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Keywords = protected geographical indication discrimination

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23 pages, 3692 KiB  
Article
Metabolic Profiling and Stable Isotope Analysis of Wines: Pilot Study for Cross-Border Authentication
by Marius Gheorghe Miricioiu, Roxana Elena Ionete, Diana Costinel, Svetlana Simova, Dessislava Gerginova and Oana Romina Botoran
Foods 2024, 13(21), 3372; https://doi.org/10.3390/foods13213372 - 23 Oct 2024
Cited by 5 | Viewed by 1466
Abstract
Globalization and free market dynamics have significantly impacted state economies, particularly in the wine industry. These forces have introduced greater diversity in wine products but have also heightened the risk of food fraud, especially in high-value commodities like wine. Due to its market [...] Read more.
Globalization and free market dynamics have significantly impacted state economies, particularly in the wine industry. These forces have introduced greater diversity in wine products but have also heightened the risk of food fraud, especially in high-value commodities like wine. Due to its market value and the premium placed on quality, wine is frequently subject to adulteration. This issue is often addressed through regulatory trademarks on wine labels, such as Protected Designation of Origin (PDO) and Protected Geographic Indication (PGI). In this context, the metabolic profiles (organic acids, carbohydrates, and phenols) and stable isotope signatures (δ13C, δ18O, D/HI, and D/HII) of red and white wines from four agroclimatically similar regions were examined. The study explored how factors such as grape variety, harvest year, and geographical origin affect wine composition, with a particular focus on distinguishing samples from cross-border areas. Multivariate statistical analysis was used to assess the variability in wine composition and to identify distinct groups of samples. Preliminary results revealed that organic acids and volatile compounds were found in lower concentrations than carbohydrates but were significantly higher than phenols, with levels ranging between 1617 mg/L and 6258 mg/L. Carbohydrate content in the wines varied from 8285 mg/L to 14662 mg/L. Principal Component Analysis (PCA) indicated certain separation trends based on the variance in carbohydrates (e.g., fructose, glucose, galactose) and isotopic composition. However, Discriminant Analysis (DA) provided clear distinctions based on harvest year, variety, and geographical origin. Full article
(This article belongs to the Special Issue Advanced Research and Development of Carbohydrate from Foods)
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12 pages, 2188 KiB  
Article
Multi-Elemental Analysis and Geographical Discrimination of Greek “Gigantes Elefantes” Beans Utilizing Inductively Coupled Plasma Mass Spectrometry and Machine Learning Models
by Eleni C. Mazarakioti, Anastasios Zotos, Vassilios S. Verykios, Efthymios Kokkotos, Anna-Akrivi Thomatou, Achilleas Kontogeorgos, Angelos Patakas and Athanasios Ladavos
Foods 2024, 13(18), 3015; https://doi.org/10.3390/foods13183015 - 23 Sep 2024
Viewed by 1083
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 [...] Read more.
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. Full article
(This article belongs to the Section Food Analytical Methods)
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11 pages, 1515 KiB  
Article
Stable Isotope Ratio Analysis for the Geographic Origin Discrimination of Greek Beans “Gigantes-Elefantes” (Phaseolus coccineus L.)
by Anna-Akrivi Thomatou, Eleni C. Mazarakioti, Anastasios Zotos, Efthimios Kokkotos, Achilleas Kontogeorgos, Angelos Patakas and Athanasios Ladavos
Foods 2024, 13(13), 2107; https://doi.org/10.3390/foods13132107 - 2 Jul 2024
Cited by 2 | Viewed by 1536
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Novel Techniques for Food Authentication)
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15 pages, 2581 KiB  
Article
A Study on Milk and Caciocavallo Cheese from Podolica Breed in Basilicata, Italy
by Giuseppe Natrella, Pasquale De Palo, Aristide Maggiolino and Michele Faccia
Dairy 2023, 4(3), 482-496; https://doi.org/10.3390/dairy4030032 - 31 Aug 2023
Cited by 7 | Viewed by 2241
Abstract
A study was undertaken on milk and caciocavallo cheese from Podolica cattle in the Basilicata Region (Southern Italy), with a view of the possible identification of specific traits useful to protect them from imitations. More than 800 individual milk samples and 29 bulk [...] Read more.
