Discrimination of yerba mate (<Emphasis Type="Italic">Ilex paraguayensis</Emphasis> St. Hil.) samples according to their geographical origin by means of near infrared spectroscopy and multivariate analysis

Near infrared reflectance (NIR) spectroscopy combined with multivariate data analysis was used to discriminate between the geographical origins of yerba mate (Ilex paraguayensis St. Hil.) samples. Samples were purchased from the local market and scanned in the NIR region (1100–2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to classify the samples based on their NIR spectra according to their geographical origin. Full cross validation was used as validation method when classification models were developed. The overall classification rates obtained were 76 and 100% using PLS-DA and LDA, respectively. The results demonstrated the usefulness of NIR spectra combined with multivariate data analysis as an objective and rapid method to classify yerba mate samples according to their geographical origin. Nevertheless, NIR spectroscopic might provide initial screening in the food chain and enable costly methods to be used more productively on suspect specimens.     

基于抗氧化活性指标的国产红葡萄酒产地识别

目的 对比分析不同产地葡萄酒抗氧化活性的差异, 以此实现葡萄酒产地的判别。方法 以新疆吐鲁番、宁夏银川和河北秦皇岛的红葡萄酒为研究对象, 采用理化检测方法比较不同产地葡萄酒抗氧化活性的差异, 并利用主成分分析(principal component analysis, PCA)、正交偏最小二乘-判别分析(orthogonal partial least squares-discriminant analysis, OPLS-DA)和支持向量机(support vector machine, SVM)建立葡萄酒的产地识别模型。结果 在对葡萄酒酒样抗氧化活性指标测定中, 不同产地酒样表现的抗氧化能力依次为: 新疆吐鲁番>宁夏银川>河北秦皇岛。基于以上述抗氧化活性指标数据进行PCA分析发现, 不同产地葡萄酒样本大致可以各自聚集成类, 并且前两个主成分的累计方差解释率可达89.6%, 基本可以反应总体数据信息; 进一步通过OPLS-DA和SVM建立产地识别模型, 以其中8个不同产地的葡萄酒样本作为测试集进行外部验证, 结果发现两个模型的识别准确率均可达87.5%, 表现出较好的产地识别能力。结论 根据抗氧化活性的差异性可以实现国产红葡萄酒的产地识别。     

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.     

Colorimetric artificial nose for identification of Chinese liquor with different geographic origins

Colorimetric artificial nose was used to characterize and identify Chinese liquors from six different geographic origins. Using chemical dyes as the sensing elements, the developed colorimetric artificial nose showed a unique pattern of color changes upon its exposure to Chinese liquors. Data analysis was performed by chemometric techniques: Hierarchical cluster analysis (HCA), principal component analysis (PCA) and linear discriminant analysis (LDA). Each category of Chinese liquor could cluster together in PCA score plot. No errors in classification by HCA were observed in 45 trials. LDA model showed a 100% of prediction ability for Chinese liquor. The results demonstrated that colorimetric artificial nose was able to classify Chinese liquors from different geographic origins.     

基于ICP-MS的甘肃不同产地小米矿物元素含量特征及判别分析

通过分析不同主产区小米矿物元素含量特征,结合化学统计学建立小米产地判别模型。该研究以甘肃省陇中地区、陇东地区和河西地区的主栽小米品种为研究对象,采用电感耦合等离子体质谱(ICP-MS)法测定了小米中18种矿物元素含量,利用方差分析、主成分分析(PCA)、正交偏最小二乘判别分析(OPLS-DA)、线性判别分析(LDA)和聚类分析(HCA)对数据进行统计分析。结果表明:小米样品18种矿物元素中有13种元素含量在3个主产区间存在显著差异(P<0.05),不同主产区小米矿物元素含量具有独特的地域分布特征;18种矿物元素之间存在较强的相关性;PCA分析共提取4个主成分,累计方差贡献率为75.82%;基于LDA和OPLS-DA的判别模型对小米产地判别正确率均为100%,基本可以实现甘肃省不同区域小米产地的精准判别,通过OPLS-DA模型确定了小米产地判别的特征元素(V、Fe、Cu、Cd、Se、Pb);基于特征元素的HCA分析可以成功地对小米产地进行判别。研究证明基于小米矿物元素含量构建的判别模型可以有效区分甘肃省不同产区的小米,为小米产地溯源和质量控制提供了科学依据。     

