Discrimination Of Geographical Origin Research Articles

Rapid discrimination of coal geographical origin via near-infrared spectroscopy combined with machine learning algorithms

Coal geographical origin information provides powerful proof for evaluating coal quality and boosting the inspections of import and export coal. Since traditional identification methods depend on a series of instruments which induce considerable time and economic cost, it is crucial to employ an efficient and effective method to discriminate coal origin. As a rapid, non-destructive, and reagent-free analytical method, near-infrared spectroscopy (NIRS) enables rapid qualitative and quantitative characterization of a wide variety of materials, such as food and fuel. In this work, different multivariate data analysis approaches based on NIRS data are investigated to identify the geographical origin of coal. Considering that raw spectra are in high-dimensional spaces, principle component analysis (PCA), isometric mapping (Isomap) and linear discriminant analysis (LDA) are introduced to extract features. However, given the classes with similar centers, it is difficult to separate them via the standard LDA because it overvalues the contributions of the edge classes to the between-class scatter matrix. To address this concern, we propose an improved LDA (iLDA) in consideration of the contribution of each class, and enhance the impact of the classes with similar centers. In addition, we combine PCA and iLDA to solve the small sample size problem. The experimental results show that nonlinear approaches outperform linear approaches generally, and kernel partial least squares discriminant analysis combined with PCA-iLDA provides the best performance with the accuracy of 97.21%. The obtained results indicate that NIRS in tandem with different machine learning algorithms are promising for the rapid and accurate identification of coal geographical origin.

Non-destructive determination of volatile oil and moisture content and discrimination of geographical origins of Zanthoxylum bungeanum Maxim. by hyperspectral imaging

Zanthoxylum bungeanum Maxim. is now known as a condiment type providing both medical applications and food. The volatile oil content and moisture content are two main quality indicators of Zanthoxylum, and the former is considered to be influenced by the growth region, climate and other environmental factors. Here, the feasibility of hyperspectral imaging in the range of 380–1040 nm to determine the volatile oil and moisture content of Z. bungeanum thus evaluating quality and distinguishing geographical origin were investigated. Regression statistical methods of partial least square, support vector regression (SVR) and extreme learning machine and wavelengths selection algorithms of competitive adaptive reweighted sampling (CARS) and variable combination population analysis were applied. The CARS-SVR models proved to be satisfying for predicting the volatile oil and moisture content. The correlation coefficient of calibration and residual predictive deviation values were 0.9059, 2.87 for volatile oil content and 0.8304, 1.94 for moisture content, respectively. Furthermore, SVM was used to classify the geographical origin of Z. bungeanum samples with an accuracy of 97.29% in the prediction set. The results suggested that HSI exhibits suitable characteristics for determining the volatile oil and moisture content in Z. bungeanum.

Deep learning for geographical discrimination of Panax notoginseng with directly near-infrared spectra image

Herbal materials have been widely used as functional food by a certain group of people for a potentially positive effect on body health regulation. Panax notoginseng as a crude material of functional food has long medical and cultivation history for more than 400 years in China and other countries. However, the quality was fluctuated with their geographical origins and Wenshan Autonomous Prefecture was regarded as the geo-authentic location with high properties. Therefore, rapid detection method is necessary for consumer to discriminate their authentic origins. In our study, 258 near infrared spectra of root powder of P. notoginseng from five main cultivation areas were used for discrimination analysis. A deep learning strategy (residual convolutional neural network) was established with 80% spectra images. Therein, the discrimination of geographical origins of the herb was first to be reported using directly spectra images instead of data matric from these spectra. The results indicated that these samples could be correctly classified as their respective categories with 100% accuracy in training set and 91% accuracy in test set. Finally, 22 samples were accurately discriminated in 25 samples of prediction set. In general, residual convolutional neural network using direct spectra image would be a feasible strategy for geographical traceability in further discrimination research.

A chemometric modeling-free near infrared barcode strategy for smart authentication and geographical origin discrimination of Chinese ginseng.

