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Background Methods Results Conclusions Assessing the potential increased risk of viral rebound (VR) in migrants requires adequate control for sex and acquisition risk groups.People living with HIV1, enrolled in the ANRS CO4‐French Hospital Database on HIV, who achieved virological suppression with antiretroviral therapy (ART) initiated between 2006 and 2016 were included. We first compared the risk of VR, with loss to follow‐up and death considered as competing events, across origin among the HIV acquisition groups, then across acquisition groups among the different origins, and finally across modality of a variable combining sex, acquisition group, and origin. Models were adjusted for clinical and biological confounding factors.We included 21 571 French natives (FRA), 10 148 migrants from sub‐Saharan Africa (SSA), 1137 migrants from the non‐French West Indies (NFWI), and 4205 other migrants (OTHER). The 5‐year probability of VR was 19% (95% confidence interval [CI] 19–20) overall, 15% in FRA, 21% in OTHER, 26% in SSA, and 34% in NFWI (p < 0.0001). It was 14% in men who have sex with men (MSM), 23% in heterosexual men, and 23% in women (p < 0.0001). After adjustment, all acquisition groups had a higher risk of VR than MSM from FRA, with men and women from NFWI having the highest risk (adjusted hazard ratio [aHR] 2.46; 95% CI 2.12–2.86 and aHR 2.59; 95% CI 2.20–3.04, respectively). Within each acquisition group, all groups of origin had a higher risk of VR than FRA. Within each region of origin, except the NFWI, heterosexual men had a higher risk of VR than MSM.After accounting for sex and acquisition group, migration, especially from NFWI, remains prognostic of VR. [ABSTRACT FROM AUTHOR]

Ginger, which is the rhizome of Zingiber officinale Roscoe, is widely distributed and consumed. The taste and aroma of ginger differ depending on its geographical origin. To distinguish the origin of ginger, ginger extracts from Korea, Peru, and China were analyzed using ultra‐performance liquid chromatography (UPLC) coupled to quadrupole time‐of‐flight mass spectrometry and nuclear magnetic resonance spectroscopy for metabolomics. Korean ginger contained more 10‐gingerol, and Peruvian ginger contained more 6‐gingerol and 8‐gingerol. Several amino acids negatively correlated with gingerols, suggesting that amino acids are related to the biosynthesis of gingerols. Sugars, which are the main energy source, positively correlated with gingerols. Organic acids and gingerols were also positively correlated, indicating that both organic acids and gingerols are used for adaptation to the environment surrounding the root. We confirmed the features of the primary and secondary metabolites by verifying the correlation between metabolites and differences in metabolites according to ginger origin. We additionally optimized a simultaneous UPLC analytical method of marker compounds for the simple and rapid quality control of ginger. This method exhibits excellent linearity, sensitivity, and reproducibility. Using metabolomics, differences in origin were observed, and a low‐end equipment analysis method for quality control can be used in the ginger industry. [ABSTRACT FROM AUTHOR]

The Jingbai pear is one of the best pear species in China with high quality and nutrition values which are closely linked to its geographical origin. With the purpose of discriminating the PGI Mentougou Jingbai pear from three other producing regions, the stable isotope ratios and elemental profiles of the pears (n = 52) and the corresponding soils and groundwater were determined using isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS), respectively. The results revealed that δ15N, δ18OJ, and Li were significantly different (p < 0.05) in samples from different regions, which indicated their potential to be used in the geographical origin classification of the Jingbai pear. The nitrogen isotopic values of the pear pulp were positively correlated with the δ15N value and nitrogen content of the corresponding soils, whilst the B, Na, K, Cr, and Cd contents of the pear pulps were positively correlated with their corresponding soils. Orthogonal partial least squares discriminant analysis (OPLS-DA) was performed in combination with analysis of the stable isotopes and elemental profiles, making it possible to distinguish the cultivation regions from each other with a high prediction accuracy (a correct classification rate of 92.3%). The results of this study highlight the potential of stable isotope ratios and elemental profiles to trace the geographical origin of pears at a small spatial scale. [ABSTRACT FROM AUTHOR]

The Chinese mitten crab (Eriocheir sinensis) is highly valued by consumers for its delicious taste and high nutritional content, including proteins and trace elements, giving it significant economic value. However, variations in taste and nutritional value among crabs from different regions lead to considerable price differences, fueling the prevalence of counterfeit crabs in the market. Currently, there are no rapid detection methods to verify the origin of Chinese mitten crabs, making it crucial to develop fast and accurate detection techniques to protect consumer rights. This study focused on Chinese mitten crabs from different regions, specifically Hongze Lake, Tuo Lake, and Weishan Lake, by collecting near-infrared (NIR) diffuse reflectance spectral data from both the abdomen and carapace regions of the crabs. To eliminate noise from the spectral data, pretreatment was performed using Savitzky–Golay (SG) smoothing, Standard Normal Variate (SNV) transformation, and Multiplicative Scatter Correction (MSC). Key wavelengths reflecting the origin of Chinese mitten crabs were selected using Competitive Adaptive Reweighted Sampling (CARS), Bootstrap Soft Shrinkage (BOSS), and Uninformative Variable Elimination (UVE) algorithms. Finally, Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Back Propagation Neural Network (BP) models were developed for rapid detection of crab origin. The results demonstrated that MSC provided the best preprocessing performance for NIR spectral data from both the abdomen and back of the crabs. For abdomen data, the SVM model developed using feature wavelengths selected by the CARS algorithm after MSC preprocessing achieved the highest accuracy (Acc) of 90.00%, with precision (P), recall (R), and F1-score for crabs from Weishan Lake at 89.29%, 86.21%, and 87.72%, respectively; for crabs from Tuo Lake at 86.96%, 95.24%, and 90.91%; and for crabs from Hongze Lake at 90.00%, 93.10%, and 91.53%. For carapace data, the SVM model based on wavelengths selected by the BOSS algorithm after MSC pretreatment achieved the best performance, with an Acc of 87.50%, and P, R, and F1 for crabs from Weishan Lake at 77.14%, 93.10%, and 84.38%; for Tuo Lake crabs at 100%, 90.47%, and 95.00%; and for Hongze Lake crabs at 92.31%, 80.00%, and 85.71%. In conclusion, NIR spectroscopy can effectively detect the origin of Chinese mitten crabs, providing technical support for developing rapid detection instruments and thereby safeguarding consumer rights. [ABSTRACT FROM AUTHOR]

The evolution of structural changes and the textural features during the ripening process of four varieties of Spanish sheep cheese were studied using Magnetic Resonance Imaging (MRI). Specifically, longitudinal (T1) and transverse (T2) relaxation times and apparent diffusion coefficient maps were analyzed. Also, proton density was used to improve the description of the structure of the cheeses. The MRI results displayed important information about cheese matrix structure, associated with different manufacturing processes (industrial vs. traditional), ripening times (RTs, from 2 to 180 days), and geographical origins. A significant interaction between RT and cheese variety related to the variations in physicochemical and textural parameters was found. Linear regression models were developed per the abundant literature. Logarithmic regression models showed the highest R2 when monitoring the dependency on T1 and T2 parameters of water content, water activity, RT, and some texture parameters. Therefore, these results support that MRI is a useful technology to monitor the ripening process, predict textural behavior and physicochemical variables, and characterize the structure of different varieties of sheep cheese. [ABSTRACT FROM AUTHOR]