Pattern recognition analysis on nutritional profile and chemical composition of edible bird’s nest for its origin and authentication

Authenticity of food is of great importance to ensure food safety and quality, and to protect consumer rights. A rapid and accurate method for authentication of edible bird’s nest (EBN) was proposed by using nutritional profile and chemical composition, and pattern recognition analysis. The authentication of EBN includes identification and classification of EBN by production origin (houses or caves), species origin (Aerodramus fuciphagus or Aerodramus maximus) and geographical origin (Peninsular Malaysia or East Malaysia) based on their active compositional content. Three pattern recognition methods, principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA), were employed to develop classification models for authentication of EBN origins. Compared to PCA and HCA, LDA is more accurate and efficient in distinguishing EBN by different production, species, and geographical origins, having classification ability of 100% and prediction ability of 92% as validated by cross-validation method. The key chemical markers for production origin differentiation are total phenolic content, zinc, valine, and calcium, while for species origin discrimination are sialic acid, serine, phenylalanine and valine, and for geographical origin differentiation are arsenic and mercury. The findings suggest that nutritional and chemical profiles combined with pattern recognition analysis are promising strategy for rapid authentication of EBN and its products.     

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。     

基于拉曼光谱的大米快速分类判别方法

以拉曼光谱技术为手段,结合化学计量学方法,对来自黑龙江、江苏、湖南3个产地共123份大米样品的光谱数据进行采集,并对得到的拉曼图谱进行主成分分析(PCA)和偏最小二乘判别分析(PLSDA),建立大米快速分类判别方法。应用主成分分析对不同种类、产地和品种的大米进行粗分类鉴别;选择不同种类、品种和产地的稻米样本建立相应的偏最小二乘判别分析模型,其中2/3的样本作为建模训练集,1/3的样本作为建模校正集,按照种类、产地、品种建立的模型其训练集样本正确判别率均为100%,校正集样本正确判别率分别为100%,100%,94.12%。因此,研究所建立的拉曼光谱技术结合化学计量学方法可以快速、有效地鉴别大米种类、品种及产地。     

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。     

基于ICP-MS的滩羊骨骼矿质元素溯源指纹图谱

采用电感耦合等离子质谱仪对内蒙古鄂托克前旗、宁夏盐池县、甘肃环县和陕西定边县滩羊骨骼中25 种（Ag、Ba、Be、Ca、Cd、Cu、Co、Cr、Cs、K、Fe、Mg、Mn、Mo、Na、Ni、P、Rb、Sb、Se、Sn、Sr、Te、V、Zn）矿质元素含量进行测定,结果表明,不同产地滩羊骨骼的组成元素各具分布特征,且相同矿质元素在不同产地间也差异显著。进一步通过主成分分析、聚类分析等多元统计分析方法,构建得到滩羊骨骼矿质元素指纹图谱为Ca、Cd、Cs、Mg、Mn、Na、P、Rb、Te、Zn,回代检验和交叉检验的整体正确判别率分别为87.50%、86.87%,说明筛选用作滩羊骨骼产地鉴别的10 种矿质元素有效,且利用该10 种元素建立的判别模型亦有效。     

