Multi-element Fingerprinting Research Articles

Elemental fingerprinting based on ICP-MS/OES and machine learning: Sample digestion-free and standard-free strategy for geographical origin identification of Chinese liquor

The significant price disparity of Chinese baijiu originating from various regions has unfortunately partially given rise to the proliferation of counterfeit and substandard products, and this may cause disturbance in the market economy. Ensuring the traceability and differentiation of baijiu by its origin is therefore of paramount importance. In this study, the Baijiu samples were straightforward dilution without sample digestion, followed by the detection of elemental signal intensities using ICP-MS and ICP-OES without accurate quantification, to rapidly construct their multi-elemental fingerprints. Heatmap and HCA (hierarchical cluster analysis) were used for data visualization and tentative classification, respectively. PCA (principal component analysis) was implemented to corroborate the classification outcomes. Subsequently, the supervised OPLS-DA (orthogonal partial least squares discrimination analysis) was deployed to enhance the identification and predictive capabilities for the Baijiu samples. The unequivocal discrimination of the three distinct Baijiu origins with 100% classification accuracy. The validation values R2 and Q2 approached 1.0, indicating a great fit and prediction capability of the OPLS-DA model. The VIP plot evaluated the importance of elemental variables, revealing that the key contributions were primarily from elements K, Ca, Na, Mg, and Al. Collectively, these findings demonstrate the efficacy of chemometric techniques, when integrated with multi-elemental fingerprints based on their signal intensities only, in identifying the geographical origins of the Baijiu. This approach holds promise for applications in quality control and anti-counterfeiting measures in the baijiu industry.

Geographical origin identification of winter jujube (Ziziphus jujuba ‘Dongzao’) by using multi-element fingerprinting with chemometrics

Winter jujube (Ziziphus jujuba ‘Dongzao’) is greatly appreciated by consumers for its excellent quality, but brand infringement frequently occurs in the market. Here, we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer (ICP-MS). As a result, 16 elements (Mg, K, Mn, Cu, Zn, Mo, Ba, Be, As, Se, Cd, Sb, Ce, Er, Tl, and Pb) exhibited significant differences in samples from different producing areas. Supervised linear discriminant analysis (LDA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA) showed better performance in identifying the origin of samples than unsupervised principal component analysis (PCA). LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64% in the testing set, respectively. By using the multilayer perceptron (MLP) and C5.0, the prediction accuracy of the models could reach 96.36 and 91.06%, respectively. Based on the above four chemometric methods, Cd, Tl, Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube. Overall, this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics, and may also provide reference for establishing the origin traceability system of other fruits.

Importance of estuaries for the horse-eye jack Caranx latus in northeastern Brazil: a case study of underestimated connectivity at the land-sea interface

This study aimed to evaluate the importance of inshore estuarine habitats for the maintenance of offshore exploited stocks of the horse-eye jack Caranx latus, a marine tropical species of high economic value. Otoliths of 143 yearlings were used to develop a data set of multi-elemental fingerprints for different juvenile habitats of C. latus in northeastern Brazil, including 7 estuaries and the coastal zone. This data set was then compared to the signatures in the juvenile part of the otolith of 40 sub-adult and adult fish caught at sea to identify their origin. Although otolith multi-elemental concentrations overlapped for some of the estuaries investigated, the maximum overall discrimination accuracy between them was only 58%. However, grouping several estuaries according to their similarity in elemental signatures increased accuracy to 80%. In both cases, correct re-assignment rates for the coastal zone were above 94%, and strontium (Sr) was the most significant element in juvenile habitat discrimination. The remaining elements (B, Ba, Co, P, Rb, and Zn) allowed us to distinguish between 3 distinct groups of estuaries, for which correct re-assignment rates were 70-88%. Juvenile fingerprints in sub-adult and adult otoliths revealed that most of the fish in the local, exploited stock (75%) originate from an inshore estuarine habitat, particularly from larger estuaries under constant marine influence (32.5%). This biological connectivity between continental and coastal waters should be considered for the conservation of tropical marine coastal stocks in general, as it is likely to apply to other exploited marine species, in Brazil and elsewhere.

