Skip to main content

Key to the conservation of calligraphy and painting relics in collection: proposing a lighting damage evaluation method

Abstract

As an important supporting material for calligraphy and painting relics in collections, the silk substrate is generally in acidification. And owing to an extremely high light response, it is very prone to different forms of lighting damage, including color and mechanical aspects. Considering that the Light Emitting Diode (LED) with flexible spectral compositions is widely used in the lighting environment of collections, the spectral responsivity of acidified silk substrates is the core issue concerning the long-term conservation of calligraphy and painting relics. In this study, based on the accelerated aging experiment of nine narrowband LEDs with different wavelengths (λ) in the visible range on silk samples with different acidification degrees (pH values), the infrared spectrum was measured non periodically. Processed with the Principal Component Analysis (PCA), the obtained ΔPC1 was used to evaluate the lighting damage of silk samples. Based on the damage change curve, its spectral responsivity function, that is, the mathematical relationship between lighting damage and λ and pH values, was further fitted. Combined with the previous research on the spectral responsivity of colorants, an evaluation method for lighting damage was proposed, which is the key to the conservation of calligraphy and painting relics.

Introduction

Calligraphy and painting relics (hereinafter referred to as relics) in collections, known as a huge stock and rich content, are an important part of the world cultural heritage and have important historical and artistic values. With the popularization of the concept of “relics first, conservation first” [1], it is particularly important to carry out conservation for the relics [2]. That is to say, identifying and evaluating environmental factors that may cause damage to the relics provides a theoretical basis for developing a series of conservation methods [3,4,5]. Lighting in the museum, as an environmental factor for the preservation and display of the relics, is an important reason for the damage to them [2, 6]. This is because their two components, the colorant covering substrate and the blank substrate, are defined as high light responsive materials [7]. Especially, the silk substrate that first appeared during the Warring States period (~ 256 B. C.) is composed of the high polymer of proteins, which are prone to different forms of lighting damage, including the color aspect like yellowing, and the mechanical aspect like deformation and embrittlement. Therefore, lighting damage evaluation is the key to the conservation of the relics.

It is mentioned in the technical report of Commission Internationale de lʹEclairage (CIE) that the lighting damage is a function of exposure and spectral responsivity [7]. For materials with high light responsivity such as colorants and silk substrates, there are recommended values of exposure in different lighting standards [7,8,9]. However, the importance of research on the spectral responsivity is highlighted by the application of Light Emitting Diodes (LEDs), for their flexible spectral compositions bring great challenges to the conservation of the relics.

To our knowledge, most of the related studies tend to focus on colorants by considering their interaction with substrates [10,11,12], with little consideration given to the blank substrate. Liu et al. studied the spectral responsivity of historical paper under irradiation of three narrowband lights by measuring reflection spectra and color difference [13]. Through the accelerated light aging experiment on silk substrates, Dang et al. obtained its spectral responsivity function based on the color difference [14]. It can be seen that the color difference is a commonly used method to evaluate the lighting damage, whereas it is only applicable to the color damage of silk substrates. Halvorson et al. evaluated changes in mechanical properties such as tensile strength of silk based on tensile tests [15]. If combined with methods for evaluating color and mechanical damage, the different damage rules obtained could bring great inconvenience and limitation to the practical application. For the silk substrate, the lighting damage on the macro level is due to changes in molecular structures caused by the absorption of photon energy [12]. Considering the advantages of non-destructive measurement without sample preparation, spectroscopy, as a means to analyze the macro properties of materials from molecular structures, is particularly suitable for evaluating the lighting damage to silk substrates [6]. Dang et al. obtained the lighting damage coefficients of four narrowband lights on paper and silk substrates by analyzing the changes in characteristic Raman peak intensity [16]. Due to the fluorescence effect of Raman spectroscopy in testing organic materials, infrared spectroscopy is a more commonly used method for studying proteins in silk substrates [17, 18]. Kopersk proposed several damage evaluation indicators based on infrared spectral peaks to assess the damage to silk under water vapor and oxygen from aspects of crystallinity, degrees of oxidation and polymerization [19]. However, in the existing studies, evaluating changes in one [20] or multiple molecular structures [19] based on calculating the infrared spectral peak intensity ratio may, on the one hand, omit unexpected but important phenomena caused by changes in other molecular structures during the damage process, and on the other hand, not be conducive to practice owing to different damage rules based on changes in multiple molecular structures.