新疆不同产地甜菜块根中元素含量的主成分和聚类分析

综合比较、评价新疆不同产区甜菜的质量。通过对新疆5大产区甜菜块根品质相关的元素(K、Na、B、N、P、Mg、Fe、Ca、Zn、Mn、Cu)的测定,以上述11个成分的含量为指标,运用相关性、主成分(PCA)和聚类分析(CA)法对不同产地的34个甜菜进行综合质量评价研究。结果表明,不同产地甜菜中元素含量有明显差异,K、Na、B、N、P、Mg、Fe、Ca、Zn、Mn、Cu的平均含量分别为:2821.1、710.0、19.8、4855.2、736.9、2362.1、58.7、2590.1、58.7、602.0、18.7 mg/kg;主成分分析得出一个3因子模型,解释了试验数据的79.45%;第1、2主成分的方差累积贡献率达67.23%;PCA和CA分析将34个甜菜样品划分为5类,在一定程度上体现了甜菜样品的亲缘关系和地域分布特征。通过测定若干元素含量数据,利用PCA和CA清晰地揭示了新疆甜菜不同产地的规律性和差异性。     

Trace mineral content of Argentinean raw propolis by neutron activation analysis (NAA): Assessment of geographical provenance by chemometrics

Multielement analysis of raw propolis samples, collected from different central Argentinean regions, was carried out by Neutron Activation Analysis (NAA) aiming at developing a reliable method in their traceability. This work presents a characterization of 96 raw propolis samples selected from three different geographical origins in middle region of Argentina. Multivariate chemometric techniques, such as Principal Component Analysis (PCA), were applied to perform a preliminary study of the data structure. Two supervised pattern recognition procedures including stepwise linear discriminant analysis (LDA) and K-nearest neighbors (kNN) were used to classify samples into the three categories considered on the basis of the chemical data. Eight trace elements (Br, Co, Cr, Fe, Rb, Sb, Sm and Zn) were selected by stepwise-LDA explaining the classification of propolis according to their geographical origin. Application of k-nearest neighbor classification procedure to these eight selected variables produced a good correlation (98% correct classification ratio) of propolis with its provenance. The trace element profiles provided sufficient information to develop classification rules for propolis identification according to their provenance.     

绿茶矿质元素特征分析及产地判别研究

本实验采用湿法消解结合ICP-AES法对来自不同产地的28种绿茶中的K、Ca、Mg、Fe、Zn、Cu等9种元素进行了分析测定,结合主成分分析及聚类分析模式识别方法进行茶叶产地的分类。结果表明,采用该方法对不同产地绿茶能够良好地区分,聚类效果明显。本法简便、准确,对茶叶产地判别以及标准化工作具有重要意义。     

基于多元素分析的贵州绿茶产地鉴别研究

目的 探究以多元素统计对贵州不同产地绿茶判别分析的有效性和可行性,筛选产地间差异性元素。方法 采用电感耦合等离子体发射光谱法(inductively coupled plasma emission spectrometry, ICP-OES)和电感耦合等离子体质谱法(inductively coupled plasma mass spectrometry, ICP-MS)对贵州4个产地63个绿茶样品中47种元素进行定量分析,结合正交偏最小二乘法判别分析(orthogonal partial least squares discriminant analysis, OPLS-DA)建立贵州绿茶的产地判别模型。结果 4个产地的绿茶中元素含量有明显的差异; K、P、Ca、Mg、Mn、Al和Fe元素含量规律相同,说明4个产地的茶叶对土壤中部分高含量元素的富集能力具有一致性;4个产地的污染物Pb、Cu、Cd、As和Cr的含量均低于茶叶相关标准的限量要求。基于元素含量建立的6组OPLS-DA分析模型可以有效区分产地,其中黔西南州与铜仁市模型(QXN-TR)参数最优,该模型用50.3%的变量可解释9...     

Classification of moroccan olive cultivars by linear discriminant analysis applied to ATR–FTIR spectra of endocarps

The potential of FTIR combined with chemometrics was studied to classify five Moroccan varieties of olives by analysis on the endocarps. Attenuated total reflectance (ATR) enabled the samples to be examined directly in the solid state. The spectral data were subjected to a preliminary derivative elaboration based on the Norris gap algorithm to reduce the noise and extract larger analytical information. Linear discriminant analysis (LDA) was adopted as classification method, and Principle component analysis (PCA) was employed to compress the original data set into a reduced new set of variables before LDA. The calibration set was built by using the IR data from seventy‐five samples scanned in reflectance mode, and the ranges 3000–2400 and 2300–600 cm^?1 were selected because furnishing the most useful analytical information. PCA allowed clustering the samples in five classes by using the first two principal components with an explained variance of 98.16%. Application of LDA on an external test set of twenty‐five samples enabled to classify them into five variety groups with a correct classification of 92.0%.     

