Determination of the geographic origin of onions between three main production areas in Japan and other countries by mineral composition

Onions (Allium cepa L.) are produced in many countries and are one of the most popular vegetables in the world, thus leading to an enormous amount of international trade. It is currently important that a scientific technique be developed for determining geographic origin as a means to detect fraudulent labeling. We have therefore developed a technique based on mineral analysis and linear discriminant analysis (LDA). The onion samples used in this study were from Hokkaido, Hyogo, and Saga, which are the primary onion-growing areas in Japan, and those from countries that export onions to Japan (China, the United States, New Zealand, Thailand, Australia, and Chile). Of 309 samples, 108 were from Hokkaido, 52 were from Saga, 77 were from Hyogo, and 72 were from abroad. Fourteen elements (Na, Mg, P, Mn, Co, Ni, Cu, Zn, Rb, Sr, Mo, Cd, Cs, and Ba) in the samples were determined by frame atomic adsorption spectrometry, inductively coupled plasma optical emission spectrometry, and inductively coupled plasma mass spectrometry. The models established by LDA were used to discriminate the geographic origin between Hokkaido and abroad, Hyogo and abroad, and Saga and abroad. Ten-fold cross-validations were conducted using these models. The discrimination accuracies obtained by cross-validation between Hokkaido and abroad were 100 and 86%, respectively. Those between Hyogo and abroad were 100 and 90%, respectively. Those between Saga and abroad were 98 and 90%, respectively. In addition, it was demonstrated that the fingerprint of an element pattern from a specific production area, which a crop receives, did not easily change by the variations of fertilization, crop year, variety, soil type, and production year if appropriate elements were chosen.     

Multielement fingerprinting as a tool in origin authentication of PGI food products: Tropea red onion

Tropea red onion ( Allium cepa L. var. Tropea) is among the most highly appreciated Italian products. It is cultivated in specific areas of Calabria and, due to its characteristics, was recently awarded with the protected geographical indications (PGI) certification from the European Union. A reliable classification of onion samples in groups corresponding to "Tropea" and "non-Tropea" categories is now available to the producers. This important goal has been achieved through the evaluation of three supervised chemometric approaches. Onion samples with PGI brand (120) and onion samples not cultivated following the production regulations (80) were digested by a closed-vessel microwave oven system. ICP-MS equipped with a dynamic reaction cell was used to determine the concentrations of 25 elements (Al, Ba, Ca, Cd, Ce, Cr, Dy, Eu, Fe, Ga, Gd, Ho, La, Mg, Mn, Na, Nd, Ni, Pr, Rb, Sm, Sr, Tl, Y, and Zn). The multielement fingerprint was processed using linear discriminant analysis (LDA) (standard and stepwise), soft independent modeling of class analogy (SIMCA), and back-propagation artificial neural network (BP-ANN). The cross-validation procedure has shown good results in terms of the prediction ability for all of the chemometric models: standard LDA, 94.0%; stepwise LDA, 94.5%; SIMCA, 95.5%; and BP-ANN, 91.5%.     

Effects of fertilization, crop year, variety, and provenance factors on mineral concentrations in onions

Mineral concentrations of onions (Allium cepa L.) grown under various conditions, including factors (fertilization, crop year, variety, and provenance), were investigated to clarify how much each factor contributes to the variation of their concentrations. This was because the mineral concentrations might be affected by various factors. The ultimate goal of this study was to develop a technique to determine the geographic origins of onions by mineral composition. Samples were onions grown under various conditions at 52 fields in 18 farms in Hokkaido, Japan. Twenty-six elements (Li, Na, Mg, Al, P, K, Ca, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sr, Y, Mo, Cd, Cs, Ba, La, Ce, Nd, Gd, W, and Tl) in these samples were determined by inductively coupled plasma atomic emission spectrometry and inductively coupled plasma mass spectrometry. Fertilization conditions and crop years of onions caused variations of P, Ni, Cu, Rb, Sr, Mo, Cs, and Tl concentrations in onions; different onion varieties also showed variations in numerous element concentrations. However, the variations of mineral compositions of onions by these factors were smaller than the differences between production places with a few exceptions. Furthermore, Na, Rb, and Cs in group IA of the periodic table, Ca, Sr, and Ba in group IIA, and Zn and Cd in group IIB showed similar concentration patterns by group; this result demonstrated that elements in the same periodic groups behaved similarly in terms of their absorption in onions.     