With the growing interest in alternative medicine, handy identification and differentiation of herbal medicines are becoming increasingly important. Here we report a chemometric modeling-free near infrared (NIR) barcode strategy for the smart identification and geographical origin discrimination of Chinese ginseng. The novel strategy demands the transformation of Chinese ginseng (standard and sample) NIR spectra into a barcode representation through assigning zero intensity to every NIR peak except the peaks having intensities greater than average peak intensity. Meanwhile, for Chinese ginseng standard NIR barcode, barcoding condition such as padding size was carefully optimized. It has been demonstrated that the padding size for each bar in the barcode is 8 cm-1. By comparing the percentage of nonzero overlap between Chinese ginseng standard barcode and sample barcodes, eight batches of samples (including Chinese ginseng, American ginseng and counterfeit) were successfully identified with 100% accuracy, respectively. Interestingly, the discrimination of the origin of ginsengs from three provinces (Jilin, Liaoning and Heilongjiang) of Northeastern China was achieved utilizing NIR barcode method. Two characteristic bars at 7750 and 8250 cm-1 were inspected in the ginseng sample from Jilin province, two specific bars at 6780 and 7015 cm-1 were displayed in the ginseng sample from Liaoning province and three distinct bars at 6560, 6910 and 7995 cm-1 were monitored in the ginseng sample from Heilongjiang province. The results indicate that the proposed method will be greatly expanded and applied as an inspecting platform for the on-site analysis and valid identification of Chinese ginseng in herbal markets by a handheld spectrometer or barcode scanner.

Geographical origin traceability of Keemun black tea based on its non-volatile composition combined with chemometrics.

Non-volatile compounds play a key role in the quality and price of Keemun black tea (KBT). The non-volatile compounds in KBT samples from different producing areas normally vary greatly. The development of rapid methods for tracing the geographical origin of KBT is useful. In this study, we develop models for the discrimination of KBT's geographical origin based on non-volatile compounds. Seventy-two KBT samples were collected from five towns in Anhui province to determine 13 KBT compounds by high-performance liquid chromatography (HPLC). Analysis of variance showed that the content of 13 compounds in KBT indicated significant differences (P < 0.05) among five towns. Three multivariate statistical models including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were built to discriminate origin. Principal component analysis effectively extracted three principal components, namely theaflavins, galloylated catechins, and simple catechins. The high sensitivity (64.5%-99.2%) was achieved of SIMCA model. To establish the discriminant functions, six variables (gallic acid, (+)-catechin, (-)-epigallocatechin gallate, theaflavin-3-gallate, theaflavin-3,3'-di-gallate, and total theaflavins) were chosen from 13 variables, and LDA was applied. This gave a satisfactory overall correct classification rate (94.4%) and cross-validation rate (88.9%) for KBT samples. The results showed that HPLC analysis together with chemometrics is a reliable approach for tracing KBT and guaranteeing its authenticity. © 2019 Society of Chemical Industry.

Geographical origin discrimination of edible bird’s nests using smart handheld device based on colorimetric sensor array

A smart handheld device based on colorimetric sensor array and smart cellphone was constructed to discriminate geographical origins of edible bird’s nest (EBN). Three hundred and twenty EBN samples were collected from Malaysia and Indonesia. The colorimetric sensor array consists of chemo-response dyes and was used to capture the odor molecule. The smart cellphone was used to obtain images and to extract red, green and blue colors using in-house software before and after contact with each sample. The differences in the nutritional (carbohydrate, protein and Sialic acid) and volatile components (VCs) between Malaysian and Indonesian EBNs were measured by conventional chemical methods. The colorimetric sensor arrays showed a unique pattern of color changes upon its exposure to EBN from different geographical origins. Data analysis was performed using pattern recognition algorithms including principal component analysis (PCA), Hierarchical cluster analysis (HCA), and partial least square regression (PLSR). The PCA and HAC were applied to investigate the similarity between sample groups. The PLS model was developed to demonstrate the relation between colorimetric responses and characteristic VCs of EBN. For the PLS model, the value of correlation coefficient is higher than 0.86 in calibration and prediction set. Results demonstrated that the smart handheld device was capable for geographical origin discrimination of EBN.

Chemometric Analysis of Elemental Fingerprints for GE Authentication of Multiple Geographical Origins.