Chemometrics is a discipline that links the measured value of a chemical system with its state through statistical or mathematical methods, that is, to extract information to the maximum extent to explain the changes in the system. It is a feasible method to extract information from the infrared spectral dataset with chemometrics [21, 22]. Principal Component Analysis (PCA) is a classical method for information extraction in data [23]. For a given infrared spectral datasets (in the form of matrix X, where each row and column represent a spectrum and wavenumber respectively), they can be projected by PCA as points in the principal component space. That is to say, the principal components are represented as linear combinations to characterize the original high-dimensional infrared spectral dataset [24], as shown in Eq. (1).

$$X = PC_{1} \cdot t_{1} + PC_{2} \cdot t_{2} + ... + PC_{n} \cdot t_{n}$$
(1)

where PC and t are the two vectors of the principal component, respectively. The score vector PC contains the score values of each spectrum, which is used to illustrate how the spectrum is related to other spectra. The load vector t reveals which spectral change characteristics in the original spectrum captured by the specific PC [25]. On the premise of the principal component achieving at least 85% interpretation of the infrared spectral dataset, its change in the score value can be used to quantify the changes in the dataset, i.e., the lighting damage to samples [26]. In addition, the load vector t captures the spectral change characteristics of samples, which explains changes in molecular structures during the process of lighting damage.

Apart from the issue concerning methods, the above study seems to ignore a common material property of silk substrates, namely acidification [27, 28]. An important source of acidification is the additives retained in the production process of silk substrates, namely, the mixture of animal glue and alum (KAl(SO4)2·12H2O) [29]. The hydrolysis of Al3+ could promote the acidification of silk substrates, seriously affecting its durability. Kim et al. pointed out that the increase of acidity is directly related to the damage to silk [30]. As far as we know, there is little study on spectral responsivity of acidified silk substrates. However, as Pastorelli et al. said, there is an interaction between the influence of environment and material properties on itself [31]. Therefore, we believe that it is urgent to study the spectral responsivity of acidified silk substrates with the spectral quantification method of infrared spectroscopy combined with PCA.

In this study, based on the accelerated aging experiment of nine narrowband LEDs in the visible range on silk samples with different acidification degrees, the spectral quantification method was proposed to study their spectral responsivity. In combination with the previous study on the spectral responsivity of colorants, an evaluation method for lighting damage to the relics was proposed.

Materials and methods

Samples

The National Museum of China has kindly provided us with several silk substrates as experimental samples. Considering the acidification sources, the solution made of different proportions of animal glue and alum was brushed on silk samples to acidify them. Using cold extraction method [32], the pH values of silk samples were determined as 3.5, 4.0, 5.0, and 6.0 respectively, basically covering the pH range involved in the literature [30]. The silk sample is shown in Fig. 1, whose specific information is shown in Table 1.

Fig. 1
figure 1

Silk samples with pH value increasing from left to right

Table 1 Proportions of animal glue and alum for silk samples with different pH values

Accelerated aging experiment

Accelerated aging experiment was carried out in the experimental box with automatic temperature and humidity adjustment function in the all-dark optical laboratory, as shown in Fig. 2a. In the experimental box divided into nine independent spaces with black partition boards, nine narrowband LEDs within the visible light range were respectively installed on the top, vertically irradiating downward silk samples at an interval of 54.5 cm. Its spectral power distributions (SPDs) were measured by the spectroradiometer (Photo Research PR-670), as shown in Fig. 2b. In order to improve the irradiation uniformity, samples in nine independent spaces were all placed on an automatic rotary table with a speed of 0.5 r/min, controlling the irradiance on sample surface about 20 W/m2. During the experiment, lighting evaluation indicator was obtained through a synchronous analysis of the infrared spectral data by PCA. On that basis, when the variation of lighting damage with exposure reaches a stable stage, the experiment was ended, with a cumulative exposure of 984 h and a total exposure of 19,680 W·h/m2.

Fig. 2
figure 2

a Experimental setup. b SPDs of nine narrowband LEDs

Data measurement

Before and after the accelerated aging experiment, the infrared spectra of silk samples were measured non periodically. Considering that the damage rate tends to change from fast to slow [7], intensive measurements were carried out in the early stage of the experiment. A total of 19 measurements was taken plus the one before irradiation.

The infrared spectrum was measured using FTIR Spectrometer (Bruker INVENIO-R), which is equipped with DLATGS detector, an accessary of Attenuated Total Reflectance (ATR), and diamond crystal. The spectrum was obtained in absorbance, recorded from 400 to 4000 cm-1, with 4 cm-1 resolution and 32 scans. The atmospheric background at the time was collected and deducted for each spectrum. By marking the ATR accessary platform and using its relative position with samples, the repeatability of each measurement was ensured as far as possible.

