# A Study on DNN-Based Practical Model for Predicting Spot Color
## 4 Discussion  
The significance of color in product packaging cannot be overstated, acting as a pivotal brand communication tool. Spot colors assure consistent color representation, offering standardization to brands. Despite this, the conventional CMYK mixing process, which is widely adopted in the packaging printing sector, presents difficulties in reproducing particular colors with precision, resulting in potential discrepancies and inefficiencies. Our study addressed the challenge of reducing color perception discrepancies in printing by introducing a deep learning model capable of predicting ink mixing outcomes, without the need for actual mixing. By exclusively using the usage data obtained through the color-matching process of a printing company, our model hopes to minimize inconsistencies in color perception. The model's performance level of 3.6679 is a promising development that, in turn, suggests its potential utility as a dependable reference tool for industry experts. The outcome emphasizes the model's ability to predict color, which, when fused with the proficiency of experts, can create a balanced combination of automation and human intervention. This can boost the precision and productivity of color mixing procedures.  
However, our approach does have noteworthy restrictions. The use of a simplified model could overlook complex relationships among factors that impact ink colors. Moreover, relying on data from only one company introduces risks associated with the particular quality and features of that dataset. Our preprocessing methods attempted to rectify incorrect data. However, the intrinsic nature of the algorithms presents challenges in detecting anomalies for uncommon patterns. Given these limitations, there are two directions to enhance the potential of this research. Firstly, it is essential to refine the model by thoroughly exploring and identifying the various factors influencing the quality of printed colors. The refined model must directly incorporate these factors and account for the intricacies of the printing process, raw materials, and other relevant attributes. Ideally, a more segmented approach would be taken in conducting such an endeavour, catering to the unique requirements of each sector or segment. Refinement of our preprocessing methods is possible, in addition to the reduction of data dependency. For example, conceptualizing each data point as a node and building edges based on pattern similarities could lead to the formation of graphs. Graph neural network (GNN) techniques can be utilized to improve anomaly detection in graphs, leading to higher data quality and potential productivity enhancements. GNN also has the potential to lay the foundation for various data-driven productivity enhancements. Incorporating these directions into future research would likely enhance the applicability and robustness of the color prediction model in real-world printing scenarios.  
Conceptualization, J.M., G.Y. and H.T.; methodology, J.M. and G.Y.; software, J.M. and G.Y.; validation, J.M. and G.Y.; formal analysis, J.M. and G.Y.; investigation, J.M. and G.Y.; resources, J.M. and G.Y.; data curation, J.M. and G.Y.; writing--original draft preparation, J.M. and G.Y.; writing--review and editing, J.M., G.Y. and H.T.; visualization, J.M. and G.Y.; supervision, H.T.; project administration, H.T.; funding acquisition, H.T. All authors have read and agreed to the published version of the manuscript.  
This study was carried out with the support of R&D Program for Forest Science Technology (Project No. 2021383A00-2323-0101)' provided by Korea Forest Service(Korea Forestry Promotion Institute). This work was supported by the Korea Institute of Industrial Technology as \"Development of holonic manufacturing system for future industrial environment [KITECH EO-230006]\".  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to legal restrictions.  
The authors declare no conflict of interest.