Multi-element Content Research Articles

Evaluation of Proximate Composition, Multielement, and Bioactive Phenolics Contents of Different Coix Seed Varieties using Multivariate Analysis Techniques

Coix lacryma-jobi L. is a plant that serves as a source of food, medicine, cosmetics, and forage in Asian countries. Due to the distinct geographic environments, Coix seed germplasm resources are extremely diverse. In this study, we evaluated the proximate composition, multi-elemental content by ICP-OES, and phenolic bioactives by UPLC-QTOF/MS of five varieties of Coix seeds from different Asian countries, including China, Thailand, Indonesia, and Malaysia. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) were used to classify the different varieties of Coix seeds. The C. lacryma-jobi var. ma-yuen seeds from Origin 1 (China) and Origin 2 (Thailand) contained high levels of energy, total fat, and calcium, while C. lacryma-jobi var. agrotis (Indonesia) and C. lacryma-jobi var. lacryma-jobi (Malaysia) had high levels of crude fiber and carbohydrates. Twenty-three phenolic compounds were identified. Protein, carbohydrate, crude fiber, magnesium, zinc, meliadanoside, and 3,4-dihydroxyphenethyl-3-O-β-D-glucopyranoside were the dominant variables and contributed the most to data variability in PCA. The HCA results were consistent with the PCA, classifying the samples into three groups: those rich in nutrients, those rich in phenolics, and those with a mixture of nutrients and phenolics. This comprehensive analysis provides valuable insights into the nutritional and bioactive composition of Coix seed varieties, with potential applications in nutrition, food science, and pharmaceuticals.

Authentication of premium Asian rice varieties: Stable isotope ratios and multi-elemental content for the identification of geographic fingerprints

Over 90 percent of the world's rice is produced and consumed in the Asia-Pacific region. Varieties such as Thai Jasmine rice and Paw San (or “Myanmar pearl rice”) are globally recognised as premium, while more local high-grade varieties include the Indonesian Ciherang and Inpari. Being able to trace the origin of these products has become necessary, since they are marketed at relatively higher prices compared to other cultivars, and they often become the target of fraudulent activities. In this work, we aimed to identify variables that could distinguish the premium-producing regions within each country, by Isotope Ratio Mass Spectrometry (IRMS) and Inductively Coupled Plasma- Mass Spectrometry (ICP-MS). Low-Level Data Fusion (LLDF) followed the analysis of more than 300 authentic samples, and (O)PLS-DA models yielded very high accuracy values. The most important geo-differentiating variables (VIP>1.4) were identified as: δ13C, δ18O, δ2H, δ34S, Co, Rb, Cu, Ba and Zn.

Effect of Soil pH on the Uptake of Essential Elements by Tea Plant and Subsequent Impact on Growth and Leaf Quality

Tea plant is an acidophilic plant, and soil pH has an important effect on the absorption and enrichment of elements, tea plant growth and quality. In this study, rhizosphere soils and leaves of tea plants from 30 tea plantations were collected to determine soil pH and multi-element content of soil and leaves of tea plants, to obtain and validate key elements that are enriched by pH affecting tea plants, and to analyze the effects of pH on the growth and quality of tea plants. The results showed that soil pH significantly affected the enrichment of 15 elements by tea plants, and the enrichment coefficients of 11 elements (C, Mg, Si, N, P, Mn, Sr, Cd, S, Ca and Sb) tended to increase significantly with the increase of soil pH, while the opposite was true for the other four elements (Cu, Rb, Ba and Al). TOPSIS analysis showed that soil pH had the greatest effect on tea plant enrichment of seven elements, namely N (100%), Mn (43.32%), C (39.22%), P (27.66%), Sr (15.30%), Mg (13.41%) and Ba (10.47%). Pot experiments with tea seedlings also verified that soil pH significantly affected the enrichment of tea leaves for seven key elements. Moreover, with the increase of soil pH, the growth indexes, photosynthesis indexes and quality indexes of tea seedlings showed a significant upward trend. Interaction analysis showed that the enhanced enrichment of N, Mn, C, P, Sr and Mg by tea plants was beneficial to increase the photosynthetic capacity of tea plants, promote the growth of tea plants and improve the quality of tea leaves. This study provides an important theoretical basis for the cultivation and management of tea plants.

