Authentication of farmed and wild turbot (Psetta maxima) by fatty acid and isotopic analyses combined with chemometrics

Fatty acid composition and stable isotope ratios of carbon (delta(13)C) and nitrogen (delta(15)N) were determined in muscle tissue of turbot (Psetta maxima). The multivariate analysis of the data was performed to evaluate their utility in discriminating wild and farmed fish. Wild (n=30) and farmed (n=30) turbot of different geographical origins (Denmark, The Netherlands, and Spain) were sampled from March 2006 to February 2007. The application of linear discriminant analysis (LDA) and soft independent modeling of class analogy (SIMCA) to analytical data demonstrated the combination of fatty acids and isotopic measurements to be a promising method to discriminate between wild and farmed fish and between wild fish of different geographical origin. In particular, IRMS (Isotope Ratio Mass Spectrometry) alone did not permit us to separate completely farmed from wild samples, resulting in some overlaps between Danish wild and Spanish farmed turbot. On the other hand, fatty acids alone differentiated between farmed and wild samples by 18:2n-6 but were not able to distinguish between the two groups of wild turbot. When applying LDA isotope ratios, 18:2n-6, 18:3n-3, and 20:4n-6 fatty acids were decisive to distinguish farmed from wild turbot of different geographical origin, while delta(15)N, 18:2n-6, and 20:1n-11 were chosen to classify wild samples from different fishing zones. In both cases, 18:2n-6 and delta(15)N were determinant for classification purposes. We would like to emphasize that IRMS produces rapid results and could be the most promising technique to distinguish wild fish of different origin. Similarly, fatty acid composition could be used to easily distinguish farmed from wild samples.     

Principal component and linear discriminant analyses of free amino acids and biogenic amines in hungarian wines

Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to classify 187 Hungarian white and red wines according to wine-making technology, geographic origin (wine-making region), grape variety, and year of vintage based on free amino acid and biogenic amine contents. Determination of free amino acids and biogenic amines was accomplished by ion-exchange chromatography. Six principal components accounted for >77% of the total variance in the data. The plots of component loadings showed significant groupings of free amino acids and biogenic amines. The component scores grouped according to wines made by different wine-making technologies. Using LDA the variables with a major discriminant capacity were determined. Almost complete classification (94.7%) was achieved concerning both white and red wines and wines made by different wine-making technologies. The results of differentiation between white wines according to geographic origin, grape variety, and year of vintage were 70.8, 62.4, and 73.5%, respectively. The same numbers for red wines according to geographic origin, grape variety, and year of vintage were 64.9, 71.6, and 82.4%, respectively.     

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.     

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.     

Multivariate analysis of CPMAS 13C-NMR spectra of soils and humic matter as a tool to evaluate organic carbon quality in natural systems

A series of humic and fulvic acids isolated from different sources, size‐fractions separated from a humic acid, and three soils of different origin were subjected to CPMAS ¹³C‐NMR spectroscopy to obtain the distribution of their carbon contents. The relative areas of chemical shift regions in NMR spectra were used to apply a principal component analysis (PCA) to the three sets of samples. The multivariate analysis was successful in efficiently differentiating samples on the basis of the quality of their organic carbon content. The PC biplots based on two principal components distinguished objectively among samples as accurately as it was possible to do by subjective qualitative evaluation of the original spectra. In the case of the soils, a discriminant analysis (DA) was applied to build a classification model that allowed the validation of the three soils according to their origin. Percentage of validation in the classification model is expected to increase when a large number of NMR spectra are accumulated and/or the concentration of organic carbon in samples is enhanced. The multivariate analyses described are likely to become a useful tool to increase the importance of CPMAS ¹³C‐NMR spectra in the appraisal of natural organic matter variations in heterogeneous natural systems.     

Chemical properties of humic matter as related to induction of plant lateral roots

A series of humic matter samples isolated from a soil sequence, different oxisols, size‐fractionated from a vermicompost humic acid and subjected to chemical modifications, were characterized by CPMAS ¹³C‐NMR spectroscopy. The relative signal areas in chemical shift regions of NMR spectra of the four sets of samples were analysed by principal component analysis (PCA). Hierarchical cluster analysis (HCA) was applied to build a classification model, which allowed the recognition of humic matter according to its origin. The relationship between carbon species and biological activity of humic acids, as promoters of lateral root emergence, was obtained by applying PLS multivariate analysis. This showed that lateral root emergence was mostly related to NMR parameters such as the hydrophobicity index (HB/HI) and the 40–110 and 160–200 ppm chemical shift regions (hydrophilic carbon HI), while the content of hydrophobic (HB) carbon in humic samples was negatively correlated with induction of lateral root hair. Our results represent a step further in the structure‐bioactivity relationship of natural humic substances and confirm their role as plant root growth promoters.     