Using stable isotopes to detect and analyze the geographical origin of insects represents an important traceability technology, which requires a rich isotope database. In this study, we representatively sampled the Chinese provinces where flighted spongy moth complex (FSMC) has been reported and, for the first time, used co‐kriging interpolation to predict the distribution patterns of FSMC δ13C values in the main distribution areas. From 2020 to 2022, we set up 60 traps in 12 provinces and cities in China and collected 795 FSMCs. Then, 6 main climatic factors were obtained by multi‐collinearity screening from 21 types of meteorological data collected at the sample plots, and a correlation analysis was carried out by combining longitude, latitude, and altitude data with the δ13C values of FSMC. Next, we performed a co‐kriging interpolation using the 2 climatic factors with the highest correlation (isothermality and altitude) and the δ13C values of FSMC. A cross‐validation was performed to systematically test 11 candidate models and select the best semi‐variogram model ("Exponential"), which was then used to build a co‐kriging interpolation model. The geographical distribution patterns of the FSMC δ13C values obtained from the 2 interpolation models (i.e., interpolated with isothermality and altitude, respectively) were almost the same. Moreover, the δ13C values varied significantly at the regional scale, showing regular changes in spatial distribution. Overall, the reference indicator map of the δ13C values generated from stable isotopes can be used to greatly improve the efficiency of discrimination analyses on the geographical origin of FSMC. [ABSTRACT FROM AUTHOR]

Seed purity assurance is an important aspect of maintaining the quality standards of wheat seeds. It relies significantly on quality parameters, like varietal classification and geographical origin identification. Hyperspectral imaging (HSI) has emerged as an advanced nondestructive technique to determine various quality parameters. In recent years, several studies have utilized HSI for varietal classification, although a limited number of varieties were considered. Additionally, no attention has been paid to determining the geographical origin of wheat seeds. To address these gaps, two separate experiments were performed for varietal classification and geographical origin identification. The seeds from 96 varieties grown across 5 different agricultural regions in India were collected. Hyperspectral images of wheat seeds were acquired in the wavelength ranging 900–1700 nm. The spectral reflectance values were obtained from the region of interest (ROI) corresponding to each seed. Subsequently, the deep learning models (convolutional neural networks [CNNs]) were established and compared with two conventional algorithms, including support vector machines (SVMs) and K‐nearest neighbors (KNNs). The experimental results indicated that the proposed CNN models outperformed the SVM and KNN models, achieving an overall accuracy of 94.88% and 99.02% for varietal classification and geographical origin identification, respectively. These results demonstrate that HSI combined with deep learning has the potential to accurately classify a large number of wheat varieties. Moreover, HSI can be used to precisely identify the geographical origins of wheat seeds. This study provides an accurate and nondestructive method that can assist in breeding, quality evaluation, and the development of high‐quality wheat seeds. This study explores the use of hyperspectral imaging (HSI) combined with deep learning for wheat seed quality assessment. We conducted experiments on 96 wheat varieties from five Indian geographical regions, utilizing convolutional neural networks (CNNs) for varietal classification and geographical origin identification. Results showed CNNs outperforming support vector machines (SVMs) and K‐nearest neighbors (KNNs), with accuracies of 94.88% and 99.02%, respectively. Our findings highlight the potential of HSI as an accurate, non‐destructive method for wheat seed quality evaluation. [ABSTRACT FROM AUTHOR]

Rheum tanguticum is one of the primary rhubarb species used for food and medicinal purposes, and it has recently been gaining more attention and recognition. This research represents the first attempt to use stable isotopes and elemental analysis via IRMS to identify the geographical origin of Rheum tanguticum. A grand total of 190 rhubarb samples were gathered from 38 locations spread throughout the provinces of Gansu, Sichuan, and Qinghai in China. The carbon content showed a decreasing trend in the order of Qinghai, followed by Sichuan, and then Gansu. Nitrogen content was notably higher, with Qinghai and Sichuan displaying similar levels, while Gansu had the lowest nitrogen levels. Significant differences were noted in the δ13C (−28.9 to −26.5‰), δ15N (2.6 to 5.6‰), δ2H (−120.0 to −89.3‰), and δ18O (16.0‰ to 18.8‰) isotopes among the various rhubarb cultivation areas. A significant negative correlation was found between %C and both longitude and humidity. Additionally, δ13C and δ15N isotopes were negatively correlated with longitude, and δ15N showed a negative correlation with humidity as well. δ2H and δ18O isotopes exhibited a strong positive correlation with latitude, while significant negative correlations were observed between δ2H and δ18O isotopes and temperature, precipitation, and humidity. The LDA, PLS-DA, and k-NN models all exhibited strong classification performance in both the training and validation sets, achieving accuracy rates between 82.1% and 91.7%. The combination of stable isotopes, elemental analysis, and chemometrics provides a reliable and efficient discriminant model for accurately determining the geographical origin of R. tanguticum in different regions. In the future, the approach will aid in identifying the geographical origin and efficacy of rhubarb in other studies. [ABSTRACT FROM AUTHOR]

Fraudulent practices concerning honey are growing fast and involve misrepresentation of origin and adulteration. Simple and feasible methods for honey authentication are needed to ascertain honey compliance and quality. Working on a robust dataset and simultaneously investigating honey traceability and adulterant detection, this study proposed a portable FTNIR fingerprinting approach combined with chemometrics. Multifloral and unifloral honey samples (n = 244) from Spain and Sardinia (Italy) were discriminated by botanical and geographical origin. Qualitative and quantitative methods were developed using linear discriminant analysis (LDA) and partial least squares (PLS) regression to detect adulterated honey with two syrups, consisting of glucose, fructose, and maltose. Botanical and geographical origins were predicted with 90% and 95% accuracy, respectively. LDA models discriminated pure and adulterated honey samples with an accuracy of over 92%, whereas PLS allows for the accurate quantification of over 10% of adulterants in unifloral and 20% in multifloral honey. [ABSTRACT FROM AUTHOR]

