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Chemical Determinants of Baking Flour Performance
Data Science and Analytics
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About
Analysing the chemical composition of flour is essential for achieving the ideal batter consistency in the production of ice cream cones. This research-derived collection investigates the relationship between three key flour components—moisture, protein, and ash—and the resulting viscosity of the batter. Based on findings published in 1988, the data provides a mathematical basis for predicting baking performance through a linear regression model. It allows for a detailed exploration of how minute variations in flour chemistry can significantly alter the mechanical properties required for high-quality cone manufacturing, ensuring that the final product meets structural and sensory standards.
Columns
- codeNum: A unique index identifier assigned to each of the 39 flour samples included in the study.
- moisture: The water content of the flour sample, recorded as a percentage (%).
- protein: The concentration of protein within the flour, expressed as a percentage (%).
- ash: The mineral content remaining after the flour is burnt, expressed as a percentage (%).
- viscosity: The dependent variable representing the thickness or resistance of the batter, measured in degrees MacMichale.
Distribution
The information is delivered in a compact CSV file titled
icecreamcone.csv, with a total file size of 942 bytes. It consists of 39 valid records across 5 distinct columns. The data maintains a high level of integrity, with 100% validity for all entries and no recorded missing or mismatched values. This is a static archive with no future updates anticipated.Usage
This resource is ideally suited for training and validating linear regression models to explore the specific formula: Viscosity = b0 + b1(Moisture) + b2(Protein) + b3(Ash). It serves as an excellent case study for exploratory data analysis within the fields of food chemistry and industrial food processing. Researchers can use it to identify which chemical factors most heavily influence batter thickness, while academic instructors can employ it to demonstrate multivariate statistical analysis using a manageable, clean real-world sample.
Coverage
The scope of the data focuses on 39 unique flour batches analysed for ice cream cone production. Temporally, the records relate to research conducted and published in 1988, providing a historical snapshot of industrial food science and quality control standards. The demographic scope is not applicable, as the data is strictly chemical and mechanical in nature, pertaining to food manufacturing materials rather than human subjects.
License
CC0: Public Domain
Who Can Use It
Food science students and researchers can leverage these figures to understand the practical applications of chemistry in industrial baking. Data science beginners will find the small, high-validity sample size perfect for practicing regression techniques and data visualisation without the requirement for extensive pre-processing. Additionally, quality control professionals in the food industry can use the findings to better understand the variables that affect batter performance.
Dataset Name Suggestions
- Ice Cream Cone Flour Viscosity Model
- Chemical Determinants of Baking Flour Performance
- Flour Moisture, Protein, and Ash Analysis (1988)
- Industrial Batter Viscosity and Flour Chemistry
- Multivariate Linear Regression: Ice Cream Cone Baking
Attributes
Original Data Source: Chemical Determinants of Baking Flour Performance
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