Review: Identification of Detergents for Forensic Fiber Analysis

Emily C. Lennert


Category: Chemistry

Keywords: fibers, trace evidence, detergent, contaminant, fluorescence, microscopy

Article to be reviewed:

  1. Heider, E.; Mujumdar, N.; Campiglia, A. Identification of detergents for forensic fiber analysis. Analytical and Bioanalytical Chemistry. 2016, 408, 7935-7943.

Additional Reference(s):

  1. Spring, K.; Davidson, M. Introduction to Fluorescence Microscopy (accessed Mar 30, 2017).

Disclaimer: The opinions expressed in this review are an interpretation of the research presented in the article. These opinions are those of the summation author and do not necessarily represent the position of the University of Central Florida or of the authors of the original article.


Trace fiber analysis may provide valuable evidence for investigators. While most trace analysis is focused on composition, color, and dye structure, external features introduced post-manufacture may provide additional information to identify the origin of the fiber. Post-manufacture external features may include structural changes, such as heat damage, or chemical contaminants, such as makeup or soaps. Laundry detergents may be classified as a chemical contaminant. Fluorescent whitening agents (FWAs) are a type of chemical detergent additive that is added to laundry detergent to make clothing appear whiter and brighter. In this study, the authors utilized fluorescence microscopy to measure the fluorescence of FWAs on fibers.

In fluorescence microscopy, a sample is placed on a fluorescence microscope, which focuses light of a specific wavelength on the sample to induce fluorescence.2 When the light from the microscope hits the sample it will excite the sample to a higher energy level which is called fluorescence. When excitation occurs, the sample will emit light at a different wavelength. The required wavelength of the light needed to excite the sample (i.e. excitation) will vary depending on the sample. This emitted light travels through a filter, which removes the excitation wavelengths, leaving only emission wavelengths from the sample. The emitted light then travels to the detector, i.e. spectrofluorimeter, where the intensities across a range of wavelengths are recorded. In this study, samples were analyzed by a fluorescence microscope that was connected to a spectrofluorimeter, to measure fluorescence intensity. A camera attached to the microscope was used to photograph the sample as it fluoresced.

Samples were prepared with known washing detergents prior to analysis. Powdered detergent samples and liquid detergent samples were prepared in solutions at concentrations matching the manufacturers’ recommendations for washing. Oxiclean (powder), Tide (powder), All (liquid), Cheer (liquid), Purex (liquid), Tide (liquid), and Wisk (liquid) were used for this study. Four individual fabric samples were prepared with each detergent: dyed nylon 361, undyed nylon 361, dyed acrylic 864, and undyed acrylic 864. For each detergent, a fabric swatch was placed in a tube containing the detergent solution and agitated with a rotary shaker for 20 minutes. Then, the detergent solution was removed from the tube and the fabric was thoroughly rinsed with purified water. After rinsing, the fabric was allowed to dry for 12 hours or overnight. When the washing process was complete, fiber samples were collected. The washing process was repeated on the same sample for a total of 6 washings, with each washing followed by fiber collection, resulting in fiber samples ranging from 1x washed to 6x washed for each fabric swatch. Samples were then analyzed via fluorescence microscopy.

Principal component analysis (PCA) was used to evaluate the data. Using PCA, data was clustered into groups, i.e. classes. Samples with more similar components, i.e. data, will cluster closer together, while samples with very different components will have a greater distance between them. Using the data from 5x washed fibers, a classification model was created. This classification model is commonly used to help examiners identify unique components that indicative of one class over the other. Then, a test set containing the data from 5x and 6x washed fibers was tested against the classification model to determine if the inclusion of 6x washed fibers would affect the classification accuracy. In other words, the authors sought to determine if fibers that had been washed more would still classify correctly with the other fibers originating from the same sample. The authors saw no incorrect classifications between the model and the test set. Single detergents were also tested against one another, e.g. All vs Tide, so that each possible detergent pair was compared, for a total of 21 pairs per fiber type. Paired comparisons were made to determine if the detergents could be distinguished from one another. Little distinction was seen in undyed fiber samples; only Wisk was distinguished from the other detergents among the undyed nylon samples. In the dyed nylon samples, 8 detergent combinations could be distinguished from one another. For the dyed acrylic samples, 5 detergent combinations were distinguishable. The authors state that the increase in differentiation among dyed vs undyed fibers is indicative of an interaction between the dye, FWA, and other detergent components. The authors suggest further study to determine if additional distinguishing features can be determined.

Scientific Highlights:

  • Fluorescence of detergent FWAs on fibers was measured to determine how well fibers could be differentiated based on the detergent used.
  • The authors found that dyed fibers were more differentiable, based on detergent FWAs, compared to undyed fibers of the same type.
  • Greater differentiation was observed among nylon fibers than was seen in acrylic fibers.

Relevance: Fiber analysis often focuses primarily on composition, color, and dye structure. However, after an item of clothing has been acquired and worn, washed, etc., the fibers will acquire additional, external features originating from post-manufacture sources. The additional, post-manufacture features may allow for greater differentiation of fibers.

Potential Conclusions:

  • Analysis of post-manufacture features of fibers can lead to greater differentiation and, in turn, may increase evidential value of fiber evidence.