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1.
Heliyon ; 10(17): e37450, 2024 Sep 15.
Article in English | MEDLINE | ID: mdl-39296179

ABSTRACT

Distinguish the geographical origin of the pear is important due to the increasingly valued brand protection and reducing the potential food safety risks. In this study, the profiles of stable isotopes (δ13C, δ15N, δ2H, δ18O) and the contents of 16 elements in pear peer from four production areas were analyzed. The δ13C, δ15N, δ2H, δ18O and 12 elements were significantly different (p < 0.05) in the four production areas. Chemometrics analysis including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and linear discriminant analysis (LDA) were exploited for geographical origin classification of samples. OPLS-DA analysis showed that crucial variables (δ13C, δ18O, δ2H, Ni, Cd, Ca, δ15N, Sr and Ga) are more relevant for the discrimination of the samples. OPLS-DA achieved pear origin accuracy rates of 87.76 % by combining stable isotope ratios and elemental contents. LDA had a higher accuracy rate than OPLS-DA, and the LDA analysis showed that the original discrimination rate reached to 100 %, while the cross-validated rate reached to 95.7 %. These studies indicated that this method could be used to assess the geographical discrimination of pear from different producing areas and could potentially control the fair trade of pear in fruit markets.

2.
Molecules ; 29(18)2024 Sep 19.
Article in English | MEDLINE | ID: mdl-39339436

ABSTRACT

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.


Subject(s)
Food Analysis , Magnetic Resonance Spectroscopy , Solanum lycopersicum , Solanum lycopersicum/chemistry , Magnetic Resonance Spectroscopy/methods , Food Analysis/methods , Reproducibility of Results
3.
Foods ; 13(18)2024 Sep 23.
Article in English | MEDLINE | ID: mdl-39335942

ABSTRACT

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.

4.
Sci Rep ; 14(1): 22708, 2024 Sep 30.
Article in English | MEDLINE | ID: mdl-39349712

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.

5.
Food Chem ; 461: 140903, 2024 Dec 15.
Article in English | MEDLINE | ID: mdl-39178543

ABSTRACT

Lycium barbarum L. (L. barbarum) is renowned worldwide for its nutritional and medicinal benefits. Rapid and accurate identification of L.barbarum's geographic origin is essential because its nutritional content, medicinal efficacy, and market price significantly vary by region. This study proposes an innovative method combining hyperspectral imaging (HSI), nuclear magnetic resonance (NMR), and an improved ResNet-34 deep learning model to accurately identify the geographical origin and geographical indication (GI) markers of L.barbarum. The deep learning model achieved a 95.63% accuracy, surpassed traditional methods by 6.26% and reduced runtime by 29.9% through SHapley Additive exPlanations (SHAP)-based feature selection. Pearson correlation analysis between GI markers and HSI characteristic wavelengths enhanced the interpretability of HSI data and further reduced runtime by 33.99%. This work lays the foundation for portable multispectral devices, offering a rapid, accurate, and cost-effective solution for quality assurance and market regulation of L.barbarum products.


Subject(s)
Deep Learning , Lycium , Magnetic Resonance Spectroscopy , Lycium/chemistry , Magnetic Resonance Spectroscopy/methods , Hyperspectral Imaging/methods , Geography
6.
Food Res Int ; 192: 114758, 2024 Sep.
Article in English | MEDLINE | ID: mdl-39147491

ABSTRACT

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.


Subject(s)
Algorithms , Hyperspectral Imaging , Panax , Support Vector Machine , Panax/classification , Panax/chemistry , Hyperspectral Imaging/methods , Light , X-Rays
7.
Molecules ; 29(15)2024 Jul 31.
Article in English | MEDLINE | ID: mdl-39125022

ABSTRACT

Olive leaves are a rich source of polyphenols with healthful properties and represent one of the most abundant waste products of olive oil production. The aims of this study were to explore the phenolic composition of olive leaves from the three main Tuscan cultivars (Leccino, Moraiolo and Frantoio) collected in Siena and Grosseto provinces and to investigate the possible use of these compounds as varietal and geographic origin markers. Discriminant factorial analysis (DFA) was used for distinguishing between different cultivars and locations. Apigenin and caffeoyl-secologanoside showed significant differences between cultivars. DFA showed that ligstroside, apigenin and luteolin have the most influence in determining the differences between sites, whereas total polyphenols, olacein and hydroxytyrosol acetate allowed for separation between leaves from the same province. The results of the present study indicate that concentrations of phenolic compounds, measured through high-resolution mass spectrometry, can be used as a marker for both the cultivar and of geographical origin of olive leaves, and possibly of olive-related products, as well as across small geographic scales (less than 50 km distance between sites).