Spatiotemporal variability in otolith elemental fingerprint and the potential to determine deepwater redfish (Sebastes mentella) origins and migrations in the Estuary and Gulf of St. Lawrence, Canada

Connectivity processes have major implications in defining the resiliency of fish populations to overexploitation. A preliminary estimate of population exchange rates can be done by identifying the natal origin of adult fish. In this study, otolith elemental fingerprints were used as natural marker of origins and movements of Deepwater redfish (Sebastes mentella) in the Gulf of St. Lawrence (GSL). We specifically targeted the strong 2011–2013 cohorts that supported the rapid recovery of the GSL stock after its collapse in the 1990s. Elemental fingerprints were extracted from the core (proxy for larval origin) and edge (proxy for capture location) of otoliths using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). We observed an East to West gradient in the multi-elemental fingerprint of the otolith edge in the GSL, as well as evidence of temporal variation between 2016 and 2018. Cluster analysis of the core fingerprint revealed the existence of two chemically distinct natal sources of variable contribution between the Saguenay Fjord, the western GSL and the eastern GSL. This new insight on the population structure of redfish in the GSL at an ecologically relevant scale constitutes important knowledge for the assessment and sustainable management of a key recovering resource.

Determination of geographical origin of Mustard oil based on multi-elemental fingerprinting using inductively coupled plasma mass spectrometry (ICP-MS) and chemometric analysis

In the present study samples of mustard were collected from 10 different provinces of India. The method is developed by using 20 different elements (Na, Mg, Al, P, Ca, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ar, Se, Rb, Sr, Mo, Cs, and Pb) using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The elemental data obtained from the ICP-MS were subjected to chemometric analysis for the classification of each sample. In this study, we have used Principal component analysis (PCA) and Hierarchical cluster analysis (HCA), in order to group the samples according to their elemental composition. HCA showed five clusters corresponding to the geographical origin of the mustard. Finally from Linear discriminant analysis (LDA) models were developed which provided 100% accuracy. The elements Mg, P, Ca, Fe, Cr, Cu & Se were found be useful and were the most discriminating elements. Hence, this study provides potential elemental marker for identification of the geographical origin of the mustard in various geographical regions.

The application of multi-elemental fingerprints and chemometrics for discriminating between cage and free-range table eggs based on atomic absorption spectrometry (AAS) and colorimetry

The application of multi-elemental fingerprints and chemometrics for discriminating between cage and free-range table eggs based on atomic absorption spectrometry (AAS) and colorimetry

Authenticating Tibetan pork in China by tracing the species and geographical features based on stable isotopic and multi-elemental fingerprints

A potential tool for authenticating Tibetan pork based on species and geographical origins was investigated by isotopic and multi-elemental fingerprints to compare Tibetan pork and plain pork from various regions in China. In this study, carbon, nitrogen, hydrogen and oxygen isotope compositions and concentrations of 50 elements of a total of 93 pork samples (Tibetan pork from Tibet and Sichuan, Jilin pork from Jilin and DLY pork from Liaoning) were determined and subjected to multivariate analysis, including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA) and correlation analysis. The trace elements Fe, Cu, Rb, Mo, Cs, Ir, Co, Sr, Sm and four stable isotopes were determined as potential markers for identifying Tibetan pork. The discriminant model revealed good discrimination among pork samples based on species and regions, and the high accuracies of K-fold cross-validation (95.74% and 97.84%) indicated the reliability and stability of classification models. This study has demonstrated the efficiency of isotopic and multi-elemental technology in identifying Chinese high-altitude indigenous pigs. • Tibetan pork from different regions was discriminated compared to plain pork. • Multi-elements and stable isotopes combined with chemometrics as effective tools. • High cross-validation accuracy for tracing species and origins. • Fe, Cu and δ 13 C are indicators for discriminating high-altitude pork samples.

Multi-element fingerprinting approach for geographical authentication of Amomum tsaoko seed

The dried ripe fruit or seed of Amomun tsaoko is a crucial food additive, spice and materia medicai in eastern and southeastern Asia, particularly in China. The geographical origin is related to its overall quality of it and has a strong effect on its commercial value. In this study, an efficient absolute quantitative approach using sixty-nine elements was established for the A. tsaoko seed by ICP-MS. An elemental profile of A. tsaoko seed including fifty-eight elements was constructed, and the correlational pattern of elements was developed. The macro essential elements exhibited a narrow range, while the nonessential elements all presented broad range of mean, except Na, S and Cr. According to mass fractions of As, Cd, Hg and Pb, the A. tsaoko seeds were edible safe. And the lanthanide could not be an available marker for A. tsaoko seed by only itself. Meanwhile, the multi-elements fingerprinting including thirty-two elements (Li, B, Na, Mg, Al, Si, P, S, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ge, As, Rb, Sr, Mo, Ag, Cd, Sn, Cs, Ba, La, Ce, W, Pb) and fifteen elements (B, Mg, Al, P, S, K, Ca, V, Mn, Fe, Co, Ni, Sr, Mo, Ba) had been constructed for geographical authentication of A. tsaoko seed. The combination with thirty-two elements could also play a crucial part in the quality control, because there were three toxic elements As, Cd, Pb contained inside.

