Calculation of SPF Value and Root Analysis on Determining Vitamin C Levels in Sunscreens Using the Fishbone Diagram

Objective: This research was conducted to determine the SPF value in 2 different samples of sunscreen and identify factors that affected the failure to determine vitamin C contents in each sample. Theoretical framework: This research is grounded in the principles of cosmetic chemistry and analytical instrumentation. Sunscreen efficacy is evaluated through SPF measurement using UV-Vis spectrophotometry. Vitamin C, a common antioxidant in sunscreens, requires precise analytical conditions for detection. Root cause analysis using the Fishbone Diagram identifies systematic errors in lab procedures, guiding improvements in cosmetic product testing and quality assurance. Literature review: Sunscreens are cosmetic preparations used to protect the skin from ultraviolet exposure to sunlight. The protective ability of sunscreen products is measured in terms of SPF (Sun Protection Factor). One of the many ingredients found in sunscreen is Vitamin C. Vitamin C is widely used in cosmetic formulations, due to its natural antioxidant content and its ability to protect the skin from damage caused by free radicals, as well as to enhance the skin regeneration process. Methods: The methodology used to determine the SPF value is UV Spectroscopy, and to analyze the problem, we conducted a root analysis using Kaoru Ishikawa’s fishbone diagram. Results: We found that the two samples contain low SPF values (MC: 2.514, DN: 1.913) which are contrary to compendia recommendations. Then for the analysis, six main root causes of vitamin C determination are identified. Implications: The findings emphasize the need for consistent analytical methods and improved quality control in cosmetic testing. Identifying the root causes of failure provides a basis for refining laboratory practices. The low SPF values also suggest a need for further evaluation of product formulation and labelling accuracy. Novelty: Our novelty in this research is the analysis of causes of failure within an experiment conducted in the lab.