Detection of mango soluble solid content using hyperspectral imaging technology

Soluble solid content (SSC) is an important indicator for evaluating mango quality. The main task of this study is to develop a partial least squares (PLS) regression model for SSC by combinating the visible and near infrared (400-1000 nm) hyperspectral imaging. The PLSR model can be used to assess the quality grading of mangoes. By comparing the performance of five preprocessing full-band models, the standard normal variable transformation (SNV) and multiplicative scatter correction algorithm (MSC) are selected for this study. Otherwise, three variable selection methods, including successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS) method, and the genetic algorithm (GA) are used for the identification of the characteristic wavelengths. The screened feature bands are used to build PLS regression models.The SNV-CARS-PLS model is found to show the best prediction performance. The correlation coefficient for the predicted value for the mango SSC and its root mean square error are determined to be 0.9001 and 0.6162, respectively. These results suggest that the SNVCARS-PLS model is an effective method for predicting mango SSC.