Smart packaging reveals product condition through color changes – precise automated color recognition opens doors to new types of indicators

Jari Isohanni’s doctoral research in computer science at the University of Vaasa demonstrates how smart packaging could become more widely available to the packaging industry in the future. Color-changing printing inks in printed packages and the machine-based recognition of subtle color changes offer cost-effective solutions for industries such as food and beverage, healthcare, logistics, and electronics. 

Previously, there has been no research on which color recognition method should be used and when. Isohanni's research filled this gap by comparing the applicability of machine learning and color difference methodologies to different situations.

– My research showed that traditional, simple computational methods work well for recognizing significant color differences. However, for subtle changes and varying conditions, the most effective methods were convolutional neural networks that are based on artificial intelligence, Isohanni explains.

The right method for the right purpose

Functional printing inks change color according to conditions – for example, when temperature rises or humidity increases. Research focused on detecting small color changes opens new possibilities for industry compared to current electronic sensors.

– The color change in printing ink is so subtle or rapid that it cannot be recognized effectively enough with current machine vision methods. By the time the ink's color change is mechanically detectable, the process may already have progressed too far or damage may have occurred, Isohanni illustrates.

AI enables automatic color recognition in industry with nearly human-eye accuracy, providing new tools for quality control, among other applications. Based on the research, solutions can also be developed for consumers to indicate the condition of food products or other goods. The ink indicator used in the doctoral research can be printed on packaging alongside regular labels, with minimal additional cost compared to electronic smart packaging solutions.

– Expensive electronic measuring devices cannot be placed on, for example, a lettuce package, as it would constitute a large portion of the product's price or could cause additional challenges for recycling. Printed indicators solve this problem, Isohanni says.

The research paves the way for an environmentally friendly alternative that can improve processes and enhance consumer information. The results can be utilized, for example, in the food industry for monitoring shelf life, in healthcare indicators, in logistics for monitoring transport conditions, and in electronics for detecting moisture and temperature damage.

Dissertation

Isohanni, Jari (2025) Recognition of Subtle Colour Differences. Acta Wasaensia 557. Doctoral dissertation. University of Vaasa.

Publication PDF

Doctoral Defense

The public examination of M.Sc. Jari Isohanni’s doctoral dissertation ”Recognition of Subtle Colour Differences: A Comparative Study of Machine Learning and Colour Difference Metrics” will be held on Wednesday 3 September 2025 at 12at the University of Vaasa, auditorium Kurten.

It is possible to participate in the defence also online: 
https://uwasa.zoom.us/j/68592609894?pwd=zTja9n1vVLWfFXhYcls3ZtmcMSlmy2.1
Password: 828150

Associate Professor Miguel Bordallo López (University of Oulu) will act as opponent and Associate Professor Jani Boutellier as custos.