A study was undertaken on milk and caciocavallo cheese from Podolica cattle in the Basilicata Region (Southern Italy), with a view of the possible identification of specific traits useful to protect them from imitations. More than 800 individual milk samples and 29 bulk milk samples were taken in spring–early summer from cows registered in the genealogical book of the breed; moreover, 18 samples of caciocavallo cheese were taken in the same geographical area, 9 of which had been manufactured from Podolica milk. The obtained results confirmed the high aptitude of Podolica milk to cheesemaking, even though the exceptional dry weather in the period of sampling decreased the fat content with respect to the literature data. The presence of the variant A of α-lactalbumin, a characteristic trait of Podolica milk, was ascertained in only 14% of the animals considered in the study, indicating that this feature is disappearing in the population under study. The results on caciocavallo gave useful indications, because some possible peculiar characteristics were identified, such as the lower protein to fat ratio and some aroma descriptors. More research is needed to assess if these characteristics can be used for developing a multi-functional protocol, to be extended to all Italian Podolica populations, able to discriminate the cheese from imitations. In this perspective, the application of selection strategies for increasing the frequency of the variant A of α-lactalbumin should be carefully evaluated. Full article
(This article belongs to the Section Milk Processing)
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15 pages, 1628 KiB  
Article
Compositional Differences of Greek Cheeses of Limited Production
by Eleni C. Pappa, Efthymia Kondyli, Athanasios C. Pappas, Elisavet Giamouri, Aikaterini Sarri, Alexandros Mavrommatis, Evangelos Zoidis, Lida Papalamprou, Panagiotis Simitzis, Michael Goliomytis, Eleni Tsiplakou and Constantinos A. Georgiou
Foods 2023, 12(12), 2426; https://doi.org/10.3390/foods12122426 - 20 Jun 2023
Cited by 7 | Viewed by 2653
Abstract
Greece has a long tradition in cheesemaking, with 22 cheeses registered as protected designation of origin (PDO), 1 as protected geographical indication (PGI), and 1 applied for PGI. Several other cheeses are produced locally without any registration, which significantly contribute to the local [...] Read more.
Greece has a long tradition in cheesemaking, with 22 cheeses registered as protected designation of origin (PDO), 1 as protected geographical indication (PGI), and 1 applied for PGI. Several other cheeses are produced locally without any registration, which significantly contribute to the local economy. The present study investigated the composition (moisture, fat, salt, ash, and protein content), color parameters, and oxidative stability of cheeses that do not have a PDO/PGI certification, purchased from a Greek market. Milk and cheese types were correctly assigned for 62.8 and 82.1 % of samples, respectively, through discriminant analysis. The most important factors for milk type discrimination were L, a and b color attributes, salt, ash, fat-in-dry-matter, moisture-in-non-fat-substance, salt-in-moisture, and malondialdehyde contents, whereas a and b, and moisture, ash, fat, moisture-in-non-fat substance contents, and pH were the most influential characteristics for sample discrimination according to cheese type. A plausible explanation may be the differences in milk chemical composition between three animal species, namely cows, sheep, and goats and for the manufacture procedure and ripening. This is the very first report on the proximate analysis of these, largely ignored, chesses aiming to simulate interest for further study and production valorization. Full article
(This article belongs to the Special Issue Cheese: Chemistry, Physics and Microbiology)
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18 pages, 3345 KiB  
Article
Retrospective Screening of Anthrax-like Disease Induced by Bacillus tropicus str. JMT from Chinese Soft-Shell Turtles in Taiwan
by Jia-Ming Tsai, Hsin-Wei Kuo and Winton Cheng
Pathogens 2023, 12(5), 693; https://doi.org/10.3390/pathogens12050693 - 10 May 2023
Cited by 7 | Viewed by 3815
Abstract
Bacillus cereus is ubiquitous in the environment and a well-known causative agent of foodborne disease. Surprisingly, more and more emerging strains of atypical B. cereus have been identified and related to severe disease in humans and mammals such as chimpanzees, apes, and bovine. [...] Read more.