Changes of physiological characteristics, element accumulation and hormone metabolism of tea leaves in response to soil pH.

Soil acidification is very likely to affect the growth of tea trees and reduce tea yield. In this study, we analyzed the effects of soils with different pH on the physiological characteristics of tea leaves and determined the multi-element content and hormone metabolomes of tea leaves by ICP-MS and LC-MS/MS, based on which we further analyzed their interaction. The results showed that increasing soil pH (3.29~5.32) was beneficial to increase the available nutrient content of the rhizosphere soil of tea tree, improve the antioxidant enzyme activity and photosynthesis capacity of tea tree leaves, and promote the growth of tea tree. Orthogonal partial least squares discriminant analysis (OPLS-DA) and bubble characteristics analysis were used to screen key elements and hormones for the effect of pH on tea leaves, which were further analyzed by redundancy analysis (RDA) and interaction network. The results showed that an increase in soil pH (3.29~5.32) favored the accumulation of seven key elements (C, K, Ca, Mg, Mn, P, S) in tea tree leaves, which in turn promoted the synthesis of six key hormones (salicylic acid, salicylic acid 2-O-β-glucoside, tryptamine, 2-oxindole-3-acetic acid, indole-3-acetic acid, trans-zeatin-O-glucoside). It can be seen that the increase in soil pH (3.29~5.32) enhanced the resistance of the tea tree itself, improved the photosynthesis ability of the tea tree, and effectively promoted the growth of the tea tree.

Preliminary Study of Pepper Types Based on Multielement Content Combined with Chemometrics.

Different types of pepper (Piper nigrum L.) and cayenne pepper (Capsicum annuum L.) are widely used spices that exhibit therapeutic properties in addition to nutritional properties. In order to characterize these foods in further detail, the content of macro- (Ca, K, Mg, Na) and microelements (Ag, Al, As, Ba, Be, Bi, Cd, Co, Cr, Cu, Fe, Ga, Li, Mn, Mo, Ni, Pb, Rb, Se, Sr, Te, Tl, V and Zn) of four pepper types was determined via inductively coupled plasma mass spectrometry (ICP-MS) after microwave-assisted digestion using nitric acid and hydrogen peroxide. The obtained results were then evaluated using chemometric methods. The content of macroelements and microelements lies in the expected ranges for such spices but differs significantly between different types. The content of macro- and microelements is characteristic for pepper types originating from different plant species, but also based on further processing. Whilst green and black pepper are similar to each other, clearly diverse patterns are obtained for white pepper (different processing method) and cayenne pepper (different plant species).

The Application of Portable X-ray Fluorescence (pXRF) for Elemental Analysis of Sediment Samples in the Laboratory and Its Influencing Factors

Several techniques, such as chemical methods and inductively coupled plasma mass spectrometry (ICP-MS), are available to accurately determine element content. However, they are time-consuming, labor-intensive, or expensive. Portable X-ray fluorescence spectrometry (pXRF) can be applied in various scenarios, with significantly higher efficiency and cost-effectiveness than laboratory methods. However, it also has limitations such as lower detection capability, relatively high detection limits, and lower accuracy than laboratory methods. In this study, we focused on applying pXRF to determine the elemental content of sediment samples and investigate its use in mineral exploration. A variety of factors influencing the results of pXRF analysis were analyzed. Our results showed that pXRF could detect more than 30 elements in stream sediments. The reliability of pXRF’s measurements was affected by factors such as the kind of element, sediment particle size, sample grinding treatment, count time, averaged element content, standard deviation of content, and range of content variation. The combination of pXRF analysis and laboratory analysis of partial samples is adequate for establishing a multi-element content inference equation. With this equation, it is possible to effectively infer the content gradient of elements, which will provide valuable support for mineral resource exploration.