The 24 solar terms are a significant component of traditional Chinese culture. Amid global warming climate change, research on the Solar Terms has gained increasing prominence. Identifying the geographical origins of the Solar Terms not only provides evidence for studies on the origins of Chinese agricultural civilization but also serves as a critical foundation for the innovative utilization of traditional culture in the modern era. Previous research has primarily relied on historical records, literature review, and field investigation, often challenged by the vast and complex data, the difficulty distinguishing authenticity, the time-consuming nature of the work, and the need for direct scientific evidence. The STTMD (Solar Terms Typical Meteorological Day) method was used for typifying solar term meteorological data sequences, supplemented by isothermal estimation and clustering analysis. This approach was further validated using key crop germplasm sites, phenological indicators, and phenological observation contour maps. The results derived from statistical methods are cross-referenced with historical documents to infer the geographical origins of the 24 Solar Terms. The findings indicate that: (1)On a larger spatial scale, the Solar Terms originated in the middle and lower reaches of the Yellow River; (2)On a smaller spatial scale, the "Luoyang-Zhengzhou-Anyang" triangle is the most probable origin area; (3)The core area of origin is hypothesized to be in present-day Xingyang, Henan Province, or slightly further north. These results are consistent with historical literature and phenological records of crops, offering a novel analysis and transformative insights into the knowledge of Solar Terms. The study provides valuable evidence or methodological inspiration for historical agricultural research in China and offers references for agricultural production and the environmental impacts of global warming. [ABSTRACT FROM AUTHOR]

Millet is one of the major coarse grain crops in China. Its geographical origin and Fusarium fungal contamination with ergosterol and deoxynivalenol have a direct impact on food quality, so the rapid prediction of the geographical origins and fungal toxin contamination is essential for protecting market fairness and consumer rights. In this study, 600 millet samples were collected from twelve production areas in China, and traditional algorithms such as random forest (RF) and support vector machine (SVM) were selected to compare with the deep learning models for the prediction of millet geographical origin and toxin content. This paper firstly develops a deep learning model (wavelet transformation-attention mechanism long short-term memory, WT-ALSTM) by combining hyperspectral imaging to achieve the best prediction effect, the wavelet transformation algorithm effectively eliminates noise in the spectral data, while the attention mechanism module improves the interpretability of the prediction model by selecting spectral feature bands. The integrated model (WT-ALSTM) based on selected feature bands achieves optimal prediction of millet origin, with its accuracy exceeding 99% on both the training and prediction datasets. Meanwhile, it achieves optimal prediction of ergosterol and deoxynivalenol content, with the coefficient of determination values exceeding 0.95 and residual predictive deviation values reaching 3.58 and 3.38 respectively, demonstrating excellent model performance. The above results suggest that the combination of hyperspectral imaging with a deep learning model has great potential for rapid quality assessment of millet. This study provides new technical references for developing portable and rapid hyperspectral imaging inspection technology for on-site assessment of agricultural product quality in the future. [ABSTRACT FROM AUTHOR]

Non-targeted NMR is widely accepted as a powerful and robust analytical tool for food control. Nevertheless, standardized procedures based on validated methods are still needed when a non-targeted approach is adopted. Interlaboratory comparisons carried out in recent years have demonstrated the statistical equivalence of spectra generated by different instruments when the sample was prepared by the same operator. The present study focused on assessing the reproducibility of NMR spectra of the same matrix when different operators performed individually both the sample preparation and the measurements using their spectrometer. For this purpose, two independent laboratories prepared 63 tomato samples according to a previously optimized procedure and recorded the corresponding 1D 1H NMR spectra. A classification model was built using the spectroscopic fingerprint data delivered by the two laboratories to assess the geographical origin of the tomato samples. The performance of the optimized statistical model was satisfactory, with a 97.62% correct sample classification rate. The results of this work support the suitability of NMR techniques in food control routines even when samples are prepared by different operators by using their equipment in independent laboratories. [ABSTRACT FROM AUTHOR]

Greek giant beans, also known as "Gigantes Elefantes" (elephant beans, Phaseolus vulgaris L.,) are a traditional and highly cherished culinary delight in Greek cuisine, contributing significantly to the economic prosperity of local producers. However, the issue of food fraud associated with these products poses substantial risks to both consumer safety and economic stability. In the present study, multi-elemental analysis combined with decision tree learning algorithms were investigated for their potential to determine the multi-elemental profile and discriminate the origin of beans collected from the two geographical areas. Ensuring the authenticity of agricultural products is increasingly crucial in the global food industry, particularly in the fight against food fraud, which poses significant risks to consumer safety and economic stability. To ascertain this, an extensive multi-elemental analysis (Ag, Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, Ge, K, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, Re, Se, Sr, Ta, Ti, Tl, U, V, W, Zn, and Zr) was performed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Bean samples originating from Kastoria and Prespes (products with Protected Geographical Indication (PGI) status) were studied, focusing on the determination of elemental profiles or fingerprints, which are directly related to the geographical origin of the growing area. In this study, we employed a decision tree algorithm to classify Greek "Gigantes Elefantes" beans based on their multi-elemental composition, achieving high performance metrics, including an accuracy of 92.86%, sensitivity of 87.50%, and specificity of 96.88%. These results demonstrate the model's effectiveness in accurately distinguishing beans from different geographical regions based on their elemental profiles. The trained model accomplished the discrimination of Greek "Gigantes Elefantes" beans from Kastoria and Prespes, with remarkable accuracy, based on their multi-elemental composition. [ABSTRACT FROM AUTHOR]

Honey is produced by honeybees from different natural resources; the quality can be considerably influenced by the geographical location. For this, the present study aimed to evaluate the quality and antioxidant activity of honeys provided from six different localities in the Bejaia region to evaluate the influence of geographical variations. The quality of the samples was determined by the analysis of physicochemical parameters, including water, ash, proteins, proline, and sugar contents, electrical conductivity, pH, acidity, invertase, diastase, hydroxymethylfurfural, and color, as well as the determination of the total phenolic content (TPC) and the antioxidant activity. The majority of tested honeys meet the Codex Alimentarius specifications, revealing that the majority of the samples were mature, authentic, and came from nectar. This study showed that the honeys tested have an interesting antioxidant activity that can be explained by their high phenolic contents. It can be concluded that the geographical origin of honey can significantly influence both physicochemical parameters and antioxidant properties. [ABSTRACT FROM AUTHOR]

A rapid and non-destructive machine learning approach was developed to identify the geographical origin and predict nutrient contents of dried wolfberries by analyzing the hyperspectral images. 1826 images of dried wolfberry samples from four different geographical origins in China were collected and used as the database for this study. The raw spectral data was pre-processed and the characteristic wavelengths between 370 and 1043 nm was extracted to establish the datasets applicable for the analysis. The novel lightweight neural network model was developed based on group convolution and average pooling. By comparing the new approach with the other two existing neural network methods, it shows that the new model performs as well as or better than the existing algorithms. This study demonstrates the feasibility of identifying the geographic origin classification and nutrient prediction based on hyperspectral data of dried wolfberries. The non-invasive method provides a reference for future research of other similar products. [ABSTRACT FROM AUTHOR]

Abstract Effective geographical origin discrimination of Chinese rice requires a large database of samples to ensure sufficient data for origin verification at a regional scale. In this study, environmental similarity was used to establish a spatial database of rice nutrient element, and then the validity of the database was verified using the back propagation artificial neural networks modeling (BPNN). The spatial distribution model of 14 rice nutrient element (Al, Ba, Ca, Cu, Cr, Fe, K, Mg, Mn, Mo, Na, Ni, Rb, and Zn) on regional scale was built using an environmental similarity method for the first time. Elemental concentrations of 692 samples were used to build a simulated geographical origin prediction model for northeastern (N‐E), middle to lower Yangtze River plain (Y‐R), southwestern (S‐W), and southeastern (S‐E) in China. The results indicated that the performance of the environmental similarity model for these four growing regions was S‐W > N‐E > S‐E > Y‐R based on the lowest ranking root mean square error (RMSE) for each region. For example, the RMSEs of Zn in S‐W, N‐E, S‐E, and Y‐R regions were 2.0, 2.4, 2.7, and 3.7 mg/kg, respectively. A case study on the traceability of Japonica rice was shown that Japonica rice could be discriminated with higher origin accuracy using a simulated database (91.8%) than by the actual database (87.0%) using the BPNN model. This indicates that a simulated rice element database could improve the accuracy of geographical origin discrimination for Chinese rice and potentially be applied to other large national‐scale crop datasets.