Bacillus cereus is ubiquitous in the environment and a well-known causative agent of foodborne disease. Surprisingly, more and more emerging strains of atypical B. cereus have been identified and related to severe disease in humans and mammals such as chimpanzees, apes, and bovine. Recently, the atypical B. cereus isolates, which mainly derive from North America and Africa, have drawn great attention due to the potential risk of zoonosis. The cluster of B. cereus carries several anthrax-like virulent genes that are implicated in lethal disease. However, in non-mammals, the distribution of atypical B. cereus is still unknown. In this study, we conducted a retrospective screening of the 32 isolates of Bacillus spp. from diseased Chinese soft-shelled turtles from 2016 to 2020. To recognize the causative agent, we used various methods, such as sequencing analysis using PCR-amplification of the 16S rRNA gene, multiplex PCR for discriminating, and colony morphology by following previous studies. Furthermore, the digital DNA-DNA hybridization (dDDH) and average nucleotide identity (ANI) values were calculated, respectively, below the 70 and 96% cutoff to define species boundaries. According to the summarized results, the pathogen is taxonomically classified as Bacillus tropicus str. JMT (previous atypical Bacillus cereus). Subsequently, analyses such as targeting the unique genes using PCR and visual observation of the bacteria under various staining techniques were implemented in our study. Our findings show that all (32/32, 100%) isolates in this retrospective screening share similar phenotypical properties and carry the protective antigen (PA), edema factor (EF), hyaluronic acid (HA), and exopolysaccharide (Bps) genes on their plasmids. In this study, the results indicate that the geographic distribution and host range of B. tropicus were previously underestimated. Full article
(This article belongs to the Special Issue Emerging Infections in Aquatic Animals)
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12 pages, 1259 KiB  
Article
Application of Stable Isotope Analysis for Detecting the Geographical Origin of the Greek Currants “Vostizza”: A Preliminary Study
by Anna-Akrivi Thomatou, Eleni C. Mazarakioti, Anastasios Zotos, Achilleas Kontogeorgos, Angelos Patakas and Athanasios Ladavos
Foods 2023, 12(8), 1672; https://doi.org/10.3390/foods12081672 - 17 Apr 2023
Cited by 2 | Viewed by 1926
Abstract
There is a plethora of food products with geographical indications registered in the European Union without any study about their discrimination from other similar products. This is also the case for Greek currants. This paper aims to analyze if stable isotope analysis of [...] Read more.
There is a plethora of food products with geographical indications registered in the European Union without any study about their discrimination from other similar products. This is also the case for Greek currants. This paper aims to analyze if stable isotope analysis of C, N, and S could discriminate the Greek currants “Vositzza”, registered as a product of Protected Designation of Origin, from two other currants registered as products of Protected Geographical Indication coming from neighboring areas. The first results show that the stable isotope ratio of sulfur is not detectable due to the very low sulfur content in the samples, and the analysis should be based on the stable isotope ratios of carbon and nitrogen to discriminate these products. The mean value of δ15N (1.38‰) of PDO “Vostizza” currants is lower than that of currants grown outside the PDO zone (2.01‰), while the mean value of δ13C of PDO “Vostizza” currants is higher (−23.93‰) in comparison to that of currants grown outside the PDO zone (−24.83‰). Nevertheless, the results indicate that with only two isotopic ratios, discrimination could not be achieved, and further analysis is required. Full article
(This article belongs to the Special Issue Food Fraud and Food Authenticity across the Food Supply Chain)
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16 pages, 1027 KiB  
Article
Multivariate Statistical Approach for the Discrimination of Honey Samples from Galicia (NW Spain) Using Physicochemical and Pollen Parameters
by Olga Escuredo, María Shantal Rodríguez-Flores, Montserrat Míguez and María Carmen Seijo
Foods 2023, 12(7), 1493; https://doi.org/10.3390/foods12071493 - 1 Apr 2023
Cited by 8 | Viewed by 2179
Abstract
Raw honey is a food with a close relation to the territory in which it is produced because of factors such as soil conditions, weather patterns, and plant communities living in the area together. Furthermore, beekeeping management affects the properties of honey. Protected [...] Read more.
