|
|
|
|
|
|
| Micro-Destructive and Rapid Chemical Identification of the Protein Binders in Colors Layers of Paintings by ATR-FTIR and Principal Component Analysis |
| XU Kun1,2,3, WANG Ju-lin1,2,3* |
1. Key Laboratory of Electrochemical Process and Technology for Materials, Beijing University of Chemical Technology, Beijing 100029, China
2. School of Materials Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
3. Key Research Base of State Administration of Cultural Heritage for Evaluation of Science and Technology in Cultural Relics Protection Field, Beijing 100029, China |
|
|
|
|
Abstract Compared with gas chromatography-mass spectroscopy (GC-MS) and high performance liquid chromatography (HPLC), attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy is an “analysis-without-separation” technique so that the analytical procedures can be simplified and the consumption of the art samples can be minimized in the analysis of color layers in paintings. This study examined the feasibility of the ATR-FTIR spectroscopy for identifying the protein binders in color layers. The fresh and accelerated UV light aged reference samples with single protein binders and the mixtures of pigment-protein binders were prepared, by using milk, animal glue, egg white as protein binders, and chalk, azurite as pigments. Second derivative infrared (SD-IR) spectral pattern recognition models were obtained by using principal component analysis (PCA). The protein binders in artificial art samples can be discriminated by the pattern recognition models, which were established based on the analysis of fresh samples. Therefore, the ATR-FTIR spectroscopy has great potential in micro-destructive and rapid chemical identification of protein binders in the field of cultural heritage.
|
|
Received: 2018-04-25
Accepted: 2018-08-21
|
|
|
|
Corresponding Authors:
WANG Ju-lin
E-mail: julinwang@126.com
|
|
[1] Zhang Y, Wang J, Zhang T. Spectroscopy Letters, 2015, 48(10): 732.
[2] Orsini S, Parlanti F, Bonaduce I. Journal of Analytical and Applied Pyrolysis, 2017, 124: 643.
[3] Přikryl P, Havlíková L, Pacáková V, et al. Journal of Separation Science, 2006, 29(17): 2653.
[4] Hu W, Zhang K, Zhang H, et al. Journal of Cultural Heritage, 2015, 16(2): 244.
[5] Elert K, Herrera A, Cardell C. Dyes and Pigments, 2018, 148: 236.
[6] Manzano E, Romero-Pastor J, Navas N, et al. Vibrational Spectroscopy, 2010, 53(2): 260.
[7] Manzano E, Navas N, Checa-Moreno R, et al. Talanta, 2009, 77(5): 1724.
[8] Kun X, Julin W. Spectroscopy and Spectral Analysis, 2018, 38(6): 1829.
[9] Qiu-ju H, LI-qin W, Ya-xu Z. Spectroscopy and Spectral Analysis, 2018, 38(2): 418.
[10] Lu-yao L, Bing-jian Z, Hong Y, et al. Spectroscopy and Spectral Analysis, 2018, 38(7): 2054.
[11] Upadhyay N, Jaiswal P, Jha S N. Journal of Molecular Structure, 2018, 1153: 275.
[12] Muyonga J H, Cole C G B, Duodu K G. Food Chemistry, 2004, 86(3): 325.
[13] Xu S, Wang J, Sun Y. Materials and Structures, 2014, 48(10): 3431. |
| [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] |
XU Qi-lei, GUO Lu-yu, DU Kang, SHAN Bao-ming, ZHANG Fang-kun*. A Hybrid Shrinkage Strategy Based on Variable Stable Weighted for Solution Concentration Measurement in Crystallization Via ATR-FTIR Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1413-1418. |
|
|
|
|