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.     

Study of the phenolic profile of cider apple cultivars at maturity by multivariate techniques.

Phenolic compounds in 46 Spanish cider apple varieties were determined by RP-HPLC with direct injection. Several pattern recognition procedures, including principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares (PLS-1), were applied to the data in an attempt to classify the samples into bitter and nonbitter categories. Reliable decision rules were obtained by both LDA and PLS-1. LDA achieved 91.3 and 85.7% correct classification respectively, for internal and external evaluation of the model.     

Application of inorganic element ratios to chemometrics for determination of the geographic origin of welsh onions

The composition of concentration ratios of 19 inorganic elements to Mg (hereinafter referred to as 19-element/Mg composition) was applied to chemometric techniques to determine the geographic origin (Japan or China) of Welsh onions (Allium fistulosum L.). Using a composition of element ratios has the advantage of simplified sample preparation, and it was possible to determine the geographic origin of a Welsh onion within 2 days. The classical technique based on 20 element concentrations was also used along with the new simpler one based on 19 elements/Mg in order to validate the new technique. Twenty elements, Na, P, K, Ca, Mg, Mn, Fe, Cu, Zn, Sr, Ba, Co, Ni, Rb, Mo, Cd, Cs, La, Ce, and Tl, in 244 Welsh onion samples were analyzed by flame atomic absorption spectroscopy, inductively coupled plasma atomic emission spectrometry, and inductively coupled plasma mass spectrometry. Linear discriminant analysis (LDA) on 20-element concentrations and 19-element/Mg composition was applied to these analytical data, and soft independent modeling of class analogy (SIMCA) on 19-element/Mg composition was applied to these analytical data. The results showed that techniques based on 19-element/Mg composition were effective. LDA, based on 19-element/Mg composition for classification of samples from Japan and from Shandong, Shanghai, and Fujian in China, classified 101 samples used for modeling 97% correctly and predicted another 119 samples excluding 24 nonauthentic samples 93% correctly. In discriminations by 10 times of SIMCA based on 19-element/Mg composition modeled using 101 samples, 220 samples from known production areas including samples used for modeling and excluding 24 nonauthentic samples were predicted 92% correctly.     

Determining the geographic origin of potatoes with trace metal analysis using statistical and neural network classifiers

The objective of this research was to develop a method to confirm the geographical authenticity of Idaho-labeled potatoes as Idaho-grown potatoes. Elemental analysis (K, Mg, Ca, Sr, Ba, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Mo, S, Cd, Pb, and P) of potato samples was performed using ICPAES. Six hundred eight potato samples were collected from known geographic growing sites in the U.S. and Canada. An exhaustive computational evaluation of the 608 x 18 data sets was carried out using statistical (PCA, CDA, discriminant function analysis, and k-nearest neighbors) and neural network techniques. The neural network classification of the samples into two geographic regions (defined as Idaho and non-Idaho) using a bagging technique had the highest percentage of correct classifications, with a nearly 100% degree of accuracy. We report the development of a method combining elemental analysis and neural network classification that may be widely applied to the determination of the geographical origin of unprocessed, fresh commodities.     

Application of (1)h NMR and multivariate statistics for screening complex mixtures: quality control and authenticity of instant coffee

Principal components analysis (PCA) followed by linear discriminant analysis (LDA) of the nuclear magnetic resonance (NMR) spectra from 98 instant spray-dried coffees, obtained from 3 different producers, correctly attributed 99% of the samples to their manufacturer. Blind testing of the PCA model with a further 36 samples of instant coffee resulted in a 100% success rate in identifying the samples from the 3 manufacturers. Coffees from one manufacturer were also assigned into 2 groups using these techniques, and the compound 5-(hydroxymethyl)-2-furaldehyde was identified as the primary marker of differentiation.     