Discrimination of the geographical origin of dry red pepper using inorganic elements: A multielement fingerprinting analysis

This study was based on inductively coupled plasma optical emission spectroscopy and mass spectrometry. The purpose of this study was to develop an accurate method for clearly discriminating the origin of dry red pepper. Sixty-four domestic and 51 imported samples were acid treated in a microwave system. The geographical origin of each sample was identified using the contents of 60 elements. The OPLS-DA results showed that the R2 and Q2 values were 0.873 and 0.765, respectively, indicating a high ratio and prediction ratio of this model. A total of 26 elements with a VIP (Variable Importance in Projection) value of greater than 1 were selected, and each element had a decisive influence on the domestic and imported groups. An ROC (Receiver Operating Characteristic) analysis confirmed the accuracy of the identification of domestic and imported products using the selected 26 elements. The accuracy based on the ROC was 98.7%. A heatmap was used to select elements that were influential in identifying domestic and imported dry red pepper samples. All of the most influential elements were rare earth elements (REEs). The REE content of dry red pepper was significantly higher in imported than in domestic samples. CDA (Canonical Discriminant Analysis) showed that the accuracy of classification by group was 100% for each group.

Application of Chemostratigraphic Methods to Floodplain Alluvial Deposits within the Big Harris Creek Basin, North Carolina

Big Harris Creek, North Carolina, possesses a geomorphic history and alluvial stratigraphic record similar to many drainages in southern Appalachian Piedmont. An approximately 1 km reach of Upper Stick Elliott Creek, a tributary to Big Harris Creek, was used herein to (1) explore the use of chemostratigraphic methods to define and correlate late Holocene alluvial deposits along this relatively uncontaminated rural stream containing legacy sediments (historic, anthropogenically derived deposits), and (2) interpret depositional floodplain processes within small (<10 km2), headwater drainages. The lithofacies within four floodplain sections were described in channel banks and sampled at about 5 cm intervals. The 128 collected samples were then analyzed for grain size and the concentration of 22 elements using X-ray fluorescence. Well-defined chemostratigraphic units (facies) were defined on the basis of a multi-elemental fingerprint using a principal component analysis (PCA) and verified using discriminant analysis (DA). Chemostratigraphic units did not reflect grain size at a site (by design) but marginally correlated to lithofacies defined by field descriptions. Of significant importance, chemostratigraphic units could be quantitatively correlated between the four stratigraphic sections at a much higher spatial resolution (~5 cm) than could be performed using other sedimentologic parameters alone. In combination, the lithostratigraphic and chemostratigraphic architecture of the floodplain is consistent with a previously proposed sequence of deposition for the legacy deposits in which extensive land-use change associated with the onset of cotton farming in the 1860s led to upstream incision and gully formation and downstream deposition on the floodplain surface. Deposition appears to have progressed downvalley as incision deepened, probably in the form of crevasse splay deposits or proximal sandsheets that were occasionally interbedded with vertically accreted sediments. The results indicate that chemostratigraphy represents a highly useful approach to the assessment of floodplain depositional processes over (at least) relatively small temporal and spatial scales, even in areas with minimal sediment contamination.

Rapid analysis of gunshot residues with single-particle inductively coupled plasma time-of-flight mass spectrometry

Gunshot residues (GSRs) from different types of ammunition have been characterized using a new method based on single-particle inductively coupled plasma time-of-flight mass spectrometry (sp-ICP-TOF-MS). This method can analyze thousands of particles per minute enabling rapid sample screening for GSR detection with minimal sample preparation. GSR particles are multi-elemental nanoparticles that are mainly defined by the elements lead, barium, and antimony. Sp-ICP-TOF-MS was also used to identify other elements contained in GSR particles while standard particle classification protocols do not consider the complexities of GSR compositions and can therefore miss out on valuable information. The proposed method can be used to support existing GSR detection methods, especially when lead-free, antimony-free, or tagged ammunition has been used; it also provides a possibility for multi-elemental fingerprinting of GSR particles.

