# Montgomery College Scientific Process Data and Communication

Montgomery College Scientific Process Data and Communication

# EXERCISE 1

Introduction to the Scientific Process, Data and Communication

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### OBJECTIVES

Upon the completion of this exercise the student should be able to:

1. explain the steps of the scientific
2. apply the different steps of scientific
3. determine the dependent and independent variables of an
4. distinguish between control and experimental groups and between positive and negative controls.
5. convert between standard and scientific
6. apply the appropriate metric units for measurements and data
7. identify and use appropriate lab instruments (pipette pump, graduated pipette, graduated cylinder, micropipette, and spectrophotometer) for data collection and
8. Construct appropriate type of graphs for different data

# I.        What is the scientific method?

The scientific method is a series of logical steps that scientists use to investigate different ideas and questions about the world we live in.

Use of the scientific method is not just limited to the laboratory. This same logic can be used in the everyday world – in the work place and your life. An important part of your education is to develop the ability to use reason rather than emotion to solve problems and make important

decisions. Learning about and using the scientific method will allow you to enhance your ability to reason and think critically, which are valuable skills.

#### Example of real world use of the scientific method:

 Observation: What  you notice by using your senses to observe the natural world. It also can include information learned by studying the results of previous research. Question: Ask a question based on the observation(s) or other relevant information available. Hypothesis: A possible explanation for observation(s) that can be tested. There is usually more than one possible hypothesis. Prediction: If my hypothesis is correct, then what will happen. “If ….then….” statement Experiment: Design a controlled way to test the hypothesis. Collect data. Analyze Results: Determine whether or not the hypothesis is supported by the data collected in Conclusions: What you conclude from the results of the experiment.

You go to your car, so that you can drive to Montgomery College. You notice that the door is only partially closed. When you try to start the car, the engine won’t turn over. Using the steps in the scientific method, reason through what you could do to get to your car started.

Car doesn’t start; door partially closed.

Why doesn’t the car start?

The car doesn’t start because the battery is dead or drained of energy.

If the battery of the car is dead, then giving the car a jump start (or recharge) will allow the car to start.

Jump start or recharge the car battery.

The car starts. So, the hypothesis is supported.

The car battery was dead and needed to be recharged.

If the results of the experiment are consistent with the hypothesis, then accept the hypothesis as true. Keep in mind, however, that there may be other explanations for the result. A hypothesis is not really proven until all other hypotheses have been tested and eliminated due to lack of evidence. When you hear scientists talking about new discoveries, it may sound to you as if they are tentative or not fully confident of their conclusions. However, a good scientist will say that their data “supports” or is “consistent with” a particular conclusion. It is not that they do not believe their data, but good scientists know that their conclusions may be modified based on future experiments.

If the results do not support the hypothesis, then you may need to revise the hypothesis and do another experiment. The example above actually happened. The next day the car failed to start again and needed another jump start. So, the hypothesis needed to be revised. The person affected proposed a new hypothesis: The battery is old and needs to be replaced. If the first hypothesis is not supported by the results, then you would go through the same reasoning as above to determine if your revised hypothesis was correct or not.

Note to Instructors: Refer to the instructor manual for this lab for the instructions for these activities or alternate activities.

# II.         Developing a hypothesis:

A hypothesis is a proposed answer to the question based on information from your own previous observations and experiments and/or reading of the scientific literature, which has observations and experiments published by other researchers. There may be

multiple hypotheses to explain the same set of observations or other information. Ideally, each hypothesis would be tested to eliminate unsupported hypotheses, leaving only one possibility.

Characteristics of a Well Written Hypothesis:

• A hypothesis must be specific. You must state clearly what you will be

#### 2)                     A hypothesis is a statement and is not written in the form of a question. No question marks!

• A good hypothesis should clearly state how the dependent variable is expected to be affected by the independent variable. There should only be one independent variable, and what it is should be stated in the

Example: “As the amount of nitrogen given to the plant increases, the rate of growth of the plant will increase.”

The hypothesis above says how the dependent variable (rate of plant growth) is expected to be affected by the independent variable (the amount of nitrogen).

Note: See section on variables below.

# III.                           What is a variable?

Variable – Any factor that is different between the experimental and control groups or samples.

