Calibration is the number one item on this list for a very important reason: it is the MOST critical means of ensuring your data and measurements are accurate. Calibration involves adjusting or standardizing lab equipment so that it is more accurate AND precise. Calibration typically requires comparing a standard to what your instrument is measuring and adjusting the instrument or software accordingly. The complexity of calibrating instruments or equipment varies widely, but, typically, user manuals have recommended recalibration recommendations.
Bitesize Bio has several articles on routine calibration, including routine calibration of pipettes and calibrating your lab scales. Even if all instruments in your lab are calibrated, odds are they need regular care to operate at their maximum accuracy and precision. For instance, pH meters need routine maintenance that can be performed by novice scientists, while more sensitive instrumentation may require shipment of parts to vendors or even on-site visits. Again, check your user manuals and call equipment manufacturers to ensure you take appropriate measures to keep lab equipment running under conditions optimal for accuracy.
Always use tools that are designed and calibrated to work in the range you are measuring or dispensing. If you are ever unsure about using an instrument to measure accurately at an extreme value, reach out to a trusted peer or mentor for advice. What if you are choosing between two tools that are both calibrated for use at a given target?
Watch our on-demand webinar on improving your pipetting technique for more information. Specifically, sig figs provide the degree of uncertainty associated with values. Keep sig figs consistent when measuring items repeatedly, and ensure the number of sig figs you are using is appropriate for each measurement.
The more samples you take for a given attribute, the more precise the representation of your measurement. Some systems are prone to drift over time. For instance, background absorption in high-performance liquid chromatography HPLC may be indicative of column failure. If you notice that measurements drift in a single direction over weeks or months, address the issue immediately by recalibration or preventative maintenance.
To minimize the inherent variability between scientists, ensure that procedures are kept up to date and are as descriptive as possible. In some cases, it may be easiest to have only one person responsible for a given measurement, but this may not always be possible. Ensure that all lab personnel are trained, especially on highly manual techniques like pipetting, to maximize accuracy and precision. While this is a relatively complicated method to gauge accuracy and precision, measurement systems analysis or gage repeatability and reproducibility analysis is the most comprehensive and statistically sound way to get a complete picture of the accuracy and precision of your measurement.
This technique mathematically determines the amount of variation that exists when taking measurements multiple times. Mistakes in the lab, whether the result of user error, equipment malfunction or mis-recording of information, can perpetuate misconceptions and make the results of your study impossible to replicate.
Ari Reid has a bachelor's degree in biology behavior and a master's in wildlife ecology. When Reid is not training to run marathons, she is operating a non-profit animal rescue organization. Reid has been writing web content for science, health and fitness blogs since Things You'll Need. Related Articles How to Calculate Precision. What is a Double-Pan Balance Scale? Reasons for Error in a Chemistry Experiment.
How to Calculate RSD. Accuracy is also important, and ideal measurement device is both accurate and precise. There are different methods and devices used in wound area measurement [ 3 — 6 ]. The newest methods are based on smart devices [ 7 ] or 3D image processing [ 8 , 9 ]. Wound area measurement with digital planimetry based on wound photographs and pixel counting inside a wound boundary is a cheap and effective method.
An accurate and precise result of measurement requires calculation of an averaged calibration coefficient [ 10 ].
This averaged coefficient is an arithmetic mean from two coefficients from each ruler. They need to put a line segment at the ruler with its ends lying at the centers of ruler ticks at the picture. Next, they need to enter the length of this line segment and note number of pixels per 1 unit of length they use in area measurement.
Moreover, users need to do this twice, and then they can calculate the mean number of pixels per 1 unit of length. Calculation of this mean has to be performed outside of the graphics software. After that, the mean coefficient may be entered into a text field and used for area calculation. The entire procedure is error-prone due to many manual operations. It could be simpler in an application designed for wound area measurement. It was earlier showed that the calibration based on 2 rulers is much more reliable than on one ruler [ 10 ].
Two ruler calibration requires an arithmetic mean from coefficients coming from each ruler. This is a very good approach in the case of a wound with regular shape, and when the rulers are at equal distances from edges of the wound. In the case of an irregular wound or unequal ruler-edge distances, a better measurement result would be achieved with a weighted average. A more detailed explanation will be delivered in the next section. The reproduced area is proportional to the cosine of this angle.
The method will be presented in the next section. The aim of the current study is to present some ways for increasing accuracy and precision of wound area measurement with digital planimetry when smart devices are used for the measurement. During wound area measurement with digital planimetry based on 2-ruler calibration of linear dimensions at the picture, the coefficient k which is the number of pixels per 1 cm, is calculated as arithmetic mean from 2 coefficients from each ruler [ 10 ].
Let us consider a small area of the figure which is at the distance y 1 from the calibrating line segment a , and at the distance y 2 from the line segment b Fig 1. The averaged coefficient k 0 for this small area should be calculated as weighted average: 1 where k a and k b are the coefficients calculated from the line segments a and b , respectively. The small area is at a distance y 1 from the line segment a , and at a distance y 2 from the line segment b. The center of gravity is at a distance y a from the line segment a , and at a distance y b from the line segment b.
