# Chemistry Beverage Lab Report Essay

1265 Words Jan 23rd, 2013 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.
3.
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…