CLUSTER ANALYSIS FOR DATABASES TYPOLOGIZATION CHARACTERISTICS

The article deals with basic concepts of cluster analysis and data clustering. The authors give brief information on the history of cluster analysis and its first applications. The article gives the classification of methods by the way of data processing and analysis in cluster analysis. The detailed description of the popular, non- hierarchical K-means algorithm is given. When developing databases, their structure should provide for the division of products into clusters based on various characteristics. It is necessary to consider the division into clusters based on other characteristics, such as allergenicity (whether the product contains an allergic component or not) or carbohydrate content (important for diabetics). The content of protein, potassium and phosphates should be taken into account when developing diets for those suffering from kidney diseases. The presence of specific amino acids - for metabolic diseases, etc. In this way, food composition data and product clustering across different categories allow nutritionists to create interchangeable lists of meals with portion sizes, or lists of permitted and prohibited food products in terms of various diseases. The authors give the clustering of the database fragment of chemical composition of food products on the example of cottage cheese products and confectionary by one of the signs – the content of carbohydrates – in the R software environment by k-means. Food clusters based on carbohydrate content are very important in shaping the diet for diabetics. A visual gradation of products into clusters is demonstrated in the form of a dendrogram showing the degree of proximity of individual clusters. The resulting dendrogram contains 5 clusters. Cluster 4 includes the largest number of products (170 items) with an average carbohydrate content of 1.8 g with a variation range from 0 to 7.1 g. Food products and dishes that fall into this cluster are the least dangerous for people with diabetes. Cluster 5 includes only 8 products with a distribution of carbohydrates within the cluster from 62.60 to 80.40 g. This category of food should be excluded when preparing a diet for people with diabetes.

Download Full-text

Towards a European Food Composition Data Interchange Platform

International Journal for Vitamin and Nutrition Research ◽

10.1024/0300-9831/a000113 ◽

2012 ◽

Vol 82 (3) ◽

pp. 209-215 ◽

Cited By ~ 9

Author(s):

Simone Bell ◽

Heikki Pakkala ◽

Michael P. Finglas

Keyword(s):

Nutrient Content ◽

Food Composition ◽

Consumer Information ◽

Health Claims ◽

Composition Data ◽

European Committee ◽

Food Properties ◽

Data Interchange ◽

Food Composition Data ◽

Definition Of

Food composition data (FCD) comprises the description and identification of foods, as well as their nutrient content, other constituents, and food properties. FCD are required for a range of purposes including food labeling, supporting health claims, nutritional and clinical management, consumer information, and research. There have been differences within and beyond Europe in the way FCD are expressed with respect to food description, definition of nutrients and other food properties, and the methods used to generate data. One of the major goals of the EuroFIR NoE project (2005 - 10) was to provide tools to overcome existing differences among member states and parties with respect to documentation and interchange of FCD. The establishment of the CENs (European Committee for Standardisation) TC 387 project committee on Food Composition Data, led by the Swedish Standards Institute, and the preparation of the draft Food Data Standard, has addressed these deficiencies by enabling unambiguous identification and description of FCD and their quality, for dissemination and data interchange. Another major achievement of the EuroFIR NoE project was the development and dissemination of a single, authoritative source of FCD in Europe enabling the interchange and update of data between countries, and also giving access to users of FCD.

Download Full-text

Mapping the Market Segments for the Consumers of Greek Cooperative Food Products

Sustainability ◽

10.3390/su13073825 ◽

2021 ◽

Vol 13 (7) ◽

pp. 3825

Author(s):

Ourania Tremma ◽

Achilleas Kontogeorgos ◽

Philippos Karipidis ◽

Fotios Chatzitheodoridis

Keyword(s):

Cluster Analysis ◽

Economic Development ◽

Food Products ◽

Agricultural Cooperatives ◽

Value For Money ◽

Market Segments ◽

Local Society ◽

Economic Development And Growth ◽

Different Characteristics ◽

Western Greece

The purpose of this study was to illustrate and understand how consumers of cooperative food products could be segmented in the region of Western Greece. For this purpose, a questionnaire survey was undertaken involving almost 500 consumers in the selected region. Consumers were segmented on the basis of their consumption of cooperative products and in association with their attributes and beliefs regarding cooperatives’ products. Τhe performed cluster analysis grouped participants into four distinct groups with different characteristics and perceptions regarding cooperatives’ products. The consumer groups were called “skeptics”, “cooperative”, “passively sensitive”, and “indifferent”. According to the results, in general, participants considered cooperative food products as a safe choice with excellent value for money and contributing to the local society, economy, and sustainability. Segmentation could allow agricultural cooperatives to focus only on those segments of consumers that are willing to pay for cooperative products by allocating all available resources in serving them. The results of this research, even with its limitations, can help directors and marketing executives of agricultural cooperatives to better understand that operating in a specific market could be more effective when targeted at specific segments to help not only the viability of agricultural cooperatives but also their economic development and growth.

Download Full-text

Changes in USDA Food Composition Data for 43 Garden Crops, 1950 to 1999

Journal of the American College of Nutrition ◽

10.1080/07315724.2004.10719409 ◽

2004 ◽

Vol 23 (6) ◽

pp. 669-682 ◽

Cited By ~ 146

Author(s):

Donald R. Davis ◽

Melvin D. Epp ◽

Hugh D. Riordan

Keyword(s):

Food Composition ◽

Composition Data ◽

Food Composition Data

Download Full-text

Global magnesium supply in the food chain

Crop and Pasture Science ◽

10.1071/cp15096 ◽

2015 ◽

Vol 66 (12) ◽

pp. 1278 ◽

Cited By ~ 15

Author(s):

Diriba B. Kumssa ◽

Edward J. M. Joy ◽

E. Louise Ander ◽

Michael J. Watts ◽

Scott D. Young ◽

...

