• Analytical precision;
  • differentiation power;
  • laser diffraction;
  • log-ratio analysis;
  • particle-size distribution


This paper presents a methodological framework for inter-instrument comparison of different particle-size analysers. The framework consists of: (i) quantifying the difference between complete particle-size distributions; (ii) identifying the best regression model for homogenizing data sets of particle-size distributions measured by different instruments; (iii) quantifying the precision of a range of particle-size analysers; and (iv) identifying the most appropriate instrument for analysing a given set of samples. The log-ratio transform is applied to particle-size distributions throughout this study to avoid the pitfalls of analysing percentage-frequency data in ‘closed-space’. A Normalized Distance statistic is used to quantify the difference between particle-size distributions and assess the performance of log-ratio regression models. Forty-six different regression models are applied to sediment samples measured by both sieve-pipette and laser analysis. Interactive quadratic regression models offer the best means of homogenizing data sets of particle-size distributions measured by different instruments into a comparable format. However, quadratic interactive log-ratio regression models require a large number of training samples (n > 80) to achieve optimal performance compared to linear regression models (n = 50). The precision of ten particle-size analysis instruments was assessed using a data set of ten replicate measurements made of four previously published silty sediment samples. Instrument precision is quantified as the median Normalized Difference measured between the ten replicate measurements made for each sediment sample. The Differentiation Power statistic is introduced to assess the ability of each instrument to detect differences between the four sediment samples. Differentiation Power scores show that instruments based on laser diffraction principles are able to differentiate most effectively between the samples of silty sediment at a 95% confidence level. Instruments applying the principles of sedimentation offer the next most precise approach.