Abstract The 24 solar terms are a significant component of traditional Chinese culture. Amid global warming climate change, research on the Solar Terms has gained increasing prominence. Identifying the geographical origins of the Solar Terms not only provides evidence for studies on the origins of Chinese agricultural civilization but also serves as a critical foundation for the innovative utilization of traditional culture in the modern era. Previous research has primarily relied on historical records, literature review, and field investigation, often challenged by the vast and complex data, the difficulty distinguishing authenticity, the time-consuming nature of the work, and the need for direct scientific evidence. The STTMD (Solar Terms Typical Meteorological Day) method was used for typifying solar term meteorological data sequences, supplemented by isothermal estimation and clustering analysis. This approach was further validated using key crop germplasm sites, phenological indicators, and phenological observation contour maps. The results derived from statistical methods are cross-referenced with historical documents to infer the geographical origins of the 24 Solar Terms. The findings indicate that: (1)On a larger spatial scale, the Solar Terms originated in the middle and lower reaches of the Yellow River; (2)On a smaller spatial scale, the "Luoyang-Zhengzhou-Anyang" triangle is the most probable origin area; (3)The core area of origin is hypothesized to be in present-day Xingyang, Henan Province, or slightly further north. These results are consistent with historical literature and phenological records of crops, offering a novel analysis and transformative insights into the knowledge of Solar Terms. The study provides valuable evidence or methodological inspiration for historical agricultural research in China and offers references for agricultural production and the environmental impacts of global warming.

The pharmacological activity and clinical efficacy of traditional Chinese medicine (TCM) are significantly influenced by the geographical origin. Therefore, the effective and reliable identification of TCM producing regions is crucial for the quality control and clinical application of TCM. Ophiopogonis Radix (Chinese name: Maidong (MD)) is a well-known TCM with various properties, such as anti-oxidation, anti-inflammation, anticancer, and cardiovascular protection. In this study, the feasibility of Raman spectroscopy combined with partial least squares discriminant analysis (PLS–DA) to distinguish Sichuan Ophiopogonis Radix (CMD) from Zhejiang Ophiopogonis Radix (ZMD) was investigated. Extensive experimental results demonstrated that the Raman peaks at 363, 465, 1345, 1458, 2893 and 2933 cm−1 corresponding to polysaccharides, homoisoflavonoids, and steroidal saponins could be used as identification chemical markers for CMD and ZMD. Additionally, the PLS–DA-based scheme proposed in this study could identify the geographical origins of CMD from ZMD with an accuracy of 92.7%. Unlike conventional morphological and microscopic identification and physicochemical analysis, the proposed method is nondestructive, involves simple sample preparation, requires a very low mass of the sample, and exhibits a high classification accuracy to discriminate between CMD and ZMD. Therefore, despite analyzing a limited number of samples, this study proposes a method by the combination of Raman spectroscopy and PLS–DA to rapidly, sensitively, and nondestructively discriminate Ophiopogonis Radix samples with different geographical origins. This study confirms that Raman spectroscopy exhibits high potential to facilitate the authentication, quality control, and supervision of herbal medicines and processed products. [ABSTRACT FROM AUTHOR]

This study aimed at the geographical origin discrimination of Jatropha mollissima saps using mid- and near-infrared spectroscopies (MIR and NIR, respectively) and Linear Discriminant Analysis (LDA). For this purpose, a total of 108 sap samples were collected in 3 different geographical regions over 12 months, and their content of total polyphenols, flavonoids, and tannins were quantified. Overall, samples from region C had consistently lower levels of these secondary metabolites throughout most of the months studied. In contrast, samples from regions A and B displayed relatively stable metabolite concentrations over the collection period. Since raw sap samples are subject to deterioration during storage, lyophilization was employed to remove moisture and consequently increase their shelf-life. Thus, MIR spectroscopy was applied to both raw and lyophilized sap samples, while NIR spectroscopy was only applied to lyophilized samples. Then, Principal Component Analysis from the secondary metabolites and spectral data indicated a trend of separation between the region C (located in Sertão) and the regions A and B (located in Agreste). Next, the Successive Projection Algorithm (SPA), Genetic Algorithm (GA), and Ant Colony Optimization (ACO) were used for variable selection in LDA. As a result, all constructed models achieved a sensitivity, specificity, and accuracy of 100 %, or very close to it, in both the training and test sets. However, the SPA-LDA models were more parsimonious, selecting fewer variables and presenting reproductive results as it is a deterministic technique. Hence, the proposed analytical methodologies align with the principles of Green Chemistry by requiring a simple sample preparation, avoiding the use of reagents and solvents, and reducing waste generation. Moreover, special attention can be given to NIR spectroscopy, as it offers a cost-effective analytical tool that can be explored in situ using a portable miniaturized device in the future. [Display omitted] • Performance of NIR and MIR was evaluated to distinguish Jatropha mollisima saps from three different geographical origins. • Discriminant power of LDA with selected variables from the AOC, GA, and SPA algorithms were evaluated and showed high sensitivity and specificity. • Evaluated techniques achieved classification errors of less than 5 %. • Methods developed are fast, quick, and efficient in solving the proposed problem. [ABSTRACT FROM AUTHOR]

Abstract 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.

AbstractPanax notoginseng (Burk.) F.H. Chen (P. notoginseng), a precious medicinal herb, provides both considerable values. There is confusion in the market regarding the geographical origins of P. notoginseng. It is of paramount importance to authenticate geographical origins of P. notoginseng, which will facilitate the rational utilization of resources and sustainable development. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to investigate the influence of the tissue and data fusion strategy for the authentication of P. notoginseng origins. The PLS-DA model employing the main root spectra demonstrated great potential for authenticating P. notoginseng origin. The PLS-DA training and test sets based on feature-level fusion (VIP) spectral data were 100% accurate, indicating excellent potential in authenticating P. notoginseng. The data fusion strategy improved the authentication. The article presents a reliable and efficacious novel methodology for authenticating P. notoginseng origins and a scientific foundation for its quality control. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study presents a new strategy to discriminate between opium samples obtained from different geographical regions. Nuclear magnetic resonance (NMR) profiling and chemometrics were applied to geographical classification of opium originating from Myanmar and Afghanistan, which are two major opium producing countries in the world. A total of 50 Myanmar and 46 Afghanistan authentic opium samples were analyzed by 1H‐NMR, and the chemical profiles were characterized. Different sample preparation procedures, data processing methods, and chemometrics were compared to obtain the best classification effect. It was found that drying and the addition of buffer solutions were unnecessary for classification purposes; thus, the gum opium samples were extracted directly with CD3OD, which shortened sample preparation time. A full discrimination between the two geographical origins was achieved by 1H‐NMR profiling and orthogonal partial least squares discriminant analysis. All 30 opium samples were classified correctly by the developed orthogonal partial least squares discriminant analysis model. Compared with traditional chromatography and mass spectrometry profiling methods, the 1H‐NMR profiling method was faster (with instrument analysis time of less than 3 min) and reproducible. This study provides new insights into the applying of NMR profiling and chemometrics to rapid drug profiling analysis. [ABSTRACT FROM AUTHOR]