These line segments are used for calculation the calibration coefficients k a and k b as number of pixels per 1 cm at ruler A and B, respectively. When an averaged coefficient is to be calculated for the entire figure, it must be divided into many small areas, as for instance in Fig 2 , and the averaged coordinate y 0 should be calculated as a weighted average: 2.
At each coordinate y 1 through y 8 there is a variable number of small areas. The total number of the small areas is In general, for k number of y coordinates, it may be written: 3 where N is the total number of small areas in the figure, and n i is the number of small areas in the row i at the coordinate y i.
The right part of Eq 3 is equal to the coordinate of center of mass [ 11 ], which in the parallel gravity field, is the same as the center of gravity. The center of gravity of a plane figure is also called the centroid or geometric center. It can be analytically calculated based on the coordinates of its vertices [ 12 ]. In the case of wound area measurement, user needs to make a wound outline around the wound at its picture. In real, this outline is a polygon with its vertices lying at wound edges.
When the coordinates of the center of gravity are known, the averaged calibration coefficient k for the wound outline Fig 1 may be calculated as: 4 where y a and y b are the distances from the center of gravity to the line segments a and b , respectively. The height h of an object at the picture in a unit of length is a quotient of the number of pixels along the object N and the coefficient of linear dimensions k. This coefficient is also called a calibration coefficient, and is expressed in pixels per unit of length.
This calculated height h is equal to the actual height h 0 of the object only when the object was photographed without camera tilt i.
After inserting the counted number of pixels N and the coefficient of linear dimensions k , one can receive: 6. Height h 0 of an observed object is correctly reproduced at the picture when it is observed at the right angle upper view. The distance between a certain point and a camera may be easily calculated after finding a mathematical relationship between distance and calibration coefficient which is distance dependent.
To get the necessary data, the ruler must be photographed at many different distances, and the calibration coefficient k must be calculated from each picture. Then using the curve fitting function the best formulae was found for the relationship of the distance and the calibration coefficient.
Fig 5 shows the camera distance equation for the Samsung Galaxy S4 smartphone with parameters a and b of the power function equal to Now, the Eq 6 may be written as: 8 where f is the calibration function of distance from camera; k 1 and k 2 are the calibration coefficients for distances z A and z B , respectively; k is given by Eq 4. In the Planimator app for Android an automatic procedure for calculating the calibration coefficient k based on the center of gravity and the correction of area measurement result based on the calculated tilt angle of camera were introduced.
Some other advantages of this app are described below. The procedure of finding a mean calibration coefficient from 2 rulers in the ImageJ software is time consuming therefore in the Planimator app it has been simplified. The user needs only to point out a beginning and an end of a line segment at each of two rulers used for calibration of linear dimensions.
This takes a few seconds instead of a few minutes as in the case of manual procedure performed in the ImageJ software. The Planimator app automatically recognizes ruler ticks along the line segment and calculates the number of pixels per 1 cm.
It also compares the distances between ruler ticks and seeks for the longest sequence of ruler ticks equally distant from the adjacent ticks. This enables to skip artifacts at the ruler which could cause a calibration error. The area A expressed in square centimeters in the Planimator app is calculated as 9 where N is the number of pixels within boundary traced around a wound, and k is the averaged number of pixels per 1 cm given by the Eq 4.
The distance between 2 adjacent ticks at calibration ruler is a parameter in the Planimator app settings. Its default value is 1. This parameter can be also used when paper rulers used for calibration are not accurate. For example, if they show Inch rulers also can be used, but the area measurement result will be displayed in cm 2. Once the measurement is completed, the Planimator app displays information about percentage change of area for the current wound, taking the first area measurement for this wound as a reference value.
This is performed for each unique wound label. All saved area measurement results are stored in a text file with time stamps. The data from this file can be imported to any statistical software or spreadsheet program. When saving numeric data a picture with traced wound outline is also saved and is labeled with the value of measured area and the current date. Accuracy and precision of area measurement by the Planimator app were tested in the same way as in previous study [ 10 ].
The wound artifacts as grey shapes printed on a white paper were used. Each shape was created in CorelDraw Corel Corp. Forty wound shapes range 0. The results of the Planimator app measurements were compared to the results from the previous study [ 10 ] obtained by the Visitrak device, the SilhouetteMobile device, the AreaMe software, and to the digital planimetry based on 2-ruler calibration. Relative differences RDs and relative errors REs in area measurement were compared.
Standard deviation SD of relative differences RDs is a measure of measurement precision repeatability. The lower is the SD of RDs the higher is the precision repeatability. The accuracy of measurement may be assessed by analyzing REs. REs of an accurate measurement method are distributed close to zero, and when the accuracy decreases, the REs move away from zero.
The lower is the median of REs the more accurate is the measurement method. The RD was calculated as a ratio of the difference between the measured and actual areas to the actual area. The RE was calculated as a ratio of the absolute difference between the measured and actual areas to the actual area. The actual value was measured with a reference method based on pixel counting in the images of resolution dpi x dpi from optical scanner.
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