Discrimination Of Geographical Origin Research Articles

Metal(loid)s Profile of Four Traditional Ethiopian Teff Brands: Geographic Origin Discrimination.

Among the most renowned Ethiopian food crops, teff (Eragrostis tef (Zucc.)Trotter) is the most nutritious and gluten-free cereal. Because of the increase in demand for teff, it is necessary to establish geographic origin authentication of traditional teff brands based on multi-element fingerprint. For this purpose, a total of 60 teff samples were analysed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Accuracy of the laboratory procedure was verified by the analysis of rice flour standard reference material (NIST SRM 1568b). In this context, four traditional teff brands (Ada'a, Ginchi, Gojam and Tulu Bolo) were analytically characterized using multi-element fingerprint and further treated statistically using linear discriminant analysis (LDA). Due to obvious extrinsic Fe, Al and V contamination, these elements were excluded from the discriminant model. Five elements (Cu, Mo, Se, Sr, and Zn) significantly contributed to discriminate the geographical origin of white teff. On the other hand, Mn, Mo, Se and Sr were used as discriminant variables for brown teff. LDA revealed 90 and 100% correct classifications for white and brown teff, respectively. Overall, multi-element fingerprint coupled with LDA can be considered a suitable tool for geographic origin discrimination of traditional teff brands.

Classification and geographic origin discrimination of Chinese prickly ash using X-ray fluorescence and machine learning

Chinese prickly ash (CPA) is a widely used seasoning with numb taste and a traditional Chinese medicine. Discrepancies among cultivating regions may pose a direct impact on the elemental contents, and ultimately on the flavor and quality of CPA. This study explored the elemental composition of CPAs by X-ray fluorescence spectrometry (XRF). Combined with chemometric methods such as principal component analysis, linear discriminant analysis, k-nearest neighbor, partial least squares discriminant analysis, support vector machine and random forest, the classification and discrimination of CPAs from different cultivation regions were realized with high accuracy. Taking advantage of XRF for direct non-destructive analysis, sample digestion essential in common analytical atomic spectrometry was omitted, which significantly simplified the sample pretreatment procedure and retained matrix components that are probably indicative of the geographic origins as well. The top ten important elements for discrimination were K, Sr, C, Ca, P, S, Fe, O, Mg and Si based on the importance score of random forest. The proposed strategy has been demonstrated as a simple, rapid yet efficient method for CPA classification, and may be applied to other food and medicinal samples.

Recent Research Advancements of Coffee Quality Detection: Targeted Analyses vs. Nontargeted Fingerprinting and Related Issues

Coffee is an important commercial product that arose various quality issues. Different techniques have been applied to detect coffee quality. This review focused on the recent updates in the detection methods of coffee from a targeted versus nontargeted perspective. This review introduced case studies of the current research progresses on targeted and nontargeted detection approaches. Their merits and demerits were evaluated as an analysis of coffee quality. The targeted approach, including liquid chromatography (LC), gas chromatography (GC), thin-layer chromatography (TLC), and capillary electrophoresis (CE), evaluates the quality of coffee by specific markers, whereas the nontargeted approach tests whether the sample is abnormal, without prior knowledge of what caused the abnormality, usually coupled with chemometrics. The nontargeted techniques commonly involve LC, GC, nuclear magnetic resonance (NMR), infrared spectroscopy (IR), ultraviolet-visible spectroscopy (UV-Vis), laser-induced breakdown spectroscopy (LIBS), and mass spectrometry (MS). This work may provide guidance for resolving most aspects of the quality problems in coffee, such as adulterant detection, species identification, and geographical origin discrimination.

Proton Nuclear Magnetic Resonance (1H NMR) Metabolic Profiles Discriminate Two Monovarietal Extra Virgin Olive Oils, Cultivars Arbequina and Koroneiki, with Different Geographical Origin

