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Modeling method for SSC prediction in pomelo using Vis-NIRS with wavelength selection and latent variable updating

International Journal of Agricultural and Biological Engineering 2024年第1期17卷 251-260页

作者： Hao Tian Shuai Wang Huirong Xu Yibin Ying College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhou 310058China Key Laboratory of On-Site Processing Equipment for Agricultural Products Ministry of Agriculture and Rural AffairsHangzhou 310058China College of Food Science Shihezi UniversityShihezi 832003XinjiangChina

The aim of this study was in-line,rapid,and non-destructive detection for soluble solid content(SSC)in pomelos using visible and near-infrared spectroscopy(Vis-NIRS).However,the large size and thick rind of pomelo affect the stability of spectral acquisition and the biological variabilities affect the robustness of *** these issues,in this study,an efficient prototype in-line detection system in transmittance mode was designed and evaluated in comparison with an off-line detection *** from the years 2019 and 2020 were used for modeling and the external validation data were obtained by the inline detection system in *** wavelength selection methods of changeable size moving window(CSMW),random frog(RF),and competitive adaptive reweighted sampling(CARS)were used to improve the prediction accuracy of partial least squares regression(PLSR)*** best performance of internal prediction was obtained by CARS-PLSR and the determination coefficient of prediction(),root mean square error of prediction(RMSEP),and residual predictive deviation(RPD)were 0.958,0.204%,and 4.821,***,all models obtained large prediction biases in external *** latent variable updating(LVU)method was proposed to update models and improve the performance in external *** samples from the external validation set were randomly selected to update the *** with the recalibration method,LVU could effectively modify the original models which matched the SSC range of the external validation *** CSMW-PLSR models were more robust in external *** off-line model with LVU performed best with a root mean square error of validation(RMSEV)of 0.599%and the in-line model with recalibration obtained RMSEV of 0.864%.These results demonstrated the application potential of the transmittance Vis-NIRS for in-line rapid prediction of SSC in pomelos and the modeling and updating methods could be applied to samples with biological variabi

关键词： Vis-NIRS in-line detection external validation wavelength selection model updating pomelo SSC

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Spectral Pretreatment and wavelength selection for Soluble Solids Content in Gannan Navel Orange

Spectral Pretreatment and Wavelength Selection for Soluble S...

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作者： Xudong Sun Hailiang Zhang Zhiyuan Gong Aiguo Ouyang Jianmin Zhou Yande Liu School of Mechatronics Engineering East China Jiaotong University

The feasibility of using visible and near-infrared(Vis-NIR) spectroscopy with the fiber optic technique for a rapid measurement of soluble solids content(SSC) in intact Gannan navel orange was *** relationships between Vis-NIR spectra and laboratory measurements of SSC in Gannan navel orange were developed by using partial least squares(PLS) and multiple linear regression(MLR) methods,based on different spectral pretreatments and wavelength selection *** this study,the model based on multiplicative scatter correction(MSC) performed slightly better than those of other spectral *** the comparison with the full spectrum model,the MLR method combined with sensitive wavelength selection and PLS method with spectral interval regions could obtain better calibration *** SECV and r of the MLR model were 0.620 Brix and0.85,while those for iPLS model were 0.580 Brix and *** same prediction results(SEP = 0.64,r = 0.85,RSE%= 4.87%) showed that MLR method combined with sensitive wavelength selection and PLS method with spectral interval regions could be used for optimizing the calibration *** importantly,MLR and iPLS may lead to simple future application.

关键词： Visible and Near-Infrared Spectroscopy Spectral Pretreatment wavelength selection Soluble Solids Content Gannan Navel Orange

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A New wavelength Selective Photoreceiver

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Chinese Journal of Lasers 2000年第5期9卷 51-55页

作者： LIU Kai HUANG Yongqing REN Xiaomin LI Jianxin GUO Wei (Optical Communication Center, Beijing University of Posts and Telecommunications, Beijing 100876, China)MA Xiaoyu KANG Xuejun LIAO Qiwei (Institute of Semiconductor, The Chinese Academy of 北京邮电大学北京中科院半导体所北京

A new kind of wavelength selective photoreceiver is proposed. It was constructed by a Fabry Perot (F P) etalon filter and a resonant cavity enhanced (RCE) photodetector. The photoreceiver′s spectral response is determined by the F P etalon filter with a FWHM of less than 4 nm. Moreover, with such a photoreceiver, the transmission loss of the F P etalon filter can be compensated to some extent. And this will benefit its applications.

关键词： wavelength selection photodetector WDM and optoelectronics device

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Modeling for mung bean variety classification using visible and near-infrared hyperspectral imaging

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International Journal of Agricultural and Biological Engineering 2018年第1期11卷 187-191页

作者： Chuanqi Xie Yong He College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhou 310058China

This study was carried out to investigate the feasibility of using visible and near infrared hyperspectral imaging for the variety classification of mung *** hyperspectral images of mung beans were acquired in the wavelengths of 380-1023 nm,and all images were calibrated by the white and dark reference *** spectral reflectance values were extracted from the region of interest(ROI)of each calibrated hyperspectral image,and then they were treated as the independent *** dependent variables of four varieties of mung beans were set as 1,2,3 and 4,*** extreme learning machine(ELM)model was established using full spectral wavelengths for *** gram-schmidt(MGS)method was used to identify effective *** on the selected wavelengths,the ELM and linear discriminant analysis(LDA)models were *** models performed excellently with the correct classification rates(CCRs)covering 99.17%-99.58% in the training sets and 99.17%-100%in the testing *** wavelengths(432 nm,455 nm,468 nm,560 nm,705 nm,736 nm,760 nm,841 nm,861 nm,921 nm,930 nm,937 nm,938 nm,959 nm and 965 nm)were recommended by *** results demonstrated that hyperspectral imaging could be used as a non-destructive method to classify mung bean varieties,and MGS was an effective wavelength selection method.

关键词： visible and near-infrared hyperspectral imaging mung bean classification modeling wavelength selection

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Rapid and Non-destructive Prediction of Protein Content in Peanut Varieties Using Near-infrared Hyperspectral Imaging Method

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Grain & Oil Science and Technology 2018年第1期1卷 40-43页

作者： WANG Yijie CHENG Junhu School of Food Science and Engineering South China University of Technology

This study was undertaken to investigate the feasibility of near-infrared(NIR) hyperspectral imaging(1 000–2 500 nm) for non-destructive and quantitative prediction of protein content in peanut kernels. Partial least squares regression(PLSR) calibration model was established between the spectral data extracted from the hyperspectral images and the reference measured protein content values, with the coefficient of determination of prediction(R_P^2) of 0.885 and root mean square error of prediction(RMSEP) of 0.465%.Regression coefficients(RC) from PLSR analysis were used to identify the most essential wavelengths that had the greatest influence on changes in the protein content. Eight optimal wavelengths were selected by RC and its corresponding simplified RC-PLSR prediction model was also obtained, showing better performance with a higher R_P^2 of 0.870 and a lower RMSEP of 0.494%. The results indicate that hyperspectral imaging with PLSR analysis can be used as a rapid and non-destructive method for predicting protein content in peanut.

关键词： Hyperspectral imaging Peanut Non-destructive Protein content wavelength selection

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