#### There are three types of variables:

• Independent Variable – The factor that is changed by the experimenter and is not affected by any other

#### A well designed basic experiment only has one independent variable.

• Dependent Variable – The factor that changes value as the independent variable (another factor) is changed by the

• Standardized Variables – Factors that the experimenter “chooses” to keep constant or

Standardized variables are particularly important in human studies where there are so many possible differences among groups of patients (e.g. age, general health, etc.).

Scientists must strive to control as many factors between the groups as possible, so that the experiment is interpretable and a valid conclusion can be made.

Example #1: A new laundry detergent (Brand A) is tested to determine whether it is more effective at removing stains than the leading brand of laundry detergent (Brand B). Both Brand A and Brand B are used to clean 50 shirts with grass, dirt and other stains.

What is the independent variable that is being tested in this experiment?

-The type of laundry detergent.

Example #2: I used 3 brands of fertilizer on my lawn: Green Grow on one third of the front lawn, and it grew 2 inches; SuperGrass brand on a different third of the front lawn, and it grew 3 inches; a generic store brand on the last third of the front lawn, and it grew 4 inches.

1. What is the independent variable?

.

1. What is the dependent variable?

Example #3: A scientist is testing whether an antibiotic is effective in killing the bacteria (Mycobacterium tuberculosis) that causes tuberculosis or TB. She puts the same number of bacteria in each sample tube. Then, she adds different concentrations of the antibiotic to each tube of bacteria. In the control tube, she adds water to the bacteria to determine how many bacteria die without the addition of antibiotic in her experiment. She waits 15 min, and then determines how many bacteria are still alive.

1. What is the independent variable?

1. What is the dependent variable?

#### Check your answers for examples 2 and 3 on the bottom of the next page.

READ SECTIONS II AND III CAREFULLY AND DO THE EXAMPLES BEFORE ATTEMPTING THE ACTIVITIES.

the independent variable and dependent variable in an experiment.

Note to Instructors: Refer to the instructor manual for this lab for the instructions for these activities or alternate activities.

Example: A group of scientists have a new medication that they think may be an antidepressant. How would they go about testing whether or not this new drug is effective as an antidepressant or not?

First, they need two groups of test subjects – one group of people would receive the new potential antidepressant and the other group of people would receive a placebo (a pill that does not contain any medication, sometimes referred to as a “sugar pill”).

#### Which group is the experimental group?

-The group that receives the new potential antidepressant medication.

#### Which group is the control group?

-The group that receives the placebo. The purpose of the control group is to determine what the baseline or background level is. In this example, the control tells the scientists how many people’s depression improves without any medication. Improvement of patients in the experimental group above this background level of the control group means that the new medication is actually having an effect in alleviating the test groups’ depression.

#### What is the variable that is being tested in this example?

-The type of pill taken by the patients: either the new potential antidepressant or the placebo.

In any experiment it is important to set up the control and experimental groups as similar as possible. This is a difficult task in an experiment like this example where patients are involved because there are so many differences or variables to control for. See section above on “Standardized Variables”.

control and experimental groups or samples.

Note to Instructors: Refer to the instructor manual for this lab for the instructions for these activities or alternate activities.

# V.  Positive and Negative Controls.

In some cases, it is useful to have both negative and positive control samples or groups as part of an experiment. The purpose is to ensure that your experiment or assay (e.g. biochemical test) is working properly and that you can draw a conclusion from the results of the experiment.

Positive control – a sample, group etc. in the experiment that is designed to produce a positive result.

A positive control is often known from a previous experiment to produce a positive result under the same experimental conditions. It confirms that the test is performed correctly and shows what a positive result looks like and that a positive result is possible (even if all experimental samples happen to be negative).

Negative control – a sample, group etc. in the experiment that is designed to produce a negative result.

A negative control is often known from a previous experiment to produce a negative result under the same experimental conditions. Sometimes, a negative control does not receive any treatment (for example a placebo).

#### To interpret the results, each experimental sample can be compared to the positive and negative controls.

Revised from Lipsitch et al. 2010. Epidemiology 21:383-388.

