Publications by authors named "Ningbo Kang"
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.
View Article and Find Full Text PDF[Study on Multi-Bands Recognition for Varieties of Mutton by Using Hyperspectral Technologies].
Guang Pu Xue Yu Guang Pu Fen Xi
September 2016
Article Synopsis
- The paper investigates the identification and classification of different mutton varieties from Tan-han hybrid sheep, Yanchi Tan-sheep, and small-tailed sheep in Ningxia using visible and near-infrared hyperspectral imaging techniques.
- It applies various pretreatment methods and algorithms, including successive projection algorithm (SPA), linear discriminant analysis (LDA), and radial basis kernel function support vector machine (RBFSVM), achieving high accuracy in identifying mutton varieties.
- Results indicate that using the 400–1,000 nm band yields better classification accuracy (up to 100%) compared to 900–1,700 nm, suggesting that visual characteristics like color and texture are more distinct than compositional differences among the mutton