Soybean has developed a reputation as a superfood due to its nutrient profile, health benefits, and versatility. Since 1960, its demand has increased dramatically, going from a mere 17 MMT to almost 358 MMT in the production year 2021/22. These extremely high production rates have led to lower-than-expected product quality, adulteration, illegal trade, deforestation, and other concerns. This necessitates the development of an effective technology to confirm soybean's provenance. This is the first review that investigates current analytical techniques coupled with multivariate analysis for origin traceability of soybeans. The fundamentals of several analytical techniques are presented, assessed, compared, and discussed in terms of their operating specifics, advantages, and shortcomings. Additionally, significance of multivariate analysis in analyzing complex data has also been discussed. [ABSTRACT FROM AUTHOR]

Date fruit (Phoenix dactylifera L.) processing generates substantial quantities of date seeds, constituting a potential source of valuable oil yet currently considered waste. This study aimed at characterizing date seed oil (DSO) as a prospective approach for date seed valorization while investigating the influence of geographical origin on its composition. Samples from three Moroccan palm groves (Allougoum, Alnif, and Errachidia) and four non‐Moroccan samples were analyzed. Liquid and gas chromatography were applied to determine the chemical composition. The prevalent fatty acids were oleic acid (Ol), ranging from 40.8% to 50.2%, followed by lauric acid (La, 14.0%–24.2%), myristic acid (My, 9.0%–12.6%), palmitic acid (Pa, 9.0%–11.6%), linoleic acid (7.1%–10.7%), and stearic acid (St, 2.4%–4.8%). Major triacylglycerols identified included LaOlLa, LaOlMy, LaOlPa, LaOlOl, MyOlPa, MyOlOl, and PaOlOl. Notably, DSO contains significant tocochromanol (424–760 mg kg−1) and phytosterol content (3422–4827 mg kg−1), with specific phytosterols identified for the first time. Multivariate analysis employing principal component analysis and hierarchical cluster analysis, supplemented by heatmaps, underscored the influence of geographical origin on DSO composition. Moroccan samples exhibited distinctive profiles rich in St, Ol, Pa, PaOlSt, MyOlPa, PaOlPa, γ‐tocotrienol, and δ‐tocotrienol, leading to a differentiation from non‐Moroccan oils. Challenges in classifying DSO within selected Moroccan palm groves were discussed, emphasizing the necessity to consider major and minor compounds, along with date varieties, in geographical effect studies. Practical Applications: By delving into the chemical properties and potential applications of DSO, this research strives to contribute to sustainable and economically viable approaches for utilizing agricultural waste products. [ABSTRACT FROM AUTHOR]

Introduction: Black mahlab (Monechma ciliatum) seed is a rich source of metabolites and minerals and is usually believed to have a similar composition between different areas of cultivation. Until now, no studies have assessed changes in black mahlab seeds (BMSs) to determine those constituents that help to discriminate them according to geographical origin. Objectives: The present study attempted to compare the metabolomics and elemental profiles of BMSs of different geographical origins and identified the potential markers using ultrahigh‐performance liquid chromatography quadrupole Orbitrap tandem mass spectrometry (UHPLC‐Q‐Orbitrap‐MS2), and inductively coupled plasma mass spectrometry (ICP‐MS) techniques and established the chemometric model to identify the potential markers and discriminate them according to cultivation sites. Material and methods: In this work, data from metabolites analysis by UHPLC‐Q‐Orbitrap‐MS2 and multi‐elemental data obtained from ICP‐MS were combined with chemometrics for tracing the geographical origin of BMSs. Principal component analysis (PCA) was used to evaluate the overall grouping of samples. In contrast, partial least squares‐discriminant analysis (PLS‐DA) and orthogonal partial least squares discriminant analysis (OPLS‐DA) were employed for authentication. Results: PLS‐DA and OPLS‐DA models were fully validated (R2Y and Q2 values > 0.5). Variable importance of various projections was applied to obtain valuable data about differential elements (seven markers were identified) and metabolites (23 markers were identified) with high discrimination potential. The outcomes presented in this study serve as an appropriate framework for developing novel discrimination approaches in food origin screening. The current work was designed to authenticate black mahlab seeds (BMSs) grown in different regions of Sudan. The fingerprinting analysis combined with chemometrics was applied to clarify the differences present in the chemical elements and metabolites of BMSs. A panel of seven chemical elements and 23 metabolites with importance in the projection score greater than 1 were identified as key markers for the discrimination of BMSs. Overall, our results demonstrate the efficacy of chemical analysis and chemometrics in authenticating the geographic origin of BMSs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The endogenous rural development paradigm is one of the newest philosophical ideas that has occupied an essential part of rural studies. However, there is still no deep understanding of the different aspects of this new paradigm. This study aims to investigate the different paradigmatic aspects of Neo-Endogenous rural development. This article completes the existing narratives about the emerging paradigm in such a way that it This study has done a systematic review and thematic analysis of 47 texts using PRISMA guidelines. Examining these texts led to the identification of four main themes and 18 sub-themes. The results show that a significant part of the Endogenous endogenous rural development literature has been produced in Europe. The main focus of the studies covers the topics of structural ontology and relational epistemology, and fewer studies have dealt with ethics and process-communicative praxis. Subjective results show that the Neo_Endogenous rural development paradigm has surpassed the previous two paradigms of exogenous development and endogenous development regarding paradigmatic characteristics. The most important lesson of this paradigm is to consider the interaction between local actors, institutions, and external forces within the networks in forming rural development policies, which can give us a deeper understanding of the rural development process. However, applying this new paradigm requires careful examination of each rural area's unique characteristics and dynamics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Verification of cowpea geographical origin has become more critical in recent times to guarantee food safety and enhance government management of potentially hazardous pesticide farming practices. Cowpea samples harvested in five major southern provinces in China were characterized by stable isotopes (δ13C, δ15N, δ2H, δ18O) and elemental contents (Ca, K, Mg, Na, P, Cu, Fe, Mn, Ni, Zn, Mo, C, N) using inductively coupled plasma mass spectrometry. A combined isotope and elemental approach reflected regional elemental differences from China's eastern seaboard that are uptaken by cowpea from the environment. Results showed that many of the isotope and elemental variables differed significantly among production regions, with Cu, Zn, δ18O, P, K, δ13C, Mo, Ca, and Mg identified as the most important classification variables of projection (VIP > 1). Stable isotopes and elemental contents were representative of regional and local climate, soil geology, and fertilization practices, respectively. The cowpea elemental contents analyzed in this study were all within the expected guideline range for Asian vegetables and posed no health concerns to consumers. Two regional models and one local model based on supervised pattern recognition method of Partial Least Squares‐Discriminant Analysis (PLS‐DA) were developed, and the overall discrimination accuracies ranged from 78.0% to 99.2%. These chemometric models provide a useful method to verify Chinese cowpea origins and improve food safety controls for consumers.

