Classification of Lingwu long jujube internal bruise over time based on visible near-infrared hyperspectral imaging combined with partial least squares-discriminant analysis

• Published in: Computers and electronics in agriculture 2021-03, Vol.182, p.106043, Article 106043
• Main Authors: Yuan, Ruirui, Liu, Guishan, He, Jianguo, Wan, Guoling, Fan, Naiyun, Li, Yue, Sun, Yourui
• Format: Article
• Language: English
• Subjects: Accuracy; Adaptive sampling; Algorithms; Bruise; Bruising; Calibration; Characteristic wavelengths; Classification; Cluster analysis; Damage detection; Discriminant analysis; Forecasting; Hyperspectral imaging; Infrared analysis; Infrared imaging; Iterative methods; Least squares; Lingwu long jujube; Model accuracy; Near infrared radiation; Partial least squares-discriminant analysis; Spectra; Two dimensional analysis; Two dimensional models; Variables
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2021.106043

•Make a quantitative damage device for Lingwu long jujube.•Spectral of samples was scanned by VIS/NIR hyperspectral system.•Using SPA, UVE, CARS, iVISSA and 2D-COS to select the characteristic wavelength.•Establish the PLS-DA classification models. Early detection of internal bruise is one of the major challenges in postharvest quality sorting processes in Lingwu long jujube. In this study, the visible/near infrared (VIS/NIR) hyperspectral imaging system (400–1000 nm) was used to rapidly detect the intact and damaged jujube at five time points after mechanical damage (2 h, 4 h, 8 h, 12 h and 24 h). The region of interest of samples was selected by ENVI software, and the average spectrum was calculated for modelling. Different preprocessing methods were used to transform and enhance the spectral signal. Partial least squares-discriminant analysis (PLS-DA) classification models of the original and preprocessed spectra were established. The de-trending-PLS-DA model had the best effect, the accuracy of the calibration set and prediction set were 85.56% and 92.22%, respectively. The spectra were pre-processed by the de-trending algorithm, and the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), cluster analysis (CA), two-dimensional correlation spectra (2D-COS), UVE-SPA, CARS-SPA, iVISSA-SPA, CA-SPA and 2D-COS-SPA method were used to select characteristic variables. The PLS-DA model based on feature variables was established. The de-trending-CARS-PLS-DA model with 63 variables was found to be the optimal model. In the de-trending-CARS-PLS-DA model, the accuracy of calibration set and prediction set were 86.67% and 91.11%, respectively. It was found that the model accurately detected bruising in jujube at 8 h after bruising, and the accuracy of calibration set and prediction set were 100%. The results showed that VIS/NIR hyperspectral imaging technology combined with PLS-DA could discriminate different stages of bruising in Lingwu long jujube. This study may help develop an online detection system of bruising in Lingwu long jujube.