Enhancement of the quality of a vegetable product through the use of near-infrared spectroscopy (NIRS) and RGB imaging
A benchmark company in the agri-food sector, dedicated to the production and marketing of fresh vegetables and greens. Its collaboration with local farmers guarantees a sustainable supply chain and products enriched with essential nutrients. The company is firmly committed to quality and offers a range of healthy foods that connect the countryside with consumers' kitchens.
Product Quality Forecasting and Monitoring
The variability in the condition of the raw material and the lack of effective product degradation control resulted in significant internal waste and customer returns.
Goals
Product quality monitoring system based on control chart to track incoming raw materials based on multivariate image analysis
Pre-packaging vegetable product degradation prediction system
Revitalising Product Quality through Multivariate Six Sigma
The implementation of the Six Sigma methodology and multivariate analysis tools enabled the identification of key points in the production process and the strengthening of the quality control system through prediction, classification models and multivariate statistical control charts for quality monitoring of vegetable products.
Improving the forecasting model
There has been a 35% improvement in the predictive model for product degradation, which has a direct impact on logistics optimisation and supplier payments
Early detection of colour-textural degradation
The detection of colour and textural degradation via RGB imaging analytics allows for the identification of changes even at the earliest stages, where human visual perception may not yet be able to discern alterations in product condition.
Utilisation of hyperspectral data
The company failed to utilise the hyperspectral camera at its disposal due to a lack of knowledge.
