Multivariate analysis to discriminate the origin of sesame seeds by multi-element analysis inductively coupled plasma-mass spectrometry

A study of the lipid profile of Coix seeds from four areas based on untargeted lipidomics combined with multivariate algorithms to enable tracing of their origin

The geographical traceability of food products is seen as a distinctive feature of the future of food which is increasingly becoming a concern for consumers. In this research, differences in the lipid composition of Coix seed samples from four major Chinese origins were investigated using non-targeted lipidomics. By multivariate statistical analysis, unsupervised PCA and OPLS-DA based differentiation between the four origins of Coix seed samples could be achieved. The OPLS-DA VIP > 1 screened 72 lipids out of 1211 lipids as potential markers to distinguish Coix seeds from different origins. In addition, the potential markers (SPH(d16:0), Cer(d18:2/20:0 + O) and PC(8:0e/8:0) were combined with statistical analysis algorithms to construct a discriminant function for rapid differentiation of Coix seed samples from different origins and a specific function for different origins with 100% discrimination accuracy. In general, a rapid and accurate method combining multivariate chemometrics and algorithms was developed based on untargeted lipidomics to determine the geographical origin of Coix seed samples, which can also be applied to other agricultural products.

Towards a Standardized Approach for the Geographical Traceability of Plant Foods Using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Principal Component Analysis (PCA)

This paper presents a systematic literature review focused on the use of inductively coupled plasma mass spectrometry (ICP-MS) combined with PCA, a multivariate technique, for determining the geographical origin of plant foods. Recent studies selected and applied the ICP-MS analytical method and PCA in plant food geographical traceability. The collected results from many previous studies indicate that ICP-MS with PCA is a useful tool and is widely used for authenticating and certifying the geographic origin of plant food. The review encourages scientists and managers to discuss the possibility of introducing an international standard for plant food traceability using ICP-MS combined with PCA. The use of a standard method will reduce the time and cost of analysis and improve the efficiency of trade and circulation of goods. Furthermore, the main steps needed to establish the standard for this traceability method are reported, including the development of guidelines and quality control measures, which play a pivotal role in providing authentic product information through each stage of production, processing, and distribution for consumers and authority agencies. This might be the basis for establishing the standards for examination and controlling the quality of foods in the markets, ensuring safety for consumers.

Geographical origin discrimination of Ethiopian sesame seeds by elemental analysis and chemometric tools

Origin discrimination of sesame seeds is becoming one of the important factors for the sesame seed trade in Ethiopia as it influences the market price. This study was undertaken to construct accurate geographical origin discriminant models for Ethiopian sesame seeds using multi-element analysis and statistical tools. The concentration of 12 elements (Na, Mg, Cr, Mn, Fe, Cu, Co, Ni, Zn, Cd, As and Pb) were determined in 93 samples which were collected from three main sesame seed-producing regions in Ethiopia, Gondar, Humera and Wollega. According to a one-way analysis of variance (ANOVA), the concentration of 10 elements showing a significant difference (p < 0.05) was taken for statistical analysis using principal component analysis (PCA) and linear discriminant analysis (LDA). PCA showed some clustering of samples according to their respective origins. Then, the follow-up LDA resulted in a 100 % correct origin classification rate for all 93 sesame seed samples obtained from three regions in Ethiopia.

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products' and Foods' Origin

Fraudulent practices are the first and foremost concern of food industry, with significant consequences in economy and human's health. The increasing demand for food has led to food fraud by replacing, mixing, blending, and mislabeling products attempting to increase the profits of producers and companies. Consequently, there was the rise of a multidisciplinary field which encompasses a large number of analytical techniques aiming to trace and authenticate the origins of agricultural products, food and beverages. Among the analytical strategies have been developed for the authentication of geographical origin of foodstuff, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) increasingly dominates the field as a robust, accurate, and highly sensitive technique for determining the inorganic elements in food substances. Inorganic elements are well known for evaluating the nutritional composition of food products while it has been shown that they are considered as possible tracers for authenticating the geographical origin. This is based on the fact that the inorganic component of identical food type originating from different territories varies due to the diversity of matrix composition. The present systematic literature review focusing on gathering the research has been done up-to-date on authenticating the geographical origin of agricultural products and foods by utilizing the ICP-MS technique. The first part of the article is a tutorial about food safety/control and the fundaments of ICP-MS technique, while in the second part the total research review is discussed.

