1. Utilizing Spectrophotometry in Life Science
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2. Why do we see colors? Visible Spectrum of Color Pigments ds
This is the radiation that can be seen with the naked eye
Radiation moving within a frequency of nm is viewed as visible light.
Colors differ due to light particles moving at different wavelengths.
Pigments
Particles that absorb certain frequencies of colors of light but reflect others.
The apple’s pigments absorb most colors but reflect red’s frequency. ds

3. Spectrophotometer Spectrophotometer Made of two instruments:
1. Spectrometer
2. Photometer
Made of two instruments:
Used to produce any frequency of light
Used to measure the intensity of light
Spectrophotometers use specially designed cuvettes that have equal density and thickness throughout the container so that they consistently refract the light the same way each time.

4. How is window tint like spectrophotometry?
More light that pass through indicates less light is being absorbed and inversely transmitted due to the varying amount of pigment present.

5. Measuring Light and Color
Light is quantified by two methods:
The significance of measuring light
1. Absorption – the amount of light that is absorbed
Measuring units are in Optical Density (O.D.)
2.Transmittance – amount of light that passes through
Measured in %
The amount of light indicates the concentration of the colored substance.
Allows for detection of very small quantities.
It is important to utilize a wavelength of light with the greatest amount of absorption as possible; this is known as λmax. This is determined through experimental trials to determine the optimal wavelength.

6. Colorimetric Assays
Assays are test that can be used to detect the presence of specific proteins in the body.
Assays are tests that can qualitatively and quantitatively assess the presence of a substance. Assay usually contain a set of substances which the quantities are known ahead of time; the values are referred to as standards and can be referenced against for a variety of solutions with an unknown quantity of a similar molecule.

7. Calibrating a Spectrophotometer
Reference Tube
Range of Concentrations
This is also known as “The Blank”
Contains everything except the compound of interest which absorbs light.
Set the absorbance to zero
Insures the absorbance is due to compound of interest only.
Prepare standard samples that have been diluted.
Start with concentrated solution then pipette small amounts out.
Light passing through the spectrophotometer may still be refracted by the cuvette and solvent. Therefore the machine needs to be calibrated to ignore the absorption of the light from the tube and solvent (very similar to setting the Tare on a electronic balance)

8. Seeing the Substance Indicators
Examples:
Biuret Solution
Benedicts Solution
These are reagents that change color in the presence of specific substance:
Indicate presence and relative concentration
Turns purple with protein
Turns orange/red with simple sugars

9. Using Standards Standards Simple Linear Regression
These are substances with a known value of the measured substance.
Substances that have unknown quantities are compared to the standards.
The absorption data is usually organized onto a scatter plot.
A line of best fit may then utilized to determine the general trend of the data.
Assays do have their limits in which to little substance is undetectable and too much substance oversaturates the solution and therefore there is no affect on the absorbance.

10. Elements of a Scatter Plot
Axis
Equal intervals (2,4,6,8…)
Each axis is labeled
Measurement (length, mass, volume…)
Units (in, cm, mi…)
Scaled to include all data
Avoid values on axis beyond range of data
Easily viewed (not too small)
Line of Best Fit
Equal number of points above and below
Drawn with ruler (or computer generated)

11. Predicting with Linear Models
Graphs can be used as a tool for prediction.
Interpolation – within data
Extrapolation – outside of data
A relationship between x and y is established.
This may be linear (straight)
y = mx + b
m = slope of line
b = y intercept

12. Why do the two bottom graphs have similar lines as the top graph?
The outlier (data point not trending with other points) shifts the slopes of the lines.
All of these graphs have data points that will produce the same statistical properties. The reason that the bottom two graphs produce the same line of best fit is due to outliers in the data. Students will need to recognize the importance of graphing data before they analyze it. Often times outliers will not be used in the statistical analysis.