Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice.
Ontology highlight
ABSTRACT:
SUBMITTER: Xu F
PROVIDER: S-EPMC8266907 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Geographical indication (GI) labeling is used to represent information about specific geographical origins of target products. This study aimed at determining the impact of GI information on sensory perception and acceptance of cooked aromatic rice samples. Ninety-nine participants evaluated cooked rice samples prepared using each of three aromatic rice varieties both with and without being provided with GI information. Participants rated the acceptance and intensity of the cooked rice samples in terms of appearance, aroma, flavor, texture, and overall liking, and also reported how important the GI information was to them. The results showed that consumers rated the cooked rice samples higher in appearance and overall liking when provided with GI information. Interestingly, participants who valued "state-of-origin" information more highly exhibited increased hedonic ratings of cooked rice samples when provided with GI information, but not when no GI information was given. Participants provided with GI information rated flavor or sweetness intensities of cooked aromatic rice samples closer to just-about-right than those without such information. This study provides empirical evidence about how GI information modulates sensory perception and acceptance of cooked aromatic rice samples. The findings will help rice industry, farmers, and traders better employ GI labeling to increase consumer acceptability of their rice products.
| S-EPMC7763253 | biostudies-literature
Project description:Elemental profiles are frequently applied to identify the geographical origin and authenticity of food products, to guarantee quality. The concentrations of fifteen major, minor, and trace elements (Na, Mg, K, Ca, Al, Fe, Mn, Cu, Zn, Rb, Sr, Li, Cd, Cs, and Ba) were determined in soils, "Meili" grapes, and wines from six regions in China by inductively coupled plasma mass spectrometry (ICP-MS). The elemental concentrations in these samples, according to the geographical origins, were analyzed by one-way analysis of variance (ANOVA) with Duncan's multiple comparisons. The bioconcentration factor (BCF) from soil to grape and the transfer factor (TF) from grape to wine were calculated. Mg, K, Ca, Cu, Zn, Rb, Sr, and Ba presented higher BCF values than the other seven elements. The TF values of six elements (Na, Mg, K, Zn, Li, and Cs) were found to be greater than one. Moreover, the correlation of element content between the pairs of soil-grape, grape-wine, and bioconcentration factor (BCF)-environmental factor were analyzed. Significant correspondences among soil, grape, and wine were observed for K and Li. Two elements (Sr and Li) showed significant correlations between BCF and environmental factor (relative humidity, temperature, and latitude). A linear discriminant analysis (LDA) with three variables (K, Sr, Li) revealed a high accuracy (>90%) to determine the geographical origin for different Chinese regions.
| S-EPMC8701803 | biostudies-literature
Project description:The demand for fish and seafood is growing worldwide. Meanwhile, problems related to the integrity and safety of the fishery sector are increasing, leading legislators, producers, and consumers to search for ways to effectively protect themselves from fraud and health hazards related to fish consumption. What is urgently required now is the availability of reliable, truthful, and reproducible methods assuring the correspondence between the real nature of the product and label declarations accompanying the same product during its market life. The evaluation of the inorganic composition of fish and seafood appears to be one of the most promising strategies to be exploited in the near future to assist routine and official monitoring operations along the supply chain. The present review article focuses on exploring the latest scientific achievements of using the multi-elemental composition of fish and seafood as an imprint of their authenticity and traceability, especially with regards to the geographical origin. The scientific literature of the last 10 years focusing on the analytical determination and statistical elaboration of elemental data (alone or in combination with methodologies targeting other compounds) to verify the identity of fishery products is summarized and discussed.
