Related Experiment Video
Updated: Aug 13, 2026

Fiber Type and Subcellular-Specific Analysis of Lipid Droplet Content in Skeletal Muscle
Published on: June 8, 2022
The analysis and comparison of blue wool fibre populations found at random on clothing
1The Forensic Science Service, 109 Lambeth Road, London, SE1 7LP, UK.
Abstract:
Fifty-eight garments were taped and searched for mid to dark blue wool fibres. These were then removed from the tapings, mounted on slides and examined using a high-power microscope (400x). A total of 2,740 blue wool fibres were identified and visible range microspectrophotometry (MSP) was performed on them. Three hundred independent blue wool populations were identified on 56 of the 58 garments searched. The lack of control fibres meant the spectral range of each population was unknown. The number of populations may have been underestimated by grouping together the fibres that had broad single peaks and a lack of distinguishing features in the spectra. Although blue wool is considered to be a common fibre type, 300 unique spectral shapes were identified by the use of microspectrophotometry alone. This demonstrates that the dyes used in the dyeing of blue wool are variable. Showing that many different populations of blue wool occur on a range of garments should ensure that the forensic scientist does not underestimate or understate the strength of evidence in cases where blue wool is found. Hopefully this work will enlighten scientists and enable them to also assess the true value of their findings when other commonly occurring fibres are encountered.
Related Concept Videos
Stratified Sampling Method
To choose a stratified sample, divide the population into groups called strata and then take a...
Karyotyping
Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