Study on origin traceability of amur cork-tree bark based on stable isotopes and multielements combined chemometrics

Amur cork-tree bark is a natural medicine in China with a large market, whereas confusion about its origin limits its cultivation and commercial value. To trace the origin, the stable isotope ratios (δ13C, δ15N, δ2H, and δ18O) and multiple elements (As, Hg, Al, Ca, Fe, K, Mg, Na, Cr, Cd, Pb, Be, Cu, Zn, Sr, and Ba) of 83 batches of Amur cork-tree bark samples from 7 habitats were determined by stable isotope mass spectrometry and inductively coupled plasmamass spectrometry. The results showed that the two-dimensional combination of δ13C and δ15N in the scatter diagram of stable isotopes could obviously distinguish Amur cork-tree bark from Sichuan Province from other producing areas, and multielement radar map analysis could also be used to distinguish Amur cork-tree bark from different geographical sources. In this article, the data of stable isotope ratios and multielement content were combined for principal component analysis (PCA), linear discriminant analysis (LDA) and orthogonal partial least square discriminant analysis (OPLS-DA). The results of principal component analysis showed that all samples could be preliminarily distinguished. The prediction model established and verified by linear discriminant analysis realized 100% accurate classification for known samples. The correct rate of analysis and prediction for unknown samples was 94.5%. The effective feature discriminant markers of Amur cork-tree bark caused by VIP > 1 (Ca, K, Pb, Mg, Cr, As) were found by orthogonal partial least square discriminant analysis. It could accurately identify Amur cork-tree bark from different producing areas.

In situ simultaneous quantitative analysis multi-elements of archaeological ceramics via laser-induced breakdown spectroscopy combined with machine learning strategy

With the characteristics of precious, rare and non-renewable, archaeological ceramics cannot be used for destructive analysis and testing. Valuable physical and chemical information can be obtained through scientific micro damage analysis of precious pottery, which plays a positive role in the protection of ceramics and archaeological research. In this study, LIBS spectroscopy combined with sequential backward selection-random forest regression (SBS-RFR) algorithm was proposed for simultaneous quantitative analysis of multi-elements in ceramic samples. Firstly, the experimental conditions for LIBS spectrum acquisition of ceramic samples were optimized, including delay time, laser pulse energy and cumulative times, and the characteristic emission lines of typical elements in LIBS spectra of ceramic samples were identified based on national institute of standards and technology (NIST) database. Four different spectral preprocessing methods are used to preprocess LIBS spectral data to find a better preprocessing method. And then, SBS algorithm is applied to eliminate irrelevant or redundant feature variables of LIBS spectrum. SBS-RFR calibration model is constructed based on the screened input variables to realize the rapid determination of multi-element content of ceramic samples. To further verify the prediction performance of the SBS-RFR calibration model, its prediction performance is compared with several other models. The results show that SBS-RFR model has better prediction performance than partial least squares regression (PLSR), support vector machine (SVM), and random forest regression (RFR), of which the R2 values of prediction set are all over or close 0.98, and the RMSE values for Mg, Al, Ca and Fe are 0.3954%, 0.4627%, 0.2532% and 0.3791% respectively. This study shows that LIBS technology combined with SBS-RFR algorithm is a feasible method for the quantitative analysis of elements in ceramic samples.

Characterization of the Maximal Nutritional Value of Lactuca sativa (Lettuce) Based upon Elemental Analysis

Multielemental content of the Lactuca sativa (lettuce) was monitored during 7 weeks of growth in order to identify the optimal harvest period from the maximum nutritional point of view. Spring and tap water, having a slight difference in terms of element content, were used for plant growth. The most important accumulation of macrominerals, in leaves, is reached during the first stage of growth, which leads to a maximum nutritional contribution, corresponding to immature plants. From the third week, the maximum elemental accumulation was concentrated in roots. This study highlights the variation of the elemental content of Lactuca sativa among growing stages.

Effect of high-voltage electrical discharge treatment on multi-element content in cocoa shell and chocolates with cocoa shell

Cocoa shell is rich in bioactive components, but also contains significant amounts of micro and macro elements, but also toxic elements. In this study, we examined the contents of different elements in cocoa shell, cocoa mass and the effect of high voltage electrical discharge (HVED) on the contents of these elements in the cocoa shell. Also, dark and milk chocolates were produced with different contents of untreated and HVED treated cocoa shell for which was also conducted ICP-MS analysis of these elements. HVED treatment caused a decrease in potassium, cadmium, uranium, cobalt, nickel, iron, molybdenum, chromium, manganese and copper content. The analysis also showed that the contents of some elements were higher after HVED treatment, and the largest increase was observed for calcium. All chocolates produced with treated and untreated cocoa shells had the contents of toxic elements in a range already reported in the literature. After the addition of cocoa shell in chocolate, contents of iron and cobalt were higher than reported in the literature. This presents that, generally, chocolates with added cocoa shell could be safe for human consumption but there are also some opened questions about HVED's influence on these metals that need to be answered.

