Chemistry Beverage Lab Report Essay

1265 Words Nov 29th, 2012 6 Pages
Joanna Park
Mrs. Carrillo
CP chemistry per. 5
September 17, 2012

i. Beverage Density Lab Report ii. Purpose: The purpose of this experiment is to determine the percentage of sugar content in beverages. iii. Materials: Distilled water, beverages (juice, soda, sport drinks), Sugar reference solutions (0, 5, 15, ad 20%) 25ml each, Balance, centigram(0.01g precision), Beaker (100-mL), Erlenmeyer flask (125-mL to collect rinse solutions), Pipet(10-mL), Pipet bulb or pipet filler iv. Procedure:
Part A 1. Place an empty 100-mL beaker on the balance and hit the “rezero” button and check if the scale reads 0.00 g 2. Suck up 10.00mL of 0% sugar (distilled water) into a pipet and move the liquid to an empty beaker.
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According to its nutrition label, orange soda contains 49g of sugar per 3550mL serving. If the density of the beverage is 1.043 g/mL, what is the percentage sugar concentration in orange soda? * X g= 355mL(1.043g/1mL) = 370g, 49g/370g * 100%= 13% The percent sugar concentration in the orange soda is 13% vi. Data Table
Table A. Solution | Mass, g | Sample Volume, mL | Density, g/mL | 0% Sugar | 10.00 g | 10.00 | 1.00 g/mL | 5% Sugar | 10.10 g | 10.00 | 1.01 g/mL | 10% Sugar | 10.30 g | 10.00 | 1.03 g/mL | 15% Sugar | 10.60 g | 10.00 | 1.06 g/mL | 20% Sugar | 10.80 g | 10.00 | 1.08 g/mL |
Table B Beverage | Mass, g | Sample Volume, mL | Density g/mL | Powerade | 10.27 g | 10.00 mL | 1.027 g/mL | Cola | 10.38g | 10.00 mL | 1.038 g/mL | vii. Results Table Beverage | Measured density | Percent sugar(from graph) | Amount of sugar () | Percent sugar | Percent error | Powerade | 1.027g /mL | 8.3% | 15g /240 | 6.1% | 22% | Cola | 1.038g /mL | 11.1% | 42g /355 | 11% | 0.9% | viii. Analysis 1. .

2. Use the graph to estimate the unknown sugar concentration in the first beverage. To do this, locate the point on the y-axis that corresponds to the density value of the beverage. Follow that point on the the y-axis across horizontally to where it meets the “best-fit” line through this data. Now
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