The geographical origin of Panax ginseng significantly influences its nutritional value and chemical composition, which in turn affects its market price. Traditional methods for analyzing these differences are often time-consuming and require substantial quantities of reagents, rendering them inefficient. Therefore, hyperspectral imaging (HSI) in conjunction with X-ray technology were used for the swift and non-destructive traceability of Panax ginseng origin. Initially, outlier samples were effectively rejected by employing a combined isolated forest algorithm and density peak clustering (DPC) algorithm. Subsequently, random forest (RF) and support vector machine (SVM) classification models were constructed using hyperspectral spectral data. These models were further optimized through the application of 72 preprocessing methods and their combinations. Additionally, to enhance the model\'s performance, four variable screening algorithms were employed: SelectKBest, genetic algorithm (GA), least absolute shrinkage and selection operator (LASSO), and permutation feature importance (PFI). The optimized model, utilizing second derivative, auto scaling, permutation feature importance, and support vector machine (2nd Der-AS-PFI-SVM), achieved a prediction accuracy of 93.4 %, a Kappa value of 0.876, a Brier score of 0.030, an F1 score of 0.932, and an AUC of 0.994 on an independent prediction set. Moreover, the image data (including color information and texture information) extracted from color and X-ray images were used to construct classification models and evaluate their performance. Among them, the SVM model constructed using texture information from X -ray images performed the best, and it achieved a prediction accuracy of 63.0 % on the validation set, with a Brier score of 0.181, an F1 score of 0.518, and an AUC of 0.553. By implementing mid-level fusion and high-level data fusion based on the Stacking strategy, it was found that the model employing a high-level fusion of hyperspectral spectral information and X-ray images texture information significantly outperformed the model using only hyperspectral spectral information. This advanced model attained a prediction accuracy of 95.2 %, a Kappa value of 0.912, a Brier score of 0.027, an F1 score of 0.952, and an AUC of 0.997 on the independent prediction set. In summary, this study not only provides a novel technical path for fast and non-destructive traceability of Panax ginseng origin, but also demonstrates the great potential of the combined application of HSI and X-ray technology in the field of traceability of both medicinal and food products.

Turkey is the leading producer of hazelnuts, contributing to 62% of the total global production. Among 18 distinct local hazelnut cultivars, Giresun Tombul is the only cultivar that has received Protected Designation of Origin denomination from the European Comission (EC). However, there is currently no practical objective method to ensure its geographic origin. Therefore, in this study NIR and Raman spectroscopy, along with chemometric methods, such as principal component analysis, PLS-DA (partial least squares-discriminant analysis), and SVM-C (support vector machine-classification), were used to determine the geographical origin of the Giresun Tombul hazelnut cultivar. For this purpose, samples from unique 118 orchards were collected from eight different regions in Turkey during the 2021 and 2022 growing seasons. NIR and Raman spectra were obtained from both the shell and kernel of each sample. The results indicated that hazelnut samples exhibited distinct grouping tendencies based on growing season regardless of the spectroscopic technique and sample type (shell or kernel). Spectral information obtained from hazelnut shells demonstrated higher discriminative power concerning geographical origin compared to that obtained from hazelnut kernels. The PLS-DA models utilizing FT-NIR (Fourier transform near-infrared) and Raman spectra for hazelnut shells achieved validation accuracies of 81.7% and 88.3%, respectively, while SVM-C models yielded accuracies of 90.9% and 86.3%. It was concluded that the lignocellulosic composition of hazelnut shells, indicative of their geographic origin, can be accurately assessed using FT-NIR and Raman spectroscopy, providing a nondestructive, rapid, and user-friendly method for identifying the geographical origin of Giresun Tombul hazelnuts. PRACTICAL APPLICATION: The proposed spectroscopic methods offer a rapid and nondestructive means for hazelnut value chain actors to verify the geographic origin of Giresun Tombul hazelnuts. This could definitely enhance consumer trust by ensuring product authenticity and potentially help in preventing fraud within the hazelnut market. In addition, these methods can also be used as a reference for future studies targeting the authentication of other shelled nuts.

Adulteration of high-value agricultural products is a critical issue worldwide for consumers and industries. Discrimination of the geographical origin can verify food authenticity by reducing risk and detecting adulteration. Between agricultural products, beans are a very important crop cultivated worldwide that provides food rich in iron and vitamins, especially for people in third-world countries. The aim of this study is the construction of a map of the locally characteristic isotopic fingerprint of giant beans, \"Fasolia Gigantes-Elefantes PGI\", a Protected Geographical Indication product cultivated in the region of Kastoria and Prespes, Western Macedonia, Greece, with the ultimate goal of the discrimination of beans from the two areas. In total, 160 samples were collected from different fields in the Prespes region and 120 samples from Kastoria during each cultivation period (2020-2021 and 2021-2022). The light element (C, N, and S) isotope ratios were measured using Isotope Ratio Mass Spectrometry (IRMS), and the results obtained were analyzed using chemometric techniques, including a one-way ANOVA and Binomial logistic regression. The mean values from the one-way ANOVA were δ15NAIR = 1.875‱, δ13CV-PDB = -25.483‱, and δ34SV-CDT = 4.779‱ for Kastoria and δ15NAIR = 1.654‱, δ13CV-PDB = -25.928‱, and δ34SV-CDT = -0.174‱ for Prespes, and showed that stable isotope ratios of C and S were statistically different for the areas studied while the Binomial logistic regression analysis that followed correctly classified more than 78% of the samples.