Subject(s)
Olea , Phenols , Plant Leaves , Olea/chemistry , Olea/classification , Plant Leaves/chemistry , Phenols/analysis , Phenols/chemistry , Italy , Polyphenols/analysis , Polyphenols/chemistry , Biomarkers , Geography , Plant Extracts/chemistry
8.
J Food Sci ; 89(8): 4806-4822, 2024 Aug.
Article in English | MEDLINE | ID: mdl-39013018

ABSTRACT

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.


Subject(s)
Corylus , Nuts , Principal Component Analysis , Spectroscopy, Near-Infrared , Spectrum Analysis, Raman , Corylus/chemistry , Spectrum Analysis, Raman/methods , Spectroscopy, Near-Infrared/methods , Discriminant Analysis , Turkey , Nuts/chemistry , Support Vector Machine , Least-Squares Analysis , Chemometrics/methods , Geography
9.
Foods ; 13(13)2024 Jul 02.
Article in English | MEDLINE | ID: mdl-38998613

ABSTRACT

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.

10.
Heliyon ; 10(12): e33094, 2024 Jun 30.
Article in English | MEDLINE | ID: mdl-38948039

ABSTRACT

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.

11.
Sci Rep ; 14(1): 13342, 2024 06 10.
Article in English | MEDLINE | ID: mdl-38858425

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.


Subject(s)
Coffea , Spectroscopy, Near-Infrared , Spectroscopy, Near-Infrared/methods , Coffea/chemistry , Discriminant Analysis , Coffee/chemistry , Seeds/chemistry
12.
Food Sci Nutr ; 12(6): 4399-4407, 2024 Jun.
Article in English | MEDLINE | ID: mdl-38873439

ABSTRACT

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.

13.
Med Trop Sante Int ; 4(1)2024 03 31.
Article in French | MEDLINE | ID: mdl-38846114

ABSTRACT

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.


Subject(s)
COVID-19 , Communicable Diseases , Tropical Medicine , Humans , COVID-19/epidemiology , France/epidemiology , Communicable Diseases/epidemiology , Clinical Reasoning , Prejudice , Social Determinants of Health , Pandemics
14.
Food Chem X ; 22: 101455, 2024 Jun 30.
Article in English | MEDLINE | ID: mdl-38798798

ABSTRACT

There is a lack of a reliable tool for quickly determining the geographical origins of saffron (SFR). Ion mobility spectrometry (IMS) has emerged as a promising method for rapid authentication. In this study, 232 Iranian SFR samples harvested in five distinct areas (Khorasan, Azerbaijan, Golestan, Fars, and Isfahan) were analyzed by IMS coupled with chemometric methods. The principal component analysis (PCA) was applied for analyzing the collected IMS data, utilizing three principle components (PCs) that accounted for 81 % of the explained variance. Moreover, the partial least squares-discriminant analysis (PLS-DA) demonstrated the average sensitivity and specificity rates, of 72.3 % to 92.5 % for the exernal test set and 75.5 % to 94.3 % for training set. The accuracy values were ≥ 85.0 % for the prediction set for all classes of samples. The results of this study revealed a successful application of IMS and chemometric methods for rapid geographical authentication of saffron samples in Iran.

15.
J Tradit Chin Med ; 44(3): 505-514, 2024 Jun.
Article in English | MEDLINE | ID: mdl-38767634

ABSTRACT

OBJECTIVE: To evaluate the quality of Moyao (Myrrh) in the identification of the geographical origin and processing of the products. METHODS: Raw Moyao (Myrrh) and two kinds of Moyao (Myrrh) processed with vinegar from three countries were identified using near-infrared (NIR) spectroscopy combined with chemometric techniques. Principal component analysis (PCA) was used to reduce the dimensionality of the data and visualize the clustering of samples from different categories. A classical chemometric algorithm (PLS-DA) and two machine learning algorithms [K-nearest neighbor (KNN) and support vector machine] were used to conduct a classification analysis of the near-infrared spectra of the Moyao (Myrrh) samples, and their discriminative performance was evaluated. RESULTS: Based on the accuracy, precision, recall rate, and F1 value in each model, the results showed that the classical chemometric algorithm and the machine learning algorithm obtained positive results. In all of the chemometric analyses, the NIR spectrum of Moyao (Myrrh) preprocessed by standard normal variation or Multivariate scattering correction combined with KNN achieved the highest accuracy in identifying the geographical origins, and the accuracy of identifying the processing technology established by the KNN method after first-order derivative pretreatment was the best. The best accuracy of geographical origin discrimination and processing technology discrimination were 0.9853 and 0.9706 respectively. CONCLUSIONS: NIR spectroscopy combined with chemometric technology can be an important tool for tracking the origin and processing technology of Moyao (Myrrh) and can also provide a reference for evaluations of its quality and the clinical use.


