Light Intensity Measurements for Light Emitting Diodes (LEDs)

I was working late one evening last week when the phone rang. I debated whether to answer it or not, because it was well after standard business hours and I was trying to finish a project, but I decided I needed a break from my computer screen. I picked up the phone and was greeted by a very polite gentleman, likely from somewhere in the southeastern U.S., as evidenced by the drawl and twang in his voice.

This potential customer said he had recently purchased an LED lighting system for his aquarium, but he was having trouble with coral burning. I’m definitely out of my realm of expertise when it comes to coral, but after talking to him about his proposed application of an Apogee quantum meter to measure the intensity of his new LED system, I figured out that the problem was that the light intensity was too high for the coral.

After explaining his desire to measure the intensity of his LED lighting system with a quantum meter, in hopes of achieving the proper light level for his coral, I started to explain how the quantum meter worked. It became clear, however, that he already knew the meter was designed to measure the total number of photons between 400 nm and 700 nm, the photosynthetically active radiation (PAR) range. What he really wanted to know was the accuracy of the quantum meter when used to measure LEDs. Not surprisingly, with the advancement of LED technology in recent years, this is a question frequently asked by customers.

A previous blog post  provided some qualitative information regarding the use of a broadband device (i.e. quantum/PAR sensors or meters) to measure a narrowband radiation source (i.e. many LEDs currently on the market), where it was stated that a spectroradiometer is the best instrument to accurately measure light intensity of LEDs. While this is true, quantum meters can be used to measure LED intensity, and many customers use them for this application. As a result, an estimate of Apogee quantum meter accuracy for measuring LEDs is very practical.

The error associated with a quantum meter (or sensor) measurement of light from a source that has a different spectrum than the source used to calibrate the meter is called spectral error. Spectral error arises because no quantum meters perfectly match the defined quantum response, meaning they do not respond to all wavelengths of light equally between 400 nm and 700 nm. Apogee quantum meters are sensitive to wavelengths between approximately 370 nm and 665 nm, with a relatively flat response between 450 nm and 650 nm due to the blue pigment used in the diffuser (Figure 1). However, they are not equally sensitive to the wavelengths within the photosynthetically active range (Figure 1). In order to determine spectral error, the spectral responses of the quantum meter, calibration light source, and light source to be measured are required, along with some spectra-dependent calculations (for details, see Federer and Tanner, 1966; Ross and Sulev, 2000).

Apogee quantum sensors and meters for electric lighting are calibrated in a custom chamber filled with T5 cool white fluorescent lamps. LEDs have a very different spectral output than T5 lamps (Figures 2, 3, and 4), thus some degree of spectral error is expected. For the narrowband, broadband, and mixed LEDs shown below, spectral errors are 8 % or less. Apogee quantum sensors and meters are less sensitive to blue wavelengths (near 400 nm) compared to longer wavelengths, and thus read low under blue LEDs. Conversely, Apogee quantum sensors and meters are more sensitive to green and red wavelengths (above 500 nm) compared to blue wavelengths, and thus read high under green and red LEDs. The broadband white LEDs output a small proportion of radiation beyond the upper end of the Apogee quantum sensor/meter sensitivity range (665 nm), and thus yield low measurements for the white LEDs.

IMPORTANT NOTE: LEDs that output a large proportion of radiation above approximately 660 nm will read very low and should not be measured with an Apogee quantum sensor/meter.

I did my best to explain, over the phone, the three preceding paragraphs worth of information to the customer. He thanked me for my time, and then ordered a quantum meter the next day. After hanging up the phone following an after-hours tech support call, I was glad to have helped a customer, and even more glad to be inspired by one at the same time.

LED	Error [%]
Blue (448 nm peak, 10 nm FWHM)	-8.5
Green (524 nm peak, 15 nm FWHM)	8.0
Red (635 nm peak, 10 nm FWHM)	6.9
Cool White	-2.0
Neutral White	-3.8
Red, Blue Mixture	4.9
Red, Green, Blue Mixture	5.6

Table 1: Theoretical Spectral Errors for Apogee Quantum Meter Measurements of Multiple LED Sources

Figure 1: Apogee quantum sensor/meter response (blue line) compared to defined quantum response (black line) of equal sensitivity at all wavelengths between 400 nm and 700 nm.