Abstract This article examined the effect of geographical (different climate conditions) and floral origins on some quality parameters of honey including the activity of diastase enzyme. Moreover, some non-quality parameters were investigated such as the pH, fructose, glucose, ratio of fructose/glucose and invertase. The honey samples were collected from Asir (cold climate) and Jazan (hot climate) regions at the southwestern part of Saudi Arabia. The geographical origin significantly affected the mean value moisture of the Acacia honey (p-value = 0.02), conductivity of the polyfloral honey (p-value = 0.03), sucrose of the Acacia honey (p-value = 0.02), diastase activity of the Acacia (p-value = 0.001), Ziziphus (p-value = 0.046) and polyfloral honey (p-value ≤ 0.001), fructose of the Acacia honey (p-value = 0.01), glucose of the Ziziphus honey (p-value = 0.03), fructose/ glucose ratio of the Ziziphus honey (p-value = 0.035), and invertase activity of the polyfloral honey (p-value ≤ 0.001). Regarding the effect of the floral origin of the honey from Asir region, the sucrose percentage of the Acacia honey was significantly more than that of the polyfloral honey (p- value = 0.003), the diastase activity of the Acacia honey was significantly more than its activity in the Ziziphus honey (p- value = 0.044), glucose percentage of the Ziziphus honey was significantly more the glucose percentage of the Acacia honey (p-value = 0.009) and the fructose/ glucose ratio of the Ziziphus honey was significantly more than that of the Acacia and polyforal honeys (p-value = 0.011 and p-value = 0.045, respectively). Concerning the significant effects of the floral origin on the quality parameters of the honey samples from Jazan region, the moisture of the Ziziphus honey was significantly increased when compared to the moisture of the Acacia honey (p-value = 0.038), the acidity of the polfloral honey was significantly more than the acidity of the Acacia honey (p-value = 0.049), the sum of fructose and glucose of the polyfloral honey was significantly increased compared to that of the Acacia honey (p-value = 0.015), the pH of the Ziziphus hiney was significantly more than the pH of the polyfloral honey (0.011) and the fructose of the polfloral honey was significantly more than that of the Acacia honey (p-value = 0.031). The effect of the geographical origin of the honey samples on their quality parameters depends on their floral origin and the effect of their floral origin differs according to their geographical origin. This article suggests considering collectively the geographical and floral origins effect when developing honey standards. However, the Codex standards for honey started considering this issue when it changed the standard concentration of HMF in honey from not more than 80–40 mg/Kg for honeys from cold climate and 80 mg/Kg for honeys from hot climates.

Abstract Background Red pepper paste is a common ingredient used in food in Korea. The discrimination of the geographical origin of agricultural products is important to protect the agricultural industry and customers from the misinformation regarding the product origin. Several studies have attempted to identify the geographical origin of red pepper based on its characteristic features using diverse methods, such as inorganic elemental analysis. However, similar studies on red pepper pastes have not been conducted thus far. Results In, this study, we established methods based on inductively coupled plasma optical emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) for determining inorganic elements in red pepper pastes. The limit of detection (LOD) of ICP-AES was in the range of 0.006–0.531 mg∙kg−1 and the limit of quantification (LOQ) was 0.017–1.593 mg∙kg−1. In addition, LOD and LOQ ranges for ICP-MS were 0.001–1.553, and 0.002–5.176 μg∙kg−1, respectively. The concentrations of Ca, K, Mg, Na, P, S, Cr, Mn, Co, Ni, Cu, Ga, As, Sr, Zr, Mo, Pd, Cd, Sn, Sb, Ce, Pt, Pb, and U were high in the Korean red pepper paste. All the employed discrimination models could clearly distinguish between Korean and Chinese red pepper pastes. In particular, among the four different models, CDA showed the most accurate ability to discriminate the geological origin of Korean and Chinese red pepper paste compared to that achieved using the other models with 100% accuracy. Conclusions Based on, the findings of this study, the use of ICP-AES and ICP-MS analyses for discriminating the inorganic elements in food products in combination with the aforementioned statistical analysis models could help the mitigation of issues associated with the misinformation of the geographical origin of agricultural products, aiding customer protection. Graphical Abstract

To compare the volatile flavor characteristics of red jujube from different origins, volatile aroma components of jujube fruit from five origins (Xinzheng, Lingbao, Yulin, Hotan and Dezhou) were analyzed by headspace solid-phase microextraction combined with gas chromatography mass spectrometry. The results showed that a total of 51 compounds were identified, among which the most volatile compounds were contained in Xinjiang Hotan jujube. Combined with multivariate statistical analysis, the differences in the overall volatile flavor substances of red jujube from different origins could be well distinguished. Under the conditions of satisfying the variable importance in projection>1 and P

To investigate the effects of origin and storage time on wine quality, HPLC and HS-SPME-GC–MS were employed to analyze the chemical compositions and VOCs of Cabernet Sauvignon wines from four sub-regions in Xinjiang and with five storage times. Results showed that most of the organic acids were reduced and the anti-oxidant activity decreased during storage. Wines from different regions exhibited differences in the contents of gallic acid, (+)-catechin and (−)-epicatechin. A total of 68 VOCs were identified, with esters and alcohols being the most dominant VOCs. In total, nine VOCs with OAV > 1 were detected in all samples, impacting wine flavor. Cabernet Sauvignon wines were differentiated using PCA and OPLS-DA. 15 and 17 chemical markers were screened based on VIP > 1 for wine of different sub-regions and bottle storage times, respectively. This research provides a data reference for quality characterization of wines obtained from different sub-regions and bottle storage times in Xinjiang.