Example: The Biuret assay is a test for the presence of protein. Specifically, the test detects peptide bonds, which are the chemical bonds that join together the subunits of proteins called amino acids.

When the Biuret reagent, which is blue, is added to a solution containing protein, then the solution turns purple.

Tube #1 – Biuret reagent + H2O

Tube #2 – Biuret reagent + gelatin (a protein)

Tube #3 – Biuret reagent + egg Tube #4 – Biuret reagent + milk

You don’t need to know the results in order to determine which samples are the controls and which samples are the experimental samples.Think about the definition of positive and negative control and answer the following questions.

• Which tube(s) is the positive control? Why?

• Which tube(s) is the negative control? Why?

• Which tube(s) is the experimental? Why?

Activity 5: Please complete the worksheets to help you understand the difference between Positive and Negative controls.

# Putting it all together:

Activity 6: Please complete the assignment that requires you to answer questions regarding all of the concepts discussed above. This assignment may be done as homework along with pre-lab homework for the next lab, if you are unable to complete it during the lab period.

# VI.      Representation of Data

Models

In biology, the term “model” may refer to any form of representation of the structure and function of a biological system or a process, and may range from molecules to ecosystems levels. Models allow us to test our understanding of particular systems and, if the models are good, to make predictions (https://evergreen.edu/catalog/offering/models-biology-20502). Models may be used, in science, to explain a process or to test a hypothesis. Here, we will use a flexible definition for the term “model” and you will see some examples below. You will come across these types of models in many of your science courses, as you progress through the degree.

1. Modeling a Structure:

_The_Central_Science_(Brown_et_al.)/02._Atoms%2C_Molecules%2C_and_Ions/2.6%3A_Molecules_and_Molecular_Compounds

1. Modeling a Chemical Reaction:

1. Tracking a process:

# D.      Data visualization to understand a phenomenon: Graphs

Flatten the curve: Epidemiology of an infectious disease

# Graphs are the most relevant models of data representation in our lab.

Numerical data (data is the plural from of the word datum, an item of information) are often best represented in the form of a graph. Graphs make it easier to see and understand numerical relationships. Therefore, graphs can be a valuable addition to any research paper. Using a graph from another source is acceptable, as long as you give credit to the original source.

In any graph, there are at least two quantities that vary; these are called the variables. For example, if you counted how many different types of trees there are on campus, the various types of trees would be one variable, and the number of each type of tree would be the other variable.

In graphing, it is customary to put the independent variable along the horizontal axis. This is the variable you (the experimenter) are controlling, the types of trees are the independent variable because you will set or control the categories before you collect the data. The dependent variable is the data you actually collect. In our example, it is the number of each

type of tree. The dependent variable usually goes on the vertical axis of the graph. The dependent variable depends on what the independent variable is (its value or type).

### Types of Graphs

If one of the variables is not continuous, you must make a bar graph*. In our example, the types of trees is not continuous, they do not have to go in a particular order. They are distinct and separate categories and do not form a continuum (Figure 1).

*called a column graph in Excel.

#### Figure 1. Bar Graph

If both of your variables are continuous then a line graph is the more appropriate choice. Time, distance, and temperature are examples of continuous variables. (Figure 2). The line graph is also ideal when illustrating responses (y-axis) over time (x-axis) for multiple groups. Each line represents the data for a defined group of subjects.

#### Figure 2. Line Graph with Multiple

Percent Change in Tumor Size from Baseline as a Function of Time and Treatment Group

10

0

-10

-20

-30

-40

-50

-60

-70

0                          2

Week

Placebo (n=50) Low Dose (n=48) High Dose (n=52)

4                          6

A best-fit line or trend line is a smooth line or curve than shows the trend of the data. The line should come as close to as many points as possible. Do not connect the dots. The line expresses a mathematical relationship between the two variables.

In our example below (figure 3) the relationship is linear, as the independent variable (temperature) increases, the dependent variable (number of flashes) increases.

#### Figure 3. Best-fit Line

45

40

35

30

25

20

15

10

5

0

Fireﬂy Flash Rate as a Function of Temperature

0                5              10             15             20             25             30             35             40             45

Temperature (°C)

Often the trend line cannot start at zero or the lowest value on the graph, but the trend line can be extended and a predication can be made, this is called extrapolating from the data. How many flashes do you predict at 5°C?