This study aims to evaluate the Proton Nuclear Magnetic Resonance (1H-NMR)-based metabolomic profiles of monovarietal olive oils obtained from Arbequina and Koroneiki, which are typically present in the Mediterranean basin and grown in Spain and Greece, respectively. Micro-milled oils were obtained from olives harvested both in their cultivar home countries (Spain and Greece) and in Italy. Investigations by 1H NMR metabolic profiling were carried out to develop a strategy for the correct interpretation of differences based on geographical origins. The NMR Spectroscopy and multivariate statistical analysis (MVA) revealed significant differences in fatty acids profile as well as the unsaponifiable fraction, not only according to the cultivars but also to the specific geographical origin of the olives used. In particular, the oils from Spain were higher in polyunsaturated fatty acids (PUFAs) content than those from Italy. Conversely, the Italian oils of Koroneiki showed higher content of monounsaturated fatty acids (MUFAs) than Greek oils. Regarding the Extra Virgin Olive Oils (EVOOs) minor fraction, for both cultivars, the olive oils obtained using olives harvested in Italy were characterized by higher relative content of phenolic compounds. It was found that each of the investigated cultivars (Arbequina and Koroneiki), very popular in super high density (SHD) planting systems, assumed a specific well-characterized metabolic EVOO profile when the olives are harvested in Italy. These results may contribute to extending and enforcing available literature data on 1H NMR-based chemometric models as powerful tools for EVOOs geographical origin discrimination.

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.

Determination of the geographic origin of 52 honey samples based on the assessment of anionic content profiling with a new algorithm using monolithic column‐based micellar nano‐liquid chromatography

In the present study, a new micellar nano LC-UV was, for the first time, reported for the separation and determination of five anions (chloride, nitrite, bromide, sulfate and nitrate) in 52 honey samples. Based on this approach, a graphene oxide-based monolithic column was prepared and applied for the samples. Various amounts of hexadecyltrimethyl-ammonium bromide (HTAB) in the mobile phase were used in order to optimize the separation conditions. The baseline separation was achieved using mobile phase with 25/75% (v/v) ACN/10mM phosphate buffer at pH 3.4, while the amount of HTAB was optimized as 0.22mM in the mobile phase. The whole method was validated and it leads to high sensitivity. The LOD values were found in the range of 0.02-0.22µg/kg, while LOQ values were found in the range of 0.06-0.18µg/kg. The method allowed to achieve sensitivity analyses of anionic content in 52 honey samples. All data were evaluated using a new algorithm for geographic origin discrimination. K-nearest neighbor algorithm (K-NN), cubic support vector classifier (K-DVS), and K-Mean cluster analysis were used for geographic origin discrimination of honeys. The accuracy of the whole model was calculated as 94.4% with the K-DVS method. The samples from five provinces were classified 100% correctly, while two of them were classified with one misclassification, with an accuracy of 89.9% and 83.3%, respectively. PRACTICAL APPLICATION: The new platforms and advanced technologies are crucial for advanced food analysis. In this article, a novel methodology was attempted for the determination of geographic origin of 52 honey samples. In this sense, micellar nano LC technique with a homemade monolithic nano-column was, for the first time, applied for the anion analysis using a new algorithm.

The complete chloroplast genomes of Tetrastigma hemsleyanum (Vitaceae) from different regions of China: molecular structure, comparative analysis and development of DNA barcodes for its geographical origin discrimination

BackgroundTetrastigma hemsleyanum is a valuable traditional Chinese medicinal plant widely distributed in the subtropical areas of China. It belongs to the Cayratieae tribe, family Vitaceae, and exhibited significant anti-tumor and anti-inflammatory activities. However, obvious differences were observed on the quality of T. hemsleyanum root from different regions, requiring the discrimination strategy for the geographical origins.ResultThis study characterized five complete chloroplast (cp) genomes of T. hemsleynum samples from different regions, and conducted a comparative analysis with other representing species from family Vitaceae to reveal the structural variations, informative markers and phylogenetic relationships. The sequenced cp genomes of T. hemsleyanum exhibited a conserved quadripartite structure with full length ranging from 160,124 bp of Jiangxi Province to 160,618 bp of Zhejiang Province. We identified 112 unique genes (80 protein-coding, 28 tRNA and 4 rRNA genes) in the cp genomes of T. hemsleyanum with highly similar gene order, content and structure. The IR contraction/expansion events occurred on the junctions of ycf1, rps19 and rpl2 genes with different degrees, causing the differences of genome sizes in T. hemsleyanum and Vitaceae plants. The number of SSR markers discovered in T. hemsleyanum was 56–57, exhibiting multiple differences among the five geographic groups. Phylogenetic analysis based on conserved cp genome proteins strongly grouped the five T. hemsleyanum species into one clade, showing a sister relationship with T. planicaule. Comparative analysis of the cp genomes from T. hemsleyanum and Vitaceae revealed five highly variable spacers, including 4 intergenic regions and one protein-coding gene (ycf1). Furthermore, five mutational hotspots were observed among T. hemsleyanum cp genomes from different regions, providing data for designing DNA barcodes trnL and trnN. The combination of molecular markers of trnL and trnN clustered the T. hemsleyanum samples from different regions into four groups, thus successfully separating specimens of Sichuan and Zhejiang from other areas.ConclusionOur study obtained the chloroplast genomes of T. hemsleyanum from different regions, and provided a potential molecular tracing tool for determining the geographical origins of T. hemsleyanum, as well as important insights into the molecular identification approach and and phylogeny in Tetrastigma genus and Vitaceae family.

