法能对墨西哥的特基拉酒进行产地判别,通过对16个样本进行判别验证,预测正确率为93.75%。研究表明,通过多元素含量和稳定同位素比值的测定,结合化学计量学分析方法,能够区分墨西哥不同产地的特基拉酒,为特基拉酒产地溯源可行性提供方法依据。     

Provenance establishment of coffee using solution ICP-MS and ICP-AES

Statistical interpretation of the concentrations of 59 elements, determined using solution based inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma emission spectroscopy (ICP-AES), was used to establish the provenance of coffee samples from 15 countries across five continents. Data confirmed that the harvest year, degree of ripeness and whether the coffees were green or roasted had little effect on the elemental composition of the coffees. The application of linear discriminant analysis and principal component analysis of the elemental concentrations permitted up to 96.9% correct classification of the coffee samples according to their continent of origin. When samples from each continent were considered separately, up to 100% correct classification of coffee samples into their countries, and plantations of origin was achieved. This research demonstrates the potential of using elemental composition, in combination with statistical classification methods, for accurate provenance establishment of coffee.     

Principal component analysis of proteolytic profiles as markers of authenticity of PDO cheeses

The casein fraction of 13 Portuguese PDO cheeses were analysed using Urea-PAGE and reverse phase-high performance liquid chromatography (RP-HPLC) and then subjected to chemometric evaluation. The chemometric techniques of cluster analysis (CA) and principal component analysis (PCA) were used for the classification studies. Peptide mapping using Urea-PAGE followed by CA revealed two major clusters according to the similarity of the proteolytic profile of the cheeses. PCA results were in accordance with the grouping performed using CA.CA of RP-HPLC results of the matured cheeses revealed the presence of one major cluster comprising samples manufactured with only ovine milk or milk admixtures. When the results of CA technique were compared with the two PCA approaches performed, it was found that the grouping of the samples was similar.Both approaches, revealed the potential of proteolytic profiles (which is an essential aspect of cheese maturation) as markers of authenticity of PDO cheeses in terms of ripening time and milk admixtures not mentioned on the label.     

基于电子鼻的食用油氧化判别分析

为实现食用油氧化快速判别分析,以市场上常见的食用油为原料,对其进行氧化处理,根据国标中过氧化值和酸值划分为氧化油与未氧化油并作为模型样品和验证样品,采用电子鼻技术测定食用油气味,同时结合聚类分析（cluster analysis,CA）、主成分分析（principal component analysis,PCA）和线性判别分析（linear discriminantanalysis,LDA）方法对不同氧化程度的食用油进行判别,并建立油脂氧化的快速判别模型。同时将检测判别结果与国标规定进行比较分析,结果表明：3 种方法建立模型判别正确率均为100%,CA、PCA和LDA模型验证的判别正确率分别为95.8%、98.9%和100%,说明基于电子鼻技术的食用油氧化判别检测是可行的。     

Adulteration detection of argan oil by inductively coupled plasma optical emission spectrometry

The singularity of the trace element profile of argan oil has been demonstrated by means of inductively coupled plasma optical emission measurement in combination with different chemometric approaches. The ability of multivariate analysis methods; such as hierarchical cluster analysis (HCA), principal component analysis (PCA), classification trees using Chi-squared Automatic Interaction Detector (CHAID) and discriminant analysis (DA) to achieve edible oils classification based on its type or variety from their elemental content have been investigated. The calculations were performed using 16 variables (contents of Na, Mg, Al, K, Ca, Ti, Fe, Co, Ni, Cu, Zn, Cd, Pr, Sm, Er and Bi at μg g⁻¹ level determined by ICP-OES). HCA is able to differentiate sunflower oil samples from the rest, however the discrimination of argan oil from olive, seeds and soya oils based on their different trace element composition is hard to achieve. The PCA analysis shows three different classes in the multidimensional space (PC1-3) representing sunflower, argan and a third group comprising olive, seeds and soya oils. CHAID method allows separating the entire vegetable oil dataset, providing a correct re-substitution rate of 94.12% for argan oil using only the concentration of K. DA performed using the same variables, provides also an acceptable average accuracy results of 93.65%, by the re-substitution method. DA has been successfully applied to the analysis adulterated argan oil by addition of cheaper vegetable oils.     