Taking the silk sample with pH 5.0 as an example, a detailed explanation of its infrared spectrum before irradiation is provided, as shown in Fig. 3. The silk substrate is composed of proteins, containing 18 amino acids, mainly glycine (Gly), alanine (Ala), serine (Ser), tyrosine (Tyr), etc. The two peptide chains of -Gly-Gly- and -Gly-Ala- form the main sequence of amino acids, with infrared spectral peaks at 1001 cm-1 and 976 cm-1, respectively [33]. 1160 cm-1 is attributed to the vibration of tyrosine phenolic hydroxyl groups [34]. These amino acids form a specific secondary structure of silk proteins, with infrared spectral peaks at 1230–1265 cm-1 (amide III, mainly C=O and C–N stretching vibration), 1515–1520 cm-1 (amide II, mainly N-H bending vibration), and 1600–1690 cm-1 (amide I, mainly C=O and C–N stretching vibration) [35,36,37]. In addition, there is infrared spectral peak attributed to N-H stretching vibration at 3290–3300 cm-1 [35]. Therefore, the infrared spectral peaks within the aforementioned wavenumber range are of concern for the analysis of lighting damage to silk samples.

Fig. 3
figure 3

Infrared spectrum of the silk sample with pH 5.0

Data analysis

The measured data of each silk sample are presented as a high-dimensional dataset of infrared spectra, which was analyzed by PCA. Before the calculation, the infrared spectral dataset of each sample was averaged and centralized, which ensures them explained by changes around the mean without changing their statistical structure [38, 39]. After the calculation using SIMCA 14.1 (Umetrics, Sweden), the corresponding principal components were obtained for each sample’s dataset. There is the highest interpretation variance in PC1 and PC2, whose score values can be used to obtain the score plot in order to observe the distribution trend of silk samples before and after the irradiation. The loading plot can be used to explain the reason for the change in the score vector, i.e., the change in the infrared spectra characterizing lighting damage.

Taking the silk sample with pH 5.0 under the irradiation of 595 nm narrowband LED as an example, the score and loading plots are explained in Fig. 4, which is similar to those under other narrowband LEDs. According to Fig. 4a, the points projected onto the principal component space by the samples pre-irradiation and post-irradiation are well separated along the PC1-axis, which means that there is lighting damage during the accelerated aging experiment. Meanwhile, the interpretation variance of PC1 has exceeds 90%. Thus, changes in the score value (ΔPC1) can be used to characterize the lighting damage to silk samples. In the loading plot in Fig. 4b, the wavenumber range with higher loading weights can be used to explain the infrared spectral changes captured by PC1, i.e., changes in the molecular structure that cause lighting damage to silk samples. Within this wavenumber range, ~ 1000 cm−1, 1160 cm−1, 1230–1265 cm−1, and 3290–3300 cm−1 belongs to the -Gly-Gly- and -Gly-Ala- peptide chains, tyrosine, amide III, and N-H stretching vibrations, respectively. Among them, the infrared spectral peak intensity ratio of I1264/I1231 in the amide III is usually used for calculating crystallinity [33]. Research has shown that the crystalline region of silk protein is surrounded by some disordered peptide chains, forming an amorphous region [40]. Considering that the crystalline and amorphous regions respectively endow silk with strength and elasticity, crystallinity is considered an important factor related to the mechanical properties of silk [41], whose change usually results from the weakening or destruction of hydrogen bonding between peptide chains [37]. In addition, the color change of silk is closely related to the photodegradation of tyrosine [42]. Therefore, the spectral change information extracted from the infrared spectral dataset by PCA can scientifically reflect the change in molecular structures that causes the lighting damage to silk samples, thus verifying the reliability of this method.

Fig. 4
figure 4

a Score plot and b Loading plot of pH 5.0 silk sample under the irradiation of 595 nm narrowband LED (numbers 0 and 1–18 represent 19 tests before and after irradiation, respectively)

Results and discussion

Variation of spectral responsivity of acidified silk substrates

Based on PCA calculation, changes in the score value (ΔPC1) were used to characterize the variation of lighting damage with exposure. As the exposure increases, the lighting damage to silk samples increase, with the rate decreasing. Thus, using the Eq. (2) of the power function, the mathematical models of lighting damage variation of silk samples with different acidification degrees with exposure were fitted, as shown in Fig. 5, whose goodness of fit R2 is between 0.85–0.98.

$$D={\text{a}} \cdot {Q^b}$$
(2)

where D represents lighting damage, a and b are constants, and Q is exposure.