Authentication of the geographical origin of sesame seeds based on proximate composition, multi-element and volatile fingerprinting combined with chemometrics

The current study was designed to discriminate and authenticate sesame seeds cultivated in China, Togo, Sudan, Mozambique and Ethiopia. Fingerprinting analysis combined with chemometrics was applied to clarify the differences present in the chemical elements and volatiles of sesame seeds. Clear separations were observed among different groups of sesame seeds. Discrimination models were established with 100 % correction rate for the authentication of investigated sesame seeds. A panel of 14 chemical elements and 12 volatile compounds with p value < 0.05 and VIP score > 1 was suggested as important contributors for the discrimination of sesame seeds from China, Togo, Sudan, Mozambique and Ethiopia. In conclusion, our data demonstrate that chemical analysis together with chemometrics is of great applicability to authenticate the geographical origins of sesame seeds.

Distinguishing Korean and Chinese red pepper powder using inductively coupled plasma and X-ray fluorescence-based analysis

This study aimed to distinguish between Korean and Chinese red pepper powder (RPP) using inorganic elemental analysis data combined with orthogonal partial least squares-discriminant analysis (OPLS-DA). Elemental concentrations were obtained for 31 Korean and 31 Chinese RPP samples that were collected in Korea. Energy dispersive X-ray fluorescence spectroscopy detected 11 elements in these samples. Rb and Cl concentrations were selected as the variables which best allowed distinguishing between Korean and Chinese RPP using an S-plot from OPLS-DA. Rb and Cl concentrations in the Korean RPP samples were ≤ 1.6 mg/100 g (measured by inductively coupled plasma-optical emission spectroscopy) and ≤ 215 mg/100 g, respectively. A blind trial demonstrated that Korean RPP containing ≥ 50 g/100 g of Chinese RPP could be identified by applying predetermined ranges of Rb and Cl concentrations, suggesting that analysis of these two elements is a possible approach to distinguish between Korean and Chinese RPP.

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.

Mycobiome analysis for distinguishing the geographical origins of sesame seeds

Sesame (Sesamum indicum) is one of the most widely cultivated crops in Asia and Africa. The identification of the geographical origins of sesame seeds is important for the detection of fraudulent samples. This study was conducted to build a prediction model and suggest potential biomarkers for distinguishing the geographical origins of sesame seeds using mycobiome (fungal microbiome) analysis coupled with multivariate statistical analysis. Sesame seeds were collected from 25 cities in Korea, six cities in China, and five sites in other countries (Ethiopia, India, Nigeria, and Pakistan). According to the expression of fungal internal transcribed spacer (ITS) sequences in sesame seeds, 21 fungal genera were identified in sesame seeds from various countries. The optimal partial least squares-discriminant analysis model was established by applying two components with unit variance scaling. Based on seven-fold cross validation, the predictive model had 94.4% (Korea vs. China/other countries), 91.7% (China vs. Korea/other countries), and 88.9% (other countries vs. Korea/China) accuracy in determining the geographical origins of sesame seeds. Alternaria, Aspergillus, and Macrophomina were suggested as the potential fungal genera to differentiate the geographical origins of sesame seeds. This study demonstrated that mycobiome analysis could be used as a complementary method for distinguishing the geographical origins of raw sesame seeds.