The feasibility of combining elemental fingerprints and chemical pattern recognition methods for authentication of the geographical origins of a Chinese herb, Gastrodia elata BI. (GE), was studied in this paper. A total of 210 GE samples were collected from 7 different producing areas. The levels of 15 mineral elements in GE, including Zn, Cd, Co, Cr, Cu, Ca, Mg, Mn, Mo, Ni, Pb, Sr, Fe, Na, and K, were determined using inductively coupled plasma mass spectrometry (ICP-MS). Using the autoscaled data of elemental fingerprints and partial least-squares discriminant analysis (PLSDA), two chemometrics strategies for multiclass classifications, One-Versus-Rest (OVR) and One-Versus-One (OVO), were studied and compared in discrimination of GE geographical origins. As a result, OVR-PLSDA and OVO-PLSDA could achieve the classification accuracy of 0.672 and 0.925, respectively. The results indicate that mineral elemental fingerprints coupled with chemometrics can provide a useful alternative method for simultaneous discrimination of multiple GE geographical origins.

Potential geo-discriminative tools to trace the origins of the dried slices of shiitake (Lentinula edodes) using stable isotope ratios and OPLS-DA

The Lentinus edodes species, known as shiitake, has a history of extensive use in many cuisines in several East Asian countries owing to its unique and pleasant flavor and texture. As international food trade increases, reliable discrimination of geographical origin is becoming increasingly crucial in Korea to identify cheaper imported Chinese shiitake. Herein, stable isotope ratios (i.e., δ13C, δ15N, δ18O, and δ34S) were measured with a stable isotope ratio mass spectrometer, and a geographical discrimination method using orthogonal projection to latent structure-discriminant analysis was developed. The externally validated discrimination method showed excellent predictability (Q2cum = 0.881) and illustrated that δ18O and δ15N were important isotope markers for the geographical discrimination of dried shiitake slices. This study extends the knowledge of geographical differences between China and Korea evidenced by the shiitake isotope signatures, thereby contributing to potential geographical authentication with broader applications for international shiitake markets.

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.

Geographic characterization of Greek wine by inductively coupled plasma–mass spectrometry macroelemental analysis

Studying wine mineral profile has been proven as a valuable tool in geographical origin discrimination and authenticity for both producers and consumers. Adulteration of wines, in terms of geographical origin or variety, is considered a major topic of extensive research. Traceability and authenticity of wines have been previously studied on the basis of typical mineral element patterns by means of chemometric methods. In this context, analytical methods were developed for the determination of mineral elements in wines by inductively coupled plasma–mass spectrometry. This study aimed at classifying selected varietal Greek wines from various regions by employing instrumental analysis. Preliminary data of wine mineral content enabled for the classification of samples according to geographical origin and variety. However, further work is required in order to draw more valid conclusions and to obtain a detailed map of the mineral element content of Greek wines according to their geographical origin and/or variety.

Discrimination of genotype and geographical origin of black rice grown in Brazil by LC-MS analysis of phenolics

Physicochemical properties, cooking time, and phenolics profile of two black rice genotypes grown at six different locations in Brazil were determined. The cultivar IAC 600 and the elite-line AE 153045 were used. The main growing locations for black rice were considered, as follows: Alegrete (ALG), Capão do Leão (CPL), Guaratinguetá (GUA), Roseira (ROS), Santa Vitória do Palmar (SVP), and Taubaté (TBT). Principal component analysis (PCA) and supervised partial least squares-discriminant analysis (PLS-DA) from liquid chromatography-mass spectrometry (LC-MS) data sets showed distinction among genotypes and locations. Quercetin-3-O-glucoside and vanillic acid were the most relevant compounds for discriminating genotypes. SVP location provided the most distinctive black rice, with greater total phenolics content. Characteristics of black rice from SVP location were associated to effects of latitude and wind conditions. Hesperetin, vanillic acid, quercetion-3-O-glucoside, and p-coumaric acid were the most relevant compounds for discriminating locations.

Geographical origin authentication of onions using stable isotope ratio and compositions of C, H, O, N, and S

Both stable isotope ratio and content of carbon (C), hydrogen (H), oxygen (O), nitrogen (N), and sulfur (S) were employed to evaluate the possibility of geographical origin discrimination for onions grown in different regions in Korea. Significantly different elemental compositions were found in H, N, and S compositions, which might be related to different agricultural practices. The stable isotope ratio of C, H, O, N, and S was significantly different among classified regional groups. A linear discriminant analysis relatively well discriminated onions produced from different regional groups based on stable isotope and elemental composition data. Better discrimination of geographical origin of onions was achieved by combining groups located at similar latitude. Inclusion of the ratio of elemental compositions such as H/C and C/N improved the discriminative power for determining geographical origin of onions. Further distinct geographical differentiation of onion origin can be achieved by additional multi-elemental analysis.