电子鼻技术在朗姆酒分类及原酒识别中的应用研究

采用PEN3电子鼻嗅觉指纹分析系统对不同产地（中国、牙买加、古巴、危地马拉、菲律宾）的朗姆酒以及4种不同工艺原酒的香气进行了检测,分析电子鼻指纹图谱各特征峰,分别利用主成分分析方法（PCA）和线性判别因子分析法（LDA）建立了识别模型,采用传感器区分贡献率（Loadings）对传感器进行研究,确认各传感器对样品区分的贡献率大小和特征香气成分。结果表明,PEN3电子鼻不仅可以很好地区分不同产地的朗姆酒,而且对4种不同工艺的原酒也做出了较好地分类判别。     

Classification of monofloral honeys from different floral origins and geographical origins based on rheometer

A rheometer was used to classify commercial honeys. Five kinds of Yichun honeys from different floral origins and five kinds of Acacia honeys from different geographical origins were classified based on a rheometer by four pattern recognition techniques: Principal Component Analysis (PCA), Cluster Analysis (CA), Partial Least Squares (PLS), and Support Vector Machines (SVM). All the samples for different floral origins or different geographical origins were demarcated clearly by PCA, PLS. The samples from different floral origins could be classified by SVM, and the samples from different geographical origins also have a high correct classification rate (97.5%). The classification rates for different floral origins and geographical origins were 95% and 97.50% by CA, respectively. Three regression models: Principal Component Regression Analysis (PCR), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR) were used for category forecast. The regression analysis showed that SVR with radial basis function kernel worked most effective.     

Varietal Differentiation of Grape Juice Based on the Analysis of Near- and Mid-infrared Spectral Data

The aim of this study was to evaluate the usefulness of visible (VIS), near-infrared reflectance (NIR) and mid-infrared (MIR) spectroscopy combined with pattern recognition methods as tools to differentiate grape juice samples from commercial Australian Chardonnay (n = 121) and Riesling (n = 91) varieties. Principal component analysis (PCA), partial least squares discriminant analysis and linear discriminant analysis (LDA) were applied to classified grape juice samples according to variety based on both NIR and MIR spectra using full cross-validation (leave-one-out) as a validation method. Overall, LDA models correctly classify 86% and 80% of the grape juice samples according to variety using MIR and VIS-NIR, respectively. The results from this study demonstrated that spectral differences exist between the juice samples from different varietal origins and confirmed that the infrared (IR) spectrum contains information able to discriminate among samples. Furthermore, analysis and interpretation of the eigenvectors from the PCA models developed verified that the IR spectrum of the grape juice has enough information to allow the prediction of the variety. These results also suggested that IR spectroscopy coupled with pattern recognition methods holds the necessary information for a successful classification of juice samples of different varieties.     

基于质地特性的羊肉产地鉴别

为实现羊肉产地快速、客观的鉴别,本文利用质地特性对甘肃山丹、东乡、兰州、靖远4个不同产地羊肉进行分析,并研究其持水性及质地特性的变化规律。对不同产地的羊肉进行定性区分和定量分析,结果表明:不同地域羊肉具有其独特的质地特性,靖远羊肉的嫩度和弹性最高;兰州羊肉的弹性、嫩度、内聚性最低,其持水性和硬度最高;羊肉的质地特征来看,尤以靖远羊肉品质为佳。采用质地特性参数结合多元统计分析(主成分分析、典则判别分析和线性判别分析)对羊肉产地进行鉴别,结果发现,主成分分析和典则判别分析均可定性识别不同产地羊肉;线性判别分析建立的羊肉地域判别模型,对四个产地羊肉的正确识别率达到70%,利用质地特性在羊肉产地的鉴别中具有可行性。此研究结果为羊肉产地的鉴别提供参考。