Sometimes data seems to have a relationship, but not perfectly. The relationship is called the correlation. The stronger the correlation, the closer that data relationship is to a perfect line or curve. In this case, the correlation is positive – meaning as the independent (horizontal, or x-axis) values increase, so do the dependent (vertical, or y-axis) values. In a negative correlation, as the independent values increase, the dependent values decrease. A perfect correlation – where every value is exactly on the line – has a correlation value of 1 (or -1 if it is a negative correlation). A correlation value of 0 means there is no linear relationship at all. (figure 4)

For the following examples in figure 4, determine if the correlation is positive , negative or there is no correlation.

### Figure 4

a                                                              b                            c

### Designing the Graph

A simple acronym to help you remember the important parts of a correct graph: TAILS

T = Title: Every graph should have a descriptive title. Titles should be informative not gimmicky. You should be able to determine what the experiment is from the graph.

The following format is useful: The variable on the Y-axis as a function of the variable on the X-axis. Review the names of the graphs shown in this lab.

Example: “Figure 2: Percent Change in Tumor Size from Baseline as a Function of Time and Treatment Group.” The title tells you that you are looking at the change in the size

of a tumor in response to different treatments. The title also tells you the experiment was done over a period of time. There is no guessing, Percent change in what?

A = Axes: The independent variable is usually placed on the X- axis (horizontal axis). The independent variable is what the experimenter changes (e.g. time, temperature, wavelength of light).

The dependent variable is usually placed on the Y- axis (vertical axis). The dependent variable changes as a result of changes in the independent variable. In other words …it depends on the independent variable.

I = Intervals: Spacing between one number and the next on each axis.

The axes should be numbered in regular intervals such as 2, 5, 10, 20 or 100. Not every line needs to labeled, for example, the X-axis might start at zero and have an interval of 1, but the lines or gradations indicating the number on the axis would be 0, 2, 4, 6, 8, 10, etc. See Figure 5.

Both axes do not have to have the same interval. The interval on the X-axis might be 1, but the interval on the Y-axis might be 10. In the example shown, the interval on the X-axis is 1 and the interval on the Y-axis is 0.5.

L = Labels: Each axis should be labeled with the independent variable or dependent variable and its units. For example, if the independent variable were time measured in minutes, then the X-axis would be labeled: Time (min). If the dependent variable is milliliters of oxygen formed, then the Y-axis would be labeled: O2 formed (ml).

S = Scale: Scale refers to setting the appropriate minimum and maximum on each axis for the data being graphed.

The minimum number on each axis should be a bit lower than the lowest number in the data graphed. The minimum does not always have to be zero (unless it is a bar graph). The maximum number on each axis should be a bit higher than the highest number in the data being graphed.

For example, if the data ranges from 2 to 43, then you might set the minimum at 0 and the maximum at 50. However, if the range of data is from 127 to 179, you might set the minimum at 100 and the maximum at 200. This method of setting a minimum value at a number other than 0 is common practice in the sciences. This is not, however, the way

graphs are designed in the field of mathematics. Speak with your math instructor to verify how he/she would like you to design a graph for your math course.

The origin, the place where the two axes meet must be labeled. This indicates the minimum value of each axis.

The scale should fill up most of the graph paper. Do not squeeze your graph together in one small section of the graph paper. If you are not using at least ½ of your graph paper, your graph is too small!

Some final notes for designing graphs and tables. Remember to include a legend when graphing data with multiple groups (See Figure 2). You can draw dotted or dashed lines, use different colors, or different symbols. Absolutely DO NOT draw a squiggly line to represent the different groups.

#### Data are often displayed in tables as well as graphs. The tables need informative titles and should be different than your graph titles.

Please watch the following videos to get a better understanding of how to plot a graph.