Comparative investigation of concentrations of major and trace elements in organic and conventional Danish agricultural crops. 1. Onions (Allium cepa Hysam) and peas (Pisum sativum ping pong)

210 samples of onions (Allium cepa Hysam) from 11 conventionally and 10 organically cultivated sites and 190 samples of peas (Pisum sativum Ping Pong) from 10 conventionally and 9 organically cultivated sites in Denmark were collected and analyzed for 63 and 55 major and trace elements, respectively, by high-resolution inductively coupled plasma mass spectrometry. Sampling, sample preparation, and analysis of the samples were performed under carefully controlled contamination-free conditions. Comparative statistical tests of the element concentration mean values for each site show significantly (p < 0.05) different levels of Ca, Mg, B, Bi, Dy, Eu, Gd, Lu, Rb, Sb, Se, Sr, Ti, U, and Y between the organically and conventionally grown onions and significantly (p < 0.05) different levels of P, Gd, and Ti between the organically and conventionally grown peas. Principal component analysis (PCA) applied to the 63 elements measured in the individual onion samples from the 21 sites split up the sites into two groups according to the cultivation method when the scores of the first and third principal components were plotted against each other. Correspondingly, for peas, a PCA applied to the 55 elements measured as mean values for each site split up the 19 sites into two groups according to the cultivation method when the scores of the third and fourth principal component were plotted against each other. The methodology may be used as authenticity control for organic cultivation after further method development.     

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.     

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.     

Analysis of the mineral composition of taro for determination of geographic origin

The mineral composition of taro ( Colocasia esculenta (L.) Schott) was analyzed to develop a method to distinguish taro produced in Japan and China. The concentrations of 15 elements (Al, Ca, Cl, Mg, Mn, Br, Co, Cr, Cs, Fe, K, Na, Rb, Sc, Zn) were assayed using instrumental neutron activation analysis. The concentrations of NO(3)(-), SO(4)(2-), H(2)PO(4)(-), Cl(-), malate, and oxalate were measured by ion chromatography. The mean concentrations of H(2)PO(4)(-), Co, Cr, and Na significantly differed (P < 0.01) between taro grown in Japan and that grown in China. Discriminant analysis was performed to identify the most efficient combination of elements and compounds to discriminate the taro geographic origin. The highest percentage of correct classification was achieved with a two-variable model including H(2)PO(4)(-) and Co (100% for Japanese, 93.75% for Chinese). Principal component analysis and cluster analysis using all of the assayed elements and compounds were also conducted to determine which elements significantly accounted for the variation of the taro mineral composition. We report on the potential of H(2)PO(4)(-) and Co concentrations to differentiate taro grown in China and Japan and discuss the sources of variability in the taro mineral composition of our samples.     

Chemical profiling with modeling differentiates wild and farm-raised salmon

Classifications of fish production methods, wild or farm-raised salmon, by elemental profiles or C and N stable isotope ratios combined with various modeling approaches were determined. Elemental analysis (As, Ba, Be, Ca, Co, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, Pb, Sr, Ti, and Zn) of wild and farm-raised salmon samples was performed using an inductively coupled plasma atomic emission spectroscopy. Isotopic and compositional analyses of carbon and nitrogen were performed using mass spectrometry as an alternative fingerprinting technique. Each salmon (king salmon, Oncorhynchus tshawytscha ; coho salmon, Oncorhynchus kisutch ; Atlantic salmon, Salmo salar ) was analyzed from two food production practices, wild and farm raised. Principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for data exploration and visualization. Five classification modeling approaches were investigated: linear discriminate function, quadratic discriminant function, neural network, probabilistic neural network, and neural network bagging. Methods for evaluating model reliability included four strategies: resubstitution, cross-validation, and two very different test set scenarios. Generally speaking, the models performed well, with the percentage of samples classified correctly depending on the particular choice of model and evaluation method used.     