Fig. 5
figure 5

Mathematical models of lighting damage variation of silk samples with a pH 3.5, b pH 4.0, c pH 5.0, and d pH 6.0 with exposure

As shown in Fig. 6, taking silk samples with pH 5.0 as an example, the curve of its damage rate and exposure was obtained through derivative. It can be found that the damage rate decreases rapidly with increasing exposure, and finally approaches zero. That is to say, there is a damage inflection point, where the lighting damage has tended to remain unchanged. When the damage rate of nine narrowband LEDs to silk samples approaches zero, that is, reaching the exposure corresponding to the damage inflection point, the spectral responsivity is basically stable. For the ancient relics, although its historical information before the excavation cannot be verified, it is generally believed that the silk substrate has experienced a certain degree of damage, resulting in a relatively slow damage rate at present. On the premise of meeting the universality requirements brought about by the huge stock of relics, in order to make the results obtained in the accelerated aging experiment appliable to the silk substrate in relics, the lighting damage characterized by the corresponding exposure of the damage inflection point should be considered to further analyze its spectral responsivity. For the determination of the damage inflection point, we can refer to the derivation method proposed by Svensson et al. [26]. When the derivative of damage rate is less than 2 × 10–12 (noise level), it can be judged that the damage inflection point is reached. The exposure corresponding to the damage inflection point of silk samples with different acidification degrees is shown in Table 2.

Fig. 6
figure 6

Mathematical models of lighting damage rate variation of silk samples at pH 5.0 with exposure

Table 2 Exposure corresponding to the damage inflection point of silk samples with different acidification degrees

The lighting damage coefficients of silk samples at the beginning, inflection point, and end of the experiment were calculated according to Fig. 5, which were normalized based on the coefficients of the 447 nm narrowband LED. The obtained relative lighting damage coefficients were used to plot the spectral response curves of silk samples with different degrees of acidification under three exposure levels, as shown in Fig. 7. It can be seen that, compared with the beginning exposure (20 W·h/m2), the response curves of silk samples at the exposure corresponding to the damage inflection point has largely approached the curve at the end (19,860 W·h/m2). Thus, the spectral responsivity at the inflection point can be regarded as relatively stable.

Fig. 7
figure 7

Response curves of silk samples with a pH 3.5, b pH 4.0, c pH 5.0, and d pH 6.0 under the three exposure levels

Spectral responsivity function of acidified silk substrates and its application

Based on the relative lighting damage coefficient at the damage inflection point in Fig. 7, the spectral responsivity of acidified silk samples was further discussed, whose mathematical model was drawn as Fig. 8. It can be found that the higher the λ and pH value is, the lower the lighting damage is. Accordingly, the spectral responsivity function of the silk samples was fitted as Eq. (3), whose goodness of fit R2 is 0.8702. On that basis, the lighting damage to silk substrates with any degree of acidification can be calculated under the irradiation of light sources with any spectral composition.

$$\begin{aligned} f\left( {\lambda ,pH} \right) = & \,\,12.16 - 0.2481 \cdot \lambda + 19.97 \cdot pH + 0.0008413 \cdot \lambda ^{2} - 0.05381 \cdot \lambda \cdot pH - 2.217 \cdot pH^{2} \\ & - \,1.106e - 06 \cdot \lambda ^{3} + 6.473e - 05 \cdot \lambda ^{2} \cdot pH + 0.002829 \cdot \lambda \cdot pH^{2} + 0.07976 \cdot pH^{3} + 4.965e - 10 \cdot \lambda ^{4} \\ & - 1.964e - 08 \cdot \lambda ^{3} \cdot pH - 3.01e - 06 \cdot \lambda ^{2} \cdot pH^{2} + 6.918e - 05 \cdot \lambda \cdot pH^{3} \\ \end{aligned}$$
(3)
Fig. 8
figure 8

Spectral responsivity mathematical model of silk samples with different acidification degrees

Considering the characteristics of the relics, it consists of two parts, blank substrates and colorants covering the substrate. Compared with the complex damage form in the former, the lighting damage of the latter is presented as a change in color, which is commonly evaluated by the color difference. In combination with the spectral responsivity function of colorants proposed by Dang et al. [10], it is feasible to propose an evaluation method for lighting damage to the relics. The specific process is as follows.

  1. (1)

    Determining essential information of the relics.

First, the colorants and substrates in relics should be determined by characteristic peaks of Raman spectroscopy and infrared spectroscopy [43, 44];Calculating spectral responsivity function of second, the respective weights Wc and Ws should be determined by counting the pixel numbers of both [45]; finally, the pH value of silk substrates should be measured.

  1. (2)

    Calculating spectral responsivity function of relics.