Discrimination of Musa banana genomic and sub-genomic groups based on multi-elemental fingerprints and chemometrics

The potential of unripe banana flour multi-elemental fingerprints for classifying banana genomic and sub-genomic groups was assessed using chemometrics. The elemental concentration of N, P, K, Mg, Ca, Zn, Cu, Mn, Fe, and B in unripe banana flour from 33 banana varieties belonging to four genome groups and 11 sub-genome groups were determined using Flame-atomic Absorption spectrometry and colorimetry. Principal component analysis (PCA) combined with linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network (ANN) was applied for classification with an 80:20 split between the calibration and verification sets (157 and 39 samples, respectively). The elements K, N, and Mg presented the highest mean concentrations of 1273 mg/100 g, 424 mg/100 g, and 132 mg/100 g, respectively. The classification model verification set samples were successfully classified based on their genome groups (100 % accuracy) and sub-genome groups (78.95–100% accuracy) for PCA-LDA, PCA-ANN, and PCA-SVM models. The results demonstrate that multi-elemental fingerprinting combined with chemometrics can be employed as an effective and feasible method for classification of Musa genomic and sub-genomic groups.

Discrimination and Recognition of Bentong Ginger Based on Multi-elemental Fingerprints and Chemometrics

Discrimination and Recognition of Bentong Ginger Based on Multi-elemental Fingerprints and Chemometrics

Provenance discrimination of Sorrento lemon with Protected Geographical indication (PGI) by multi-elemental fingerprinting

Multielement analysis and chemometric methods were proposed to discriminate the Sorrento lemon (PGI) juices according to geographical origin. In 2018 and 2019, 169 fruits from three farms in PGI area and two in not-PGI area were collected and analysed for essential and not-essential elements by ICP-MS. The PCA of multielement fingerprinting grouped lemon juices from PGI farms revealing a strong differentiation at small geographical scale. The S-LDA discriminated lemon juices for Mo, Ba, Rb, Mg, Co, Ca, Fe, Sr on the two production years, giving 97.7% correct classification, 98.5% accuracy and 93.8% external validation. The good correlation lemon juice vs cultivation soil and the soil discrimination by not-essential elements suggested the use of these elements as reliable indicators of lemon juice provenances. Despite lowering the number of variables, constituted by not-essential elements Ba, Rb, Ti, Co, the use of S-QDA discriminated the lemons juices with 87.5% accuracy and 83.9% validation.

Inorganic Elements in Mytilus galloprovincialis Shells: Geographic Traceability by Multivariate Analysis of ICP-MS Data.

The international seafood trade is based on food safety, quality, sustainability, and traceability. Mussels are bio-accumulative sessile organisms that need regular control to guarantee their safe consumption. However, no well-established and validated methods exist to trace mussel origin, even if several attempts have been made over the years. Recently, an inorganic multi-elemental fingerprint coupled to multivariate statistics has increasingly been applied in food quality control. The mussel shell can be an excellent reservoir of foreign inorganic chemical species, allowing recording long-term environmental changes. The present work investigates the multi-elemental composition of mussel shells, including Al, Cu, Cr, Zn, Mn, Cd, Co, U, Ba, Ni, Pb, Mg, Sr, and Ca, determined by inductively-coupled plasma mass-spectrometry in Mytilus galloprovincialis collected along the Central Adriatic Coast (Marche Region, Italy) at 25 different sampling sites (18 farms and 7 natural banks) located in seven areas. The experimental data, coupled with chemometric approaches (principal components analysis and linear discriminant analysis), were used to create a statistical model able to discriminate samples as a function of their production site. The LDA model is suitable for achieving a correct assignment of >90% of individuals sampled to their respective harvesting locations and for being applied to counteract fraud.

Discrimination of pistachio cultivars based on multi-elemental fingerprinting by pattern recognition methods

The potential of multi-elemental fingerprinting based on inductively coupled plasma optical emission measurements (ICP OES) was examined to classify pistachio cultivars. Five pistachio cultivar samples were collected during the harvesting period 2017–2018 from Kerman in Iran. The ability of multivariate data analysis approaches, such as principal component analysis (PCA) and principal component analysis-linear discriminant analysis (PCA-LDA) have been investigated in order to achieve discrimination of different cultivars. Eighteen variables i.e. the contents of Al, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Pb, Sr and Zn at mg g−1 level, determined by ICP OES were used. The results showed that the five pistachio cultivars can be identified based on the multi-elemental fingerprints. The loading plot of PCA illustrated that Pb, Cr, Co, Cd, and Ba have the lowest contributions in discrimination of the different pistachio cultivars. Almost all samples were correctly classified by the PCA-LDA model using cross validation (99.0%). The mean sensitivity and specificity were 98.0% and 99.6%, respectively, indicating the satisfactory performance of the model. The results demonstrate multi-elemental fingerprinting combined with multivariate data analysis methods can be employed as an effective and feasible method for classification of Iranian pistachio based on their cultivars.

