Nondestructive detection for egg freshness based on hyperspectral imaging technology combined with harris hawks optimization support vector regression

2021 ◽  
Author(s):  
Kunshan Yao ◽  
Jun Sun ◽  
Lin Zhang ◽  
Xin Zhou ◽  
Yan Tian ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hyperspectral Imaging ◽  
Support Vector ◽  
Nondestructive Detection ◽  
Imaging Technology ◽  
Egg Freshness

Nondestructive detection for egg freshness grade based on hyperspectral imaging technology

2020 ◽  
Vol 43 (7) ◽  
Author(s):  
Kunshan Yao ◽  
Jun Sun ◽  
Xin Zhou ◽  
Adria Nirere ◽  
Yan Tian ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Nondestructive Detection ◽  
Imaging Technology ◽  
Egg Freshness

scholarly journals Visual detection of microbial community during three bacteria mixed fermentation through hyperspectral imaging technology

eFood ◽  
2021 ◽  
Author(s):  
Yanxiao Li ◽  
Xuetao Hu ◽  
Jiyong Shi ◽  
Baijing Qiu ◽  
Jianbo Xiao
Keyword(s):  
Microbial Community ◽  
Hyperspectral Imaging ◽  
Support Vector ◽  
Counting Method ◽  
Mixed Fermentation ◽  
Imaging Technology ◽  
Linear Discriminant ◽  
Identification Rate ◽  
Distribution Maps ◽  
Specific Strain

Hyperspectral imaging technology with chemometrics was used for identifying and counting each species in microbial community during mixed fermentation. Hyperspectral images of microbial community of <i>Enterobacter</i> sp, <i>Acetobacter pasteurianus</i>, and <i>Lactobacillus paracasei</i> colonies were obtained and the spectra of strain colonies were extracted. Identification models were developed using linear discriminant analysis (LDA) and least-squares support vector machine (LS-SVM) by using 23 variables selected by genetic algorithm. The optimal LS-SVM model with identification rate of 96.67 % was used to identify colonies and prepare colony distribution maps in color for strains counting. The counting results by hyperspectral imaging technology agree with that of the manual counting method with average relative error of 3.70 %. The developed counting method has been successfully used to identify and count the specific strain from the mixed strains simultaneously. The hyperspectral imaging technology has a great potential to monitor changes in the microbial community structure.

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

2018 ◽  
Vol 8 (10) ◽  
pp. 1793 ◽  
Author(s):  
Jinnuo Zhang ◽  
Xuping Feng ◽  
Xiaodan Liu ◽  
Yong He
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Projection Algorithm ◽  
Rapid Screening ◽  
Support Vector ◽  
Calibration Model ◽  
Imaging Technology ◽  
Characteristic Wavelength ◽  
Full Band

Near-infrared (874–1734 nm) hyperspectral imaging technology combined with chemometrics was used to identify parental and hybrid okra seeds. A total of 1740 okra seeds of three different varieties, which contained the male parent xiaolusi, the female parent xianzhi, and the hybrid seed penzai, were collected, and all of the samples were randomly divided into the calibration set and the prediction set in a ratio of 2:1. Principal component analysis (PCA) was applied to explore the separability of different seeds based on the spectral characteristics of okra seeds. Fourteen and 86 characteristic wavelengths were extracted by using the successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. Another 14 characteristic wavelengths were extracted by using CARS combined with SPA. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were developed based on the characteristic wavelength and full-band spectroscopy. The experimental results showed that the SVM discriminant model worked well and that the correct recognition rate was over 93.62% based on full-band spectroscopy. As for the discriminative model that was based on characteristic wavelength, the SVM model based on the CARS algorithm was better than the other two models. Combining the CARS+SVM calibration model and image processing technology, a pseudo-color map of sample prediction was generated, which could intuitively identify the species of okra seeds. The whole process provided a new idea for agricultural breeding in the rapid screening and identification of hybrid okra seeds.

scholarly journals Polysaccharide determination and habitat classification for fresh Dendrobiums with hyperspectral imagery and modified RBFNN

RSC Advances ◽  
2022 ◽  
Vol 12 (2) ◽  
pp. 1141-1148
Author(s):  
Yuzhen Wei ◽  
Wenjun Hu ◽  
Feiyue Wu ◽  
Yi He
Keyword(s):  
Hyperspectral Imaging ◽  
Hyperspectral Imagery ◽  
Nondestructive Detection ◽  
Imaging Technology ◽  
Habitat Classification

This research aimed to study the visual and nondestructive detection of mannose (MN) and Dendrobium polysaccharides (DP) in Dendrobiums by using hyperspectral imaging technology.

scholarly journals Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Donge Zhao ◽  
Shuyan Liu ◽  
Xuefeng Yang ◽  
Yayun Ma ◽  
Bin Zhang ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Spatial Information ◽  
Imaging System ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Practical Significance ◽  
Support Vector ◽  
Operational Environment ◽  
Imaging Technology ◽  
Training Samples

Hyperspectral imaging technology can obtain the spatial information and spectral information of the simulated operational background and its camouflage materials at the same time and identify and classify them according to their differences. In this paper, we collected the hyperspectral images (400–1000 nm) of the desert background, jungle background, desert camouflage netting, jungle camouflage netting, and jungle camouflage clothing through the hyperspectral imaging system, and the samples were preprocessed by denoising and black-and-white correction. Then, we analysed the region of interest (ROI) of the training samples by principal component analysis (PCA). After the pixels in the region of interest and their surrounding areas were averaged, 60% of the data was used as the training samples, and the remaining 40% was used as the test samples. According to their similarities and differences between them and referenced spectrum, the models of classification were established by combining the Naive Bayes (NB) algorithm, K-nearest neighbour (KNN) algorithm, random forest (RF) algorithm, and support vector machine (SVM) algorithm. The results show that among the four models, SVM model has the highest accuracy of classification and the recognition rate of jungle camouflage clothing is the highest. This study verifies the scientific and feasibility of hyperspectral imaging technology for camouflage identification and classification in a simulated operational environment, which has some practical significance.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection
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