Classification of maize kernels using NIR hyperspectral imaging

Williams, Paul J.; Kucheryavskiy, Sergey

doi:10.1016/j.foodchem.2016.04.044

Cited by 89 publications

(62 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other words, these score images are the products of the PCA scores. In the PCA score images, score values can be presented in color gradients, and differences among samples are observed [23,24]. Support vector machine (SVM) is a widely used pattern recognition algorithm in spectral data analysis [25,26].…”

Section: Data Analysis Methodsmentioning

confidence: 99%

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

Hong

2020

Applied Sciences

View full text Add to dashboard Cite

Longjing tea is one of China’s protected geographical indication products with high commercial and nutritional value. The geographical origin of Longjing tea is an important factor influencing its commercial and nutritional value. Hyperspectral imaging systems covering the two spectral ranges of 380–1030 nm and 874–1734 nm were used to identify a single tea leaf of Longjing tea from six geographical origins. Principal component analysis (PCA) was conducted on hyperspectral images to form PCA score images. Differences among samples from different geographical origins were visually observed from the PCA score images. Support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) models were built using the full spectra at the two spectral ranges. Decent classification performances were obtained at the two spectral ranges, with the overall classification accuracy of the calibration and prediction sets over 84%. Furthermore, prediction maps for geographical origins identification of Longjing tea were obtained by applying the SVM models on the hyperspectral images. The overall results illustrate that hyperspectral imaging at both spectral ranges can be applied to identify the geographical origin of single tea leaves of Longjing tea. This study provides a new, rapid, and non-destructive alternative for Longjing tea geographical origins identification.

show abstract

Section: Data Analysis Methodsmentioning

confidence: 99%

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

Hong

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Marena [7] et al (2011) used NIR-HIS to examine single whole kernels of three cereals (barley, wheat and sorghum) with varying topographic complexity. Paul [15] et al (2016) used NIR hyperspectral imaging to classify maize kernels of three hardness categories with two approaches, pixel-wise and object-wise, however, in their research, three categories and 20–40 kernels of each category were insufficient to establish robust discriminative models and characteristic wavelengths were missing to simplify the models. Stephen [16] et al (2013) used the near-infrared hyperspectral technique to measure the flour yield, softness and sucrose content of wheat and achieved a reliable evaluation of wheat milling quality.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

View full text Add to dashboard Cite

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.

show abstract

“…PCA projects original variables onto a set of new variables (principal components, PCs) which are orthogonal to each other and can keep maximum variation of the data points in the original spectral space. 14 In the PC space, the scores represent the weighted sums of the original variables without signi¯cant loss of useful information. PCA score plots could give an indication of clustering of groups comprised of similar pixels.…”

Section: Multivariate Data Analysis Methodsmentioning

confidence: 99%

Evaluation of growth characteristics of Aspergillus parasiticus inoculated in different culture media by shortwave infrared (SWIR) hyperspectral imaging

Chu

Wang

et al. 2018

J. Innov. Opt. Health Sci.

View full text Add to dashboard Cite

The growth characteristics of Aspergillus parasiticus incubated on two culture media were examined using shortwave infrared (SWIR, 1000–2500[Formula: see text]nm) hyperspectral imaging (HSI) in this work. HSI images of the A. parasiticus colonies growing on rose bengal medium (RBM) and maize agar medium (MAM) were recorded daily for 6 days. The growth phases of A. parasiticus were indicated through the pixel number and average spectra of colonies. On score plot of the first principal component [Formula: see text] and PC2, four growth zones with varying mycelium densities were identified. Eight characteristic wavelengths (1095, 1145, 1195, 1279, 1442, 1655, 1834 and 1929[Formula: see text]nm) were selected from PC1 loading, average spectra of each colony as well as each growth zone. Furthermore, support vector machine (SVM) classifier based on the eight wavelengths was built, and the classification accuracies for the four zones (from outer to inner zones) on the colonies on RBM were 99.77%, 99.35%, 99.75% and 99.60% and 99.77%, 99.39%, 99.31% and 98.22% for colonies on MAM. In addition, a new score plot of PC2 and PC3 was used to differentiate the colonies incubated on RBM and MAM for 6 days. Then characteristic wavelengths of 1067, 1195, 1279, 1369, 1459, 1694, 1834 and 1929[Formula: see text]nm were selected from the loading of PC2 and PC3. Based on them, a new SVM model was developed to differentiate colonies on RBM and MAM with accuracy of 100.00% and 99.99%, respectively. In conclusion, SWIR hyperspectral image is a powerful tool for evaluation of growth characteristics of A. parasiticus incubated in different culture media.

show abstract

Classification of maize kernels using NIR hyperspectral imaging

Cited by 89 publications

References 23 publications

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

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

Evaluation of growth characteristics of Aspergillus parasiticus inoculated in different culture media by shortwave infrared (SWIR) hyperspectral imaging

Contact Info

Product

Resources

About