First, the corresponding spectral responsivity function Dc(λ) and Ds(λ,pH) should be obtained respectively according to the composition of colorants and silk substrates in relics, substituting the measured pH value in the latter; second, the spectral responsivity function of relics should be calculated according to the Eq. (4).

$$f(\lambda ,pH)={W_{\text{c}}} \cdot {f_c}(\lambda )+{W_s} \cdot {f_s}(\lambda ,pH)$$
(4)
  1. (3)

    Calculating lighting damage to relics.

The technical report of CIE defines the lighting damage of relics as a function of exposure and spectral responsivity, only considering the lighting damage from the perspective of light sources [7]. However, this study verified that the degree of acidification, a material property, also has an impact on the spectral response of silk substrates, which further affects the relics.

First, the S(λ) (i.e., SPD) of the light source should be measured with the PR-670 spectroradiometer; second, according to Eq. (5), the lighting damage D to relics can be calculated.

$$D=Q \cdot \int_{{380}}^{{780}} {S(\lambda )} \cdot f(\lambda ,pH)d\lambda$$
(5)

Discussions

The results indicate that the spectral responsivity of silk substrates exhibits the rule that damage decreases with increasing wavelength. It is consistent with the research results of Dang [14] and Wang [46] on silk substrates in the visible light range, although they chose the color difference as the evaluation indicator. The results of this study indicate that the spectral responsivity of silk substrates with different degrees of acidification varies, which means that it is necessary to consider its own degree of acidification when calculating the lighting damage to silk substrates.

Relics, visitors, and staff, as the main elements of museums, have different concerns about the lighting environment, which brings it with the multi-objective lighting requirements. The current study and application trend is to prioritize reducing lighting damage while also improving their lighting quality and efficiency [2, 6]. Gargano proposed a new lighting method by reducing the spectral composition absorbed by materials in the light source, which not only reduces lighting damage but also improves lighting efficiency [47]. Lucchi proposed a simplified evaluation method that assesses the environmental and energy quality of museums, identifies potential risks, and determines conservation methods. Among them, lighting is an important cause of damage to the relics [5]. The lighting damage evaluation is the theoretical basis for carrying out conservation practices from the perspective of lighting. It achieves a reasonable selection of lighting sources by comparing the lighting damage caused by them on the relics, especially in the context of LEDs with flexible spectral compositions widely used in museums.

Durmus proposed a voxelated 3-D volume to balance the multi-objective lighting requirements, which quantifies the volume formed by indicators such as lighting damage, lighting quality, and lighting efficiency to compare the performance of different light sources. This method involves establishing a universal damage calculation model (DCM) [48]. The scholar pointed out that various non-invasive spectral technologies (such as FTIR, hyperspectral imaging, etc.) can bring new perspectives to the evaluation of lighting damage to materials [48, 49]. The lighting damage evaluation method proposed in this study can be used to establish a DCM, whose key lies in the spectral responsivity function of the two components of substrates and colorants. The limitations of color difference for evaluating lighting damage to substrates can be addressed by the proposed method of FTIR combined with PCA. However, the color difference as a commonly used method for colorants also has well-documented limitations, such as abnormal hue angle shifts [48], and transformation needs before interpreting and comparing the results in different studies using different color difference formulas [13, 50]. In fact, the reflection spectrum of a material reflects its physical property in terms of spectral selective absorption and reflection of incident light, which is the basis for color measurement and calculation such as color difference [51]. The reflection spectrum, as the “fingerprint” spectrum of the color of colorants, fundamentally determines the color change and can be used to accurately identify its color damage. It is feasible to evaluate the lighting damage to colorants using a spectral quantification method combining reflection spectra with PCA in the subsequent studies. It is worth noting that since the late 1990s, micro fading spectroscopy has been developed to evaluate the stability of colorants. On that basis, the reflection spectrum can be effectively measured to evaluate the lighting damage in a very short time, which is a significant advantage compared to traditional accelerated aging experiments that typically require longer time [52].

Conclusions

In this study, based on the accelerated aging experiment of nine narrowband LEDs in the visible range on silk samples with different acidification degrees, the infrared spectrum was measured non periodically, whose data processing was carried out with PCA. The ΔPC1 calculated was used to evaluate the lighting damage. Based on the derivation of the damage rate, the damage inflection point and the corresponding exposure were determined. On that basis, the spectral responsivity of silk substrates with different acidification degrees was quantized to obtain its function, namely, the mathematical relationship between lighting damage and λ and pH values. In combination with the previous study on the spectral responsivity of colorants, an evaluation method for lighting damage was proposed, which is the key to the conservation of calligraphy and painting relics.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. National Cultural Heritage Administration of the People’s Republic of China. The 14th five-year plan for cultural relics safety prevention and control. 2022.

