Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology

Abstract

Hyperspectral imaging (HSI) technology has increasingly been applied as an analytical tool in fields of agricultural, food, and Traditional Chinese Medicine over the past few years. The HSI spectrum of a sample is typically achieved by a spectroradiometer at hundreds of wavelengths. In recent years, considerable effort has been made towards identifying wavelengths (variables) that contribute useful information. Wavelengths selection is a critical step in data analysis for Raman, NIRS, or HSI spectroscopy. In this study, the performances of 10 different wavelength selection methods for the discrimination of Ophiopogon japonicus of different origin were compared. The wavelength selection algorithms tested include successive projections algorithm (SPA), loading weights (LW), regression coefficients (RC), uninformative variable elimination (UVE), UVE-SPA, competitive adaptive reweighted sampling (CARS), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), and genetic algorithms (GA-PLS). One linear technique (partial least squares-discriminant analysis) was established for the evaluation of identification. And a nonlinear calibration model, support vector machine (SVM), was also provided for comparison. The results indicate that wavelengths selection methods are tools to identify more concise and effective spectral data and play important roles in the multivariate analysis, which can be used for subsequent modeling analysis.

A Hyperspectral Imaging Approach for Classifying Geographical Origins of Rhizoma Atractylodis Macrocephalae Using the Fusion of Spectrum-Image in VNIR and SWIR Ranges (VNIR-SWIR-FuSI)

“…VNIR-SWIR-FuSI was also used to discriminate origins in SPA bands. As shown in Table 3, when only spectral features were used, the classification results of SPA bands were weaker than those of full bands, and these results were consistent with the empirical results of others [9,10]. This could be attributed to the loss of partial information after SPA features extraction.…”

“…However, these studies focus on HSI systems within the visible and near-infrared (VNIR, about 400–1000 nm) range, or the short-wave near-infrared (SWIR, about 900–1700 nm) range. Moreover, there have been few HSI studies on comparing the classification performance of spectrum and image fusion with VNIR and SWIR range combined, especially in the realm of traditional Chinese medicine (TCM) due to lack of appropriate methodology [8,9,10].…”

Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

“…The sensitive wavelengths or specific vegetation indices are analyzed by multiple algorithms to classify the growth stages or evaluate the nutrient status [ 23 ]. In contrast to correlation analysis, several algorithms were proposed to reduce the problems of multicollinearity between adjacent wavelengths, such as principle component analysis, Monte Carlo uninformative variable elimination [ 24 ], competitive adaptive reweighted sampling [ 25 ], random frog (RF) [ 26 ], and successive projection algorithm (SPA) [ 27 ]. The RF and SPA were usually used to select sensitive wavelengths of crop growth parameters [ 28 , 29 ].…”

Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance Spectroscopy

“…On the one hand, band selection can avoid instrument hardware interference because the presence of interference signals such as the noise makes the signal-to-noise ratio of some bands low and the quality of the collected spectrum poor. On the other hand, a small number of variables are beneficial to effectively extract information and eliminate noninformation [36]. Here, the whole spectra band is divided into 5 subbands, 10 subbands, 15 subbands, and 20 subbands, respectively.…”