Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging

Abstract

Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874–1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine−SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree.

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

“…To find the most representative information of Longjing fresh teas and to reduce unnecessary calculations, principal component analysis (PCA) (Feng et al, ) was performed on hyperspectral images. As can be seen in Figure , the first two principal components (PC) images shown contain significant information (e.g., Fenkou Zhongcha 108), whereas the latter two images are basically noise.…”

Detection of Sulfite Dioxide Residue on the Surface of Fresh-Cut Potato Slices Using Near-Infrared Hyperspectral Imaging System and Portable Near-Infrared Spectrometer

“…The object-wise spectra and pixel-wise spectra were analyzed. Pixel-wise analysis and object-wise analysis were conducted [37]. The average of the pixel-wise spectra was calculated to represent the sample as the object-wise spectra.…”

“…For PCA of hyperspectral image, there are object-wise analysis and pixel-wise analysis [37]. Object-wise analysis is to use the average spectrum of one sample instead of individual pixels for PCA to get the scatter plot.…”

“…Samples of different PCs are scattered in different areas, and then the differences among the samples were explored [42]. Pixel-wise analysis is based on individual pixels of the hyperspectral image to obtain the score of individual pixels in the hyperspectral image of each PC to form a score visualization image, which can intuitively observe the differences among the samples [37]. In this study, PCA was performed on the spectra collected by portable NIR spectrometer and the object-wise spectra and pixel-wise spectra extracted from hyperspectral images obtained by NIR-HSI system.…”

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].…”