| S-EPMC7912245 | biostudies-literature
Project description:The replication strategy of metazoan genomes is still unclear, mainly because definitive maps of replication origins are missing. High-throughput methods are based on population average and thus may exclusively identify efficient initiation sites, whereas inefficient origins go undetected. Single-molecule analyses of specific loci can detect both common and rare initiation events along the targeted regions. However, these usually concentrate on positioning individual events, which only gives an overview of the replication dynamics. Here, we computed the replication fork directionality (RFD) profiles of two large genes in different transcriptional states in chicken DT40 cells, namely untranscribed and transcribed DMD and CCSER1 expressed at WT levels or overexpressed, by aggregating hundreds of oriented replication tracks detected on individual DNA fibres stretched by molecular combing. These profiles reconstituted RFD domains composed of zones of initiation flanking a zone of termination originally observed in mammalian genomes and were highly consistent with independent population-averaging profiles generated by Okazaki fragment sequencing. Importantly, we demonstrate that inefficient origins do not appear as detectable RFD shifts, explaining why dispersed initiation has remained invisible to population-based assays. Our method can both generate quantitative profiles and identify discrete events, thereby constituting a comprehensive approach to study metazoan genome replication.
| S-EPMC8266662 | biostudies-literature
Project description:This study presents the comprehensive elemental profile of Greek Graviera (Gruyère) cheeses. In total, 105 samples from nine different geographic regions produced from sheep, goat and cow milk and their mixtures were assessed. Elemental signatures of 61 elements were investigated for determination of geographic origin and milk type. Regional and milk type classification through Linear Discriminant Analysis was successful for almost all cases, while a less optimistic cross validation exercise presented lower classification rates. That points to further research using a much larger sample set, increasing confidence for cheese authentication utilizing also bioinformatics tools under development. This is the first study reporting signatures of 61 elements in dairy products including all sixteen rare earth elements and all seven precious metals. Safety and quality were assessed regarding toxic and nutritive elements. According to both EU and USA regulations and directives, Graviera is a nutritional source for trace and macro elements with low levels of toxic elements.
| S-EPMC6412278 | biostudies-literature
Project description:With increasing demands for more rapid and practical analyses, various techniques of ambient ionization mass spectrometry have gained significant interest due to the speed of analysis and abundance of information provided. Herein, an ambient ionization technique that utilizes corona discharge was applied, for the first time, to analyze and categorize whole seeds of black and white peppers from different origins. This setup requires no solvent application nor gas flow, thus resulting in a very simple and rapid analysis that can be applied directly to the sample without any prior workup or preparation. Combined with robust data pre-processing and subsequent chemometric analyses, this analytical method was capable of indicating the geographical origin of each pepper source with up to 98% accuracies in all sub-studies. The simplicity and speed of this approach open up the exciting opportunity for onsite analysis without the need for a highly trained operator. Furthermore, this methodology can be applied to a variety of spices and herbs, whose geographical indication or similar intellectual properties are economically important, hence it is capable of creating tremendous impact in the food and agricultural industries.
| S-EPMC8352875 | biostudies-literature
Project description:Iron (Fe) and zinc (Zn) are essential micronutrients required for proper development in both humans and plants. Rice (Oryza sativa L.) grains are the staple food for nearly half of the world's population, but a poor source of metals such as Fe and Zn. Populations that rely on milled cereals are especially prone to Fe and Zn deficiencies, the most prevalent nutritional deficiencies in humans. Biofortification is a cost-effective solution for improvement of the nutritional quality of crops. However, a better understanding of the mechanisms underlying grain accumulation of mineral nutrients is required before this approach can achieve its full potential. Characterization of gene function is more time-consuming in crops than in model species such as Arabidopsis thaliana. Aiming to more quickly characterize rice genes related to metal homeostasis, we applied the concept of high throughput elemental profiling (ionomics) to Arabidopsis lines heterologously expressing rice cDNAs driven by the 35S promoter, named FOX (Full Length Over-eXpressor) lines. We screened lines expressing candidate genes that could be used in the development of biofortified grain. Among the most promising candidates, we identified two lines ovexpressing the metal cation transporter OsZIP7. OsZIP7 expression in Arabidopsis resulted in a 25% increase in shoot Zn concentrations compared to non-transformed plants. We further characterized OsZIP7 and showed that it is localized to the plasma membrane and is able to complement Zn transport defective (but not Fe defective) yeast mutants. Interestingly, we showed that OsZIP7 does not transport Cd, which is commonly transported by ZIP proteins. Importantly, OsZIP7-expressing lines have increased Zn concentrations in their seeds. Our results indicate that OsZIP7 is a good candidate for developing Zn biofortified rice. Moreover, we showed the use of heterologous expression of genes from crops in A. thaliana as a fast method for characterization of crop genes related to the ionome and potentially useful in biofortification strategies.