Geographical origin discrimination of wheat kernel and white flour using near‐infrared reflectance spectroscopy fingerprinting coupled with chemometrics

SummaryWheat kernel and flour with three genotypes across 4 years procured from three different geographical areas of China were analysed using near‐infrared reflectance (NIR) spectroscopy coupled with chemometrics to better classify wheat according to the origin, production year and genotypes, respectively. For this purpose, principle component analysis‐linear discriminant analysis and multi‐way anova were applied to the NIR data. The best classification percentages were obtained for flour matrix both for geographical origin and production years with the correct percentages of 100% and 73%, respectively. For genotypes, wheat whole kernel showed better classification percentage (98.2%). All the samples were validated using external validation procedure and the obtained percentages were found satisfactory with the average prediction abilities of &gt;85% in all regions indicating the suitability of the developed model. Multivariate anova showed that NIR fingerprints of wheat kernels and flours were significantly influenced by regions, years, genotypes and their interactions. In conclusion, white flour showed better performance in discriminating the geographical origin as compared to wheat whole kernel.

Electronic Nose in Combination with Chemometrics for Characterization of Geographical Origin and Agronomic Practices of Table Grape

Nowadays, the protection of food quality attributes (such as geographical origin or method of production) from frauds and adulterations is one of the main concerns of honest producers and aware consumers. In this study, table grape samples were analyzed by using an electronic nose aiming to evaluate the usefulness of sensor data in combination with statistical analysis in discriminating the agronomic practice (conventional vs. organic farming) and the geographical origin of table grape. Principal component analysis (PCA) showed inadequate clustering of samples according to places of production or agronomic practice; thus for classification purpose, a supervised approach was carried out. In particular, linear discriminant analyses (LDA) was used, resulting in mean prediction abilities of 83.6% and 84.6% for the discrimination of farming method and geographical origin, respectively. Considering the results obtained herein, it can be concluded that sensor data combined with chemometrics showed a good potential in discriminating origin as well as method of production of table grapes especially if compared with other analytical techniques both in terms of time and cost of analyses.

Discrimination of geographical origin of hop (Humulus lupulus L.) using geochemical elements combined with statistical analysis.

Beer is a popular alcoholic beverage worldwide, traditionally made from water, barley and hop (Humulus lupulus L.) strobili. The strobili contain lupulin glands whose components (mostly bitter acids and polyphenols) confer unique and locally different flavours to beer types. It is therefore relevant for brewers and consumers to precisely know the geographical origin of hop plants used for high-quality beer. Hop plants belonging to the variety Hallertau Perle, grown in two locations, Cavalese and Imèr, of the Trentino Region (Italy) were analysed to establish a direct relationship between the chemical elements detected in soil and in plant parts. Chemical elements were determined by X-ray fluorescence and inductively coupled plasma mass spectrometry in soil, leaf and strobili samples from Cavalese and Imèr. The data from the two areas were compared by a nonparametric test (Mann-Whitney) and multivariate statistics (principal component analysis and partial least squares discriminant analysis). The geochemical characterization and the statistical analyses showed different concentrations of major and trace elements in soil and plant parts from the two areas. A reliable correlation could be established between some elements in soil and strobili samples, that is Nb, Fe, Rb and Zr for Cavalese and Mg, Ni, Zn and Zr for Imèr. These elements could therefore be used as geochemical fingerprints to identify the geographical origin of strobili from the two study areas, an approach useful to verify the origin of hop plants for the production of high-quality beer.

The Quality Control of Tea by Near-Infrared Reflectance (NIR) Spectroscopy and Chemometrics

Tea is known to be one of the most popular beverages enjoyed by two-thirds of the world’s population. Concern of variability in tea quality is increasing among consumers. It is of great significance to control quality for commercialized tea products. As a rapid, noninvasive, and nondestructive instrumental technique with simplicity in sample preparation, near-infrared reflectance (NIR) spectroscopy has been proved to be one of the most advanced and efficient tools for the control quality of tea products in recent years. In this article, we review the most recent advances and applications of NIR spectroscopy and chemometrics for the quality control of tea, including the measurement of chemical compositions, the evaluation of sensory attributes, the identification of categories and varieties, and the discrimination of geographical origins. Besides, challenges and future trends of tea quality control by NIR spectroscopy are also presented.