Cooperative combination of LIBS-based elemental analysis and near-infrared molecular fingerprinting for enhanced discrimination of geographical origin of soybean paste

Laser-induced breakdown spectroscopy (LIBS) and near-infrared (NIR) spectroscopy were combined to enhance discrimination of soybean paste samples according to geographical origin. Since element and organic component compositions of soybean pastes depend on soybean cultivation areas and fermentation conditions, utilization of two complementary spectroscopic signatures would be synergetic for the discrimination. When the areas of C (AC) and Ca (ACa) peaks in the LIBS spectra were used as the inputs for linear discriminant analysis, the accuracy was 95.4%. The accuracy became 92.1%, when the principal component (PC) scores obtained by principal component analysis of the NIR spectra were employed. To enhance NIR discrimination, two-trace two-dimensional (2T2D) correlation analysis was adopted to recognize minute spectral differences. With using the 1st/2nd PC scores of 2T2D slice spectra, accuracy increased to 95.0%. When the ratios of ACa/AC and the 2nd PC scores of the samples were combined together, the accuracy improved to 99.6%.

Isotopic Characterization of Cedrela to Verify Species and Regional Provenance of Bolivian Timber

With increasing concerns about sustainable exploitation of tropical timber, there is a need for developing independent tools to check their origin. We evaluated the potential of tree-ring stable isotopes for identifying four Cedrela species (C. balansae, C. fissilis, C. odorata, and C. saltensis) and for identifying geographic origin of C. fissilis and C. odorata, two of the most intensively exploited species. We studied differences in δ13C and δ18O of wood among 11 forest sites (163 trees). We quantified isotope composition of 10-year bulk samples, and for a subset we also evaluated isotopic annual fluctuations for the last 10 years. Although annual isotopic variability was not correlated to precipitation or elevation, we found a significant relationship between the 10-year bulk stable-isotope composition and average precipitation and elevation. However these relationships were not consistent across all sites. We also explored isotopic site and species differentiation using Kernel Discriminant Analyses. Site discrimination was low: 30% accuracy for C. odorata, and 40% for C. fissilis sites. However, species discrimination was 57.5% for C. odorata and 95.3% for C. fissilis. These results suggest that although δ13C and δ18O isotopic analyses hold potential to verify species identification, discrimination of geographical origin within a country may still be challenging.

Elemental Profiling for Discrimination of Geographical Origin of Tea (Camellia sinensis) in north-east region of India by ICP-MS coupled with Chemometric techniques

The study aimed to discriminate the teas originated from eight north-east region of India using 67 elemental profiling through the use of ICP-MS and process the large data with chemometric software. Among the 67 elements, concentrations of 18 elements were significantly different in the eight regions (Darjeeling, Tripura, Upper Assam, South Bank, Terai, North Bank, Dooars and Cachar). Selected 18 elements belongs to alkali metal (Rb, Cs), alkaline earth metal (Mg, Sr, Ba), transition metal (V, Cr, Mn, Co, Ni, Zn, Mo), basic metal (Al), metalloid (B), non-metal (Se) and lanthanide (La, Ce, Nd). Multivariate chemometric analysis with PCA, LDA, SVM & OPLS DA were performed on the concentration of 18 elements on 307 authentic sample set. The correct classification rate for the training set (307) and test set (24) tea samples were 100% for SVM, LDA and OPLS DA model and confidence measure for SVM and LDA model were 99.4% and 88.97%, respectively. Variable importance in projection (VIP) values ranged between 1.04-1.51 for five elements (Cr, Mo, Rb, Mn and Ni), which can be considered as significant parameter for discrimination. The results demonstrated that ICP-MS with chemometric software could be used to discriminate the geographical origin of tea.

