Dataset Information

Clustering of commercial fish sauce products based on an e-panel technique.

ABSTRACT: Fish sauce is a brownish liquid seasoning with a characteristic flavor that is produced in Asian countries and limited areas of Europe. The types of fish and shellfish and fermentation process used in its production depend on the region from which it derives. Variations in ingredients and fermentation procedures yield end products with different smells, tastes, and colors. For this data article, we employed an electronic panel (e-panel) technique including an electronic nose (e-nose), electronic tongue (e-tongue), and electronic eye (e-eye), in which smell, taste, and color are evaluated by sensors instead of the human nose, tongue, and eye to avoid subjective error. The presented data comprise clustering of 46 commercially available fish sauce products based separate e-nose, e-tongue, and e-eye test results. Sensory intensity data from the e-nose, e-tongue, and e-eye were separately classified by cluster analysis and are shown in dendrograms. The hierarchical cluster analysis indicates major three groups on e-nose and e-tongue data, and major four groups on e-eye data.

SUBMITTER: Nakano M

PROVIDER: S-EPMC5734694 | biostudies-other | 2018 Feb

REPOSITORIES: biostudies-other

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Clustering of commercial fish sauce products based on an e-panel technique.

Nakano Mitsutoshi M Sagane Yoshimasa Y Koizumi Ryosuke R Nakazawa Yozo Y Yamazaki Masao M Ikehama Kiyoharu K Yoshida Koichi K Watanabe Toshihiro T Takano Katsumi K Sato Hiroaki H

Data in brief 20171202

Fish sauce is a brownish liquid seasoning with a characteristic flavor that is produced in Asian countries and limited areas of Europe. The types of fish and shellfish and fermentation process used in its production depend on the region from which it derives. Variations in ingredients and fermentation procedures yield end products with different smells, tastes, and colors. For this data article, we employed an electronic panel (e-panel) technique including an electronic nose (e-nose), electronic ...[more]

PMID: 29270450

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Project description:BackgroundVitamin K antagonist (warfarin) is the most classical and widely used oral anticoagulant with assuring anticoagulant effect, wide clinical indications and low price. Warfarin dosage requirements of different patients vary largely. For warfarin daily dosage prediction, the data imbalance in dataset leads to inaccurate prediction on the patients of rare genotype, who usually have large stable dosage requirement. To balance the dataset of patients treated with warfarin and improve the predictive accuracy, an appropriate partition of majority and minority groups, together with an oversampling method, is required.MethodTo solve the data-imbalance problem mentioned above, we developed a clustering-based oversampling technique denoted as DBCSMOTE, which combines density-based spatial clustering of application with noise (DBCSCAN) and synthetic minority oversampling technique (SMOTE). DBCSMOTE automatically finds the minority groups by acquiring the association between samples in terms of the clinical features/genotypes and the warfarin dosage, and creates an extended dataset by adding the new synthetic samples of majority and minority groups. Meanwhile, two ensemble models, boosted regression tree (BRT) and random forest (RF), which are built on the extended dataset generateed by DBCSMOTE, accomplish the task of warfarin daily dosage prediction.ResultsDBCSMOTE and the comparison methods were tested on the datasets derived from our Hospital and International Warfarin Pharmacogenetics Consortium (IWPC). As the results, DBCSMOTE-BRT obtained the highest R-squared (R2) of 0.424 and the smallest mean squared error (mse) of 1.08. In terms of the percentage of patients whose predicted dose of warfarin is within 20% of the actual stable therapeutic dose (20%-p), DBCSMOTE-BRT can achieve the largest value of 47.8% among predictive models. The more important thing is that DBCSMOTE saved about 68% computational time to achieve the same or better performance than the Evolutionary SMOTE, which was the best oversampling method in warfarin dose prediction by far. Meanwhile, in warfarin dose prediction, it is discovered that DBCSMOTE is more effective in integrating BRT than RF for warfarin dose prediction.ConclusionOur finding is that the genotypes, CYP2C9 and VKORC1, no doubt contribute to the predictive accuracy. It was also discovered left atrium diameter, glutamic pyruvic transaminase and serum creatinine included in the model actually improved the predictive accuracy; When congestive heart failure, diabetes mellitus and valve replacement were absent in DBCSMOTE-BRT/RF, the predictive accuracy of DBCSMOTE-BRT/RF decreased. The oversampling ratio and number of minority clusters have a large impact on the effect of oversampling. According to our test, the predictive accuracy was high when the number of minority clusters was 6 ~ 8. The oversampling ratio for small minority clusters should be large (> 1.2) and for large minority clusters should be small (< 0.2). If the dataset becomes larger, the DBCSMOTE would be re-optimized and its BRT/RF model should be re-trained. DBCSMOTE-BRT/RF outperformed the current commonly-used tool called Warfarindosing. As compared to Evolutionary SMOTE-BRT and RF models, DBCSMOTE-BRT and RF models take only a small computational time to achieve the same or higher performance in many cases. In terms of predictive accuracy, RF is not as good as BRT. However, RF still has a powerful ability in generating a highly accurate model as the dataset increases; the software "WarfarinSeer v2.0" is a test version, which packed DBCSMOTE-BRT/RF. It could be a convenient tool for clinical application in warfarin treatment.

Project description:The herbal products, sold worldwide as medicines or foods, are perceived as low risk because they are considered natural and thus safe. The quality of these products is ineffectively regulated and controlled. The growing evidence for their lack of authenticity is causing deep concern, but the scale of this phenomenon at the global, continental or national scale remains unknown. We analyzed data reporting the authenticity, as detected with DNA-based methods, of 5,957 commercial herbal products sold in 37 countries, distributed in all six inhabited continents. Our global survey shows that a substantial proportion (27%) of the herbal products commercialized in the global marketplace is adulterated when their content was tested against their labeled, claimed ingredient species. The adulterated herbal products are distributed across all continents and regions. The proportion of adulterated products varies significantly among continents, being highest in Australia (79%), South America (67%), lower in Europe (47%), North America (33%), Africa (27%) and the lowest in Asia (23%). The commercial HPs' authenticity among the 37 countries included in our global analysis ranges between 0 and 100% from the total number of product reported for each specific national marketplace. For 9 countries, more than 100 products were successfully DNA-based authenticated and reported. From these countries, the highest percentage of adulterated commercial HPs was reported for Brazil (68%), followed distantly by Taiwan (32%), India (31%), USA (29%), followed closely by Malaysia (24%), Japan (23%), South Korea (23%), Thailand (20%), and China (19%). Our results confirm the large-scale presence of adulterated herbal products throughout the global market. The adulterated herbal products contain undeclared contaminant, substitute, and filler species, or none of the labeled species, which all may be accidental or intentional, economically-motivated and fraudulent. Due to the ever-increasing analytical sensitivity of the high throughput DNA sequencing, increasingly used for the untargeted, simultaneous multi-taxa identification, the proportion of adulterated HPs detected on the global market is expected to increase. In the context of the increasing demand for HPs, the limited supply of raw materials derived from many plant species, some of which being already nationally or internationally protected and having various degrees of trade restrictions, adds up to the differences and discrepancies between national HPs' regulatory frameworks and further increases the risks of adulteration of many types of herbal products. The globally widespread adulteration is a serious threat to consumers' well-being and safety, in spite of herbal products' claimed or expected health benefits.

Dataset Information

Clustering of commercial fish sauce products based on an e-panel technique.

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Clustering of commercial fish sauce products based on an e-panel technique.

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