The open source R programing language is a free software environment for statistical computing and graphics, and is easy to master. The official website is https://www.r-project.org/ . It can run on a wide variety of UNIX platforms, Windows and MacOS.
On September 24, 2016, this blog site published an article on how to use R to generate random numbers (https://consultglp.com/2016/09/24/how-to-use-r-to-generate-random-numbers/) . In light of the newly revised ISO/IEC 17025 accreditation standards embracing sampling as another important criterion for technical competence assessment, the random number function of R becomes very handy for cargo surveyors and samplers to prepare their sampling plan on cargo shipment.
We can use the random number function of R to create a random number table to suit the needs in randomly selecting samples for laboratory quality analysis.
For example, there is a shipment of 1000 bags of coffee beans in a warehouse to be surveyed prior to be dispatched to port. The buyer requires a 5% sampling for laboratory quality testing. That means some 50 bags have to be random selected before composite a portion of each bag into a suitable sized test sample through a quartering sub-sampling process.
The sampling plan, therefore, can be the following process:
[,1] [,2] [,3] [,4] [,5]
[1,] 154 424 84 486 82
[2,] 78 214 275 498 388
[3,] 93 104 478 148 258
[4,] 229 283 96 479 489
[5,] 487 211 216 59 263
[6,] 94 450 47 201 105
[7,] 330 121 130 276 56
[8,] 11 415 303 240 407
[9,] 427 60 71 142 409
[10,] 101 238 228 441 355
By definition, sampling involves selecting a portion of material (i.e. sample or samples) from a substance, material, product or even a consignment of goods to represent or provide information about that larger body of material (i.e. population).
Although the ISO/IEC 17025 accreditation standards and also its latest revision are still concerned about the technical competence of organizations that conduct laboratory activities, it is reckoned that the reliability of testing result lies on how representative the sample analyzed is for the bulk material of interest. As the saying goes : “The result is not better than the sample that it is based on”.
A question has thus been asked: Can sampling be considered as a stand alone activity or should sampling activities always be associated with testing or calibration?
It is obvious that although the scope of this accreditation standards is with laboratory activities, sampling indeed has an inevitable connection to the laboratory’s analytical process that produces the test result which is important to the end users. It is usually impossible to analyze the whole bulk (or lot) of material (statistically called ‘population’ or ‘sampling target’). Therefore, proper sampling plays an essential role to ensure the validity of the final test result.
The FDIS requires laboratory to have a sampling plan and documented procedures for sampling in their field of testing. The laboratory is allowed to state “analyzed as received” in the report if it has not been responsible for the sampling stage. Also, laboratory performing sampling or testing activities shall evaluate measurement uncertainty, i.e. the uncertainty of sampling process is to be evaluated and forms an additional uncertainty contributor to the measurement uncertainty evaluation of the whole testing or calibration process.
Even though a laboratory does not get involved in the sampling of a population outside its premises, it often carries out sub-sampling process before the start of the analytical procedure. Therefore the subject of sampling cannot be ignored also in such laboratory.
As said earlier, since the main purpose of laboratory analysis is to estimate the value of analyte concentration in a sampling target, sample taken should be as representative of the sampling target under study as possible. This is to ensure that the property that each sample has the same probability of being drawn from the population as another sample.
So, in order to optimize the whole measurement process including taking a good sample for analysis, the sampling planner needs to gather information of the sampling target and to decide on appropriate sampling protocols.
Ask the following questions during information gathering:
To decide on the appropriate sampling protocols, one must try to deal with the following subjects:
We shall discuss these sampling strategies in more details in the next blog.
New FDIS 17025 version and its impact on Measurement Uncertainty