|
|
|
|
|
|
Fourier Transform Infrared (FTIR) Spectroscopy of Genomic DNA from Forty-One Camellia Varieties |
QIU Lu1,2, XIE Mei-hua1,2, ZHAO Guo-yi1 |
1. College of Chemistry and Life Science, Chuxiong Normal University, Chuxiong 675000, China
2. Innovative Research Team (in Science and Technology), Universities of Yunnan Province, Chuxiong 675000, China |
|
|
Abstract The purpose of this research is to cluster and identify forty-one Camellia varieties by Fourier Transform Infrared (FTIR) spectroscopy of genomic DNA. We discovered FTIR spectra of genomic DNA are different among forty-one varieties tested. FTIR spectra can therefore act as a fingerprint for Camellia. Anova confirmed that the differences among FTIR data are significant. We set up the standard clustering and identifying model of forty-one Camellia varieties by hierarchical cluster combined with principal component analysis. The accuracy rate of clustering by using average spectra from one hundred and twenty-three genomic DNA samples is 92.68%. The identification accuracy rate is 100%. Clustering results showed that the forty-one Camellia varieties fall into nine categories based on a 1.0 cluster distance limit, and into three bigger categories at a 15.0 cluster distance limit. The genetic relationship analysis illustrates that the current Chuxiong population of C. reticulata Lindl. comes from Chuxiong, Tengchong, and Dali. We concluded that hierarchical clustering combined with principal component analysis based on FTIR spectra of genomic DNA can be used to quickly cluster and identify Camellia.
|
Received: 2017-12-22
Accepted: 2018-08-17
|
|
|
[1] Zhang H D,Ren S.X. Flora of China. Beijing: Science Press, 1998, 49(3):1.
[2] Min T L. The Study of the Genus Camellia. Kunming: Yunnan Science and Technology Press, 2000. 1.
[3] Talari A C S, Martinez M A G, Movasaghi Z, et al. Applied Spectroscopy Reviews, 2017, 52(5): 456.
[4] Risoluti R, Fabiano M A, Gullifa G, et al. Applied Spectroscopy Reviews, 2017, 52(1): 39.
[5] Qiu Lu, Zhao Yi, Yang Shengjie, et al. Spectroscopy and Spectral Analysis, 2017,37(5): 1612.
[6] Paston S V, Shulenina O V, Tankovskaya S A, et al. In Febs. J. 2015, 282:56.
[7] Kelly J G, Trevisan J, Scott A D, et al. J. Proteome Res.,2011, 10(4): 1437.
[8] Gao J G, Wu Y H, Xu G D, et al. Biochem. Syst. Ecol.,2012, 40: 184.
[9] Dziuba B, Babuchowski A, Nacz D, et al, Int. Dairy J. 2007, 17(3): 183.
[10] Naumann A. Analyst, 2009, 134(6): 1215.
[11] Emura K, Yamanaka S, Isoda H, et al. Breeding Sci.,2006, 56(4): 399.
[12] Kondepati V R, Keese M, Heise H M, et al. Vib. Spectrosc.,2006, 40(1): 33.
[13] Kelly J G, Martin-Hirsch P L, Martin F L,et al. 2009, 81(13): 5314.
[14] Hammiche A, German M J, Hewitt R, et al. Biophys. J.,2005, 88(5): 3699.
[15] Shen J B, Lue H F, Peng Q F, et al. J. Syst. Evol.,2008, 46(2): 194.
[16] Tyagi G, Jangir D K, Singh P, et al. DNA and Cell Boil.,2010, 29(11): 693.
[17] Pascolo L, Bedolla D E, Vaccari L, et al. Reproductive Toxicology, 2016, 61:39.
[18] Qiu Lu, Yang Haiyan, Yang Chunsheng, et al. Spectroscopy and Spectral Analysis, 2014, 34(4):967.
[19] Qiu L, Wang Z, Liu P, et al. Spectroscopy Letters, 2015, 48(2): 120.
[20] Banyay M,Gräslund A., J. mol. Boil.,2002, 324(4): 667.
[21] Geinguenaud F, Mondragon-Sanchez J A,Liquier J, et al. Spectrochim. Act. A. Mol. Biom. Spectrosc.,2005, 61(4): 579.
[22] Elmarzugi N A, Adali T, Bentaleb A M, et al. Journal of Applied Pharmaceutical Science, 2014, 4(8):6. |
[1] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[2] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[3] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[4] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
[5] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[6] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
[7] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[8] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[9] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[10] |
CAO Qian, MA Xiang-cai, BAI Chun-yan, SU Na, CUI Qing-bin. Research on Multispectral Dimension Reduction Method Based on Weight Function Composed of Spectral Color Difference[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2679-2686. |
[11] |
ZHANG Zi-hao1, GUO Fei3, 4, WU Kun-ze1, YANG Xin-yu2, XU Zhen1*. Performance Evaluation of the Deep Forest 2021 (DF21) Model in
Retrieving Soil Cadmium Concentration Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2638-2643. |
[12] |
CHEN Wan-jun1, XU Yuan-jie2, LU Zhi-yun3, QI Jin-hua3, WANG Yi-zhi1*. Discriminating Leaf Litters of Six Dominant Tree Species in the Mts. Ailaoshan Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2119-2123. |
[13] |
WANG Yu-hao1, 2, LIU Jian-guo1, 2, XU Liang2*, DENG Ya-song2, SHEN Xian-chun2, SUN Yong-feng2, XU Han-yang2. Application of Principal Component Analysis in Processing of Time-Resolved Infrared Spectra of Greenhouse Gases[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2313-2318. |
[14] |
HU Hui-qiang1, WEI Yun-peng1, XU Hua-xing1, ZHANG Lei2, MAO Xiao-bo1*, ZHAO Yun-ping2*. Identification of the Age of Puerariae Thomsonii Radix Based on Hyperspectral Imaging and Principal Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1953-1960. |
[15] |
LIU Yu-juan1, 2, 3 , LIU Yan-da1, 2, 3, SONG Ying1, 2, 3*, ZHU Yang1, 2, 3, MENG Zhao-ling1, 2, 3. Near Infrared Spectroscopic Quantitative Detection and Analysis Method of Methanol Gasoline[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1489-1494. |
|
|
|
|