Pub Date : 2020-01-01DOI: 10.3964/J.ISSN.1000-0593(2020)09-2937-06
Lei Yu, Jin-hao Chen, Longbo Li, Chao Li, Yi-zhuo Zhang
{"title":"Prediction Model of Wood Absolute Dry Density by Near-Infrared Spectroscopy Based on IPSO-BP","authors":"Lei Yu, Jin-hao Chen, Longbo Li, Chao Li, Yi-zhuo Zhang","doi":"10.3964/J.ISSN.1000-0593(2020)09-2937-06","DOIUrl":"https://doi.org/10.3964/J.ISSN.1000-0593(2020)09-2937-06","url":null,"abstract":"","PeriodicalId":21846,"journal":{"name":"光谱学与光谱分析","volume":"18 1","pages":"2937"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81837724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.3964/J.ISSN.1000-0593(2020)08-2415-06
Juncheng Guo, Yongjie Ma, Zhiming Guo, Hua Huang, Shi Yong, Jun Zhou
{"title":"Watercore Identification of Xinjiang Fuji Apple Based on Manifold Learning Algorithm and Near Infrared Transmission Spectroscopy","authors":"Juncheng Guo, Yongjie Ma, Zhiming Guo, Hua Huang, Shi Yong, Jun Zhou","doi":"10.3964/J.ISSN.1000-0593(2020)08-2415-06","DOIUrl":"https://doi.org/10.3964/J.ISSN.1000-0593(2020)08-2415-06","url":null,"abstract":"","PeriodicalId":21846,"journal":{"name":"光谱学与光谱分析","volume":"41 1","pages":"2415"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82348092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.3964/J.ISSN.1000-0593(2020)09-2862-07
Ming Li, Kai Qin, Ning Zhao, Feng Tian
{"title":"Study on the Relationship Between Black Soil Emissivity Spectrum and Total Potassium Content Based on TASI Thermal Infrared Data","authors":"Ming Li, Kai Qin, Ning Zhao, Feng Tian","doi":"10.3964/J.ISSN.1000-0593(2020)09-2862-07","DOIUrl":"https://doi.org/10.3964/J.ISSN.1000-0593(2020)09-2862-07","url":null,"abstract":"","PeriodicalId":21846,"journal":{"name":"光谱学与光谱分析","volume":"49 1","pages":"2862"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86354838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.3964/J.ISSN.1000-0593(2020)09-2781-05
Yan Jun, S. Qing, Xueqing Yan, F. Shibin, Shen Jiawei, Zhang Jian
{"title":"The Categories of the UV-Vis Reflectance Spectra of Seawater Cultured Black Pearl and Its Unique PL Spectral Characteristics","authors":"Yan Jun, S. Qing, Xueqing Yan, F. Shibin, Shen Jiawei, Zhang Jian","doi":"10.3964/J.ISSN.1000-0593(2020)09-2781-05","DOIUrl":"https://doi.org/10.3964/J.ISSN.1000-0593(2020)09-2781-05","url":null,"abstract":"","PeriodicalId":21846,"journal":{"name":"光谱学与光谱分析","volume":"43 1","pages":"2781"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89154145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.3964/J.ISSN.1000-0593(2020)06-1889-06
Zhe Liu, N. Luo, Jiulin Shi, Yubao Zhang, Xingdao He
{"title":"Quantitative Analysis of Fuel Blends Based on Raman and Near Infrared Absorption Spectroscopy","authors":"Zhe Liu, N. Luo, Jiulin Shi, Yubao Zhang, Xingdao He","doi":"10.3964/J.ISSN.1000-0593(2020)06-1889-06","DOIUrl":"https://doi.org/10.3964/J.ISSN.1000-0593(2020)06-1889-06","url":null,"abstract":"","PeriodicalId":21846,"journal":{"name":"光谱学与光谱分析","volume":"16 1","pages":"1889"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82672504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.3964/J.ISSN.1000-0593(2020)03-0809-04
Bao-feng Zheng, Xiao-yun Yang, Chungang Min, Cui Xiaoying, Kun Dong
{"title":"Study on Raman Spectra and Fluorescence Spectra of Dy3Al2(AlO4)3 in Aluminosilicate Substrates","authors":"Bao-feng Zheng, Xiao-yun Yang, Chungang Min, Cui Xiaoying, Kun Dong","doi":"10.3964/J.ISSN.1000-0593(2020)03-0809-04","DOIUrl":"https://doi.org/10.3964/J.ISSN.1000-0593(2020)03-0809-04","url":null,"abstract":"","PeriodicalId":21846,"journal":{"name":"光谱学与光谱分析","volume":"56 1","pages":"809"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88310537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the problem of incomplete root image information because of blocking by the soil, the paper proposed a root phenotypic method by using thermal image combined with improved Criminisi algorithm for root image repair, and studied the relationship between the root phenotype and seed vigor. First, an annular double-layer quartz culture device adapted to maize root configuration was designed to push maize roots to grow along the inner 3d and 6d were planted in the annular culture device respectively. Base on the significant difference of heat capacity between soil and water, water was