The article discusses two types of colour‐formulation strategies: the tristimulus colorimetric strategy (ΔX,ΔY,ΔZ) → (0,0,0) and the least‐squares curve fitting Σjw2j[ΔR(λj)]2 → min. Then a new matching strategy (ΔED65)2 + (ΔEA)2 + (ΔEWWF)2 → min is stated, which tries to combine the advantages of the first two strategies: more “balanced” colour differences for several different illuminants (the case of curve fitting) and small colour differences for the illuminant involved in the matching process (the case of tristimulus matching). An algorithm following this new general strategy is constructed using the case of single‐constant Kubelka‐Munk theory as an example. Results of a few numerical experiments are included for illustration.

The article examines the concepts of the following three quantities: partial colour sensitivity of a recipe to a particular colorant, colour balance of a recipe, and the overall colour sensitivity and the related property of colour robustness of a recipe. the way to calculate numerical estimates of the above quantities is extended from the case of CIE L *a*b* to the case CMC(l:c) colour difference formula. Results of a few numerical experiments are included for illustration and some possible practical consequences are discussed.

The general concept of predicting the colour sensitivity to random colorant concentration errors and the colour correctability of a colour matching recipe are reviewed and generalised in this paper. The treatment of both quantities is unified either in the concentration space or, equivalently, in colour space. The concept of the recipe's colour balance is revised. Oulton's concept of recipe colour sensitivity to proportional concentration errors is also briefly reviewed and extended to obtain another measure of recipe sensitivity to random concentration errors. The differences and connections among the two measures of recipe sensitivity to random errors and Oulton's measure of recipe sensitivity to proportional errors are discussed and illustrated by numerical examples. Estimates of maximal colour error, caused by given maximal weighing and strength errors, are developed.

Interlaboratory comparisons are the most powerful tools for determining the competences of laboratories performing calibrations and testing. Performance metrics is based on statistical analysis, which can be very complex in certain cases, especially for testing where transfer standards (samples) are prepared by the pilot laboratory. Statistical quantities are calculated using different kinds of software, from simple Excel applications to universal or specific commercial programmes. In order to ensure proper quality of such calculations, it is very important that all computational links are recognized explicitly and known to be operating correctly. In order to introduce a traceability chain into metrology computation, the European project EMRP NEW 06 TraCIM was agreed between the EC and the European Metrology Association (EURAMET). One of the tasks of the project was also to establish random datasets and validation algorithms for verifying software applications in regard to evaluating interlaboratory comparison results. The statistical backgrounds for resolving this task, and the basic concept of the data generator are presented in this paper. Background normative documents, calculated statistical parameters, boundary conditions for generating reference data sets are described, as well as customer interface.

Indices for describing the degree of metamerism are based on either the deviation of the spectra of a metameric pair or the colour difference of the pair under test conditions. The magnitude of illuminant metamerism is commonly evaluated by measuring the colour difference under the test illuminant. The calculated colour differences absolutely vary with the selected test illuminants, so the measured (special) index of metamerism could be considered as a test‐illuminant‐dependent value. The spectral‐based indices of metamerism act as a single‐number value, but most of them are criticised for their poor correlation with visual assessment. In this paper, a general metric is developed for evaluating the upper limit of the degree of illuminant metamerism. The suggested approach combines the advantages of general and special indices, avoiding their drawbacks at the same time. The performance of the formula is analysed in a number of numerical experiments, as well as by practical testing.

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A regularity of the predicted sensitivities to random and proportional dye concentration errors in regard to the position of target colour has been observed for the case of dyeing acrylic with basic dyes. The sensitivity of the recipe colour to random dye concentration errors is highest for light neutral target colours and is almost negligible for dark‐shade recipes. Recipes for less saturated targets are slightly more sensitive than recipes for more saturated targets of equal lightness. The span of the sensitivities to weighing error of the recipes matching a given target varies with the position of the target in colour space. By contrast, the sensitivity of recipes to dye strength error is the highest at medium to low lightness for neutral and near neutral target shades. The span of sensitivities of the recipes for any such particular target is broad, with some having low sensitivity to strength errors. Recipes for the target colours at the ‘lighter’ part of the gamut border were the least sensitive to strength errors. Received: 10 June 2005; Accepted: 11 July 2005.

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