Classification of Frozen Corn Seeds Using Hyperspectral VIS/NIR Reflectance Imaging

Abstract

A VIS/NIR hyperspectral imaging system was used to classify three different degrees of freeze-damage in corn seeds. Using image processing methods, the hyperspectral image of the corn seed embryo was obtained first. To find a relatively better method for later imaging visualization, four different pretreatment methods (no pretreatment, multiplicative scatter correction (MSC), standard normal variation (SNV) and 5 points and 3 times smoothing (5-3 smoothing)), four wavelength selection algorithms (successive projection algorithm (SPA), principal component analysis (PCA), X-loading and full-band method) and three different classification modeling methods (partial least squares-discriminant analysis (PLS-DA), K-nearest neighbor (KNN) and support vector machine (SVM)) were applied to make a comparison. Next, the visualization images according to a mean spectrum to mean spectrum (M2M) and a mean spectrum to pixel spectrum (M2P) were compared in order to better represent the freeze damage to the seed embryos. It was concluded that the 5-3 smoothing method and SPA wavelength selection method applied to the modeling can improve the signal-to-noise ratio, classification accuracy of the model (more than 90%). The final classification results of the method M2P were better than the method M2M, which had fewer numbers of misclassified corn seed samples and the samples could be visualized well.

Multi-Objective Optimization of Process Parameters of Longitudinal Axial Threshing Cylinder for Frozen Corn Using RSM and NSGA-II

“…Frost is formed on the surface of corn ears, and kernel gaps are filled with frosts. Frozen corn kernels are often damaged by low temperatures [1], which reduces corn quality and causes mildew [2]. The threshing loss rate is improved with a higher kernel splash speed, as the collision coefficient restitution of frozen kernels is increased [3].…”

Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics

“…Image information can be gained while acquiring spectral information, so that the sample can be fully analyzed from a three‐dimensional data block (He et al, ; Wang, Sun, Pu, & Zhu, ). This technology has been widely used in agriculture and forestry research (Caporaso, Whitworth, Grebby, & Fisk, ; Zhang, Dai, & Cheng, ; Zhang, Feng, Liu, & He, ). It also has been used for tea identification.…”

“…This technology has been widely used in agriculture and forestry research (Caporaso, Whitworth, Grebby, & Fisk, 2017;Zhang, Dai, & Cheng, 2019;Zhang, Feng, Liu, & He, 2018). It also has been used for tea identification.…”

Advances in Near Infrared Spectroscopy and Related Computational Methods

“…However, for sake of clarity a tentative and brief overview of these contributions may be helpful to present the Special Issue to the readership. The majority of the articles focuses on applied qualitative and quantitative analyses in a variety of fields [9,11,12,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Roughly, half of these may be associated with pharmaceutical and medical applications [17][18][19][20][21][22][23][24].…”

“…Roughly, half of these may be associated with pharmaceutical and medical applications [17][18][19][20][21][22][23][24]. Most of the remaining applied studies were directed at agricultural applications [25][26][27][28][29][30][31][32][33][34], well reflecting the ever-growing significance of NIR spectroscopy in this area; a good perspective of this topic is included in a focused review article published in the Special Issue [30]. Modern strategies for food analysis also rely on this technique, and few contributions touched that field as well [9,12,33,34].…”