Identification of the botanical origin of raw spirits produced from rye, potato, and corn based on volatile compounds analysis using a SPME-MS method

Determination of the botanical origin of raw spirit used for alcoholic beverage production is of great importance for rectifying units, control laboratories, and proper product labeling. Raw spirit samples (138) produced from rye, corn, and potato were analyzed using a solid phase microextraction-mass spectrometry (SPME-MS) method, which involved volatiles preconcentration by SPME with subsequent volatile fraction characterization by MS without particular compounds separation by GC. Obtained data were treated using principal component analysis and linear discriminant analysis (LDA) to test the possibility of sample classification. SPME sampling conditions allowed rapid extraction in 2 min at 50 °C using a carboxen/divinylbenzene/polydimethylsiloxane fiber, followed by rapid MS analysis. Use of LDA made possible the classification of raw spirits based on the material they were produced from. The classification ability of the developed SPME-MS method was 100%, whereas its prediction ability was 96%.     

1H NMR metabolite fingerprint and pattern recognition of mullet (Mugil cephalus) bottarga

Nuclear magnetic resonance (NMR) spectroscopy combined with multivariate data analysis (MVA) was used to investigate the molecular components of the aqueous extract of samples of bottarga, that is, salted and dried mullet (Mugil cephalus) roe, manufactured in Sardinia (Italy) from mullets of known and unknown geographical provenience. Principal component analysis (PCA) applied to the processed (1)H NMR spectra indicated that samples tend to cluster according to their geographical origin and also on the basis of storage and manufacturing procedures. The most important metabolites that characterized grouping of samples are the free amino acids methionine (Met), glutamate (Glu), histidine (His), phenylalanine (Phe), tyrosine (Tyr), and isoleucine (Ile); trimethylamine (TMA) and dimethylamine (DMA), both biomarkers of degradation; nucleotides and derivatives; choline (Cho) and phosphorylcholine (P-cho); and lactate (Lac).     

Determination of authenticity, regional origin, and vintage of Slovenian wines using a combination of IRMS and SNIF-NMR analyses

The authenticity and geographical origin of wines produced in Slovenia were investigated by a combination of IRMS and SNIF-NMR methods. A total of 102 grape samples of selected wines were carefully collected in three different wine-growing regions of Slovenia in 1996, 1997, and 1998. The stable isotope data were evaluated using principal component analysis (PCA) and linear discriminant analysis (LDA). The isotopic ratios to discriminate between coastal and continental regions are the deuterium/hydrogen isotopic ratio of the methylene site in the ethanol molecule (D/H)(II) and delta(13)C values; including also delta(18)O values in the PCA and LDA made possible separation between the two continental regions Drava and Sava. It was found that delta(18)O values are modified by the meteorological events during grape ripening and harvest. The usefulness of isotopic parameters for detecting adulteration or watering and to assess the geographical origin of wines is improved only when they are used concurrently.     

Application of cryoprobe 1H nuclear magnetic resonance spectroscopy and multivariate analysis for the verification of corsican honey

Proton nuclear magnetic resonance spectroscopy ((1)H NMR) and multivariate analysis techniques have been used to classify honey into two groups by geographical origin. Honey from Corsica (Miel de Corse) was used as an example of a protected designation of origin product. Mathematical models were constructed to determine the feasibility of distinguishing between honey from Corsica and that from other geographical locations in Europe, using (1)H NMR spectroscopy. Honey from 10 different regions within five countries was analyzed. (1)H NMR spectra were used as input variables for projection to latent structures (PLS) followed by linear discriminant analysis (LDA) and genetic programming (GP). Models were generated using three methods, PLS-LDA, two-stage GP, and a combination of PLS and GP (PLS-GP). The PLS-GP model used variables selected by PLS for subsequent GP calculations. All models were generated using Venetian blind cross-validation. Overall classification rates for the discrimination of Corsican and non-Corsican honey of 75.8, 94.5, and 96.2% were determined using PLS-LDA, two-stage GP, and PLS-GP, respectively. The variables utilized by PLS-GP were related to their (1)H NMR chemical shifts, and this led to the identification of trigonelline in honey for the first time.     

Geographical characterization of italian extra virgin olive oils using high-field (1)H NMR spectroscopy.