Raw honey is a food with a close relation to the territory in which it is produced because of factors such as soil conditions, weather patterns, and plant communities living in the area together. Furthermore, beekeeping management affects the properties of honey. Protected Geographical Indication Miel de Galicia protects the honey produced in Galicia (Northwest Spain). Various types of honeys (362 samples) from this geographical area were analyzed using chemometric techniques. Principal component analysis was favorable to analyzing the physicochemical and pollen variables with the greatest weight in the differentiation of honey. The linear discriminant analysis correctly classified 89.8% of the samples according to the botanical origin using main pollen spectra and physicochemical attributes (moisture, pH, electrical conductivity, diastase content, phenols, flavonoids, and color). Regarding unifloral honey, blackberry, eucalyptus, and heather honeys were correctly grouped, while five chestnut honeys and fourteen samples of honeydew honeys were misclassified. The chestnut and honeydew honeys have similar physicochemical properties and frequently similar pollen spectra profiles complicating the differentiation. Experimental evidence suggests the potential of multivariate statistics in the characterization of honey of the same geographical origin. Therefore, the classification results were good, with electrical conductivity, total phenol content, total flavonoid content and dominant pollens Eucalyptus, Erica, Rubus and Castanea sativa as the variables of higher importance in the differentiation of botanical origin of honeys. Full article
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16 pages, 4636 KiB  
Article
Protected Geographical Indication Discrimination of Zhejiang and Non-Zhejiang Ophiopogonis japonicus by Near-Infrared (NIR) Spectroscopy Combined with Chemometrics: The Influence of Different Stoichiometric and Spectrogram Pretreatment Methods
by Qingge Ji, Chaofeng Li, Xianshu Fu, Jinyan Liao, Xuezhen Hong, Xiaoping Yu, Zihong Ye, Mingzhou Zhang and Yulou Qiu
Molecules 2023, 28(6), 2803; https://doi.org/10.3390/molecules28062803 - 20 Mar 2023
Cited by 13 | Viewed by 2152
Abstract
This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 Ophiopogon japonicus samples from five major production areas in China were analyzed by NIR [...] Read more.
