Applying Multivariate Statistical Techniques in the Study of Chocolate and Its P...
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Applying Multivariate Statistical Techniques in the Study of Chocolate and Its Potential Effects on Cardiovascular and Neurovascular Disease ( US 2018 215 )
Nov 2, 2018 7:24 AM
| Last Modified: Nov 26, 2018 11:46 AM
Level: Intermediate Mason Chen, Stanford University OHS Program Charles Chen, Lean Six Sigma Master Black Belt and Industry Consultant
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Many people like eating chocolate, but may have concerns about health risks, especially regarding cardiovascular or neurovascular diseases. Using JMP 14, multivariate statistical techniques were applied to define a health biometric to help with choosing a healthy chocolate for patients with heart disease. Chocolate, made from cocoa beans, contains flavonoids. Flavonoids are the most abundant polyphenols in the human diet and have antioxidant properties that can prevent aging. Flavonoids are also beneficial for heart disease and diabetes patients, as is a diet that is low in saturated fat, trans fat, sodium and cholesterol, and high in dietary fiber. Cocoa flavanols, a class of flavonoid, promote healthy blood flow from head to toe. The heart, brain and muscles depend on a healthy circulatory system. Data were collected on more than 20 chocolate ingredients from over 60 different types of chocolate. A multivariate correlation study has found a strong negative correlation between cocoa and sugar, and a strong positive correlation between dietary fiber and iron. Most dark chocolate contains more cocoa and less sugar. Dietary fiber and iron are highly correlated because of the high cocoa concentration. The above two correlations can be further explained by conducting a hierarchical clustering analysis to separate the dark, milk and white chocolates by cocoa and calcium factors.