Rapid and Non-destructive Detection of Rice Protein Content Based on Near Infrared Spectroscopy
【Objective】It aims to establish a non-destructive and rapid screening technique for rice kernel protein.【Method】A total of 317 rice germplasm resources were selected. Based on near infrared spectroscopy (NIRS), four pretreatment methods were used: first-order smooth derivative (SG1), second-order sm...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Guangdong Academy of Agricultural Sciences
2024-10-01
|
| Series: | Guangdong nongye kexue |
| Subjects: | |
| Online Access: | http://gdnykx.cnjournals.org/gdnykx/ch/reader/view_abstract.aspx?file_no=202410011 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841560388426006528 |
|---|---|
| author | Siping TAN Jicheng YUE Ying CHEN Cuihong HUANG Danhua ZHOU Huijuan ZHANG Guili YANG Hui WANG |
| author_facet | Siping TAN Jicheng YUE Ying CHEN Cuihong HUANG Danhua ZHOU Huijuan ZHANG Guili YANG Hui WANG |
| author_sort | Siping TAN |
| collection | DOAJ |
| description | 【Objective】It aims to establish a non-destructive and rapid screening technique for rice kernel protein.【Method】A total of 317 rice germplasm resources were selected. Based on near infrared spectroscopy (NIRS), four pretreatment methods were used: first-order smooth derivative (SG1), second-order smooth derivative (SG2), standard normal variable (SNV) and detrend algorithm (Detrend). The near infrared detection model of rice protein contents in rice, brown rice and milled rice were established by using partial least square (PLS) method.【Result】Combined SG1+SNV+Detrend and SNV+Detrend+SG1 pretreatment methods had the best modeling effect. The corrected correlation coefficients (R2) and standard deviations (SEP) of the near infrared detection model for determination of protein content in rice and brown rice were 0.882 and 0.926, and 0.239 and 0.213, respectively. The calibration R2 and SEP of NIR detection models for determination of protein content in rice and milled rice were 0.900 and 0.925, and 0.267 and 0.224, respectively. The internal cross-validation correlation coefficients (R2) and internal cross-validation standard deviations (SECV) of NIR detection models for determination of protein content in rice and brown rice were 0.859 and 0.917, and 0.266 and 0.227, respectively. The internal cross-validation R2 and SECV of NIR detection models for determination of protein content in rice and milled rice were 0.880 and 0.916, and 0.296 and 0.238, respectively. The external validation correlation coefficients (R2) and external validation SEP of NIR detection models for determination of protein content in rice and brown rice were 0.902 and 0.923, and 0.422 and 0.311, respectively. The external validation R2 and external validation SEP of NIR detection models for determination of protein content in rice and milled rice were 0.950 and 0.981, and 0.364 and 0.197, respectively.【Conclusion】The prediction model of rice protein content based on near infrared spectroscopy can be used for the preliminary screening of protein content in large samples of breeding materials, which can provide reference for rice nutritional quality breeding. |
| format | Article |
| id | doaj-art-1a4f727bab0a4dba98c30861962bda32 |
| institution | Kabale University |
| issn | 1004-874X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Guangdong Academy of Agricultural Sciences |
| record_format | Article |
| series | Guangdong nongye kexue |
| spelling | doaj-art-1a4f727bab0a4dba98c30861962bda322025-01-04T07:39:37ZengGuangdong Academy of Agricultural SciencesGuangdong nongye kexue1004-874X2024-10-01511011112310.16768/j.issn.1004-874X.2024.10.011202410011Rapid and Non-destructive Detection of Rice Protein Content Based on Near Infrared SpectroscopySiping TANJicheng YUEYing CHENCuihong HUANGDanhua ZHOUHuijuan ZHANGGuili YANGHui WANG【Objective】It aims to establish a non-destructive and rapid screening technique for rice kernel protein.