Traceability of the geographical origin of Siraitia grosvenorii based on multielement contents coupled with chemometric techniques

Siraitia grosvenorii (LHG) is widely used as a medicinal and edible material around the world. The objective of this study was to develop an effective method for the authentication of the geographical origin of LHG in its main producing area Guangxi, China, which is identified as Chinese Protected Designation of Origin product, against other producing regions in China. The content of 14 elements (K, Na, Ca, P, Mg, Al, B, Ba, Cu, Fe, Mn, Ni, Zn, and Sr) of 114 LHG samples was determined by inductively coupled plasma optical emission spectrometry. Multivariate analysis was then performed to classify the geographical origin of LHG samples. The contents of multielement display an obvious trend of clustering according to the geographical origin of LHG samples based on radar plot and principal component analysis. Finally, three supervised statistical techniques, including linear discriminant analysis (LDA), k-nearest neighbours (k-NN), and support vector machine (SVM), were applied to develop classification models. Finally, 40 unknown LHG samples were used to evaluate the predictive ability of model and discrimination rate of 100%, 97.5% and 100% were obtained for LDA, k-NN, and SVM, respectively. This study indicated that it is feasible to attribute unknown LHG samples to its geographical origin based on its multielement content coupled with chemometric techniques.

Multielement Principal Component Analysis and Origin Traceability of Rice Based on ICP-MS/MS

In this experiment, inductively coupled plasma tandem mass spectrometry (ICP-MS/MS) was used to determine the content of 30 elements in rice from six places of production and to explore the relationship between the multielement content in rice and the producing area. The contents of Ca, P, S, Zn, Cu, Fe, Mn, K, Mg, Na, Ge, Sb, Ba, Ti, V, Se, As, Sr, Mo, Ni, Co, Cr, Al, Li, Cs, Pb, Cd, B, In, and Sn in rice were determined by ICP-MS/MS in the SQ and MS/MS mode. By passing H2, O2, He, and NH3/He reaction gas into the ICP-MS/MS, respectively, the interference was eliminated by means of in situ mass spectrometry and mass transfer. The detection limit of each element was 0.0000662–0.144 mg/kg, and the limit of quantification was in the range of 0.000221–0.479 mg/kg, the linear correlation coefficient was greater or equal to 0.9987 (R2 ≥ 0.9987), and the detection results had low detection limit and great linear regression. Recovery of the method was in the range of 80.6% to 110.5% with spike levels of 0.10–100.00 mg/kg, and relative standard deviations were lower than 10%. For the multielement content of rice from different producing areas, the principal component factor analysis can get six principal component factors, 87.878% cumulative contribution rate, and the distribution of the principal component scores of each element and different producing areas. Based on the multielement content and cluster analysis, the samples were accurately divided into two major categories and six subcategories according to the places of production, which proved that there was a significant correlation between the multielement content in rice and the place of production, so that the place of rice origin can be traced.

Geographical Origin Identification of Tequila Based on Multielement and Stable Isotopes

To explore the regional characteristics of multielement and stable isotopes in tequila and the viability of tracing the geographical origin, both inductively coupled plasma mass spectrometry (ICP-MS) and stable isotope ratio mass spectrometry (IRMS) were applied in this study to determine the multielement content and stable isotope ratios of tequila. Then, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) as chemometrics were conducted in combination to establish a model for determining the geographical origin of Mexican tequila. According to the analytical results, the content of 22 elements varied, as did the ranges of stable isotope δ13C and δ18O values for the tequila obtained from the 4 producing areas. Besides, there were regional characteristics shown to some extent. PCA and PLS-DA methods can be adopted to identify the geographical origin of tequila in Mexico. According to the discriminant verification of 16 samples, the accuracy of prediction reached 93.75%. It has been demonstrated that it is possible to distinguish tequila of different geographical origins in Mexico by combining the determination of the multielement content and stable isotope ratios with chemometric analysis, which lays a foundation for tracing the geographical origin of tequila.