*A Beginner’s Guide to Graphing Data by Bozeman Science, Nov. 25, 2012, 10 minutes

https://youtu.be/9BkbYeTC6Mo

Graph Types and Guidelines by BiologyMonk, Aug. 9, 2013, 9 minutes https://youtu.be/2RCy5hTxuYA

*Graphing Data by Hand by Bozeman Science, Nov. 25, 2012, 5 minutes https://youtu.be/GUYRMdcEs00

Choosing a Scale by C Annable Math Page, Nov. 1, 2016, 5 minutes https://youtu.be/pG3W6S4b1t8

How to Use Proper Scale for Graph by Ting by HC Ting, May 20, 2014, 10 minutes https://youtu.be/CSUYMZaaJs4

# Line of Best Fit Video

*How to Draw Line of Best Fit (Scatterplot) by Moomoo Math and Science, Mar. 7, 2018, 3 minutes https://youtu.be/l2BOZDosuIk

# How to draw a graph using excel

In this course, most of your graphs will be generated manually, on a graph paper. It is important to learn how to generate a graph using excel as well. Please follow the instructions as given in the video below and also practice drawing at least one of your graphs in excel

https://youtu.be/DM52xjSLGqs

• Scientific Notation

Our Sun is 150,000,000 kilometers from Earth, and the mass of the average mitochondrion is 0.00000000000308 grams. Scientists constantly work with extremely large and small numbers. Scientific notation is a useful system for working with these otherwise cumbersome numbers.

#### In scientific notation, numbers are composed of three components:

• The coefficient
• The base
• The exponent

#### There are also three rules when writing in scientific notation:

1. The coefficient must be greater than 1 and less than 10
2. The base is always 10
3. The exponent reflects the number of places the decimal has to be moved. The exponent is positive if the original number is greater than 1. The exponent is negative if the original numbers is a fraction of

Examples:

• 1000 is expressed as 1 x 103 (1000 is greater than 1, so the exponent is positive)
• 001 is expressed as 1 x 10-3 (0.001 is a fraction of 1, so the exponent is negative)

Note: you cannot just count the number of 0s, you need to count the number of places the decimal moves to make the coefficient be greater than 1.

• Now lets combine all 3 rules: 0.00000000000308 is expressed as 3.08 x 10-12The coefficient is 08

The base is 10

The exponent is –12 (the original number is a fraction of 1, so the exponent is negative)

Scientific Notation Practice Problems

1. Convert 25000 to scientific
2. Convert 0.00025 to scientific
3. Convert 12,495,000,000 to scientific
4. Convert 0.0000000238 to scientific
5. Convert 2.54 x 106 to
6. Convert 5.3 x 10-9 to

### VIII. The Metric System

The United States is one of the few countries that still use the awkward and antiquated English system of measurement based upon units of inches, pounds, gallons, and Fahrenheit. The metric system is a system based on units of 10 and is the most common system of measurement used in science and industry. Conversions between the units of the metric system can be performed by simply shifting the decimal place. The basic units of measurement in the metric system are:

Length = Meter (m) Mass = Gram (g) Volume = Liter (L)

Greek or Latin prefixes are used to denote powers of 10.

#### Table 1: Commonly Used Metric Prefixes

 Prefix Symbol Factor (m, L or g) Equivalent Name kilo k 103 1000 thousand basic unit m, L or g 100 1 one centi c 10-2 .01 hundredth milli m 10-3 .001 thousandth micro µ 10-6 .000001 millionth nano n 10-9 .000000001 billionth

Metric System Practice Problems – identifying abbreviations and conversions from one unit to another (to be done for HW)

1. Using Lab Instruments

Measuring Volume of a Liquid

Volume is a measure of the space occupied by an object. The basic unit of volume in the metric system is the liter. In the lab you will most commonly be measuring items in milliliters (1 x 10-3 L) and microliters (1 x 10-6 L). In the laboratory, several devices are used to measure volume including flasks, beakers, graduated cylinders, pipettes and micropipettes

Figure 6

Laboratory beakers are generally used for stirring, mixing and heating liquids as part of the lab protocol. If the experiment calls for approximate volumes, one can use a beaker to measure the volume as it has markings on its side.

Conical flasks can be used for chemical reactions as the long, narrow neck of the flask can prevent spillage. In addition, flasks have the ability to be capped or corked, therefore solutions can be stored for long periods of time.