Figure 2: T5 cool white fluorescent spectrum (lamp used by Apogee for electric light calibration of quantum meters; black line) compared to narrowband color LEDs (blue, green, red lines) and defined quantum response (gray line).

Figure 3: T5 cool white fluorescent spectrum (lamp used by Apogee for electric light calibration of quantum meters; black line) compared to broadband white LEDs (cool white fluorescent – blue line, neutral white fluorescent – green line, warm white fluorescent – red line) and defined quantum response (gray line).

Figure 4: T5 cool white fluorescent spectrum (lamp used by Apogee for electric light calibration of quantum meters; black line) compared to mixtures of narrowband color LEDs (red/blue – blue line, red/green/blue – red line) and defined quantum response (gray line).

Federer, C.A. and C.B. Tanner, 1966. Sensors for measuring light available for photosynthesis. Ecology 47:654-657.

Ross, J. and M. Sulev, 2000. Sources of errors in measurements of PAR. Agricultural and Forest Meteorology 100:103-125.

 



Mark Blonquist

Chief Science Officer

Revisiting the Clear Sky Calculator

I usually spend every other Saturday in my hometown, working as a butcher in my Dad and Uncle’s grocery store. Last time I was there, during an afternoon lull, I noticed a calibration verification sticker on the scale used to weigh meat (not unlike the verification stickers found on gas pumps). Accurate calibration of the meat scale is required to ensure that the customer gets what they are paying for, and to ensure my Dad and Uncle are making a reasonable profit on goods being sold. I’ve never been around to witness the USDA inspector verify the calibration of the scale, but I can imagine he or she carries a small set of reference weights that are placed on the scale to determine the scale accuracy and need for recalibration.

Customers often contact Apogee Instruments to inquire about how often pyranometers and quantum sensors should be recalibrated. While it is safe practice to follow the general recommendation to recalibrate radiation sensors every two years, it may not be necessary to recalibrate on a fixed schedule if a sensor consistently matches a reference. The challenge for many pyranometer and quantum sensor users is they don’t have a handy reference, something analogous to weights that can be placed on a meat scale.

In response to customer inquiries regarding recalibration requirements, in 2009 Apogee Instruments developed the Clear Sky Calculator (www.clearskycalculator.com), an online tool that can be used to estimate the intensity of solar radiation (either total global shortwave radiation, measured by pyranometers, or global photosynthetic photon flux density, measured by quantum sensors) incident on a horizontal surface at any time of the day, at any location in the world. The equations used to estimate clear sky solar radiation with the Clear Sky Calculator come from the clear sky solar radiation model used to calculate net radiation in the ASCE Standardized Reference Evapotranspiration Equation (http://www.kimberly.uidaho.edu/water/asceewri/index.html). The only input requirements to the calculator are site elevation, latitude, longitude, reference longitude, and air temperature and relative humidity measurements or estimates. These data are typically easy to obtain, making the Clear Sky Calculator a simple solar radiation reference that can be used to estimate pyranometer and quantum sensor accuracy and determine the need for recalibration.

When used near solar noon over multiple clear, unpolluted days during spring and summer months, accuracy of the Clear Sky Calculator is estimated to be ± 4 % in all climates and locations around the world. As an example, modeled incoming shortwave radiation (SWi) from the Clear Sky Calculator closely tracked measured SWi (data from a heated and ventilated Kipp & Zonen CM21 pyranometer) for a clear day (April 21, 2012) in Logan, Utah (Figure 1). The ratio of measured SWi to modeled SWi was between 1.00 and 1.05 (0 % and 5 %) from 9 AM to 6 PM (solar zenith angles less than 65°) (Figure 2). The average ratio from two hours before solar noon to two hours after solar noon was 1.02 ± 0.01 (2 ± 1 %). A more detailed discussion of Clear Sky Calculator accuracy is given on the webpage (http://clearskycalculator.com/model_accuracy.htm), where the necessary accuracy of the required inputs is discussed.

Apogee strongly encourages our customers to use the Clear Sky Calculator as an effective way to monitor pyranometer and quantum sensor performance and determine the need for sensor recalibration. If a sensor is consistently different from the Clear Sky Calculator by more than a few percent, please contact us about recalibration.