【Method】A total of 317 rice germplasm resources were selected. Based on near infrared spectroscopy (NIRS), four pretreatment methods were used: first-order smooth derivative (SG1), second-order smooth derivative (SG2), standard normal variable (SNV) and detrend algorithm (Detrend). The near infrared detection model of rice protein contents in rice, brown rice and milled rice were established by using partial least square (PLS) method.【Result】Combined SG1+SNV+Detrend and SNV+Detrend+SG1 pretreatment methods had the best modeling effect. The corrected correlation coefficients (R2) and standard deviations (SEP) of the near infrared detection model for determination of protein content in rice and brown rice were 0.882 and 0.926, and 0.239 and 0.213, respectively. The calibration R2 and SEP of NIR detection models for determination of protein content in rice and milled rice were 0.900 and 0.925, and 0.267 and 0.224, respectively. The internal cross-validation correlation coefficients (R2) and internal cross-validation standard deviations (SECV) of NIR detection models for determination of protein content in rice and brown rice were 0.859 and 0.917, and 0.266 and 0.227, respectively. The internal cross-validation R2 and SECV of NIR detection models for determination of protein content in rice and milled rice were 0.880 and 0.916, and 0.296 and 0.238, respectively. The external validation correlation coefficients (R2) and external validation SEP of NIR detection models for determination of protein content in rice and brown rice were 0.902 and 0.923, and 0.422 and 0.311, respectively. The external validation R2 and external validation SEP of NIR detection models for determination of protein content in rice and milled rice were 0.950 and 0.981, and 0.364 and 0.197, respectively.【Conclusion】The prediction model of rice protein content based on near infrared spectroscopy can be used for the preliminary screening of protein content in large samples of breeding materials, which can provide reference for rice nutritional quality breeding.http://gdnykx.cnjournals.org/gdnykx/ch/reader/view_abstract.aspx?file_no=202410011ricenear infrared spectroscopyprotein contentnear infrared detection modelpartial least square methodcorrelation coefficient |
| spellingShingle | Siping TAN Jicheng YUE Ying CHEN Cuihong HUANG Danhua ZHOU Huijuan ZHANG Guili YANG Hui WANG Rapid and Non-destructive Detection of Rice Protein Content Based on Near Infrared Spectroscopy Guangdong nongye kexue rice near infrared spectroscopy protein content near infrared detection model partial least square method correlation coefficient |
| title | Rapid and Non-destructive Detection of Rice Protein Content Based on Near Infrared Spectroscopy |
| title_full | Rapid and Non-destructive Detection of Rice Protein Content Based on Near Infrared Spectroscopy |
| title_fullStr | Rapid and Non-destructive Detection of Rice Protein Content Based on Near Infrared Spectroscopy |
| title_full_unstemmed | Rapid and Non-destructive Detection of Rice Protein Content Based on Near Infrared Spectroscopy |
| title_short | Rapid and Non-destructive Detection of Rice Protein Content Based on Near Infrared Spectroscopy |
| title_sort | rapid and non destructive detection of rice protein content based on near infrared spectroscopy |
| topic | rice near infrared spectroscopy protein content near infrared detection model partial least square method correlation coefficient |
| url | http://gdnykx.cnjournals.org/gdnykx/ch/reader/view_abstract.aspx?file_no=202410011 |
| work_keys_str_mv | AT sipingtan rapidandnondestructivedetectionofriceproteincontentbasedonnearinfraredspectroscopy AT jichengyue rapidandnondestructivedetectionofriceproteincontentbasedonnearinfraredspectroscopy AT yingchen rapidandnondestructivedetectionofriceproteincontentbasedonnearinfraredspectroscopy AT cuihonghuang rapidandnondestructivedetectionofriceproteincontentbasedonnearinfraredspectroscopy AT danhuazhou rapidandnondestructivedetectionofriceproteincontentbasedonnearinfraredspectroscopy AT huijuanzhang rapidandnondestructivedetectionofriceproteincontentbasedonnearinfraredspectroscopy AT guiliyang rapidandnondestructivedetectionofriceproteincontentbasedonnearinfraredspectroscopy AT huiwang rapidandnondestructivedetectionofriceproteincontentbasedonnearinfraredspectroscopy |