Quality Detection of Fresh-Cut Pineapples During Cold Storage Based on Diffuse Reflectance and Fluorescence Hyperspectral Imaging Technique

Abstract

The fresh-cut fruits and vegetables industry has developed rapidly in recent years, with pineapple being a popular fruit. The quality of pineapple plays a crucial role in determining its market value. This research explored the potential of diffuse reflectance hyperspectral imaging (HSI) and fluorescence hyperspectral imaging (FHSI) in monitoring L*a*b*, pH, and soluble solids content (SSC) of fresh-cut pineapples during cold storage. Fresh-cut pineapples were stored at a temperature of 4°C for 0–5 days, with daily acquisition of diffuse reflectance and fluorescence hyperspectral images at 380–1000 nm, along with measurement of quality indices. Changes in spectral properties and quality indices during cold storage were analyzed. Seven preprocessing algorithms were used to preprocess two types of spectra. Subsequently, five feature selection methods were employed to extract feature variables from the preprocessed spectra. The Partial Least Squares Regression (PLSR) models were constructed to predict the various quality indices. Data fusion methods were introduced to leverage the complementary information from different spectra. Both feature-level and decision-level fusion methods demonstrated improvement in model accuracy. The hybrid fusion method, combining the advantages of these two fusion methods, effectively enhanced the prediction accuracy of all quality indices. The determination coefficients (R²) for predicting L*a*b* consistently exceeded 0.8, while the R² for predicting pH was close to 0.8, and the R² for predicting SSC reached 0.91. In summary, the quality indices of fresh-cut pineapples can be accurately predicted using diffuse reflectance HSI and FHSI, with model accuracy further enhanced through multi-level data fusion methods.