钙水平对大葱生长及氮代谢的影响

【目的】 通过探讨钙对大葱生长及氮代谢的影响,明确钙在提高大葱产量和品质中的作用,为优化大葱施肥技术提供理论依据。 【方法】 以‘昭和’和‘章丘’大葱为试材,进行了砂培试验和田间试验。用砂培试验营养液钙水平设 0、4、6、8 mmol/L 4 个处理。于大葱越夏期 (7 月 17 日)、叶丛速生期 (9 月 11 日) 及假茎充实期 (10 月 20 日)取大葱叶片测定不同形态氮含量以及硝酸还原酶(NR)、谷氨酰胺合成酶(GS)、谷氨酸合酶(GOGAT)和谷氨酸脱氢酶(GDH)活性,于收获期 (11 月 13 日) 测定大葱生长量、产量及品质。田间试验设置 0、225、450、675 kg/hm2 4 个水平 (以 CaO 计),于收获期 (11 月 13 日) 测定产量。 【结果】 砂培大葱株高、茎粗、根及茎叶鲜重均随营养液钙水平提高而显著增加,至钙水平达 6 mmol/L 时表现较好,单株生长量达 211.13 g,钙水平继续增加至 8 mmol/L 时,单株生长量仅 185.83 g,与 4 mmol/L 的 183.29 g 无显著差异;除根系鲜重外,‘章丘’大葱株高、茎粗及茎叶鲜重均显著高于‘昭和’大葱。适量增加钙水平亦可显著提高大葱叶片 GOGAT、GDH、NR、GS 活性,以叶丛速生期影响最大。大葱叶片铵态氮(NH₄+-N)、硝态氮 (NO₃--N)、可溶性蛋白、游离氨基酸含量均随营养液钙水平提高而呈先增加后降低,均以叶丛速生期最高。两品种大葱品质相关指标均以钙水平 6 mmol/L 时最高,8 mmol/L 时有所降低。钙水平为 6 mmol/L 时,盆栽大葱产量显著高于其它处理,‘章丘’和‘昭和’分别较对照增产 79.94% 和 74.42%。大田试验,‘章丘’和‘昭和’大葱均以施用 CaO 450 kg/hm2 产量最高,分别较对照提高 12.30%、19.00%。 【结论】 适量施钙可显著促进大葱生长,提高叶片氮代谢酶活性及不同形态氮含量,提高产量及品质。综合分析表明,以营养液钙水平 6 mmol/L、土壤施钙 450 kg/hm2 时最有利于大葱的生长及产量品质的提高。      

Preliminary studies for the differentiation of apple juice samples by chemometric analysis of solid-phase microextraction-gas chromatographic data

A combination of gas chromatography (GC) and chemometrics was evaluated for its ability to differentiate between apple juice samples on the basis of apple variety and applied heat treatment. The heat treatment involved exposure of 15 mL juice samples for 30 s in a 900 W domestic microwave oven. The chromatographic results were subjected to two chemometric procedures: (1) partial least squares (PLS) regression and (2) linear discriminant analysis (LDA) applied to principal component (PC) scores. The percent correct classification of samples were obtained from PLS and LDA in terms of separation on the basis of apple variety and applied heat treatment. PLS gave the highest level of correct classification of the apple juice samples according to both variety and heat treatment, 92.5% correct classification in each case. When LDA was performed on the PC scores obtained from GC analysis, 87.5% and 80% of samples were correctly classified according to apple variety used and applied heat treatment, respectively.     

Discrimination between Japanese and Chinese Products using trace elements concentration of kernel in pickled Japanese apricot

pp. 875–880

The trace-element composition of kernel in pickled Japanese apricot (Prunus mume Sieb. et Zucc.) was determined using an inductively coupled plasma optical emission spectrometer in order to distinguish between Japanese products and Chinese products.

Strontium and barium concentrations in the kernels of Chinese products were 10 or more times those of the Japanese ones. When based on 8.0 mg kg^?1 of strontium concentration in kernel, 93.2% of sample was distinguished as Japanese products or Chinese ones.

Applying principal component analysis using 9 elements (Mn, Zn, Fe, Ni, Ba, Sr, Cu, Co, Cr), the pickled Japanese apricots tend to separate into two countries. Linear discriminant analysis (LDA) using 9 elements allowed a reasonable classification of pickled Japanese apricots according to the country of production.

The result of the analysis of K-nearest neighbors (KNN) was better than that of LDA.     