The use of vegetal tissue multi-element content as an indicator of soil or substrate type employed to cultivate Cannabis sativa L. (marijuana)

Brazil plays a significant role in the cultivation and production of marijuana. The Brazilian Federal Police (BFP) statistics demonstrate an increase in the apprehension of ready-to-use marijuana and cannabis seeds, indicating that indoor cultivation is becoming more common. The present research aimed to test the use of multi-element content in vegetal tissue samples as an indicator of soil or substrate cultivation and, more specifically, to determine whether it is possible to differentiate cannabis plants grown in different soil types. To do so, seeds seized by the Brazilian postal authorities were germinated and cultivated indoor under controlled conditions by the BFP. Then, cannabis plants were grown in two soil (Ultisol and Chernosol) and one substrate comprising organic soil, earthworm humus, and sand. In total, forty-nine (49) cannabis plant samples were analyzed. After digestion, 19 elements were analyzed by inductively-coupled plasma optical emission spectrometry (ICP-OES), and the data were compared by the Kruskal-Wallis H test followed by a multiple discriminant analysis (MDA) to classify samples cultivated with different types of soil and substrate. The model correctly classified 100% of samples in their respective group with an uncertainty of ≤ 2.2%. The MDA demonstrated that manganese, nickel, calcium, magnesium, barium, copper, and zinc were the best set of elements to discriminate Cannabis plants grown in different soil types or substrates. Therefore, the multi-element content of the plant material combined with a robust statistical tool can be applied as an indicator of the type of soil or substrate used to cultivate marijuana.

Physiological Response and Multi-Elemental Content in Lichens Growing on Agricultural Fences: A Pilot Study Comparing No-Tillage and Organic Cropping.

The objective of this study is to compare the physiological response (content and degradation of photosynthetic pigments, membrane oxidation products and soluble proteins) and multi-element content of Ramalina celastri (lichenized fungi) growing on agricultural fences with no-tillage (associated with transgenic crops and agrochemical application), organic cropping and a non-cultivated area. We found that R. celastri did not differ in its physiological response to agricultural practices, except for the contents of chlorophyll b and phaeophytin a which were high in both cultivated areas. Lichens growing in organic cropping fields have higher arsenic, chromium, uranium and internal transition elements common in the earth's crust, possibly due to the greater resuspension of the material during soil tillage. Lichens that grow on posts close to no-tillage field had higher bromine contents (present in numerous pesticides). We found evidence that R. celastri behaves as a tolerant species to air pollution in agricultural environments.

Evolution of the Multielemental Content along the Red Wine Production Process from Tempranillo and Grenache Grape Varieties

In the present work, 38 elements were quantified in the different fractions generated by applying amateur winemaking methods. Inductively Coupled Plasma Mass Spectrometry was used as detection technique. Grapes were analyzed and separate metal profiles were also obtained for the skin and seeds. Additional vinification fractions included musts before and after the fermentation process. Meanwhile, solid fractions corresponded to the so-called hat, pressed pomace and the lees obtained after gravitational settling at the tank bottom. Wine was further analyzed. The obtained results revealed a different repartition depending on the particular element and winemaking solid and liquid fraction evaluated. The studies included vinification in presence and in absence of added yeast and grape geographical origin. Principal component analysis helped to discriminate among fractions and to determine the critical elements behaving differently. Finally, a mass balance allowed to unequivocally detect the migration of a given element to the winemaking fractions.

Redistribution of corrosion products on steel bar/concrete interfaces in repaired concrete under influenced of aggressive ions

The important cases contribute to deterioration of reinforced concrete structure were due to corrosion attack to the reinforcing steel bar. Instead of penetration of aggressive ions for example chlorides, sulphates, acids and others; the selection of materials used for repair should be concern as well. Due to this factor, the understanding on the electrochemical behavior of reinforcing steel bar using different materials for repairing for deteriorated concrete structures will be considered. To understand this phenomenon, the ready-cast specimens mixed with chloride as prepared earlier will be demolished at 301 days of concrete age. Surprisingly, potential measurement of reinforcing steel bars was reoriented to a lower corrosion risk at 301 days of concrete age. The chemical and microstructural factors related to corrosion products were quantitatively identified and correlated it using electrochemical measurements results. For corroding reinforcing steel bar by fully and partially influenced of chloride ions, the corrosion products were mainly contained wuestite, magnetite and lepidocrocite confirmed by XRD patterns. Depending on morphology and multi-elemental content, it shows that chloride ion is approaching the reinforcing steel bar that lead to greater corrosion rate in Reference specimen compared to repaired ones. However, the existence of Ca(OH) in the vicinity between concrete/reinforcing steel bar helps to prevent further corrosion attacks