Investigation of geographic origin of wine from border regions: Results from investigation of two vintages

Investigation on discrimination of geographic origin in border areas is carried out by investigating and comparing authentic wine samples from the border areas of Austria, Czech Republic, Slovakia (and from Serbia) by applying different physico-chemical methods (e.g. IRMS, NMR, ICP-MS, ICP-OES, EPR, HPLC, UV-VIS, etc…). The comparison of the data sets from the participating countries and the different applied methods demonstrates, to which extent a differentiation can still be achieved in the border areas of the respective countries, which methods prove most efficient and sensitive and if a combination of methods leads to an increased sensitivity. Furthermore, we also investigate the potential causes for the differentiation, as the environment, different agricultural practices or enology. Analysis of two consecutive vintages shows significant variations between the two years, complicating direct comparison of regions beyond individual vintages. Still, several parameters are identified enabling correct and complete differentiation. However, some of them have been found to represent lab effects, whereas others are interpreted as resulting from different agricultural practices or environmentally driven. Only the latter two ones, if consistent, can be used for discrimination of geographic origin.

Investigation of geographic origin of wine from border regions: Potential limitations and possibilities of different analytical methods and combinations of methods to identify the correct side of the border

In the presented project the investigation on discrimination of geographic origin of wine in from border areas is carried out. Authentic wine samples from the border areas of Austria, Czech Republic, Slovakia (and from Serbia) are investigated and compared by applying different physico-chemical methods (e.g. IRMS, NMR, ICP-MS, ICP-OES, EPR, HPLC, UV-VIS, etc.). The comparison of the data sets from the participating countries and the different applied methods will show, to which extent a differentiation can still be achieved in the border areas of the respective countries, which methods prove most efficient and sensitive and if a combination of methods will lead to an increased sensitivity. Furthermore, we also investigate the causes for the potential differentiation, which might be environmentally determined, due to different agricultural and/or enological practices. Results from the first investigated vintage 2016 show a good separation of the respective geographic origins by isotope ratios and element concentrations as well as by other parameters. The relevant parameters for differentiation now have to be critically evaluated to exclude potential lab influences.

TARGETED AND NON-TARGETED ANALYSIS BASED ON ULTRA HIGH PERFORMANCE LIQUID CHROMATOGRAPHY AND FOURIER TRANSFORM INFRARED SPECTROSCOPY FOR PARIS SPECIES OF DIFFERENT GEOGRAPHICAL ORIGINS

Various species of Paris have been used in treatments for their antitumor, antimicrobial, hemostatic, anthelmintic and immune-stimulating properties, among others. The bioactive markers of these plants including paris saponins I (PSI), paris saponins II (PSII), paris saponins VI (PSVI) and paris saponins VII (PSVII) are responsible for inhibiting nasopharyngeal carcinoma epithelial cell and ethanol-induced gastric lesions. This paper intends to comprehensively characterize the distribution of bioactive markers and illustrate the relationship between chemical profile and Paris spp.. A targeted quantitative analysis by ultra high performance liquid chromatography (UHPLC) was established for the simultaneous separation and determination of PSI, PSII, PSVI and PSVII. Non-targeted analysis based on Fourier transform infrared (FT-IR) spectroscopy, with the aid of partial least squares discriminant analysis (PLS-DA), was also performed. In addition, similarity analysis combining UHPLC and FT-IR spectroscopy were employed to evaluate Paris of different species and geographical origins. Quantitative analysis indicated that the content of saponins varied markedly, and the average total saponins of Paris axialis (PA) of a specific region (Baoshan) was higher than the other species. In addition, total saponins of P. polyphylla var. yunnanensis (PPY) collected from Xishuangbanna was higher than the same species of other regions. Non-targeted analysis indicated that PA was different from other species and PPY in the Baoshan differed from other regions. The results indicate that comprehensive analyses for geographical origin discrimination of herbal medicines are expected to be employed in further investigations.

Geographical origin discrimination methods of planting trees by chloroplast DNA analysis for revegetation considering endemic genetic characters

Geographical origin discrimination methods of planting trees by chloroplast DNA analysis for revegetation considering endemic genetic characters