  2. Lucchi E. Review of preventive conservation in museum buildings. J Cult Herit. 2018;29:180–93. https://doi.org/10.1016/j.culher.2017.09.003.

    Article  Google Scholar 

  3. Lucchi E. Environmental risk management for museums in historic buildings through an innovative approach: a case study of the Pinacoteca di Brera in Milan (Italy). Sustainability. 2020;12(12):5155. https://doi.org/10.3390/su12125155.

    Article  Google Scholar 

  4. Lucchi E. Multidisciplinary risk-based analysis for supporting the decision making process on conservation, energy efficiency, and human comfort in museum buildings. J Cult Herit. 2016;22:1079–89. https://doi.org/10.1016/j.culher.2016.06.001.

    Article  Google Scholar 

  5. Lucchi E. Simplified assessment method for environmental and energy quality in museum buildings. Energy Build. 2016;117:216–29. https://doi.org/10.1016/j.enbuild.2016.02.037.

    Article  Google Scholar 

  6. Tan H, Dang R. Review of lighting deterioration, lighting quality, and lighting energy saving for paintings in museums. Build Environ. 2022;208:108608. https://doi.org/10.1016/j.buildenv.2021.108608.

    Article  Google Scholar 

  7. Commission Internationale de lÉclairage (CIE). Control of damage to museum objects by optical radiation. Technical Report No. 157. : 2004. Vienna, 2004.

  8. China National Standardization Administration. Code for lighting design of museum. Technical Report No. GB/T 23863-2009  Beijing: China Standard Press; 2009.

    Google Scholar 

  9. Illuminating Engineering Society. Recommended practice lighting museum. New York: Technical Report No. RP-30-20; 2020.

    Google Scholar 

  10. Dang R, Wang BP, Song XY, et al. The mathematical expression of damage law of museum lighting on dyed artworks. Sci Rep. 2021;11(1):10951. https://doi.org/10.1038/s41598-021-90520-z.

    Article  CAS  Google Scholar 

  11. Mayorga Pinilla S, Vázquez D, Álvarez, Fernández-Balbuena A et al. Spectral damage model for lighted museum paintings: Oil, acrylic and gouache. J Cult Herit. 2016; 22: 931–939; doi: https://doi.org/10.1016/j.culher.2016.05.005.

  12. Matthias F, Marcello B, Oliver H. Light damage to selected organic materials in display cases: a study of different light sources. Stud Conserv. 2016;61(suppl):83–93. https://doi.org/10.1179/2047058414Y.0000000148.

    Article  Google Scholar 

  13. Liu Y, Fearn T, Strlič M. Spectral sensitivity of the discoloration of historical rag paper. Talanta Open. 2021;4:100058. https://doi.org/10.1016/j.talo.2021.100058.

    Article  Google Scholar 

  14. Dang R, Zhang FH, Yang D, et al. Spectral damage model for lighting paper and silk in museum. J Cult Herit. 2020;45(0):249–53. https://doi.org/10.1016/j.culher.2020.03.001.

    Article  Google Scholar 

  15. Halvorson BG, Kerr N. Effect of light on the properties of silk fabrics coated with parylene-C. Stud Conserv. 1994;39(1):45–56. https://doi.org/10.1179/sic.1994.39.1.45.

    Article  CAS  Google Scholar 

  16. Dang R, Tan HJ, Gang L, et al. Effects of illumination on paper and silk substrates of traditional chinese painting and calligraphy measured with Raman spectroscopy. LEUKOS. 2019;16(1):87–95. https://doi.org/10.1080/15502724.2019.1570851.

    Article  Google Scholar 

  17. Casadio F, Toniolo L. The analysis of polychrome works of art: 40 years of infrared spectroscopic investigations. J Cult Herit. 2001;2(1):71–8. https://doi.org/10.1016/S1296-2074(01)01107-4.

    Article  Google Scholar 

  18. Kong JL, Yu SN. Fourier transform infrared spectroscopic analysis of protein secondary structures. ACTA Bioch Bioph Sin. 2007;39(8):549–59. https://doi.org/10.1111/j.1745-7270.2007.00320.x.

    Article  CAS  Google Scholar 

  19. Koperska MA, Pawcenis D, Bagniuk J, et al. Degradation markers of fibroin in silk through infrared spectroscopy. Polym Degrad Stabil. 2014;105:185–96. https://doi.org/10.1016/j.polymdegradstab.2014.04.008.