Multivariate analysis for yield and yield-related traits of sesame (Sesamum indicum L.) genotypes

Sesame production under irrigation is limited in Ethiopia because of in availability of high yielding varieties, inadequate and inefficient irrigation schemes, and insignificant awareness of producers. This study, comprising 13 sesame genotypes, was conducted around Humera and Werer during 2018 and 2019 under irrigation. The design was randomized completely block design with three replications and the objectives were to develop high yielding genotypes and identify important agronomic traits. Multivariate statistical methods like Additive Main Effect and Multiplicative Interaction (AMMI) model, Principal Component Analysis, Cluster and factor analyses were used. The genotypes (6.22%), environments (42.62) and Genotype × Environment Interactions (25.09%) were statistically (p < 0.001) significant for the agronomic traits. The grain yield in each observation varied from 383 kg/ha to 2044 kg/ha and the grand mean yield was 820.19 kg/ha. The highest mean yield was recorded from G12 (948.6 kg/ha) followed by G4 (938.9 kg/ha) while the lowest was recorded from G8 (703.1 kg/ha). G1, G4, G12, G5, G8, G11 and G13 are identified as unstable genotypes while G2, G3, G6, and G9 are stable genotypes. The genotypes were grouped in to four clusters and cluster-II was characterized as the high yielding cluster and it was also associated with grain yield, pods per plant, branches per plant and thousand seed weight. Branches per plant, pods per plant and thousand seed weight may be most determinant and crucial in developing high yielding sesame varieties. This finding recommends that G4 and G6 are desirable genotypes and can be used for irrigation production.

Geographic origin determination of Brazilian Cannabis sativa L. (Marihuana) by multi-element concentration

The Marihuana Polygon production of Cannabis sativa L. supplies the northeastern region of Brazil and represents 30% of the nation's market. The international trend of indoor cultivation is also occurring in Brazil, and the Brazilian Federal Police (BFP) has been increasing its apprehension of cannabis seeds sent by mail. The present work aims to assess the utility of the multi-element composition of different cannabis plant parts and soil samples where the plants were cultivated to determine their geographic origin. Statistical tools were applied to classification of marijuana samples from distinct geographic regions within northeastern Brazil, including indoor cultivated samples. The multi-element quantification was determined using inductively-coupled plasma - optical emission spectrometry (ICP-OES), and the data were compared by the Kruskal-Wallis H test, and subsequently, multiple discriminant analysis (MDA). The results of the multi-element concentration of cannabis plant samples were also subjected to a principal component analysis (PCA) and an orthogonal partial least squares discriminant analysis (OPLS-DA). The cannabis plant samples from the Marihuana Polygon could be clearly separated from those cultivated indoors, and the distance between them was detectable. The MDA revealed that phosphorus, calcium, magnesium, selenium, and arsenic concentrations were used as variables for this separation. Our results demonstrate that multi-element composition analysis can be used to indicate the origin or cultivation location of cannabis plants. Routine laboratory analyses consisting of multi-element composition combined with statistical analyses provide a reliable tool by which C. sativa movement, cultivation, and interdiction efforts in Brazil may be assessed.

Metabolite Profiling and Chemometric Study for the Discrimination Analyses of Geographic Origin of Perilla (Perilla frutescens) and Sesame (Sesamum indicum) Seeds

Perilla and sesame are traditional sources of edible oils in Asian and African countries. In addition, perilla and sesame seeds are rich sources of health-promoting compounds, such as fatty acids, tocopherols, phytosterols and policosanols. Thus, developing a method to determine the geographic origin of these seeds is important for ensuring authenticity, safety and traceability and to prevent cheating. We aimed to develop a discriminatory predictive model for determining the geographic origin of perilla and sesame seeds using comprehensive metabolite profiling coupled with chemometrics. The orthogonal partial least squares-discriminant analysis models were well established with good validation values (Q² = 0.761 to 0.799). Perilla and sesame seed samples used in this study showed a clear separation between Korea and China as geographic origins in our predictive models. We found that glycolic acid could be a potential biomarker for perilla seeds and proline and glycine for sesame seeds. Our findings provide a comprehensive quality assessment of perilla and sesame seeds. We believe that our models can be used for regional authentication of perilla and sesame seeds cultivated in diverse geographic regions.