Classification of gilthead sea bream (Sparus aurata) from 1H NMR lipid profiling combined with principal component and linear discriminant analysis

The combination of (1)H NMR fingerprinting of lipids from gilthead sea bream (Sparus aurata) with nonsupervised and supervised multivariate analysis was applied to differentiate wild and farmed fish and to classify farmed specimen according to their areas of production belonging to the Mediterranean basin. Principal component analysis (PCA) applied on processed (1)H NMR profiles made a clear distinction between wild and farmed samples. Linear discriminant analysis (LDA) allowed classification of samples according to the geographic origin, as well as for the wild and farmed status using both PCA scores and NMR data as variables. Variable selection for LDA was achieved with forward selection (stepwise) with a predefined 5% error level. The methods allowed the classification of 100% of the samples according to their wild and farmed status and 85-97% to geographic origin. Probabilistic neural network (PNN) analyses provided complementary means for the successful discrimination among classes investigated.     

Classification of German white wines with certified brand of origin by multielement quantitation and pattern recognition techniques

A procedure is proposed for the determination of the authenticity of white wines from four German wine-growing regions (Baden, Rheingau, Rheinhessen, and Pfalz) based on their content of some major, trace, and ultratrace elements. One hundred and twenty-seven white wine samples possessing a certificate of origin, all of the 2000 vintage, were analyzed. The concentrations of 13 elements (Li, B, Mg, Ca, V, Mn, Co, Fe, Zn, Rb, Sr, Cs, and Pb) were determined in wine diluted 1:20 by sector field inductively coupled plasma mass spectrometry (SF-ICP-MS). Indium was routinely used as internal standard. Supervised pattern recognition techniques such as discriminant analysis and classification trees were applied for the interpretation of the data. A quadratic discriminant analysis (QDA) allowed the four regions to be discriminated with 83% accuracy when using only eight variables (Li, B, Mg, Fe, Zn, Sr, Cs, and Pb), and the prediction ability for classifying new samples was 76%. By use of a second method, a decision tree, the classification of samples coming from the four regions could be performed with an accuracy of 84% when only four elements were used: Li (very low in samples from Baden), Zn (abnormally low in the samples from the Rheingau), and Mg and Sr (both important for the differentiation between Pfalz and Rheinhessen samples). For this method, the prediction ability was only 74% in the identification of unknown samples. The robustness of the QDA model was not good enough, and therefore the tree is better recommended for the classification of new wine samples from these areas of German wine production.     

Chemical authentication of extra virgin olive oil varieties by supervised chemometric procedures.

This work has focused on discriminating extra virgin olive oils from Sabina (Lazio, Italy) by olive fruit variety (cultivar). A set of oils from five of the most widespread cultivars (Carboncella, Frantoio, Leccino, Moraiolo, and Pendolino) in this geographical area was analyzed for chemical composition using only the Official Analytical Methods, recognized for the quality control and commercial classification of this product. The obtained data set was converted into a computer-compatible format, and principal component analysis (PCA) and a method based on the Fisher F ratio were used to reduce the number of variables without a significant loss of chemical information. Then, to differentiate these samples, two supervised chemometric procedures were applied to process the experimental data: linear discriminant analysis (LDA) and artificial neural network (ANN) using the back-propagation algorithm. It was found that both of these techniques were able to generalize and correctly predict all of the samples in the test set. However, these results were obtained using 10 variables for LDA and 6 (the major fatty acid percentages, determined by a single gas chromatogram) for ANN, which, in this case, appears to provide a better prediction ability and a simpler chemical analysis. Finally, it is pointed out that, to achieve the correct authentication of all samples, the selected training set must be representative of the whole data set.     

Inductively coupled plasma emission spectroscopic determination of nine elements in infant formula: collaborative study

Results from a collaborative study of a method for the determination of 9 elements in infant formula, using inductively coupled plasma emission spectroscopy, are reported. Six collaborators analyzed 6 infant formulas for native and spiked levels of Ca, Cu, Fe, Mg, Mn, P, K, Na, and Zn. The within-laboratory and between-laboratory coefficients of variation were generally (69 of 108 samples) below 9% for all elements determined in all samples. Most of the average recoveries of the elements from spiked samples ranged from 90 to 105%. The method has been adopted official first action for determining Ca, Cu, Fe, Mg, Mn, P, K, Na, and Zn in infant formula.     