The unique floral fingerprint embedded within honey holds valuable clues to its geographical and botanical origin, playing a crucial role in ensuring authenticity and detecting adulteration. Honey from native Apis cerana and Heterotrigona itama bees in Karangasem, Indonesia, was examined utilizing pollen DNA metabarcoding for honey source identification. In this study, we used ITS2 amplicon sequencing to identify floral DNA in honey samples. The finding reveals distinct pollen signatures for each bee species. Results analysis showed A. cerana honey generated 179,267 sequence reads, assembled into Amplicon Sequence Variants (ASVs) with a total size of 485,932 bp and an average GC content of 59 %. H. itama honey generated 177,864 sequence reads, assembled into ASVs with a total size of 350,604 bp and an average GC content of 57 %. A. cerana honey exhibited a rich tapestry of pollen from eleven diverse genera, with Schleichera genus dominating at an impressive relative read abundance of 72.8 %. In contrast, H. itama honey displayed a remarkable mono-dominance of the Syzygium genus, accounting for a staggering 99.95 % of its pollen composition or relative read abundance, highlighting their distinct foraging preferences and floral resource utilization. Notably, all identified pollen taxa were indigenous to Karangasem, solidifying the geographical link between honey and its origin. This study demonstrates pollen DNA metabarcoding may identify honey floral sources. By using pollen profiles from different bee species and their foraging patterns, we may protect consumers against honey adulteration and promote sustainable beekeeping in Karangasem district. Future research could explore expanding the database of reference pollen sequences and investigating the influence of environmental factors on pollen composition in honey. Investigating this technology\'s economic and social effects on beekeepers and consumers may help promote fair trade and sustainable beekeeping worldwide.

Diguo (Ficus tikoua Bur.), an ancient wild fruit, is widely spread in southwest China. However, there is little information on the phenotypic traits, quality characteristics, and aroma compounds available to diguo fruit. The present study is an investigation into the effects of geographical origin on the phenotypic traits and quality characteristics of wild diguo fruit collected from southwest China. The volatile compounds in the mixed fruit samples were also investigated using gas chromatography-mass spectrometry. Our results indicated that significant variation existed among the sampling materials in all the phenotypic parameters. Fruit fresh weight ranged between 2.06 and 4.59 g. Moreover, significant variation existed among the selected materials in all macronutrients (dry matter, total soluble solids, crude protein, crude fat, and ash) and some nutritional parameters (glutamate, arginine, total soluble solids, maltose, and mannose, etc.). Regardless of their geographical origin, diguo fruit is relatively low in fat and fructose and high in fiber and glutamate. A total of 95 volatile constituents were identified in the frozen diguo fruit. In conclusion, diguo fruit with rich nutritional attributes has a promising future for commercial-scale production. The variability of the observed morphological and nutritional features of diguo fruit provides important characteristics for improving the breeding of diguo as a modern fruit crop.

Yemeni smallholder coffee farmers face several challenges, including the ongoing civil conflict, limited rainfall levels for irrigation, and a lack of post-harvest processing infrastructure. Decades of political instability have affected the quality, accessibility, and reputation of Yemeni coffee beans. Despite these challenges, Yemeni coffee is highly valued for its unique flavor profile and is considered one of the most valuable coffees in the world. Due to its exclusive nature and perceived value, it is also a prime target for food fraud and adulteration. This is the first study to identify the potential of Near Infrared Spectroscopy and chemometrics-more specifically, the discriminant analysis (PCA-LDA)-as a promising, fast, and cost-effective tool for the traceability of Yemeni coffee and sustainability of the Yemeni coffee sector. The NIR spectral signatures of whole green coffee beans from Yemeni regions (n = 124; Al Mahwit, Dhamar, Ibb, Sa\'dah, and Sana\'a) and other origins (n = 97) were discriminated with accuracy, sensitivity, and specificity ≥ 98% using PCA-LDA models. These results show that the chemical composition of green coffee and other factors captured on the spectral signatures can influence the discrimination of the geographical origin, a crucial component of coffee valuation in the international markets.