This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 Ophiopogon japonicus samples from five major production areas in China were analyzed by NIR spectroscopy, and divided into 2127 from Zhejiang and 1530 from other areas (‘non-Zhejiang’). Principal component analysis (PCA) was selected to screen outliers and eliminate them. Monte Carlo cross validation (MCCV) was introduced to divide the training set and test set according to a ratio of 3:7. The raw spectra were preprocessed by nine single and partial combination methods such as the standard normal variable (SNV) and derivative, and then modeled by partial least squares regression (PLSR), a support vector machine (SVM), and soft independent modeling of class analogies (SIMCA). The effects of different pretreatment and chemometrics methods on the model are discussed. The results showed that the three pattern recognition methods were effective in geographical origin tracing, and selecting the appropriate preprocessing method could improve the traceability accuracy. The accuracy of PLSR after the standard normal variable was better, with R2 reaching 0.9979, while that of the second derivative was the lowest with an R2 of 0.9656. After the SNV pretreatment, the accuracy of the training set and test set of SVM reached the highest values, which were 99.73% and 98.40%, respectively. The accuracy of SIMCA pretreated with SNV and MSC was the highest for the origin traceability of Ophiopogon japonicus, which could reach 100%. The distance between the two classification models of SIMCA-SNV and SIMCA-MSC is greater than 3, indicating that the SIMCA model has good performance. Full article
(This article belongs to the Special Issue Applied Analytical Chemistry)
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20 pages, 4109 KiB  
Article
A Statistical Approach to Identify Appropriate Sampling Scheme Capable of Geographical Identification Analysis of the Protected Origin Pulse Crops in Greece
by George Tsirogiannis, Anastasios Zotos, Eleni C. Mazarakioti, Efthimios Kokkotos, Achilleas Kontogeorgos, Angelos Patakas and Athanasios Ladavos
Appl. Sci. 2023, 13(6), 3623; https://doi.org/10.3390/app13063623 - 12 Mar 2023
Cited by 2 | Viewed by 1317
Abstract
In this study, we aimed to develop a sampling method that could be used in geographical discrimination studies of Protected Geographical Indication (PGI) dry beans (Phaseolus vulgaris L.) by considering the geoclimatic variability within the cultivation zone of the analyzed product. The [...] Read more.
In this study, we aimed to develop a sampling method that could be used in geographical discrimination studies of Protected Geographical Indication (PGI) dry beans (Phaseolus vulgaris L.) by considering the geoclimatic variability within the cultivation zone of the analyzed product. The Regional Unit of Kastoria in Greece, a major area of protected designation origin of pulse crops, was selected for detailed investigation. Meteorological data were collected from five weather stations in different subregions of Kastoria (Argos Orestiko, Kalochori, Lakkomata, Lithia, and Polykarpi), over a period of six years (2015 to 2020), along with data of soil texture. The collected data were analyzed in order to determine statistically significant differences among the subregions with regard to the aforementioned parameters. A seasonality pattern was observed for all subregions concerning the microclimate, which splits the data into two clusters. Moreover, a significant variation of the soil textures was revealed, frequently affecting farmers’ decision regarding agronomic practices, leading to the unique stable-isotope ratios and multi-elemental composition. This study guides the dry bean sample collection and will enable the designation of the boundaries of protected origin regions and enable future sampling schemes for stable-isotope and multi-elemental analysis. Full article
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14 pages, 1406 KiB  
Article
Tenderness of PGI “Ternera de Navarra” Beef Samples Determined by FTIR-MIR Spectroscopy
by María José Beriain, María Lozano, Jesús Echeverría, María Teresa Murillo-Arbizu, Kizkitza Insausti and Miguel Beruete
Foods 2022, 11(21), 3426; https://doi.org/10.3390/foods11213426 - 29 Oct 2022
Cited by 2 | Viewed by 2067
Abstract
Understanding meat quality attribute changes during ageing by using non-destructive techniques is an emergent pursuit in the agroindustry research field. Using beef certified samples from the protected geographical indication (PGI) “Ternera de Navarra”, the primary goal of this study was to use Fourier [...] Read more.
