Determination of sugar content in Lingwu jujube by NIR–hyperspectral imaging

Abstract Near infrared hyperspectral imaging (NIR‐HSI) with a spectral range of 900 to 1700 nm was for the first time used to predict the changes of sugar content in Lingwu jujube during storage. Monte Carlo method was adopted to detect outliers, and multiple scattering correction (MSC), standard normal variate transformation (SNV), and Baseline were used to optimize modeling. Competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), and interval random frog (IRF) were used to select optimal wavelengths. In addition, partial least square regression (PLSR) and support vector machine (SVM) modeling based on optimal wavelengths were compared. The results showed that 30, 30, and 24 wavelengths were selected by CARS; 106, 87, and 112 feature wavelengths were selected by iVISSA; and 96, 71, and 83 optimal wavelengths were selected by IRF for sucrose, fructose, and glucose, respectively. The CARS–PLSR models provided the best results for fructose and glucose, and iVISSA–SVM model was better for sucrose. The results indicated that NIR–HSI model may be used as a rapid and nondestructive method for the determination of sugar content in jujubes.