    Article  CAS  Google Scholar 

  20. Dang R, Wang JX, Zhang T. Optimal LED spectrum for lighting chinese paper cultural relics in museums. J Cult Herit. 2021;51:81–96. https://doi.org/10.1016/j.culher.2021.07.006.

    Article  Google Scholar 

  21. Carlesi S, Ricci M, Cucci C, et al. Multivariate analysis of combined reflectance FT-NIR and micro-raman spectra on oil-paint models. Microchem J. 2016;124(0):703–11. https://doi.org/10.1016/j.microc.2015.10.023.

    Article  CAS  Google Scholar 

  22. Muehlethaler C, Massonnet G, Esseiva P. The application of chemometrics on infrared and Raman spectra as a tool for the forensic analysis of paints. Forensic Sci Int. 2011;209(1–3):173–82. https://doi.org/10.1016/j.forsciint.2011.01.025.

    Article  CAS  Google Scholar 

  23. Bacci M, Fabbri M, Picollo M, et al. Non-invasive fibre optic Fourier transform-infrared reflectance spectroscopy on painted layers: identification of materials by means of principal component analysis and Mahalanobis distance. Anal Chim Acta. 2001;446(1–2):15–21. https://doi.org/10.1016/S0003-2670(01)01057-1.

    Article  CAS  Google Scholar 

  24. Chen Y, Zhang TN, Guo P, et al. Quantitative analysis for nonlinear fluorescent spectra based on principal component analysis. Acta Optica Sinica. 2009;29:1285–91.

    Article  CAS  Google Scholar 

  25. Svensson O, Josefson M, Langkilde FW. Reaction monitoring using Raman spectroscopy and chemometrics. Chemometr Intell Lab. 1999;49(1):49–66. https://doi.org/10.1016/S0169-7439(99)00025-8.

    Article  CAS  Google Scholar 

  26. Svensson O, Josefson M, Langkilde FW. The synthesis of metoprolol monitored using Raman spectroscopy and chemometrics. Eur J Pharm Sci. 2016;11(2):141–55. https://doi.org/10.1016/S0928-0987(00)00094-4.

    Article  Google Scholar 

  27. Leksophee T, Supansomboon S, Sombatsompop N. Effects of crosslinking agents, dyeing temperature, and pH on mechanical performance and whiteness of silk fabric. J Appl Polym Sci. 2004;91(2):1000–7. https://doi.org/10.1002/app.13256.

    Article  CAS  Google Scholar 

  28. Garside P, Wyeth P, Zhang XM. The inherent acidic characteristics of silk, part II - weighted silks. e-Preservation Sci. 2010;7:126–31.

    CAS  Google Scholar 

  29. Li J. Study of ink absorption property on silk for painting. Suzhou: Soochow University; 2014.

    Google Scholar 

  30. Kim JJ, Zhang XM, Wyeth P. The inherent acidic characteristics of aged silk. e-Preservation Sci. 2008;5:41–6.

    CAS  Google Scholar 

  31. Pastorelli G, Cao S, Cigić IK, et al. Development of dose-response functions for historic paper degradation using exposure to natural conditions and multivariate regression. Polym Degrad Stabil. 2019;168:108944. https://doi.org/10.1016/j.polymdegradstab.2019.108944.

    Article  CAS  Google Scholar 

  32. China National Standardization Administration. Textiles - determination of pH of aqueous extract. Technical Report No. GB/T 7573 – 2009. Beijing: China Standard Press; 2009.

    Google Scholar 

  33. Arai T, Freddi G, Innocenti R, et al. Biodegradation of bombyx mori silk fibroin fibers and films. J Appl Polym Sci. 2010;91(4):2383–90. https://doi.org/10.1002/app.13393.

    Article  CAS  Google Scholar 

  34. Shao J, Zheng J, Liu J, et al. Fourier transform Raman and Fourier transform infrared spectroscopy studies of silk fibroin. J Appl Polym Sci. 2010;96(6):1999–2004. https://doi.org/10.1002/app.21346.

    Article  CAS  Google Scholar 

  35. Zhang XM, Yuan SX. Research on the infrared spectrometry of aging silk fabrics. Spectrosc Spect Anal. 2004;24:1528–32.

    CAS  Google Scholar 

  36. Tsuboi Y, Ikejiri T, Shiga S, et al. Light can transform the secondary structure of silk protein. Appl Phys A-Mater. 2001;73(5):637–40. https://doi.org/10.1007/s003390100984.