A graduated cylinder is a standard piece of laboratory glassware used to measure the volume of an object or amount of liquid. Graduated cylinders are specifically designed to make accurate volume measurements. For preparation of solutions that require exact volumes of liquid to be added, a graduated cylinder must be used and not a beaker.

• To read a graduated cylinder, place the cylinder on a flat surface and bend down so you are eye level with the top of the solution. You will notice the solution curves, this is known as the

#### The volume in this cylinder is 50 mL

(http://waterdecontamination.tpub.com/TM-5-6630-215-12/css/TM-5-6630-215-12_45.htm)

The meniscus results from the interaction of the solution and the sides of the glass cylinder (surface tension and adhesion). A meniscus does not form in a plastic graduated cylinder.

Graduated pipette (also known as a serological pipette): On one side of the pipette you will notice it reads 0 – 9 (top to bottom) and on the other, 10 – 1 (top to bottom). You may read either side as long as you understand what the numbers are measuring. For example, for the pipette shown in figure 6 on the previous page, if you draw up liquid to the 6 mL mark you are drawing up 4 mL (10 mL – 6 mL = 4 mL).

If you need to measure less than 1 mL do not use the last milliliter in the pipette. You must be able to read the gradations on the pipette to accurately determine the volume of liquid. To verify that you are using the pipettes properly you will use a graduated cylinder.

Using a pipette pump and graduated pipette

• Make sure the plunger on the pipette pump is

• Insert the pipette into the pipette pump. The end with the cotton plug should befirmly inserted into the pipette

• You should always hold the pipette pump with the pipette pointing down. NEVER tip the pipette up. You never want liquid running into the pipette

Watch the video: https://youtu.be/4VTTE_oWs58?list=TLPQMjgwNjIwMjArQr6Y5wf8XQ

Using a micropipette

The most common instrument used for manipulating small volumes (< 1mL) of liquids in a laboratory is a micropipette. The scales on micropipettes are in microliters (µL). These are delicate, expensive instruments costing \$250 – \$300 a piece. Please follow all directions given by your instructor and handle the micropipettes with care.

Micropipettes come in different sizes, which are capable of pipetting different ranges of volumes. For example:

#### P20 = 0.5 – 20 µL

P100 = 20 – 100 µL

P200 = 20 – 200 µL

P1000 = 200 – 1000 µL

It is extremely important that you never exceed the upper or lower limits of the micropipettors. Always select the SMALLEST size pipettes that will handle the volume you wish to move. Accuracy decreases as you use unnecessarily large pipettes for small volumes.

1. Which micropipette would you use to measure the following volumes?

1. 250 μL

1. 75 μL

1. 189 μL

 \
• 5 μL

1. Determine the following window settings or the appropriate micropipette to be used given the following

Examples:

Micropipette with a tip

1. A) P- 1000 B) P- 200 C) P-

 0 1 7 9 3 8 _________ mL     D) P- ______ _ L     E) P- 20 920 mL     F) P- 1 5 0 50 µL L 19.5 mL

Modified from Institute for Systems Biology (http://baliga.systemsbiology.net/drupal/ education/?q=content/lesson-5-spectrophotometer-and-micropipette-use)

Micropipetting video:

Using a Spectrophotometer

Spectrophotometry is one of the most useful methods of quantitative analysis in various fields such as chemistry, physics, biochemistry, material and chemical engineering and clinical applications. A spectrophotometer is used to measure the amount of light that a sample absorbs. The instrument operates by passing a beam of light through a sample and measuring the intensity of light reaching a detector. As the beam of light passes through a solution, it can encounter a molecule of solute, and there is a chance the molecule will absorb the light. The spectrophotometer can calculate how much light has been absorbed at different wavelengths.

Visible light, the light your eyes can detect, travels in waves that vary between 390 – 750 nanometers. Our brain interprets the various wavelengths of light as different colors. When visible light is passed through a prism it is separated into its various wavelengths, and we see the rainbow colors: red, orange, yellow, green, blue, and violet.

The amount of light a molecule will absorb depends on the wavelength of light. Different molecules absorb different wavelengths of light. A spectrophotometer is used to determine what wavelengths of light a molecule absorbs. If a molecule does not absorb light, we say the light is reflected or transmitted. If transmittance is 100 % , this indicates an absorbance of 0%. When a molecule reflects a wavelength of light, it bounces off the substance, and then we see that color of light. The spectrophotometer has special filters that can separate white light into individual wavelengths. The basic principle is that each compound absorbs light over a certain range of wavelengths.