Multi-element fingerprinting of soils can reveal conversion of wetlands to croplands

Little is known about the influence of conversion of wetlands to farmlands on concentrations and distribution of elements other than those most commonly studied, partly because of the lack of stratification in wetland soils. In this study, in the Sanjiang Plain in northeastern China, we determined the concentrations of 63 elements along soil profiles at three depths: 0–20 cm, 20–40 cm, and below the depth to which farmers would plow, at 40–60 cm, under four land uses: natural wetland, drained wetland, wetland converted to soybean field and subsequently to rice paddy field. Based on our previous work, we expected that changes in organic matter content would be an important factor affecting element concentrations, but that changes in land uses also led to decoupling of the influence of organic matter on elements. This would lead to other factors, such as changes in redox conditions, changes in hydrology and mixing of soils due to plowing, becoming more important factors affecting element distributions. Our study confirmed these expectations. Changes in organic matter content directly or indirectly affected many elements, explaining 67% of variation. Arsenic, cobalt, iron and nickel concentrations were dramatically higher and sulfur concentrations lower when wetland was converted to paddy field. Co and Ni were identified as potential chemical indicators of wetland conversion. Our research is the first to use multi-element fingerprinting to study effects of conversion of wetlands to croplands in China and showed that this relatively simple approach highlights the complexity of the many interacting factors in reclamation of wetland soils for agricultural uses.

Ionome signatures discriminates the geographical origin of jackfruits (Artocarpus heterophyllus Lam.)

Jackfruits are nutritionally rich fruit crop indigenous to the humid tropics, known by their place of origin. In the present study, using multielemental profiling of fruit samples, we demonstrated the discrimination of different jack fruit germplasm based on their geographical origin in India. The concentration of 24 elements in soil and fruit were determined by inductively coupled plasma mass spectrometry (ICP-MS). ANOVA revealed the significant difference of these 24 elements amongst the geographical locations both in soils and fruits. The correlation between soil and fruit ionome indicated the major influence of germplasm and other locational factors on the acquisition and accumulation of fruit multi elemental characteristics with minimal contribution of soil elements. Among the multivariate analysis tools, linear discriminant analysis (LDA) of fruit multi elemental fingerprint was found to be an efficient tool for discrimination of geographical origin of Indian jackfruits.

Origin traceability of peanut kernels based on multi-element fingerprinting combined with multivariate data analysis.

Multi-elements have been widely used to identify the geographical origins of various agricultural products. The objective of this study was to investigate the feasibility of identifying the geographical origins of peanut kernels at different regional scales by using the multi-element fingerprinting technique. The concentrations of 20 elements [boron (B), magnesium (Mg), phosphorus (P), potassium (K), calcium (Ca), etc.] were determined in 135 peanut samples from Jilin Province, Jiangsu Province, and Shandong Province of China. Data obtained were processed by one-way analysis of variance (ANOVA), principal components analysis (PCA), k nearest neighbors (k-NN), linear discriminant analysis (LDA), and support vector machine (SVM). Peanut kernels from different regions had their own element fingerprints. The k-NN, LDA, and SVM were all suitable to predict peanut kernels according to their grown provinces with the total correct classification rates of 91.2%, 91.1%, and 91.1%, respectively. While SVM was the best to identify different grown cities of peanut kernels with the prediction accuracy of 91.3%, compared to 72.2% and 78.3% for k-NN and LDA, respectively. It was an effective method to identify producing areas of peanut kernels at different regional scales using multi-element fingerprinting combined with SVM to enhance regional capabilities for quality assurance and control. © 2020 Society of Chemical Industry.

Spatio-temporal resolution of spawning and larval nursery habitats using otolith microchemistry is element dependent

Otolith chemistry is frequently employed in the reconstruction of fish environmental histories. While some elements have been strongly correlated with environmental factors (e.g. salinity, temperature, water chemistry), others may not indicate exogenous factors and simply add endogenous variability to a data set. Several commonly assessed elements were previously identified as being only present in the proteinaceous fraction of endolymph from black bream Acanthopagrus butcheri, suggesting that the choice of elements in otolith multi-elemental fingerprinting could influence their utility as natural environmental markers. To test this hypothesis, we performed several cluster analyses based on different sets of trace element data extracted from both the core and larval region of otoliths of juvenile black bream. We clustered in 3 different ways: (1) all elements analysed; (2) elements identified as being primarily in the salt fraction of the endolymph (i.e. inorganic); and (3) elements identified as being associated with the protein fraction of the endolymph. We subsequently assessed the power of the resulting clusters to resolve cohort identity and/or collection location based on differences in otolith chemistry using multinomial logistic regression. Our results indicate that clustering based solely on salt-fraction elements is best for resolving spatio-temporal variability in spawning sources and larval nursery habitats of black bream.