Metalloprotein and multielemental content profiling in serum samples from diabetic and hypothyroid persons based on PCA analysis

Diabetes and hypothyroidism are both metabolic diseases with great incidence worldwide. Metalloproteins and metals play key roles in normal glucose metabolism and thyroid hormone synthesis, which are altered in their respective pathologies. The aim of this work was to establish the corresponding multielemental and metalloprotean profiles in a control group (n=20) compared with a diabetic (n=20) or hypothyroidism group (n=20), by exploring a multivariate principal components model. Classification to discriminate these groups was possible based in the quantification of 23 elements (Mg, Al, K, Ca, V, Cr, Zn, Fe, Se, Rb, Pb, Cu, Mn, Co, Ni, U, Sr, Mo, Sb, Ba, Tl, Cd, Ag), and alternatively on the metalloprotein profiles obtained by SEC-ICPMS. Determinations were assessed by means of QQQ-ICP and SEC-ICPMS for total and metalloprotean content, respectively. Samples were classified using Principal Component Analysis chemometric tool. Results showed that there were statistical differences in transitional elements concentrations, such as Zn, Cu, Co, Mn, V, and Cr. For the metal associated protein study, the expression of the fractions of the same transitional elements also were statistically different when compared between control vs diabetic patients, and control vs hypothyroid patients. Se levels showed no differences in both studies among groups. This screening study demonstrates that mass spectrometry methods and data analysis with chemometrics tools may be valuable in order to find possible biomarkers in serum samples of diabetic and hypothyroid patients. Future proteomics analysis are necessary to complete these findings.

Geographical characterization of Spanish PDO paprika by multivariate analysis of multielemental content

A multielemental analytical method has been proposed to determine the contents of Al, B, Ca, Cu, Fe, K, Mg, Mn, Na, Ni, P, Pb, Sr and Zn in paprika samples from the two Protected Designations of Origin recognized in Spain, such as Murcia and La Vera (Extremadura). The samples are mineralized by acid wet digestion using a mixture of perchloric and nitric acids and analyzed by means of inductively coupled plasma atomic emission spectroscopy. The method performance has been checked studying the absence of matrix effect, trueness, precision, linearity, limit of detection and limit of quantification. The proposed method has been applied to analyze samples of sweet, hot and hot/sweet paprika from the considered production areas. Differences between paprika samples from Murcia and Extremadura were found and pattern recognition methods, such as linear discriminant analysis, linear support vector machines, soft independent modeling of class analogy and multilayer perceptrons artificial neural networks, has been used to obtain classification models. Sweet and hot/sweet paprika types were differentiated by means of linear models and hot paprika was differentiated by using artificial neural networks. A model based on artificial neural networks is proposed to differentiate the geographical origin of paprika, with independence of the type, leading to an overall classification performance of 99%.

Multielemental composition of suet oil based on quantification by ultrawave/ICP-MS coupled with chemometric analysis.

Suet oil (SO) has been used commonly for food and medicine preparation. The determination of its elemental composition has became an important challenge for human safety and health owing to its possible contents of heavy metals or other elements. In this study, ultrawave single reaction chamber microwave digestion (Ultrawave) and inductively coupled plasma-mass spectrometry (ICP-MS) analysis was performed to determine 14 elements (Pb, As, Hg, Cd, Fe, Cu, Mn, Ti, Ni, V, Sr, Na, Ka and Ca) in SO samples. Furthermore, the multielemental content of 18 SO samples, which represented three different sources in China: Qinghai, Anhui and Jiangsu, were evaluated and compared. The optimal ultrawave digestion conditions, namely, the optimal time (35 min), temperature (210 °C) and pressure (90 bar), were screened by Box-Behnken design (BBD). Eighteen samples were successfully classified into three groups by principal component analysis (PCA) according to the contents of 14 elements. The results showed that all SO samples were rich in elements, but with significant differences corresponding to different origins. The outliers and majority of SO could be discriminated by PCA according to the multielemental content profile. The results highlighted that the element distribution was associated with the origins of SO samples. The proposed ultrawave digestion system was quite efficient and convenient, which could be mainly attributed to its high pressure and special high-throughput for the sample digestion procedure. Our established method could be useful for the quality control and standardization of elements in SO samples and products.