<![CDATA[UV-Visible spectrophotometry (UV-Vis) is one of the most widely used instrumental analytical methods in the fields of pharmacy, food science, and analytical chemistry, particularly for determining the concentration of active compounds in various types of samples. The working principle of UV-Vis spectrophotometry is based on the ability of a compound to absorb light at a specific wavelength, resulting in an absorbance value proportional to the concentration of the substance in solution. This method is considered efficient due to its relatively simple operation, short analysis time, and lower cost compared to other instrumental methods such as HPLC, AAS, or GC-MS. In addition, UV-Vis spectrophotometry can be applied to a wide range of compounds containing chromophoric groups, making it suitable for analyzing herbal products that consist of complex natural matrices. This study aims to review the application of UV-Vis spectrophotometry in the analysis of active compounds in herbal products through a literature review of ten relevant and recent scientific journals. Based on the results of the review, it was found that UV-Vis spectrophotometry is widely used to analyze compounds such as flavonoids, vitamin C, caffeine, paracetamol, and oxalates derived from natural sources such as tea, fruits, vegetables, as well as traditional herbal preparations such as jamu and supplements. In addition, this method is also applied in solubility studies of active compounds within drug delivery systems such as Self-Nanoemulsifying Drug Delivery Systems (SNEDDS), analytical method validation, and evaluation of compound stability in final formulations. Several studies also demonstrated the ability of UV-Vis spectrophotometry to detect the presence of synthetic contaminants in herbal products quickly and simply. From the overall findings, it can be concluded that UV-Vis spectrophotometry plays a significant role in the development, formulation, and quality testing of herbal-based products. This method is not only practical and efficient but also capable of producing reliable quantitative data that can be applied in various research and industrial contexts.]]>