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. [ABSTRACT FROM AUTHOR]

Geographic origins together with environmental conditions (temperature, rainfall, humidity, altitude) during harvest, could alter the olive oil quality, nutritional value, and traceability. The impact of geographic region (Bursa PDO, Hatay, Mersin, and Izmir NPDO) and conditions at harvest years (2016 and 2017) on the quality and traceability of Gemlik virgin olive oils were investigated. Environmental conditions (high temperature and relative humidity, low rainfall) resulted in low quality due to increase in FFA, linoleic acid, PUFA, Δ7‐stigmastenol, stigmasterol and total sterols in oils obtained from southeast (Hatay and Mersin) NPDO regions. Bursa PDO and Izmir NPDO Gemlik virgin olive oils were high in oleic acid, MUFA, hydroxtytyrsol, tyrosol, antioxidant activity and, ∆5‐avenesterol and discriminated by 99% accuracy with discriminant analysis. PCA and DA can easily detect and distinguish differences in virgin olive oils quality and purity coming from different growing regions and subjected to inverse environmental conditions. Bursa PDO and Izmir NPDO growing regions have better conditions for Gemlik olives cultivation to obtain high quality and oxidative resistant Gemlik virgin olive oils. [ABSTRACT FROM AUTHOR]

The present study aimed to differentiate Mediterranean honey samples with regards to their botanical and geographical origin, using UV-vis spectroscopy, combined with multivariate statistical analysis and chemometrics. A total of 151 commercial honey samples were collected from different countries (Greece, Malta, Spain, Tunisia, and Turkey), which were characterized as thyme, pine, or polyfloral. Discrimination of geographical and botanical origin was achieved successfully with Principal Component Analysis (PCA) together with Random Forest (RF), Partial Least Squares – Discriminant Analysis (PLS – DA), and Data Driven – Soft Independent Modelling of Class Analogies (DD-SIMCA) using the spectral range of 220–550 nm. DD-SIMCA achieved very high accuracy, sensitivity, and specificity on the discrimination of both the geographical and botanical origin of honey samples. The results support the good predictive capability of UV-vis spectroscopy combined with chemometrics for the determination of honey origin. This methodology could be utilized for the rapid and non-destructive assessment of Mediterranean honey origin. [ABSTRACT FROM AUTHOR]

Recent studies on the origin of food rarely focus on the source of counties or even small-scale districts, but traceability of small-scale districts of food is the research trend and difficulty for future research. The geographical origin of Boletus bainiugan is of great significance to its safety and economic value. The aim of this study was to provide a new way for the traceability of B. bainiugan in small-scale districts using synchronous two-dimensional correlation spectroscopy (2D-COS) images combined with residual convolutional neural network (ResNet) model. In our study, 550 wild-grown B. bainiugan mushrooms were collected in 28 small-scale districts and their fourier transform near infrared (FT-NIR) were collected. First, samples from nine regions in Yunnan province were identified, and then samples from Chuxiong, Kunming and Yuxi were identified. Most of these models had a 100% accuracy rate and were suitable for identifying the geographical origin of B. bainiugan. In addition, the combination of 7000–4000 cm−1 and 15 contour lines achieves the most reliable accuracy in continuous classification model, with 100% accuracy rate in the training set, the test set and the external verification set, and the loss values were all close to zero (0.018, 0.069, 0.040 and 0.028). The results indicated that the synchronized two-dimensional correlation spectroscopy images combined with the ResNet model had excellent discriminant ability, and this analysis method provided a possibility for food identification in small-scale districts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

[Objective]To establish the characteristic fingerprint of tobacco traceability by the determination of mineral elements in tobacco leaves combined with chemometrics method, and to verify the accuracy and reliability of the method. [Method]The contents of inorganic elements in 25 tobacco leaf samples planted in soils from five different areas were analyzed by ICP-OES combined with microwave digestion method. The data matrix was sequentially analyzed by one-way-ANOVA, principal component analysis and discriminant analysis. [Result]There were significant differences among tobacco leaves planted in different soils about the contents of mineral elements. The first two principal components obtained by principal component analysis could approximately reflect the information of original data, and realized the traceability of tobacco planted in soils from different regions by analyzing the mineral elements’ contents. Fisher discriminant analysis and leave-one-out cross-validation test were performed to validate these results, ideal verification results were obtained, and the correct discrimination rate were 100%, respectively. [Conclusion]Based on the specific differences of inorganic elements could discriminate the geographical origins of tobacco. [ABSTRACT FROM AUTHOR]

Traceability in the coffee supply chain is crucial for ensuring transparency and authenticity. It helps safeguard the interests of both producers and consumers by minimizing the risk of fraudulent practices and ensuring fair trade. Different geographical origins can lead to variances in coffee taste, quality of product and economic value. Just because controlling the authenticity of the geographical origin of coffee beans is of great importance for producers and consumers worldwide. This study determined parameters (caffeine, chlorogenic acids, total antioxidant capacity (TAC), total polyphenols content (TPC) volatile compounds, pH, water activity aw and dry matter) based on which was testing classification of geographical origin of coffee beans. For this research, six samples of 100% Coffea arabica medium roasting coffee beans, from the two major growing country of America and Africa were studied. As part of the research, variability in chemical composition based on geographical origin was confirmed. For America samples were significant parameters TAC, 4.5- dicaffeoylquinic acid, 3.5-dicaffeoylquinic acid, caffeine and chlorogenic acids. Second group was sample from Africa. For these samples was determine parameter like TPC, cryptochlorogenic acid, neochlorogenic acid and pH. Volatile substances, caffeine, pH, chlorogenic acids were considered the most important parameters that can help to identify the geographical origin. [ABSTRACT FROM AUTHOR]

Background: Red pepper paste is a common ingredient used in food in Korea. The discrimination of the geographical origin of agricultural products is important to protect the agricultural industry and customers from the misinformation regarding the product origin. Several studies have attempted to identify the geographical origin of red pepper based on its characteristic features using diverse methods, such as inorganic elemental analysis. However, similar studies on red pepper pastes have not been conducted thus far. Results: In, this study, we established methods based on inductively coupled plasma optical emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) for determining inorganic elements in red pepper pastes. The limit of detection (LOD) of ICP-AES was in the range of 0.006–0.531 mg∙kg−1 and the limit of quantification (LOQ) was 0.017–1.593 mg∙kg−1. In addition, LOD and LOQ ranges for ICP-MS were 0.001–1.553, and 0.002–5.176 μg∙kg−1, respectively. The concentrations of Ca, K, Mg, Na, P, S, Cr, Mn, Co, Ni, Cu, Ga, As, Sr, Zr, Mo, Pd, Cd, Sn, Sb, Ce, Pt, Pb, and U were high in the Korean red pepper paste. All the employed discrimination models could clearly distinguish between Korean and Chinese red pepper pastes. In particular, among the four different models, CDA showed the most accurate ability to discriminate the geological origin of Korean and Chinese red pepper paste compared to that achieved using the other models with 100% accuracy. Conclusions: Based on, the findings of this study, the use of ICP-AES and ICP-MS analyses for discriminating the inorganic elements in food products in combination with the aforementioned statistical analysis models could help the mitigation of issues associated with the misinformation of the geographical origin of agricultural products, aiding customer protection. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the influence of cultivar and environment on the chemical composition of Arbequina and Empeltre olive oils, and their contribution to geographical identification of olive oils from Aragon. A total of 260 olive oil samples from different cultivars (Arbequina, Empeltre, Royal de Calatayud, Alquezrana, and Royeta de Asque) from the three main oil‐producing areas of Aragon, located in northeast Spain, were selected. Fatty acid and sterol composition were analyzed in the course of three crop years (2017, 2018, and 2019). Cultivar was found the main factor influencing the variability of palmitic, palmitoleic, and linolenic fatty acid content, whereas geographic origin was the main contributor to variation in oleic and linoleic fatty acids in Arbequina and Empeltre olive oils. Cultivar also had a significant impact on sterol composition, although the effect of the production area also showed a significant effect on these oils. Crop year showed limited relevance, except for oleic and linoleic fatty acids. The interaction between the environment (e.g., crop year and geographical factors) and the cultivar (Arbequina and Empeltre) exerted a significant influence on oleic/linoleic (O/L) ratio and Δ7‐stigmastenol content, particularly in the southeast area of Aragon during the crop year with higher temperatures and drier conditions. Principal component analysis (PCA) and discriminant analysis (DA) confirmed the discriminative potential of the geographic production zone as a factor enabling the differentiation of olive oils from Aragon based on the major fatty acids and sterols. [ABSTRACT FROM AUTHOR]