Keyword(s):

Food Composition ◽

Published Data ◽

Mg Deficiency ◽

Individual Level ◽

Household Food ◽

Dietary Surveys ◽

Food Composition Data ◽

Per Capita ◽

Potential Inhibitors ◽

Dietary Deficiency

Magnesium (Mg) is an essential mineral micronutrient in humans. Risks of dietary Mg deficiency are affected by the quantity of Mg ingested and its bioavailability, which is influenced by the consumption of other nutrients and ‘anti-nutrients’. Here, we assess global dietary Mg supplies and risks of dietary deficiency, including the influence of other nutrients. Food supply and food composition data were used to derive the amount of Mg available per capita at national levels. Supplies of Mg were compared with estimated national per capita average requirement ‘cut points’. In 2011, global weighted mean Mg supply was 613 ± 69 mg person–1 day–1 compared with a weighted estimated average requirement for Mg of 173 mg person–1 day–1. This indicates a low risk of dietary Mg deficiency of 0.26% based on supply. This contrasts with published data from national individual-level dietary surveys, which indicate greater Mg deficiency risks. However, individuals in high-income countries are likely to under-report food consumption, which could lead to overestimation of deficiency risks. Furthermore, estimates of deficiency risk based on supply do not account for potential inhibitors of Mg absorption, including calcium, phytic acid and oxalate, and do not consider household food wastage.

Download Full-text

The use of food composition data in the Choices International Programme

Food Chemistry ◽

10.1016/j.foodchem.2015.06.082 ◽

2016 ◽

Vol 193 ◽

pp. 196-202 ◽

Cited By ~ 10

Author(s):

Léon Jansen ◽

Annet J.C. Roodenburg

Keyword(s):

Food Composition ◽

Composition Data ◽

International Programme ◽

Food Composition Data

Download Full-text

Abstract TP201: The Relationship Between Chronic Kidney Diseases and the Presence of Cerebral Small Vessel Disease in Patients With Acute Ischemic Stroke

Stroke ◽

10.1161/str.48.suppl_1.tp201 ◽

2017 ◽

Vol 48 (suppl_1) ◽

Author(s):

Kana Ueki ◽

Asako Nakamura ◽

Masahiro Yasaka ◽

Takahiro Kuwashiro ◽

Seiji Gotoh ◽

...

Keyword(s):

Ischemic Stroke ◽

White Matter ◽

Acute Ischemic Stroke ◽

White Matter Lesion ◽

Kidney Diseases ◽

Univariate Analysis ◽

Small Vessel ◽

Chronic Kidney Diseases ◽

History Of ◽

The Relationship

Introduction: Cerebral small vessel diseases (SVDs) i.e. white matter lesion and cerebral microbleeds (CMBs) are related to the patients with stroke more deeply than those without. In general population, in addition to age, hypertension, diabetes chronic kidney diseases (CKD) is well known to be related to SVDs, but it remains unclear in patients with stroke. We investigated the relationship between CKD and the presence of SVDs in patients with acute ischemic stroke. Methods: We enrolled 493 patients with acute ischemic stroke patients or transient ischemic attack patients (mean age 71; 60% male) who had undergone 1.5T MR imaging within a week of the index events from April 2013 to march 2015. We evaluated kidney function by estimated glomerular filtration rate (eGFR) with the modification of diet in Renal Disease. CKD was defined as an eGFR less than 60mil/min/1.73m 2 . CMBs were defined as focal areas of very low signal intensity smaller than 10mm. White matter lesion as Periventricular hyper intensity (PVH)>grade 2 and Deep and Subcortical White Matter Hyper intensity (DSWMH)> grade 2 were defied as advanced PVH and advanced DSWMH, respectively. We investigated relationship between CKD and CMBs, advanced PVH and advanced DSWMH using a logistic regression analysis. Results: We noted CMBs in 173 patients (35%), PVH in 81 (16%), and DSWMH in 151 (31%). An univariate analysis revealed that the age, CKD, history of stroke, and antiplatelet agents were associated with presence of CMBs, advanced PVH and severe DSWMH . The multivariate analysis revealed that CMBs, advanced PVH and advanced DSWMH were associated with age (CMBs: odds ratio(OR) ; 1.32 ; 95% confidence interval(CI), 1.10-1.60, p=0.004, advanced PVH : OR ; 3.00 ; 95% CI, 2.17-4.26, p<0.01, advanced DSWMH: OR ; 1.94; 95% CI, 1.56-2.45, p<0.01 ), history of stroke(CMBs : OR ; 2.01 ; 95% CI, 1.21-3.34, p=0.007, advanced PVH : OR ; 2.25 ; 95% CI, 1.18-4.27, p=0.01, advanced DSWMH: OR ; 1.78 ; 95% CI, 1.03-3.06, p=0.038). CKD was associated with CMBs (OR ; 1.62 ; 95% CI, 1.04-2.52, p=0.03), but PVH and DSWMH were not. Conclusions: It seems that age and history of stroke are related to CMBs, advanced PVH and advanced DSWMH, and that CKD is associates with CMBs but not with either advanced PVH or advanced DSWMH.

Download Full-text