| S-EPMC6037872 | biostudies-literature
Project description:Geographical indication (GI) is used to identify a product's origin when its characteristics or quality are a result of geographical origin, which includes agricultural products and foodstuff. Metabolomics is an "omics" technique that can support product authentication by providing a chemical fingerprint of a biological system, such as plant and plant-derived products. The main purpose of this article is to verify possible contributions of metabolomic studies to the marketing field, mainly for certified regions, through an integrative review of the literature and maps produced by VOSviewer software. The results indicate that studies based on metabolomics approaches can relate specific food attributes to the region's terroir and know-how. The evidence of this connection, marketing of GIs and metabolomics methods, is viewed as potential tool for marketing purposes (e.g., to assist communication of positive aspects and quality), and legal protection. In addition, our results provide a taxonomic categorization that can guide future marketing research involving metabolomics. Moreover, the results are also useful to government agencies to improve GIs registration systems and promotion strategies.Supplementary informationThe online version contains supplementary material available at 10.1007/s00217-021-03782-2.
| S-EPMC8204615 | biostudies-literature
Project description:Tea (Camellia sinensis (L.) Kuntze-surname of German origin) is a popular beverage consumed worldwide due to its health benefits. Its quality depends on measuring features that may discriminate teas from distinct provenances. Protected designation of origin (PDO) is therefore a very useful label for tea quality evaluation. In the present work, antioxidant activity profiles obtained from microfluidic paper-based analytical devices (µPADs) were analyzed by chemometrics to determine the tea geographic origin. Based on the existing literature, we constructed a database containing chemical data from 26 samples and evaluated it by principal component analysis (PCA) coupled to linear discriminant analysis (LDA). Antioxidant activity was an effective LDA predictor for sample discrimination accomplishing accuracies from 75 to 82%. Modeling performance was favored by an external validation method. The best classification model was found using the first nine PCs as input variables. Training samples achieved a perfect success rate, while the test ones were predicted with 83% specificity, 100% sensitivity, and 90% overall accuracy. The modeling robustness was verified by integrating AUC (0.943) from ROC curve. The PCA-LDA approach taken here demonstrated that the teas coming from different countries can be correctly authenticated through µPADs, thus contributing to certificate samples PDO.Graphical abstractSupplementary informationThe online version contains supplementary material available at 10.1007/s13197-022-05440-1.
| S-EPMC9525549 | biostudies-literature
Project description:Rice (Oryza sativa L.) is a widely consumed food source, and its geographical origin has long been a subject of discussion. In our study, we collected 44 and 20 rice samples from different regions of the Republic of Korea and China, respectively, of which 35 and 29 samples were of white and brown rice, respectively. These samples were analyzed using nuclear magnetic resonance (NMR) spectroscopy, followed by analyses with various data normalization and scaling methods. Then, leave-one-out cross-validation (LOOCV) and external validation were employed to evaluate various machine learning algorithms. Total area normalization, with unit variance and Pareto scaling for white and brown rice samples, respectively, was determined as the best pre-processing method in orthogonal partial least squares-discriminant analysis. Among the various tested algorithms, support vector machine (SVM) was the best algorithm for predicting the geographical origin of white and brown rice, with an accuracy of 0.99 and 0.96, respectively. In external validation, the SVM-based prediction model for white and brown rice showed good performance, with an accuracy of 1.0. The results of this study suggest the potential application of machine learning techniques based on NMR data for the differentiation and prediction of diverse geographical origins of white and brown rice.
| S-EPMC9693418 | biostudies-literature