The volatile and olfactory profiles of three Portuguese olive oils with protected designations of origin (PDO) were studied: "Azeite do Alentejo Interior", "Azeites da Beira Interior", and "Azeite de Trás-os-Montes". Seven classes of volatiles were identified, with aldehydes, followed by hydrocarbons and alcohols, the most prevalent (5.63, 2.92, and 2.79 mg/kg olive oil, respectively). The "Azeites da Beira Interior" oils exhibited the highest amount of volatiles (18.2 ± 4.6 mg/kg olive oil) compared to the oils from the other two PDOs. Ten positive olfactory sensations were detected, and a significant effect of the PDO on the intensities of fruity, apple, cabbage, tomato, dry and fresh herbs was observed. Specifically, "Azeite do Alentejo Interior" PDO oils were characterized as fruity-ripe, while "Azeite de Trás-os-Montes" PDO oils were labelled as fruity-green. Conversely, "Azeites da Beira Interior" PDO oils encompassed both fruity-ripe and fruity-green oils. Unique volatile and olfactory fingerprints were established for each PDO, allowing the linear discrimination of the oils according to the PDO, with a predictive sensitivity of 98.0 ± 4.2% (repeated K-fold-CV). Furthermore, a lab-made electronic nose successfully discriminated the studied oils based on the PDO, with a predictive accuracy of 99.7 ± 2.0% (repeated K-fold-CV). This device also allowed predicting the concentrations of the three main volatile classes found in the oils through multiple linear regression models (R2 ≥ 0.923 ± 0.101 and RMSE ≤ 1.32 ± 0.72 mg/kg oil; repeated K-fold-CV). These findings underscore the potential of the electronic nose as a reliable traceability tool to authenticate the PDO declaration of Portuguese olive oils, and broaden its use beyond non-PDO oils from a specific geographical area to encompass a national scale. [ABSTRACT FROM AUTHOR]

Consumer trust in fruit industry is partly depended on the labeling of its geographical origin. The accurate label is not only of great significance to consumers and retailers, but also for their brands shaping. This work evaluated the utility of Fourier transform near-infrared spectroscopy (FT-NIRS) in identifying the geographical origins of Chinese pears including Laiyang pear (Pyrus bretschneideri cv. Laiyang), Dangshan pear (Pyrus bretschneideri Rehd. cv. Dangshansuli), and Korla pear (Pyrus sinkiangensis T. T. Yu). FT-NIR spectra of 316 pears from three different geographical origins were obtained in the spectral range of 10,000–4000 cm−1. Preliminary investigation was first conducted based on principal component analysis (PCA) to confirm the similar spectral features of samples within one class, and the results showed that PC1 and PC2 were effective for the identification. Partial least square discriminant analysis (PLS-DA) models based on full spectra indicated that the optimal classification based on second-order derivative (Der2) preprocessed spectra achieved 100% correct classification rates (CCRs) in all datasets. After that, the multivariate selection strategies including two-dimensional correlation spectroscopy (2D-COS), competitive adaptive reweighted sampling (CARS) combined with successive projection algorithm (SPA), and uninformative variable elimination (UVE) combined with SPA were individually optimized for selecting the characteristic wavenumbers from full FT-NIR spectra. On the basis of simplified PLS-DA models establishment, the CCRs in calibration, cross-validation, and prediction sets of the selected CARS-SPA-PLS-DA model achieved 98.58%, 98.11%, and 98.08%, respectively. This research demonstrated that FT-NIRS combined with multivariate analysis can be successfully employed to identify the geographical origins of Chinese pears. [ABSTRACT FROM AUTHOR]

To compare the volatile flavor characteristics of red jujube from different origins, volatile aroma components of jujube fruit from five origins (Xinzheng, Lingbao, Yulin, Hotan and Dezhou) were analyzed by headspace solid-phase microextraction combined with gas chromatography mass spectrometry. The results showed that a total of 51 compounds were identified, among which the most volatile compounds were contained in Xinjiang Hotan jujube. Combined with multivariate statistical analysis, the differences in the overall volatile flavor substances of red jujube from different origins could be well distinguished. Under the conditions of satisfying the variable importance in projection>1 and P<0.05, 13 differential characteristic aroma substances, including methyl caproate, methyl decanoate, hexanal, and benzaldehyde, were screened out from red jujube samples, which imparted unique almond, sweet, and fruity flavors to red jujube. The results of this study clarify the chemical substance basis of the aroma quality of red jujube from different origins, provide a new idea for the origin tracing study of red jujube, and further provide a scientific basis for the rational processing and utilization of jujube fruit resources. [ABSTRACT FROM AUTHOR]

Abstract: Gemlik is a cultivar that grows in a distinct region of Turkiye and is ideal for brine fermentation of brine black table olives. Bursa Protected Designated Origin (PDO) and Izmir non-PDO Gemlik table olives have high levels of oleic acid (74%), total phenol (190 mg/kg), and dry matter (57%), while being low in linoleic acid (8%). The pH values and salt contents were observed to be in the range of 4.1 to 4.3 and 3.9% to 4.8%, respectively. During the fermentation of Gemlik table olives, a mass transfer occurred, resulting in a reduction in reducing sugar and total sugar contents as well as an increase in the salt content of the olives. Despite the reduction of phenolic content in both Gemlik PDO and non-PDO table olives, their antioxidant capacity remains high after fermentation. The oil content, antioxidant activity, phenolic contents, palmitic, palmitoleic, oleic, and linoleic acids were all found to be significant variables in distinguishing between Gemlik PDO and non-PDO table olives using PLS-DA analysis. There is a statistically significant correlation between the phenolic content and oleic (0.588) and linoleic (–0.659) acids (p < 0.05). Bursa PDO and Izmir non-PDO exhibit enhanced nutritional quality and antioxidant activity, unequivocally differentiating them from Hatay and Mersin non-PDO Gemlik table olives with 98% accuracy through discriminant analysis (p < 0.05). PLS-DA and DA can effectively identify variations in the quality of Turkish-style black table olives preserved in brine, originating from PDO and non-PDO growing areas. [ABSTRACT FROM AUTHOR]

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Copyright of Xinjiang Agricultural Sciences is the property of Xinjiang Agricultural Sciences Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)