Geographical origin discrimination of pepper (Capsicum annuum L.) based on multi-elemental concentrations combined with chemometrics

The concentrations of twenty-four elements in twenty-five peppers from three cultivated regions in Guizhou Province (China) were analyzed. The chemometric data processing, including one-way analysis of variance, principal component analysis, linear discriminant analysis (LDA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were executed to differentiate the peppers. Consequently, the contents of 16 elements (Arsenic, Ba, Cu, Co, Cr, Ni, Pb, Sn, Sb, Mo, Sr, Y, Zn, Ca, P, and Fe) were significantly different among the three regions (p < 0.05). The correct discrimination rates of 25 peppers were 92.0% for LDA and 96.0% for OPLS-DA. The variable importance in the projection (VIP) values were ranged from 1.063 to 1.262 for seven elements (Tin, Fe, Zn, Y, Cr, Sr, and Mo) indicating that they played an important role for the geographical origin traceability of pepper. To sum up, multi-element concentrations together with chemometric data-processing can be promising for the geographical origin differentiation of pepper.

Discrimination of the species and authenticity of Rhizoma Coptidis based on stable isotope and multielement fingerprinting and multivariate statistical analysis

It is essential to be able to identify the source species and to determine the authenticity of traditional Chinese medicines (TCM) in order to prevent the use of false or inferior medicines. In this work, a stable and reliable method of discriminating among the three source species of Rhizoma Coptidis and checking the authenticity of Rhizoma Coptidis samples was established. The technique involved evaluating stable isotope ratios and the contents of multiple elements in samples along with the use of multivariate statistical techniques. The stable isotope ratios δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O and the concentrations of various inorganic elements (Li, B, Na, Mg, Al, P, Si, K, Ca, Ti, Mn, Fe, Cu, Zn, Sr, and Ba) in authentic Rhizoma Coptidis samples from three source species (n = 56) and in counterfeit Rhizoma Coptidis samples (n = 39) were determined. The results showed that there were significant differences between the samples from different source species according to multivariate statistical analysis. The three species were clearly distinguished using hierarchical cluster analysis (HCA). Employing stepwise linear discriminant analysis (SLDA), a classification model for differentiating the three species was developed, and this model achieved 100% classification accuracy when applied to samples. In addition, authentic samples and counterfeit samples were successfully discriminated using stable isotope and multielement fingerprint analysis and orthogonal projections to latent structures discriminant analysis (OPLS-DA), and OPLS-DA models for checking the authenticity of Rhizoma Coptidis were established and verified. Therefore, stable isotope and multielement analysis combined with multivariate statistical analysis was shown to be a promising method of discriminating among the three source species of Rhizoma Coptidis and of establishing the authenticity of Rhizoma Coptidis samples. Graphical abstract.

Multielemental Analysis Associated with Chemometric Techniques for Geographical Origin Discrimination of Tea Leaves (Camelia sinensis) in Guizhou Province, SW China

This study aimed to construct objective and accurate geographical discriminant models for tea leaves based on multielement concentrations in combination with chemometrics tools. Forty mineral elements in 87 tea samples from three growing regions in Guizhou Province (China), namely Meitan and Fenggang (MTFG), Anshun (AS) and Leishan (LS) were analyzed. Chemometrics evaluations were conducted using a one-way analysis of variance (ANOVA), principal component analysis (PCA), linear discriminant analysis (LDA), and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that the concentrations of the 28 elements were significantly different among the three regions (p < 0.05). The correct classification rates for the 87 tea samples were 98.9% for LDA and 100% for OPLS-DA. The variable importance in the projection (VIP) values ranged between 1.01–1.73 for 11 elements (Sb, Pb, K, As, S, Bi, U, P, Ca, Na, and Cr), which can be used as important indicators for geographical origin identification of tea samples. In conclusion, multielement analysis coupled with chemometrics can be useful for geographical origin identification of tea leaves.