Figure 1

Figure 1 illustrates the basic structure of spectrophotometers. Visible light (a light bulb) passes through a prism, which splits the light into its different wavelengths. A specific wavelength can be selected to pass through the substance in a cuvette. The amount of light absorbed by the

substance is displayed on the spectrophotometer. (Modified from UC Davis ChemWiki licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.)

# Click on the link below for a simulation with colored solutions. Click on the

Beer’s Law tab to get started.

In today’s activity you will be using the spectrophotometer to measure the amount of light that is absorbed by fruit/vegetable extracts at various wavelengths. The objective of this activity is for you to learn to use a spectrophotometer and to describe the relationship between wavelengths of light, light absorption, and color of a substance. You are measuring the amount of light absorbed by the fruit/vegetable extract at different wavelengths of visible light. The wavelengths you are testing correspond to specific colors.

Before a spectrophotometer can calculate the amount of light absorbed, you need to show the spectrophotometer what 0 absorbance looks like – a negative control. A cuvette that contains only the solvent for your sample is used as reference or blank cuvette. The blank cuvette is inserted into the spectrophotometer and you adjust the reading to 0 absorbance. You must use the blank cuvette before the sample cuvette every time you make a change to the spectrophotometer.

The videos below explain how this is done

## How to use a spectrophotometer I

How to use a Genesys Spectrophotometer

For remote classes: Look at the set of data for different food extracts in the table below. Copy the values for the extract your group picks in the table provided on page 26, answer the questions below and plot a graph using this information, to complete the assignment in class or as homework.

 wavelength purple cabbage spinach carrot Blueberry 350 0.068 0.0795 0.065 0.03 400 0.051 0.599 0.123 0.05 450 0.052 0.573 0.454 0.032 500 0.076 0.324 0.525 0.085 550 0.111 0.127 0.120 0.42 600 0.076 0.141 0.08 0.22 650 0.020 0.217 0.065 0.08 700 0.010 0.112 0.064 0.021 750 0.004 0.058 0.01 0.001

Student Activity

Which extract did your group select?

What variable will be manipulated or changed with the use of the spectrophotometer?

What variable will be measure with the use of the spectrophotometer?

Review the information about the spectrophotometer and your answers to the above questions. What do you think will happen when you measure the absorbance of the extract in the spectrophotometer? Your answer to this question should be testable and falsifiable. This is your hypothesis for this experiment.

Hypothesis

Data Collection

When designing an experiment it is important to think about how you will record the data you will be collecting. Often the data are recorded in a table and then presented in a graph. Graphs provide a compact method of synthesizing all of the data into a single image (http://www.ncsu. edu/labwrite).

23

A Table to record your results

For this experiment we have designed a table for you, however in future activities you may need to design your own table.

Title of Table 1:  Absorbance values of Blueberry                                                                            extract at different wavelengths of visible light.

 The independent Variable is   Wavelength The dependent Variable is   Absorbance _ 350 0.03 400 0.05 450 0.032 500 0.085 550 0.42 600 0.22 650 0.08 700 0.021 750 0.001

1. What is the independent variable? Wavelength

1. Does this go on the X-axis or Y-axis? X-axis

1. What is the dependent variable? Absorbance value of blueberry extract

1. Does this go on the X-axis or Y-axis? Y-axis

1. Is the independent variable quantitative? Yes

1. Is the independent variable continuous? Yes

1. What kind of graph would be best to use? Circle one

#### BAR GRAPH           LINE GRAPH

1. Use the TAILS checklist and graph the data on the following

• Title
• Axes
• Intervals
• Labels
• Scale

Conclusion:

Should you accept or reject your hypothesis? Explain.

What color of light corresponds to the lowest amount of light absorbed?

If the light is not being absorbed, what is happening to it?

Explain the relationship between the color of a substance and the wavelengths of light the substance absorbs.

Why do you think green plants are green? What wavelength of light is being reflected or transmitted?