Subject(s)
Spectroscopy, Near-Infrared , Spectroscopy, Near-Infrared/methods , Principal Component Analysis , Chemometrics/methods , Drugs, Chinese Herbal/chemistry , Geography , Algorithms , China
16.
Food Chem X ; 22: 101445, 2024 Jun 30.
Article in English | MEDLINE | ID: mdl-38764786

ABSTRACT

The aim of this study was the valorisation of cactus (or prickly pear, Opuntia ficus-indica) seeds growing in six different regions of Morocco. Moisture, proteins, lipids profile, total polyphenols content, oxidative stability, and antioxidant activity were investigated. The Folin-Ciocalteu test highlighted the abundant presence of phenolic compounds (165 to 225 mg EAG/100 g of extract) and a significant antioxidant capacity against DPPH free radicals. The seeds contained protein (7-9.25%) and lipids (2.7-5%). Cactus oil quality indices such as acidity and peroxide value were below 1.2% and 10 mEq.O2/kg, respectively. GC analysis revealed that linoleic and oleic acid percentages ranged from 57.1 to 63.8%, and 13.5 to 18.7%, respectively. Cactus seed oil was rich in tocopherols (500-680 mg/kg) and phytosterols (8000-11,100 mg/kg) with a predominance of γ-tocopherols and ß-sitosterol. Triacylglycerols, fatty acids and sterols composition showed small variation depending on the geographical origin, while the individual tocopherol profile was significantly influenced.

17.
Food Chem X ; 22: 101396, 2024 Jun 30.
Article in English | MEDLINE | ID: mdl-38699585

ABSTRACT

With the proliferation of the consumer's awareness of wine provenance, wines with unique origin characteristics are increasingly in demand. This study aimed to investigate the influence of geographical origins and climatological characteristics on grapes and wines. A total of 94 anthocyanins and 78 non-anthocyanin phenolic compounds in grapes and wines from five Chinese viticultural vineyards (CJ, WH, QTX, WW, and XY) were identified by UHPLC-QqQ-MS/MS. Chemometric methods PCA and OPLS-DA were established to select candidate differential metabolites, including flavonols, stilbenes, hydroxycinnamic acids, peonidin derivatives, and malvidin derivatives. CCA showed that malvidin-3-O-glucoside had a positive correlation with mean temperature, and quercetin-3-O-glucoside had a negative correlation with precipitation. In addition, enrichment analysis elucidated that the metabolic diversity in different origins mainly occurred in flavonoid biosynthesis. This study would provide some new insights to understand the effect of geographical origins and climatological characteristics on phenolic compounds in grapes and wines.

18.
Spectrochim Acta A Mol Biomol Spectrosc ; 318: 124480, 2024 Oct 05.
Article in English | MEDLINE | ID: mdl-38781824

ABSTRACT

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.


Subject(s)
Algorithms , Machine Learning , Oryza , Spectroscopy, Near-Infrared , Oryza/chemistry , Oryza/classification , Spectroscopy, Near-Infrared/methods , Discriminant Analysis , Least-Squares Analysis , Geography , Principal Component Analysis , Neural Networks, Computer , Thailand
19.
Food Chem X ; 22: 101412, 2024 Jun 30.
Article in English | MEDLINE | ID: mdl-38707779

ABSTRACT

Identifying the geographic origin of a wine is of great importance, as origin fakery is commonplace in the wine industry. This study analyzed the mineral elements, volatile components, and metabolites in wine using inductively coupled plasma-mass spectrometry, headspace solid phase microextraction gas chromatography-mass spectrometry, and ultra-high-performance liquid chromatography-quadrupole-exactive orbitrap mass spectrometry. The most critical variables (5 mineral elements, 13 volatile components, and 51 metabolites) for wine origin classification were selected via principal component analysis and orthogonal partial least squares discriminant analysis. Subsequently, three algorithms-K-nearest neighbors, support vector machine, and random forest -were used to model single and fused datasets for origin identification. These results indicated that fused datasets, based on feature variables (mineral elements, volatile components, and metabolites), achieved the best performance, with predictive rates of 100% for all three algorithms. This study demonstrates the effectiveness of a multi-source data fusion strategy for authenticity identification of Chinese wine.

20.
Antioxidants (Basel) ; 13(5)2024 Apr 26.
Article in English | MEDLINE | ID: mdl-38790631

ABSTRACT

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.

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