Stable isotope and trace element profiling combined with classification models to differentiate geographic growing origin for three fruits: effects of subregion and variety

Classifications of geographic growing origin of three fresh fruits combining elemental profiles with various modeling approaches were determined. Elemental analysis (Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, V, and Zn) of strawberry, blueberry, and pear samples was performed using inductively coupled plasma argon atomic emission spectrometer. Bulk stable carbon and nitrogen isotope analyses in pear were performed using mass spectrometry as an alternative fingerprinting technique. Each fruit, strawberry (Fragaria x ananassa), blueberry (Vaccinium caesariense/corymbosum), and pear (Pyrus communis), was analyzed from two growing regions: Oregon vs Mexico, Chile, and Argentina, respectively. Principal component analysis and canonical discriminant analysis were used for data visualization. The data were modeled using linear discriminant function, quadratic discriminant function, neural network, genetic neural network, and hierarchical tree models with successful classification ranging from 70 to 100% depending on commodity and model. Effects of Oregon subregional and variety classification were investigated with similar success rates.     

Enhancing the Reliability of Laboratory Phosphorus Filter Tests: Effect of Influent Properties and Interpretation of Effluent Parameters

The distribution of selected elements in individual fractions of organic matter from anthropogenically contaminated soils was investigated. The attention was paid especially at Hg. Furthermore, contents of S, Mg, Mn, Fe, Cu, Zn and Pb were also measured. The decomposition of organic matter to particular fractions was carried out by the resin DAX-8. Ten soil samples were collected, and the Advanced Mercury Analyzer (AMA-254) was used for the determination of the total Hg content. The two highest Hg values reached up to the concentration 10.5 mg kg^?1, and in the highest one, it was almost 29 mg kg^?1. In each extract, mercury was measured by inductively coupled plasma mass spectrometry (ICP-MS), for other elements, inductively coupled plasma optical emission spectrometry (ICP-OES) was applied. Results of the analysis show that the Hg content bound to the humic acids is inversely proportional to the content of Mg, Mn, Fe and Cu. However, this dependence was not confirmed by the samples with the mercury content above 10 mg kg^?1. In the case of fulvic acids, the relationship between Hg and S was observed and has again an inverse character.     

Determining the geographic origin of wheat using multielement analysis and multivariate statistics

The element contents of wheat from four major wheat-producing regions of China were analyzed and used in multivariate statistical analysis to classify wheat according to geographical origin. The concentrations of 15 elements (Be, Na, Mg, Al, K, Ca, V, Mn, Fe, Cu, Zn, Mo, Cd, Ba, and Th) in 240 samples from the 2007/2008 and 2008/2009 harvests were determined by inductively coupled plasma mass spectrometry. The analysis of variance and linear discriminant analysis were applied to classify wheat origin, and the effects of region, variety, and harvest year on the element contents were analyzed in this study. It was concluded that the multielement analysis is a promising method to provide reliable origin information for wheat, although the element profiles and discriminant models were affected by wheat varieties, harvest years, and agricultural practices.     

Usefulness of near-infrared reflectance (NIR) spectroscopy and chemometrics to discriminate fishmeal batches made with different fish species

Near-infrared reflectance (NIR) spectroscopy combined with chemometrics was used to identify and authenticate fishmeal batches made with different fish species. Samples from a commercial fishmeal factory (n = 60) were scanned in the NIR region (1100-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), dummy partial least-squares regression (DPLS), and linear discriminant analysis (LDA) based on PCA scores were used to identify the origin of fishmeal produced using different fish species. Cross-validation was used as validation method when classification models were developed. DPLS correctly classified 80 and 82% of the fishmeal samples. LDA calibration models correctly classified >80% of fishmeal samples according to fish species The results demonstrated the usefulness of NIR spectra combined with chemometrics as an objective and rapid method for the authentication and identification of fish species used to manufacture the fishmeal.     

Geographic classification of spanish and Australian tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis

Visible (vis) and near-infrared (NIR) spectroscopy combined with multivariate analysis was used to classify the geographical origin of commercial Tempranillo wines from Australia and Spain. Wines (n = 63) were scanned in the vis and NIR regions (400-2500 nm) in a monochromator instrument in transmission. Principal component analysis (PCA), discriminant partial least-squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) based on PCA scores were used to classify Tempranillo wines according to their geographical origin. Full cross-validation (leave-one-out) was used as validation method when PCA and LDA classification models were developed. PLS-DA models correctly classified 100% and 84.7% of the Australian and Spanish Tempranillo wine samples, respectively. LDA calibration models correctly classified 72% of the Australian wines and 85% of the Spanish wines. These results demonstrate the potential use of vis and NIR spectroscopy, combined with chemometrics as a rapid method to classify Tempranillo wines accordingly to their geographical origin.