![]() ![]() (A) In case of a gas-chromatogram, the feature is the total abundance released by each compound, as identified in the elution time axis. Examples of feature selections in popular fingerprint-generating instruments. Depending on customer demands, even Δ E > 4 is acceptable under certain circumstances.įig. In general and independent of the type of color deviation formula, two colors can be optically distinguished if Δ E ≥ 1 Δ E > 3 is perceived as a significant color deviation. But it also has to be borne in mind that visual assessment by different observers is also a subject of great variation and that the reproducibility of such a valuation is limited ( Strocka, 1971). It is also a well-known fact that different color difference formulas lead to very diverse results. Many articles have shown that the correlation between visual assessment and calculated color difference leaves often much to be desired. Unfortunately, the existing color difference formulas have practical limitations. There is no doubt about the advantages such a method can offer: the magnitude and direction of the color difference can be stated in quantitative terms so that colored products can be controlled by objective standards ( Strocka, 1971). The assessment of color differences by means of color difference formulas is one of the most important aims of industrial color measurement. The relationship between the human perception and instrumental measurement results is an important subject. Michael Dattner, Daniel Bohn, in Printing on Polymers, 2016 20.1.4 Color Deviation Formulas The ability to utilize digital color values transmitted over the internet such as Gates described in Business the Speed of Thought 18 has enormous competitive advantage by shortening the supply chain and reducing the time to market. Today as we engage in globalization and standardization, such as ISO, correlation becomes increasing important. A model that could provide such a vehicle is of interest to the color community and has commercial value. Statistical studies of the customer’s databases for these cases show a weak correlation between different modalities, configurations, and different manufacturer’s instruments. A form of Kulbeka-Munk 17 equations uses these values to compute the desired pigment concentrations. The K in the K/S represents the absorption component of the mixture and the S represents the scattering component of the mixture. In this case the K/S values are calculated from measured spectral reflectance values at each wavelength. Typically three to six colorants will be used to create each color. These colorizing methods involve the preparation of multiple calibrations (let downs) for each colorant in a formula. The fifth case involves companies who regularly utilize computer color matching or batch correction. Colorant manufacturers and large manufacturers require data compatibility from around the globe as they use the colorant information for computer color matching and product colorization. The fourth case involves customers who utilize a single universal database. Utilizing the old database values with the new instrumentation enables them to utilize standardized methods without introducing confusion. Often they are told that the database has to be ‘scrapped’ because the values obtained with the new instrumentation do not correlate with the values obtained with the old instrument. In this case, the customer wants to preserve the integrity of the database. The third case is customers who have large product databases. It is desirable to correlate values from any instrument with any modality to those values generated by a central laboratory instrument. Not included but also appropriate are a plethora of non-standard modalities. For instance, there are six popular modalities: SIN 11, SEX 12, d/8° 13, d/0° 14, 45/0° 15 and multi-angle 16. The second case involves customers who in this business climate of corporate acquisition acquire companies that have spectrometers of different modalities. In many industries manufacturing facilities are off-shore while the central laboratories are located in the US, for instance. In this case, customers want identical color values from multiple color measuring spectrometers around the globe so that data can be compared and analyzed. The first case involves customers with multiple manufacturing sites. There are at least five situations where improvement of instrumental spectrometer values would be beneficial. The correlation of instrumental measurements is desired and yet has been elusive. The authors are professionally engaged in the instrumental measurement of color. Ladson, in Colour Measurement, 2010 8.7 Introduction to improving accuracy ![]()
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