Details

Autor(en) / Beteiligte

• Dziki, Pawel
• Pieszczek, Lukasz
• Daszykowski, Michal

Titel

Toward more efficient and effective color quality control for the large‐scale offset printing process

Ist Teil von

• Journal of chemometrics, 2024-04, Vol.38 (4), p.n/a

Ort / Verlag

Chichester: Wiley Subscription Services, Inc

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Wiley Blackwell Single Titles

Beschreibungen/Notizen

• This study illustrates at‐line application of hyperspectral imaging in the visible range for quality control of large‐scale offset printing. In particular, the measurement stability of a competitive device is assessed and compared to traditional handheld and desktop spectrophotometers. The performance of the commercially available instruments was assessed based on collected spectra and their corresponding L*, a*, and b* values. The printing process was described by hyperspectral images (in visible range) of selected regions from template color fields acquired at 17 sampling occasions. Spectra constituting hyperspectral images were visualized and evaluated in the space of significant principal components obtained from the principal component analysis. Furthermore, confidence ellipses were constructed for each set of spectra characterizing a specific moment of the printing process. Comparing their mutual locations, shapes, orientations, and sizes enabled effective visualization of process variability and was more comprehensive regarding the classic approach based on information provided by desktop and handheld spectrometers. This study demonstrates the effectiveness of hyperspectral imaging for real‐time quality control in large‐scale offset printing, in particular by studying sampling distributions. By assessing measurement stability and comparing competitive devices with traditional spectrophotometers, it reveals the advantages of hyperspectral imaging in measurement speed, spatial dimension inclusion, and process variability visualization. Chemometric analysis aids in effective color assessment, while local segmentation can offer insights into color perception, crucial for process monitoring and detection of various sources of variation.