    Article  CAS  Google Scholar 

  37. Gong DC, Liu L, Zhu Y. Application of infrared spectroscopy in characterizing fiber aggregation structure of ancient silk fabrics. Sericultural Sci. 2015; (4): 694–700.

  38. Cséfalvayová L, Strlič M, Karjalainen H. Quantitative NIR chemical imaging in heritage science. Anal Chem. 2011;83(13):5101–6. https://doi.org/10.1021/ac200986p.

    Article  CAS  Google Scholar 

  39. Marengo E, Manfredi M, Zerbinati O, et al. Development of a technique based on multi-spectral imaging for monitoring the conservation of cultural heritage objects. Anal Chim Acta. 2011;706(2):229–37. https://doi.org/10.1016/j.aca.2011.08.045.

    Article  CAS  Google Scholar 

  40. Luxford N. Reducing the risk of open display optimising the preventive conservation of historic silks. Southampton: University of Southampton; 2009.

    Google Scholar 

  41. Timar-Balazsy A, Eastop D. Chemical principles of textile conservation. Abingdon: Taylor and Francis Group; 1998.

    Google Scholar 

  42. Wang Z, Chen W, Cui Z, et al. Studies on photoyellowing of silk fibroin and alteration of its tyrosine content. J Text I. 2016;107(4):413–9. https://doi.org/10.1080/00405000.2015.1034933.

    Article  CAS  Google Scholar 

  43. Vahur S, Teearu A, Leito I. ATR-FT-IR spectroscopy in the region of 550 – 230 cm-1 for identification of inorganic pigments. Spectrochim Acta A. 2009;75(3):1061–72. https://doi.org/10.1016/j.saa.2009.12.056.

    Article  CAS  Google Scholar 

  44. Burgio L, Clark RJH. Library of FT-Raman spectra of pigments, minerals, pigment media and varnishes, and supplement to existing library of Raman spectra of pigments with visible excitation. Spectrochim Acta A. 2001;57(7):1491–521. https://doi.org/10.1016/S1386-1425(00)00495-9.

    Article  CAS  Google Scholar 

  45. Dang R, Tan HJ, Wang N, et al. Raman spectroscopy-based method for evaluating LED illumination-induced damage to colorants in high-light-sensitivity art. Appl Opt. 2020;59(15):4599–605. https://doi.org/10.1364/AO.379398.

    Article  Google Scholar 

  46. Wang YL, Zhao F, Tu HX. Light- aging experiment on silk. Sci Conserv Archaeol. 2006;18(1):9–16.

    Google Scholar 

  47. Gargano M, Scotuzzi S, Angelin EM, et al. A new lighting method for cultural materials using selective chromatic light. Lighting Res Technol. 2022;54(3):287–305. https://doi.org/10.1177/14771535211025404.

    Article  Google Scholar 

  48. Durmus D. Characterizing color quality, damage to artwork, and light intensity of multi-primary LEDs for museums. Heritage. 2021;4(1):188–97. https://doi.org/10.3390/heritage4010011.

    Article  Google Scholar 

  49. Fernandez-Balbuena AA, Vazquez-Molini D. Optical technologies applied to cultural heritage. Heritage. 2022;5(1):359–61. https://doi.org/10.3390/heritage5010020.

    Article  Google Scholar 

  50. Degani L, Gulmini M, Piccablotto G, et al. Stability of natural dyes under light emitting diode lamps. J Cult Herit. 2017;26:12–21. https://doi.org/10.1016/j.culher.2017.02.002.

    Article  Google Scholar 

  51. Villmann B, Weickhardt C. Wavelength dependence of light induced changes in reflectance spectra of selected dyes and pigments. Stud Conserv. 2018;63(2):104–12. https://doi.org/10.1080/00393630.2017.1345088.

    Article  CAS  Google Scholar 

  52. Del Hoyo-Meléndez JM, Mecklenburg MF. Micro-fading spectrometry: a tool for real-time assessment of the light-fastness of dye/textile systems. Fiber Polym. 2012;13(8):1079–85. https://doi.org/10.1007/s12221-012-1079-7.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by the Instructional Reform Item of Tianjin University of Technology (Grant number ZD20-17).

Author information

Authors and Affiliations

Authors

Contributions

ZW was the project leader and was a major contributor in writing the manuscript. ZF performed the acquisition, analysis, and interpretation of data. HT designed the work and substantively revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Huijiao Tan.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wei, Z., Feng, Z. & Tan, H. Key to the conservation of calligraphy and painting relics in collection: proposing a lighting damage evaluation method. Herit Sci 11, 102 (2023). https://doi.org/10.1186/s40494-023-00945-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40494-023-00945-0

Keywords