AI-Based Synthetic Data Analysis of Erionyl Blue Absorbance Using Quartz and Acrylic Cuvettes

Abstract

The use of UV-Vis spectrophotometry in absorbance determination is a fundamental yet critical technique within the textile industry, particularly for quality control in dyeing processes. Recent advancements in Big Data and Artificial Intelligence (AI) offer opportunities to enhance the precision of spectrophotometric analysis through more extensive, accurate, and adaptive data processing. This study compares the performance of two cuvette materials, quartz and acrylic in the absorbance measurement of the synthetic textile dye Erionyl Blue. Samples were measured at six concentration levels with ten repetitions each, generating 120 data points. The dataset was analyzed using conventional statistical methods (two-way ANOVA with replication) and further enriched through AI-assisted data profiling to examine microvariance patterns.The ANOVA results indicated a statistically significant difference between quartz and acrylic cuvettes (p < 0.0001). However, AI-based profiling revealed that the effect size of this difference was minimal (<1.5%), suggesting negligible practical impact on absorbance interpretation. Therefore, acrylic cuvettes can be considered an economical alternative for academic and educational laboratory use, especially in high-throughput measurement scenarios.