Evaluation of Colorimetry in Cheeses Using Machine Vision and OpenCV: A Non-destructive Approach to Food Quality Control

Lozada-Vilca R.A.; Castillo-Alarcón O.; Luque-Vilca O.M.; Pampa-Vilca W.J.; Montalvo-Atco M.A. · Smart Innovation, Systems and Technologies (2025) · DOI: 10.1007/978-3-031-92651-8_29

Resumen

Machine vision technology has proven to be a powerful tool in the food industry, facilitating the improvement of manufacturing and quality control processes. This study presents a non-destructive approach to color evaluation in cheeses by analyzing images using OpenCV, an open-source computer vision library. An image capture system was implemented in a controlled environment, using an RGB camera and color spaces such as LAB, BGR, and HSV to analyze cheeses such as Pariah, Tilsit, Edam, and Andino. The technique used allowed the separation of color channels, the elimination of irrelevant information, and the calculation of central trend measurements, showing that the LAB channel is the most suitable for the detection of color variations related to product quality. The results indicate that this method can be effectively applied in the industry to ensure uniformity and standardization of color in cheeses, thus improving their acceptability in the market. The use of OpenCV not only reduces food waste by avoiding destructive techniques but also optimizes quality control and competitiveness in the food sector. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Instituciones: Universidad Nacional de San Agustín de Arequipa; Universidad Nacional de Juliaca