Understanding meat quality attribute changes during ageing by using non-destructive techniques is an emergent pursuit in the agroindustry research field. Using beef certified samples from the protected geographical indication (PGI) “Ternera de Navarra”, the primary goal of this study was to use Fourier transform infrared spectroscopy on the middle infrared region (FTIR-MIR) as a tool for the examination of meat tenderness evolution throughout ageing. Samples of the longissimus dorsi muscle of twenty young bulls were aged for 4, 6, 11, or 18 days at 4 °C. Animal carcass classification and sample proximate analysis were performed to check sample homogeneity. Raw aged steaks were analyzed by FTIR-MIR spectroscopy (4000–400 cm−1) to record the vibrational spectrum. Texture profile analysis was performed using a multiple compression test (compression rates of 20%, 80%, and 100%). Compression values were found to decrease notably between the fourth and sixth day of ageing for the three compression rates studied. This tendency continued until the 18th day for C20. For C80 and C100, there was not a clear change in the 11th and 18th days of the study. Regarding FTIR-MIR as a prediction method, it achieved an R2 lower than 40%. Using principal component analysis (PCA) of the results, the whole spectrum fingerprint was used in the discrimination of the starting and final ageing days with correct maturing time classifications. Combining the PCA treatment together with the discriminant analysis of spectral data allowed us to differentiate the samples between the initial and the final ageing points, but it did not single out the intermediate points. Full article
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19 pages, 5109 KiB  
Article
Deep Learning Model for Soil Environment Quality Classification of Pu-erh Tea
by Xiaobo Cai, Wenxia Yuan, Xiaohui Liu, Xinghua Wang, Yaping Chen, Xiujuan Deng, Qi Wu, Ke Han, Zhiyong Cao, Wendou Wu and Baijuan Wang
Forests 2022, 13(11), 1778; https://doi.org/10.3390/f13111778 - 27 Oct 2022
Cited by 2 | Viewed by 2359
Abstract
Pu-erh tea, Camellia sinensis is a traditional Chinese tea, one of the black teas, originally produced in China’s Yunnan Province, named after its origin and distribution center in Pu-erh, Yunnan. Yunnan Pu-erh tea is protected by geographical Indication and has unique quality characteristics. [...] Read more.
Pu-erh tea, Camellia sinensis is a traditional Chinese tea, one of the black teas, originally produced in China’s Yunnan Province, named after its origin and distribution center in Pu-erh, Yunnan. Yunnan Pu-erh tea is protected by geographical Indication and has unique quality characteristics. It is made from Yunnan large-leaf sun-green tea with specific processing techniques. The quality formation of Pu-erh tea is closely related to the soil’s environmental conditions. In this paper, time-by-time data of the soil environment of tea plantations during the autumn tea harvesting period in Menghai County, Xishuangbanna, Yunnan Province, China, in 2021 were analyzed. Spearman’s correlation analysis was conducted between the inner components of Pu’er tea and the soil environmental factor. The analysis showed that three soil environmental indicators, soil temperature, soil moisture, and soil pH, were highly significantly correlated. The soil environmental quality evaluation method was proposed based on the selected soil environmental characteristics. Meanwhile, a deep learning model of Long Short Term Memory (LSTM) Network for the soil environmental quality of tea plantation was established according to the proposed method, and the soil environmental quality of tea was classified into four classes. In addition, the paper also compares the constructed models based on BP neural network and random forest to evaluate the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of the indicators for comparative analysis. This paper innovatively proposes to introduce the main inclusions of Pu’er tea into the classification and discrimination model of the soil environment in tea plantations, while using machine learning-related algorithms to classify and predict the categories of soil environmental quality, instead of relying solely on statistical data for analysis. This research work makes it possible to quickly and accurately determines the physiological status of tea leaves based on the establishment of a soil environment quality prediction model, which provides effective data for the intelligent management of tea plantations and has the advantage of rapid and low-cost assessment compared with the need to measure the intrinsic quality of Pu-erh tea after harvesting is completed. Full article
(This article belongs to the Special Issue Dynamics of Upland Soil for Agroforestry Crops)
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11 pages, 766 KiB  
Article
Multi-Elemental Composition Data Handled by Chemometrics for the Discrimination of High-Value Italian Pecorino Cheeses
by Francesca Di Donato, Martina Foschi, Nadia Vlad, Alessandra Biancolillo, Leucio Rossi and Angelo Antonio D’Archivio
Molecules 2021, 26(22), 6875; https://doi.org/10.3390/molecules26226875 - 15 Nov 2021
Cited by 8 | Viewed by 2422
Abstract
The multi-elemental composition of three typical Italian Pecorino cheeses, Protected Designation of Origin (PDO) Pecorino Romano (PR), PDO Pecorino Sardo (PS) and Pecorino di Farindola (PF), was determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The ICP-OES method here developed allowed the [...] Read more.
