Research on Citrus Fruit Freshness Detection Based on Near-Infrared Spectroscopy

The study developed a novel method for evaluating the freshness of citrus fruits by integrating near-infrared spectroscopy with the non-linear data processing capabilities of a BP neural network. This approach utilizes specific wavelength analysis to distinguish between fresh and non-fresh fruits effectively. Advanced pre-processing techniques are employed to remove spectral anomalies, enhancing the network's ability to accurately identify crucial quality indicators like sugar content. Concurrently, an experiment utilizing a mathematical computing software -based BP neural network optimized the number of hidden layer nodes, identifying 61 as optimal. This configuration achieves impressive indicators, including a mean square error of 0.0025665 and a root mean square error of 49.8214. More than 1000 training iterations were performed on 100 citrus samples, and the learning rate was 80%. The model demonstrated a high accuracy rate of 97.6275%, confirming its precision and reliability in assessing citrus freshness. This synergy between advanced neural network processing and spectroscopic techniques marks a significant advancement in agricultural quality assessment, setting new standards for speed and efficiency in data processing.