used to irrigate the seedings along their stems followed by short time hot air thermal excitation, and then infrared thermal images were captured based on the temperature difference between the soil and interstitial water flow around the roots. Secondly, the endpoints of the root thermal images after preprocessed were selected and matched for connecting using improved Criminisi algorithm to repair root image. Finally, different aged-day maize seeds were applied for seeding root phenotyping detection to verify the mentioned method which results shown that the proposed thermal infrared imaging method can help to enhance the root phenotypic image information which improve the precision of phenotypic parameters about 0.5-10% compared with color image. The was no significant difference of Root Total Length (RTL) and Root Total Number (RTN) after 1 d aging, but there were remarkable difference of RTL and RTN after 3 d and 6 d aging which decreased about 20-35% and 10-55% respectively. In general, the maize root phenotypic parameters such as RTN and RTL were significantly negative with the aging-day which can be used as important index parameters of seed vigor. Furthermore, RTN is more sensitive to impress a seed vigor. Root number of 1d/3d and 6d aging days increasing delayed about 1day and 2 day compared with 0 aging-day seeds respectively. The proposed root phenotypic detection method based on the thermal infrared imaging combined with improved Criminisi algorithm for root image repair can be used in root high throughput nondestructive detection which has a broad application prospect.
{"title":"Maize Root Phenotypic Detection Based on Thermal Imaging and Root Gap Repair Algorithm","authors":"Wei Lu, Zhao Han, X. Jian, Ji Zhou, Dong Jiang, Yanfeng Ding","doi":"10.3964/J.ISSN.1000-0593(2020)09-2845-06","DOIUrl":"https://doi.org/10.3964/J.ISSN.1000-0593(2020)09-2845-06","url":null,"abstract":"Aiming at the problem of incomplete root image information because of blocking by the soil, the paper proposed a root phenotypic method by using thermal image combined with improved Criminisi algorithm for root image repair, and studied the relationship between the root phenotype and seed vigor. First, an annular double-layer quartz culture device adapted to maize root configuration was designed to push maize roots to grow along the inner 3d and 6d were planted in the annular culture device respectively. Base on the significant difference of heat capacity between soil and water, water was used to irrigate the seedings along their stems followed by short time hot air thermal excitation, and then infrared thermal images were captured based on the temperature difference between the soil and interstitial water flow around the roots. Secondly, the endpoints of the root thermal images after preprocessed were selected and matched for connecting using improved Criminisi algorithm to repair root image. Finally, different aged-day maize seeds were applied for seeding root phenotyping detection to verify the mentioned method which results shown that the proposed thermal infrared imaging method can help to enhance the root phenotypic image information which improve the precision of phenotypic parameters about 0.5-10% compared with color image. The was no significant difference of Root Total Length (RTL) and Root Total Number (RTN) after 1 d aging, but there were remarkable difference of RTL and RTN after 3 d and 6 d aging which decreased about 20-35% and 10-55% respectively. In general, the maize root phenotypic parameters such as RTN and RTL were significantly negative with the aging-day which can be used as important index parameters of seed vigor. Furthermore, RTN is more sensitive to impress a seed vigor. Root number of 1d/3d and 6d aging days increasing delayed about 1day and 2 day compared with 0 aging-day seeds respectively. The proposed root phenotypic detection method based on the thermal infrared imaging combined with improved Criminisi algorithm for root image repair can be used in root high throughput nondestructive detection which has a broad application prospect.","PeriodicalId":21846,"journal":{"name":"光谱学与光谱分析","volume":"34 1","pages":"2845"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87108795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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