Headspace GC/MS and fast GC e-nose combined with chemometric analysis to identify the varieties and geographical origins of ginger (Zingiber officinale Roscoe)

Food authenticity regarding different varieties and geographical origins is increasingly becoming a concern for consumers. In this study, headspace gas chromatography-mass spectrometry (HS-GC–MS) and fast gas chromatography electronic nose (fast GC e-nose) were used to successfully distinguish the varieties and geographical origins of dried gingers from seven major production areas in China. By chemometric analysis, a distinct separation between the two varieties of ginger was achieved based on HS-GC–MS. Furthermore, flavor information extracted by fast GC e-nose realized the discrimination of geographical origins, and some potential flavor components were selected as important factors for origin certification. Moreover, several pattern recognition algorithms were compared in varietal and regional identification, and random forest (RF) led to the highest accuracies for discrimination. Overall, a rapid and precise method combining multivariate chemometrics and algorithms was developed to determine varieties and geographical origins of ginger, and it could also be applied to other agricultural products.

Methylnigakinone content determination and geographical origin discrimination for P. quassioides via fluorescence fingerprint and principal component analyses

Picrasma quassioides is used as a bittersweet stomach medicine. Because it is a natural product obtained from various geographical regions, the production area is important when P. quassioides is used as a crude drug. Herein, we developed a method to determine the content of methylnigakinone, one of the major active ingredients in P. quassioides, and a protocol for discriminating the geographical origin of this natural product using a fluorescence fingerprint analysis and principal component analysis (PCA). Because methylnigakinone is fluorescent (excitation wavelength: 352 nm, emission wavelength: 458 nm), the content of this molecule can be determined in the concentration range of 0.1–1 μg/mL. The quantification results of methylnigakinone obtained using the developed method were similar to those obtained from an HPLC analysis. Furthermore, the PCA of the fluorescence fingerprint of P. quassioides produced a score plot with the three different geographical origins (Kyushu island (Japan), Shikoku island (Japan), and China) plotted in the regions. Thus, it was possible to discriminate the geographical origin of the P. quassioides samples. The developed method is simple, quick, and has a minimal environmental impact. Therefore, the developed method will be useful for confirming the origin of P. quassioides.

Investigation of the data fusion of spectral and textural data from hyperspectral imaging for the near geographical origin discrimination of wolfberries using 2D-CNN algorithms

Deep convolutional neural networks have been applied to hyperspectral imaging (HSI) and have significantly improved modelling performance in many spectral analysis tasks due to their automatic extraction of relevant features. Using visible and near infrared hyperspectral (Vis-NIR) data, two-dimensional convolutional neural network (2D-CNN) discrimination models between the spectra of wolfberries and their corresponding classes of geographical origins were established and optimized using various variable selection and data fusion methods. The interval variable iterative space shrinking analysis (iVISSA), the uninformative variable elimination (UVE) algorithm, competitive adaptive reweighted sampling (CARS) and the iterative retained information variable (IRIV) algorithms were used to extract the feature wavelengths and compare the modelling effects; and then the 72 optimal wavelengths were extracted by the iVISSA algorithm. To extract the textural features of images, grey-level co-occurrence matrix (GLCM) analysis was conducted on the first principal component image. Models using variable selection methods based on low-level fusion data were superior to the corresponding methods based on single spectral data. The model based on iVISSA achieved the best result on mid-level fusion, the prediction set accuracy and mean F1 were 97.34% and 100%, respectively. Finally, optimized models of spectral-textural data were employed to identify the geographical origins of wolfberries. In general, the results showed that 2D-CNN model combined with fusion data of spectral and textural information can obtain excellent identification effect for the near geographical origins of wolfberries. This study may help develop an online detection system of near geographical origins of wolfberries.