The multi-elemental composition of three typical Italian Pecorino cheeses, Protected Designation of Origin (PDO) Pecorino Romano (PR), PDO Pecorino Sardo (PS) and Pecorino di Farindola (PF), was determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The ICP-OES method here developed allowed the accurate and precise determination of eight major elements (Ba, Ca, Fe, K, Mg, Na, P, and Zn). The ICP-OES data acquired from 17 PR, 20 PS, and 16 PF samples were processed by unsupervised (Principal Component Analysis, PCA) and supervised (Partial Least Square-Discriminant Analysis, PLS-DA) multivariate methods. PCA revealed a relatively high variability of the multi-elemental composition within the samples of a given variety, and a fairly good separation of the Pecorino cheeses according to the geographical origin. Concerning the supervised classification, PLS-DA has allowed obtaining excellent results, both in calibration (in cross-validation) and in validation (on the external test set). In fact, the model led to a cross-validated total accuracy of 93.3% and a predictive accuracy of 91.3%, corresponding to 2 (over 23) misclassified test samples, indicating the adequacy of the model in discriminating Pecorino cheese in accordance with its origin. Full article
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12 pages, 1205 KiB  
Article
Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques
by Muhammad Hilal Kabir, Mahamed Lamine Guindo, Rongqin Chen and Fei Liu
Foods 2021, 10(11), 2767; https://doi.org/10.3390/foods10112767 - 11 Nov 2021
Cited by 38 | Viewed by 4169
Abstract
Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need [...] Read more.
Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (n = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way. Full article
(This article belongs to the Topic Future Food Analysis and Detection)
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12 pages, 2095 KiB  
Article
Tracing the Geographical Origin of Thai Hom Mali Rice in Three Contiguous Provinces of Thailand Using Stable Isotopic and Elemental Markers Combined with Multivariate Analysis
by Supalak Kongsri, Phitchan Sricharoen, Nunticha Limchoowong and Chunyapuk Kukusamude
Foods 2021, 10(10), 2349; https://doi.org/10.3390/foods10102349 - 1 Oct 2021
Cited by 21 | Viewed by 4443
Abstract
Rice is a staple food for more than half of the world’s population. The discrimination of geographical origin of rice has emerged as an important issue to prevent mislabeling and adulteration problems and ensure food quality. Here, the discrimination of Thai Hom Mali [...] Read more.
Rice is a staple food for more than half of the world’s population. The discrimination of geographical origin of rice has emerged as an important issue to prevent mislabeling and adulteration problems and ensure food quality. Here, the discrimination of Thai Hom Mali rice (THMR), registered as a European Protected Geographical Indication (PGI), was demonstrated. Elemental compositions (Mn, Rb, Co, and Mo) and stable isotope (δ18O) in the rice were analyzed using inductively coupled plasma mass spectrometry (ICP-MS) and elemental analyzer isotope ratio mass spectrometry (EA-IRMS), respectively. The recoveries and precisions of all elements were greater than 98% and lower than 9%, respectively. The analytical precision (±standard deviation) was below ±0.2‰ for δ18O measurement. Mean of Mn, Rb, Co, Mo, and δ18O levels was 14.0 mg kg−1, 5.39 mg kg−1, 0.049 mg kg−1, 0.47 mg kg−1, and 25.22‰, respectively. Only five valuable markers combined with radar plots and multivariate analysis, linear discriminant analysis (LDA) could distinguish THMR cultivated from three contiguous provinces with correct classification and cross-validation of 96.4% and 92.9%, respectively. These results offer valuable insight for the sustainable management and regulation of improper labeling regarding geographical origin of rice in Thailand and other countries. Full article
(This article belongs to the Special Issue Food Origin Analysis with Isotope Fingerprints)
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