Prediction of soluble solids content in navel orange using Laser-Induced Fluorescence Spectroscopy

Abstract

Hyperspectral imaging is a non-contact, non-destructive technique that combines spectroscopy and imaging to extract information from a sample. This technology has recently emerged as a powerful technique for food analysis. In this study, the potential of Laser-Induced Fluorescence Spectroscopy (LIFS) to predict navel orange Soluble Solid Content (SSC) was investigated. The relationship between SSC and LIFS of navel orange were analyzed via partial least squares (PLS) regression. PLS regression model was used to predict SSC in navel orange. The correlation coefficient (Rc) and root mean standard error of calibration (RMSEC) for SSC in calibration set were 0.9006 and 0.5355, respectively, Rp and root mean standard error of prediction (RMSEP) for SSC in prediction set were 0.8410 and 0.6870, respectively. It is verified that the combination of LIFS and PLS model can be used to provide a technique of convenient, nondestructive and rapid analysis for prediction of SSC in the wavelength range of 481.4–780.23nm.