1H high-field nuclear magnetic resonance (NMR) was used to analyze 216 extra virgin olive oils collected in three years (1996, 1997, and 1998) in different Italian areas in order to evaluate the potential contribution of this technique to the geographical characterization of olive oils. A statistical procedure performed on the intensity of selected NMR peaks has been proposed. Tree clustering analysis of NMR data performed without any a priori hypothesis showed the existence of reliable parameters able to group the olive oils according to the location of olive oil production. Linear discriminant analysis applied to selected NMR parameters of olive oils of the same year of production allowed the grouping of samples according to their geographical origin with only very few errors. Moreover, a satisfactory grouping is reached by combining the NMR data of olive oils from two different years (1996 and 1997). Operating on appropriate sampling, a careful analysis of data yielded the conclusion that the place of olive production could be singled out as a discriminating factor regardless of the cultivars from which the olive oils are derived.     

Multivariate analysis of NMR and FTIR data as a potential tool for the quality control of beer

In this work, principal component analysis (PCA) is applied to the FTIR-ATR and the (1)H NMR spectra of 50 beers differing in label and type (ale, lager, alcohol-free), to identify the spectral parameters that may provide rapid information about factors affecting beer production. PCA of FTIR data resulted in the separation of beers mainly according to their alcoholic content, providing little information on components other than ethanol contributing to the variability within the samples. PCA of (1)H NMR spectra, performed on the region where major beer components resonate (3.0-6.0 ppm), resulted in the separation of samples into four groups: two groups characterized by the predominance of dextrins, one group of alcohol-free beers characterized by the predominance of maltose, and one group where glucose was found to predominate. By performing PCA on aliphatic and aromatic regions, the contribution of minor components was highlighted. In particular, most ales, lagers, and alcohol-free samples could be distinguished based on their aromatic composition, thus reflecting the high sensitivity of the low-field NMR region toward different types of beer fermentation.     

Differentiation of tea (Camellia sinensis) varieties and their geographical origin according to their metal content

The metal content of 46 tea samples, including green, black, and instant teas, was analyzed. Al, Ba, Ca, Cu, Fe, K, Mg, Mn, Na, Sr, Ti, and Zn were determined by ICP-AES. Potassium, with an average content of 15145.4 mg kg(-1) was the metal with major content. Calcium, magnesium, and aluminum had average contents of 4252.4, 1978.2, and 1074.0 mg kg(-1), respectively. The average amount of manganese was 824.8 mg kg(-1). There were no clear differences between the metal contents of green and black teas. Pattern recognition methods such as principal component analysis (PCA), linear discriminant analysis (LDA), and artificial neural networks (ANN), were applied to differentiate the tea types. LDA and ANN provided the best results in the classification of tea varieties. These chemometric procedures were also useful for distinguishing between Asian and African teas and between the geographical origin of different Asian teas.     

Detection of extra virgin olive oil adulteration with lampante olive oil and refined olive oil using nuclear magnetic resonance spectroscopy and multivariate statistical analysis

High-field 31P NMR (202.2 MHz) spectroscopy was applied to the analysis of 59 samples from three grades of olive oils, 34 extra virgin olive oils from various regions of Greece, and from different olive varieties, namely, 13 samples of refined olive oils and 12 samples of lampante olive oils. Classification of the three grades of olive oils was achieved by two multivariate statistical methods applied to five variables, the latter being determined upon analysis of the respective 31P NMR spectra and selected on the basis of one-way ANOVA. The hierarchical clustering statistical procedure was able to classify in a satisfactory manner the three olive oil groups. Subsequent application of discriminant analysis to the five selected variables of oils allowed the grouping of 59 samples according to their quality with no error. Different artificial mixtures of extra virgin olive oil-refined olive oil and extra virgin olive oil-lampante olive oil were prepared and analyzed by 31P NMR spectroscopy. Subsequent discriminant analysis of the data allowed detection of extra virgin olive oil adulteration as low as 5% w/w for refined and lampante olive oils. Further application of the classification/prediction model allowed the estimation of the percent concentration of refined olive oil in six commercial blended olive oils composed of refined and virgin olive oils purchased from supermarkets.     

Toward the determination of the geographical origin of Emmental(er) cheese via high resolution MAS NMR: a preliminary investigation

We tested the potential of High-Resolution MAS NMR spectroscopy to study 20 samples of Emmental cheeses from 7 different geographical regions. Principal component analysis (PCA) and discriminant analysis (DA) were used to analyze the data set of 1H HRMAS NMR spectra and succeeded in grouping the studied samples according to their geographical origins.