Morrill Learning Center, Santa Clara, California
Stanford University Online High School, Palo Alto, California
Statistical Modeling Analysis of Chocolate Nutrition Science and Chemistry
This paper adopts STEAM (Science, Technology, Engineering, Artificial Intelligence, Math) approach. The objectives of this paper are to use Multivariate Clustering Statistics to study the Chocolate Science and Products. Chocolate contains flavonoids and antioxidants which can prevent aging and beneficial to heart disease and diabetes patients. Antioxidants can prevent heart disease is because it reduces free radical formation. Data has been collected on 20+ chocolate ingredient nutrition contents from 60+ different types of chocolate. Both Clustering Variables and Principle Component Analysis methods are utilized to cluster (1) chocolate nutrition, (2) chocolate product types. Chocolate nutrition are clustered into four clusters which is consistent with Chocolate science research and can explain the common chocolate food science very well. Chocolate products can also be clustered into 4 clusters which can distinguish the major chocolate types (dark, milk, white). Five clustering distance algorithms are studied and compared based on the impact of clustering sequence and patterns. Number of clusters are also studied in order to determine which clustering distance algorithm can provide the best clustering pattern to explore the chocolate science research. This paper has demonstrated the effectiveness and power of adopting STEAM approach on the general Scientific Research.