Figure 1: Comparison of measured incoming shortwave radiation (SWi) (red line) and modeled SWi on April 21, 2012 in Logan, Utah. Measured SWi is from a heated and ventilated blackbody pyranometer (Kipp & Zonen model CM21). Modeled SWi is from the Clear Sky Calculator.

Figure 2: Ratio of measured shortwave radiation (SWi) to modeled SWi over the course of a clear day (April 21, 2012) in Logan, Utah. Mean ratio = 1.02 ± 0.01 (2 ± 1 %) for measurements averaged from two hours before solar noon to two hours after solar noon (solar noon occurred at approximately 13.5). The dip in the morning near 7 is due to mountains on the east side of the valley where Logan is located.

 

Mark Blonquist
Chief Science Officer

A Brief Review of Temperature Measurements

The properties of materials and nearly all biological, chemical, and physical processes are temperature dependent. As a result, temperature is perhaps the most widely measured environmental variable, and there are multiple sensors, or thermometers, available to measure temperature. Some of the common thermometers for automated temperature measurement are:

Thermocouple: two different metals or alloys connected at the ends (see Figure 1 below) to form a simple electrical circuit (current loop). A temperature difference (thermal energy gradient) between the two ends of the circuit produces a voltage, called an electromotive force (emf), that is proportional to the temperature difference.

Thermistor: electrical resistor, often ceramic (see Figure 1 below), where resistance changes with temperature.

Platinum Resistance Thermometer (PRT): platinum coil, where resistance changes with temperature. Thermistors and PRTs are similar and operate via the same mechanism, but are made with different materials.

For temperature measurement with the thermometers listed, changes in the physical property (voltage, electrical resistance) related to temperature changes must be measureable, repeatable, and stable. These thermometers require a meter to make the electrical measurement and convert it to temperature. This is desirable for environmental monitoring, where many meters can log data, and automated data collection of high frequency and/or long-term data sets are often required. A summary table (Table 1) is provided to highlight the advantages and disadvantages of thermocouples and thermistors. The advantages and disadvantages of PRTs are similar to or the same as those for thermistors, except that the sensitivity (resistance change) in PRTs is much smaller and potentially more difficult to measure accurately.

Figure 1. Size comparison of thermocouples and thermistors. From left to right: human hair (for scale), fine wire ceramic thermistor with thin epoxy coating, ceramic thermistor with epoxy bead coating, 30‐ AWG (0.2546 mm) type‐E thermocouple, and 24‐AWG (0.5106 mm) type‐E thermocouple. Thermocouple junctions are often coated with epoxy for electrical isolation and waterproofing, however, those in this picture are bare wires.

Table 1. Advantages and Disadvantages of Thermistors and Thermocouples

 Some of the advantages and disadvantages listed in Table 1 are dependent on the circumstances of the specific measurement and application. Multiple datalogger program steps for many thermistors are avoided with dataloggers that have ‘canned’ thermistor instructions. A ‘canned’ instruction is a set of pre‐programmed datalogger codes that allow use of specific sensors with only a single instruction. Also, the power requirement of thermistors is extremely small. For example, a commonly used thermistor in environmental applications only uses 0.056 mW at 20 C. The maximum current draw across a wide range of temperatures is approximately 0.090 mA. A commonly used datalogger can source 25 mA. Based on this specification, the datalogger could accommodate over 250 thermistors if there were enough measurement channels available.

The reference temperature required for thermocouples is available on many dataloggers. Accurate reference temperature measurements are then dependent on the accuracy of the sensor used to measure it. This is often a thermistor. Periodic recalibration of the datalogger is recommended to ensure the thermistor is accurate. Also, the datalogger wiring panel (where the thermocouples are connected) should be kept isothermal. This is best accomplished by installing the logger in an insulated, weatherproof box that shields the logger from solar radiation. The small output signal of a thermocouple relative to the large output signal of a thermistor is only a disadvantage when a low resolution datalogger is used to make measurements. Some dataloggers have adequate resolution to make highly accurate thermocouple measurements. The datalogger program required for thermocouples is only simple because it is generally always available as a ‘canned’ instruction. If thermistor and thermocouple datalogger programs had to be written from scratch, the number of program steps and the difficulty level would be similar. Because thermocouples require a differential channel and thermistors do not, this is always a disadvantage. This means twice as many thermistors can be connected to the same number of datalogger channels

 

 

Mark Blonquist
Chief Science Officer