Integration of multiplatform metabolomics and multivariate analysis for geographical origin discrimination of Panax ginseng

As the health food industry grows, the market for ginseng also expands globally. Korean ginseng (Panax ginseng C. A. Meyer) is cultivated in East Asian countries such as Korea, China, and Japan. The metabolic profile of plants can vary depending on the cultivation environment. As such, in this study, we aimed to compare the differences in the metabolic profiles of P. ginseng cultivated in Korea, China, and Japan, and to construct a library of these metabolite data. Using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS), we profiled 62 types of secondary metabolites, ginsenosides, using optimized analysis conditions to separate peaks with a high-resolution for about 30 min. In addition, we selected ginsenosides showing differences between their origins were selected among the possible origins in the S-line plot of orthogonal partial least squares discriminant analysis (OPLS-DA), which we quantitatively analyzed using UPLC-tandem mass spectrometry (UPLC-MS/MS). The contents of ginsenosides Ra1, Ra2, Ra3, and Rk1 were high in Korean P. ginseng; the contents of ginsenosides Rb1, Rb2, and Rc were high in Japanese P. ginseng; and the contents of the ginsenoside Ro was high in Chinese P. ginseng. We also analyzed the primary metabolite contents using nuclear magnetic resonance (NMR) spectroscopy and gas chromatography time-of-flight mass spectrometry (GC-TOF/MS). Japanese P. ginseng showed a high sucrose content and Korean P. ginseng showed high contents of most amino acids and organic acids. In the PLS-DA results of multivariate statistical analysis using the data obtained from each analysis instrument, we observed a clear clustering among the three origins. Although a genetically identical species, the metabolic profile substantially differs depending on the cultivation environment. Because ginsenoside, having many biological activities, showed origin-dependent origins, when P. ginseng is used for medicinal purposes, its content by origin should be considered. After disclosing the profiling results of these metabolites, we expect that they will be used in future ginseng research.

Chemometric origin classification of Chinese garlic using sulfur-containing compounds, assisted by stable isotopes and bioelements

Geographical origin discrimination of agro-products is essential to guarantee food safety and fair trade. Garlic samples cultivated in six provinces or major production regions in China were characterized for stable isotopes (δ13C, δ2H, δ18O, δ15N, and δ34S), bioelemental contents (% C, % N and % S), and sulfur-containing compounds (8 organosulfur components and 2 amino acids). Results showed that many of the 18 analyzed garlic variables had significant differences among production regions. Some sulfur-containing compounds found in garlic from different provinces had a strong correlation with sulfur isotopes, suggesting garlic sulfur isotopes were also affected by geographical origin. Two supervised pattern recognition models (PLS-DA and k-NN) were developed using stable isotopes, elemental contents, and sulfur-containing compounds, and had a discrimination accuracy of 93.4 % and 87.8 %, respectively. Chemometric classification models using multi-isotopes, elements and sulfur-containing compounds provides a useful method to authenticate Chinese garlic origins.

Real-time traceability of sorghum origin by soldering iron-based rapid evaporative ionization mass spectrometry and chemometrics.

Sorghum is an important grain with a high economic value for liquor production. Tracing the geographical origin of sorghum is vital to guarantee the liquor flavor. Soldering iron-based rapid evaporative ionization mass spectrometry (REIMS) combined with chemometrics was developed for the real-time discrimination of the sorghum's geographical origin. The working conditions of soldering iron-based ionization were optimized, and then the obtained MS profiling data were processed using chemometrics analysis methods, including principal component analysis-linear discriminant analysis and orthogonal projection to latent structures discriminant analysis (OPLS-DA). A recognition model was established, and discriminations of sorghum samples from 10 provinces in China were achieved with a correct rate higher than 90%. On the basis of OPLS-DA, the specific ions of m/z 279.2327, 281.2479, and 283.2639 had relatively strong discrimination power for the geographical origins of sorghum. The developed method was successfully applied in the discrimination of sorghum origins. The results indicated that the soldering iron-based REIMS technique combined with chemometrics is a useful tool for direct, fast, and real-time ionization of poor conductivity samples and acquisition of metabolic profiling data.

Discrimination of Geographical Origin of Agricultural Products From Small-Scale Districts by Widely Targeted Metabolomics With a Case Study on Pinggu Peach.