Healthcare discriminations based on one\'s ethnic background is increasingly being studied in medicine. The scale of the Covid-19 pandemic has played an important role in bringing them to light. Data, although scarce, exist in France. These discriminations have an impact on the care pathway and contribute to the renunciation of care by the most affected populations. The issue of discrimination is particularly relevant in infectious diseases. Although the epidemiology of infectious diseases is unevenly distributed worldwide, erroneous social representations are prevalent and expose to a harmful prejudice against migrants with regard to infectious diseases. The transmissible nature of some infectious diseases reinforces their stigmatizing potential. In this context, it seems important to discuss the dimension to be given to social determinants, geographical origin, phenotype, and ethnicity in teaching and medical reasoning. The English-speaking world uses the concept of \"race\" in a structural way, whereas this \"international standard\" has not been applied in France until now. To improve the care of people from minority groups, it seems important to better document and teach a more nuanced clinical reasoning based on origin, without neglecting the importance of collecting and taking into account social determinants of health and environmental factors.

The features for assessing the authenticity of wines by region of origin are studied, based on the relationship between the mineral composition of the wine, the grapes, and the soil profile (0 to 160 cm) from the place of growth of Cabernet Sauvignon grapes. Soil, grape, and wine samples were taken from the territories of six vineyards in the Anapa district of Krasnodar Territory, Russia. Using the methods of ICP-OES, thermal, and X-ray phase analysis, the soils were differentiated into three groups, differing in mineralogical and mineral compositions. The soil samples of the first group contained up to 31% quartz, the second group up to 25% quartz and 19% mixed calcite, and the third group up to 32% calcite and 15% quartz. The formation of the elemental image of the grapes was studied, taking into account the total content and mobile forms of metals in the soil. The territorial proximity of the vineyards did not affect the extraction of elements from the soil into the grape berry, and the migration of metals for each territory was selective. According to the values of the biological absorption coefficient, the degree of transition of metals from the soil to a berry was estimated. For K, Ti, Zn, Rb, Cu, and Fe in all berries, the coefficient was higher than 1.00, which means that the berry extracts contained not only mobile-form, but also difficult-to-dissolve metal compounds. The migration of macro-components from the soil to the berry was low, and amounted to 6-7% for Ca, 0.8-3.0% for Na, and 25-70% for Mg of the concentration of their mobile forms. For all territories, the maximum correlation between metal concentrations in grapes and soil was observed for samples from a depth of 0-40 cm. The discriminant model based on concentrations of Rb, Al, K, Sr, Co, Na, Pb, Ca, and Ni showed the formation of clusters in the territories of vineyard cultivation. The developed model allow the problems of identifying wines by region to be solved with high accuracy, using their elemental image.

The aim of this work was to investigate the influence of two locations and seven cultivars on the profiling of metabolites in organically grown plums (Prunus domestica L.) fruit in Norway. P, K, and Ca were most abundant in the studied fruits, while Ba and Sr formed a clear line between the locations. The most abundant sugars were glucose, fructose, sucrose, and sorbitol, which together accounted for up to 97.00%. Quinic acid and malic acid were the predominant organic acids, while chlorogenic acid, rutin, and kaempferol-3-O-glucoside were the most abundant polyphenols. Plums from Ullensvang were characterized by a higher content of minerals, sugars, organic acids, total polyphenol content (TPC), and radical scavenging activity (RSA), while plums from Telemark had a higher content of quantified polyphenols. The cultivar \'Mallard\' had the highest mineral and radical scavenging activity, \'Opal\' had the sweetest fruit, \'Jubileum\' had the highest acidity, \'Excalibur\' had the highest TPC content, and \'Valor\' stored the highest content of quantified polyphenols, especially chlorogenic acid. These results provide comprehensive information on the chemical profiles of selected plum cultivars, suggesting that organic plums are a rich source of beneficial compounds that can have a positive impact on human health.

The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers), it is vital to identify geographical origin. Near infrared spectroscopy can be used to detect the specific content of organic moieties in agricultural and food products. The present study implemented the combinatorial method of FT-NIR spectroscopy with chemometrics to identify geographical origin of Khao Dawk Mali 105 rice. Rice samples were collected from 2 different region including the north and northeast of Thailand. NIR spectra data were collected in range of 12,500 - 4,000 cm-1 (800-2,500 nm). Five machine learning algorithms including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), C-support vector classification (C-SVC), backpropagation neural networks (BPNN), hybrid principal component analysis-neural network (PC-NN) and K-nearest neighbors (KNN) were employed to classify NIR data of rice samples with full wavelength and selected wavelength by Extremely Randomized Trees (Extra trees) algorithm. Based on the findings, geographical origin of rice could be specified quickly, cheaply, and reliably using combination of NIRS and machine learning. All models creating by full wavelength and selected wavelength exhibited accuracy between 65 and 100 % for identifying geographical region of rice. It was proven that NIR spectroscopy may be used for the quick and non-destructive identification of geographical origin of Khao Dawk Mali 105 rice.