Application of near-infrared hyperspectral imaging to discriminate different geographical origins of Chinese wolfberries

Abstract

Near-infrared (874–1734 nm) hyperspectral imaging (NIR-HSI) technique combined with chemometric methods was used to trace origins of 1200 Chinese wolfberry samples, which from Ningxia, Inner Mongolia, Sinkiang and Qinghai in China. Two approaches, named pixel-wise and object-wise, were investigated to discriminative the origin of these Chinese wolfberries. The pixel-wise classification assigned a class to each pixel from individual Chinese wolfberries, and with this approach, the differences in the Chinese wolfberries from four origins were reflected intuitively. Object-wise classification was performed using mean spectra. The average spectral information of all pixels of each sample in the hyperspectral image was extracted as the representative spectrum of a sample, and then discriminant analysis models of the origins of Chinese wolfberries were established based on these average spectra. Specifically, the spectral curves of all samples were collected, and after removal of obvious noise, the spectra of 972–1609 nm were viewed as the spectra of wolfberry. Then, the spectral curves were pretreated with moving average smoothing (MA), and discriminant analysis models including support vector machine (SVM), neural network with radial basis function (NN-RBF) and extreme learning machine (ELM) were established based on the full-band spectra, the extracted characteristic wavelengths from loadings of principal component analysis (PCA) and 2nd derivative spectra, respectively. Among these models, the recognition accuracies of the calibration set and prediction set of the ELM model based on extracted characteristic wavelengths from loadings of PCA were higher than 90%. The model not only ensured a high recognition rate but also simplified the model and was conducive to future rapid on-line testing. The results revealed that NIR-HSI combined with PCA loadings-ELM could rapidly trace the origins of Chinese wolfberries.

Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology

“…In order to explore the separability of different okra seeds, the PCA program that could minimize the interference of other useless data was applied to extract the critical components from the various spectral data [10,29,30]. The three-dimensional (3D) principal component (PC) score plot of all the samples is illustrated in Figure 2.…”

“…Spectroscopic and spectral imaging techniques provide comprehensive structural information on the components and properties of samples at the molecular level [10]. Nowadays, near-infrared hyperspectral technology has been widely used in food detection and the identification of varieties [11][12][13][14][15].…”

Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds

“…19 NIR-HSI can simultaneously acquire NIR spectra (one-dimensional spectral information) and image information (two-dimensional spatial information). 20 Each pixel in a hyperspectral image has spectral information. The spectral information of each pixel combined with the corresponding spatial information can realize the visualization of sample features, which can intuitively show the differences among samples.…”

Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging

“…The other spectral ranges (in NIR and SWIR) are linked to physical and chemical characteristics, such as cellulose and lignin[57], as well as carbohydrates, proteins, and water content[58]. Wavelengths in the ranges 966-977, 983-1009, 1216-1265, 1254-1270, and 1330-1390 nm are linked to the O-H stretch in water, while the range 1047-1178 nm is linked to the N-H stretch of proteins[59]. The prevalence of pigment and water content related wavelengths indicates the importance of these variables for classification of plants into age groups and drought stress.…”