Geographical indications of agricultural products are characterized by high quality and regional attributes, while they are more likely to be counterfeited by similar products from nearby regions. Accurate discrimination of origin on small geographical scales is extremely important for geographical indications of agricultural products to avoid food fraud. In this study, a widely targeted metabolomics based on ultra-high-performance liquid chromatography–tandem mass spectrometry combined with multivariate statistical analysis was used to distinguish the geographical origin of Pinggu Peach of Beijing and its two surrounding areas in Heibei province (China). Orthogonal partial least squares-discriminant analysis (OPLS-DA) based on 159 identified metabolites showed significant separation from Pinggu and the other adjacent regions. The number of the most important discriminant variables (VIP value >1) was up to 62, which contributed to the differentiation model. The results demonstrated that the metabolic fingerprinting combined with OPLS-DA could be successfully implemented to differentiate the geographical origin of peach from small-scale origins, thus providing technical support to further ensure the authenticity of geographical indication products. The greenness of the developed method was assessed using the Analytical GREEnness Metric Approach and Software (ARGEE) tool. It was a relatively green analytical method with room for improvement.

Analysis of active compounds and geographical origin discrimination of Atractylodes macrocephala Koidz. by using high performance liquid chromatography-diode array detection fingerprints combined with chemometrics

In this work, a simple and effective strategy for the determination of 12 active compounds of Atractylodes macrocephala Koidz. (AM) was proposed by using high performance liquid chromatography-diode array detection (HPLC-DAD) combined with alternating trilinear decomposition (ATLD) algorithm. Utilizing the "second-order advantage", three common problems in HPLC could be resolved, namely baseline drifts, peak overlaps, and unknown interferences. 12 compounds were rapidly eluted within 12.5 min, and the average spiked recoveries were 80.8–109.9%. The figures of merit reflected the feasibility of the proposed method. Compared with the results of the traditional univariate calibration method based on HPLC-UV technique, the proposed strategy further verified the reliability and simplicity of the mathematical separation. On this basis, partial least squares-discriminant analysis (PLS-DA) was applied to discriminate 113 AM samples from different geographical origins, and variable importance in projection (VIP) was used to further screen the main differential components that affect the regional division of AM. A series of results show that the AM samples from the three regions have obviously different clustering trends. Overall, the strategy is expected to provide a scientific basis for the modern research of medicinal materials, and it is also conducive to the clinical use and market supervision of AM.

Geographic origin discrimination of red swamp crayfish Procambarus clarkii from different Chinese regions using mineral element analysis assisted by machine learning techniques

To explore the application prospects of mineral element analysis assisted by machine learning in the geographical origin traceability of red swamp crayfish (Procambarus clarkii) and establish an origin traceability model for it, we used inductively coupled plasma mass spectrometry (ICP-MS) to determine the content of 24 mineral elements, including Cu, Zn, Ga, As, Se, Rb, Sr, and Mo, in red swamp crayfish muscle tissues from Anhui, Hunan, and Hubei provinces of China. The ten characteristic elements, screened by the principal component analysis (PCA), were incorporated into the linear discriminant analysis (LDA) to establish an origin discriminant model of red swamp crayfish. We have verified the feasibility of the mineral element fingerprinting to trace the geographical origin of red swamp crayfish in three regions of China. To improve the discrimination performance, three machine learning classification algorithms, the support vector machine (SVM), the feed-forward neural networks (FNN), and the back-propagation neural network (BPNN), were combined to classify the red swamp crayfish of different origins based on mineral element content. The discriminative accuracy of the training models of both FNN and BPNN reached 100%, and their cross-validation accuracy were 91.88% and 93.13%, respectively, with the area under the receiver operating characteristic (ROC) curve (AUC) values close to 1. Thus, the overall performance of the FNN and BPNN was better than that of SVM. Taken together, our results showed that mineral elements combined with neural networks showed great advantages and potential in geographic origin traceability of red swamp crayfish.

Geographical origin discrimination of Agaricus bisporus produced by the complete medium: A pilot study in South Korea

The complete medium used for mushroom cultivation is important for reliable crop production. We aimed to identify how the origin of Agaricus bisporus grown in Korea was affected by complete media produced in different geographical regions using stable isotope ratios (SIRs). We found that the SIR features of A. bisporus significantly depended on the complete media origin used; in particular, it appeared the high δ34S in the Chinese complete medium, low δ34S in the Dutch complete medium, and high δ15N in the Korean complete medium (P < 0.05). The support vector machine method appeared better geo-origin classification of A. bisporus by the complete media compared to a linear discriminant analysis. A large-scale study should be conducted to establish a reliable origin identification model for A. bisporus grown in complete media to improve the global mushroom marketplace.