不同品种青稞炒制后挥发性风味物质GC-MS分析

为探究不同品种青稞炒制后挥发性风味成分的差异,筛选风味化合物丰富的青稞品种。采用气相色谱-质谱联用技术对10 个不同品种青稞炒制样挥发性成分进行分离鉴定。首先探讨炒制对昆仑15号风味的影响,其次结合香气分析、聚类分析和主成分分析对不同品种归类。结果表明：不同样品共检出140 种香气成分,包括杂环类（32 种）、酯类（26 种）、醇类（21 种）、醛类（19 种）、酮类（16 种）、酸类（13 种）、烃类（9 种）、腈类（4 种）。炒制后昆仑15号香气物质由48 种增加到60 种,主要增加的是吡嗪类物质。不同品种炒制青稞中相对含量最高为杂环类（38.59%～64.42%）,其次为酯类（1.85%～35.98%）和醛类（7.56%～21.79%）。香气分析显示炒制青稞以可可香、烤香和坚果香为主,未炒制昆仑15号果香与甜香较强。青稞焙炒香气与青稞品种及基本化学组成等有关,肚里黄为本研究参试青稞品种中最适青稞炒制加工品种。分类分析显示样品可分为两大类,昆仑15号和藏青320相似度较高,为一类,其他品种在欧式距离为9时为另一类。     

基于仿生嗅觉特征的黄酒产地判别研究

采用GC-Flash型电子鼻结合化学计量学方法对不同产地的黄酒进行区分判别。以主成分分析和判别因子分析法建立了产地判别模型。分析结果表明,在主成分分析判别模型中,绍兴原产地（地理标志）、非原产地（非地理标志）与绍兴以外地区的黄酒样品分别占据着不同位置;绍兴原产地和非原产地各自有较大的相对集中分布区间,存在一定的边缘交集,但是他们与绍兴以外地区的黄酒样品分界明显。判别因子法所建产地判别模型中,绍兴原产地、绍兴非原产地和绍兴以外地区的黄酒样品得到正确判别。研究表明,电子鼻结合化学计量学方法可较好地用于黄酒产地的判别。     

Mineral content and electrical conductivity of the honeys produced in Northwest Morocco and their contribution to the characterisation of unifloral honeys

In this study the mineral content, ash content and electrical conductivity of 98 honey samples from Northwest Morocco were studied. Using inductively coupled plasma atomic emission spectrometry (ICP‐AES), six minerals were identified and quantified: K, Mg, Mn, Cu, Fe and Zn. Potassium was the predominant mineral (accounting for 80% of the total minerals quantified), followed by Mg and Fe (9 and 3% respectively). The ash content values were lower than 0.6% in 95 of the samples. The higher electrical conductivity values corresponded to the honeydew honeys ( x = 1734 µS cm^?1). In addition, characterisation of the main unifloral honeys by principal component analysis (PCA), linear discriminant analysis (LDA) and multilayer perceptrons (MLP) was carried out. The PCA showed that the cumulative variance was approximately 67%. On the other hand, the LDA and MLP allowed perfect classification of the honeydew (100% correct classification) and Eucalyptus (92%) honeys. © 2003 Society of Chemical Industry     

Multivariate analysis to discriminate the origin of sesame seeds by multi-element analysis inductively coupled plasma-mass spectrometry

In this study, inductively coupled plasma-mass spectrometry (ICP-MS) was used to determine the concentration of 15 elements (Mg, Al, K, Ca, Cr, Mn, Co, Ni, Cu, Zn, Rb, Sr, Cd, Ba, and Pb) of sesame seeds. Multivariate analysis was then performed to discriminate the origin of sesame seeds. Korean (48), Chinese (44), and Indian (21) samples were used to develop the calibration model. Another 10 samples were used to validate this model. All elements were significantly different (p<0.05) among the samples from three countries, and all elements were subjected to both principal component analysis (PCA) and discriminant analysis. The concentrations of multi-element showed a trend of clustering according to the origin of samples based on PCA. They showed a discrimination rate of 92.0% in the discriminant analysis. The results demonstrated that a combination of ICP-MS multi-element determination and multivariate analysis could be used to discriminate the sesame seed origin.     

Fracture Resistance of Cashew Nuts as Influenced by Pre-Shelling Treatment

The fracture resistance of raw and pre-treated cashew nuts during uni-axial compressive loading was investigated. Cashew nut samples were subjected to two pre-shelling treatments, namely: steam boiling and roasting in hot cashew nut shell liquid. Two loading rates of 2.5 and 50 mm/min and two loading orientations (longitudinal and transverse) were considered for fracture resistance of pre-treated cashew nuts using a 50 kN capacity Instron testing machine. The data obtained were subjected to analysis of variance. The average values at 2.5 mm/min were 342 and 318 N for raw nuts, 321 and 242 N for roasted nuts, and 341 and 309 N for steam boiled nuts during longitudinal and transverse loading, respectively; whereas corresponding values at 50 mm/min were 784 and 763 N for raw nuts, 517 and 464 N for roasted nuts, and 436 and 398 N for steam boiled nuts, respectively. In each of the pre-treatment methods and loading rates, more force was required to crack cashew nuts during longitudinal loading than transverse loading; and for each loading rate, pre-treated nuts generally required less force than raw nuts.