A non-destructive method for detecting the degree of aging of a parchment

Abstract

The embodiment of the application provides a kind of nondestructive testing method of the aging degree of the parchments, it is related to the technical field of cultural relics detection, the method comprises: the parchments leaf sample is grouped and is fast aging treatment to obtain the standard sample of different aging degree, obtains the corresponding bending strength before aging, bending strength after aging and near infrared spectrum after aging of each parchments leaf sample group;Bending index retention rate is calculated according to the bending strength before aging and the bending strength after aging, and near infrared data model is constructed according to bending index retention rate and near infrared spectrum after aging;If the error between the actual aging degree of standard sample and the predicted bending index retention rate is less than the preset error, then near infrared data model is used as parchments aging degree prediction model;The actual near infrared spectrum of parchments leaf to be detected is obtained, and the actual near infrared spectrum is input into near infrared data model to obtain predicted bending index retention rate.The application can realize the nondestructive aging detection of parchments.

Description

Technical Field

[0001] This invention relates to the field of cultural relic testing technology, and more specifically, to a non-destructive testing method for the degree of aging of palm leaves. Background Technology

[0002] Palm-leaf manuscripts are a form of written literature written on the leaves of palm trees or similar palm species. During long-term preservation and use, palm-leaf manuscripts are affected by various environmental factors, resulting in degradation and aging, leading to decreased mechanical strength, brittleness, and even breakage of the leaves. Accurately assessing the degree of aging is fundamental to evaluating the preservation status of palm-leaf manuscripts and developing conservation measures. Given the precious value and non-renewable nature of these artifacts, a non-destructive testing method is needed to assess the aging status of the palm-leaf manuscript leaves. Existing methods for assessing the aging of palm-leaf manuscripts typically require sampling, which can cause irreversible damage. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a non-destructive testing method for the aging degree of palm leaves, aiming to solve the problems in the prior art. Based on the characteristic that the bending strength of plant leaves decreases after aging, this invention prepares aging samples of different degrees, establishes a test model for the retention rate of bending strength of palm leaf material based on near-infrared detection technology, and combines the correlation between near-infrared spectroscopy and the degree of aging and the retention rate of bending strength to quickly and non-destructively determine the aging degree of palm leaf artifacts.

[0004] To achieve the aforementioned objective, the technical solution adopted in the embodiments of the present invention is as follows: This invention provides a non-destructive testing method for the degree of aging of beech leaves, comprising: Leaf samples of *Begonia spp.* were prepared by selecting leaves from *Begonia spp.* in Xishuangbanna, Yunnan Province. After grouping and rapid aging treatment, multiple standard samples with different aging degrees were obtained. The bending strength before aging, bending strength after aging, and near-infrared spectrum of each standard sample were obtained. The flexural index retention rate is calculated based on the flexural strength before aging and the flexural strength after aging, and a near-infrared data model is constructed based on the flexural index retention rate and the near-infrared spectrum after aging. If the error between the actual aging degree of the standard sample and the predicted flexural index retention rate is less than the preset error, then the near-infrared data model will be used as the Bayeux aging degree prediction model. The actual near-infrared spectrum of the leaf blade to be tested is obtained, and the actual near-infrared spectrum is input into the aging degree prediction model of the leaf blade. The predicted bending index retention rate corresponding to the actual near-infrared spectrum is output.

[0005] In an optional implementation, the rapid aging process includes: Obtain multiple groups of Betelgeuse blade samples and the pre-aging bending strength of each group of Betelgeuse blade samples; Each of the aforementioned Betelgeuse leaf sample groups was subjected to different degrees of aging treatment to obtain multiple Betelgeuse leaf aged sample groups.

[0006] In an optional implementation, the step of subjecting each of the *Begonia spp.* leaf sample groups to different degrees of aging treatment to obtain multiple *Begonia spp.* leaf aging standard sample groups includes: Each of the aforementioned beech leaf sample groups was immersed in an alkaline aging solution of the same concentration. When the soaking time of each of the Betel leaf sample groups reaches the preset time, the sample groups are washed and dried to obtain multiple aged Betel leaf sample groups, each with a different preset time.

[0007] In an optional embodiment, the step of calculating the flexural index retention rate based on the flexural strength before aging and the flexural strength after aging includes: Obtain the flexural strength before aging and the flexural strength after aging for each of the standard samples; Calculate the ratio of the flexural strength after aging to the flexural strength before aging for each standard sample to obtain the flexural index retention rate for each standard sample.

[0008] In an optional implementation, the step of constructing a near-infrared data model based on the flexural index retention rate and the near-infrared spectrum after aging includes: Based on the flexural index retention rate and the near-infrared spectrum after aging corresponding to the standard sample, the correspondence between the flexural index retention rate and the near-infrared spectrum after aging is determined. An initial data model is constructed, and the parameters of the initial data model are adjusted according to the flexural index retention rate, the near-infrared spectrum after aging, and the corresponding relationship to obtain the near-infrared data model.

[0009] In an optional implementation, the step of determining the actual aging degree of the palm leaf to be tested based on the actual near-infrared spectrum includes: The actual near-infrared spectrum of the standard sample is input into the near-infrared data model, and the predicted flexural index retention rate corresponding to the actual near-infrared spectrum is output and compared with the actual flexural index retention rate of the standard sample for verification. If the error between the actual aging degree and the predicted bending index retention rate is less than a preset error, then the near-infrared data model will be used as the Bayeux aging degree prediction model.

[0010] In an optional implementation, the method further includes: Near-infrared spectral data of real palm leaf manuscripts were collected non-destructively using fiber optic probes. The corresponding bending strength retention rate was predicted by the palm leaf manuscript aging degree prediction model, which served as an indicator for quantitatively evaluating the aging degree of real manuscripts.

[0011] This invention provides a method for detecting the aging of palm leaves. Palm leaf samples are prepared using palm leaves and aged to different degrees. The bending strength of the samples before and after aging, as well as their near-infrared spectra, are obtained. The bending index retention rate is calculated based on the bending strength before and after aging. A near-infrared data model is constructed based on the correspondence between the bending index retention rate and the near-infrared spectrum after aging. The near-infrared spectrum of actual leaf samples and the actual bending index retention rate are used to verify the near-infrared data model's prediction of the bending index retention rate, confirming that the error in the bending index retention rate is within an acceptable range. Then, based on the near-infrared spectrum of the actual palm leaf sample and its actual aging degree, the method is further verified by comparing the actual and predicted values ​​of the bending index retention rate. Finally, it is determined that the near-infrared data model can be used for non-destructive and quantitative aging detection of palm leaves.

[0012] To make the objectives, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A flowchart of a non-destructive testing method for the degree of aging of bay leaves provided by an embodiment of the present invention is shown; Figure 2 This diagram illustrates the retention rate of bending strength of blades at different aging degrees, as provided in an embodiment of the present invention. Figure 3 A flowchart of a near-infrared data prediction model construction method provided by an embodiment of the present invention is shown; Figure 4 The flowchart illustrates a method for evaluating a Bayeton leaf aging degree prediction model provided by an embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0016] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0017] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0018] Please refer to Figure 1 , Figure 1 A flowchart of a non-destructive testing method for the aging degree of bay leaves provided in this embodiment is included. The method includes: S101. Select leaves of the palm tree from Xishuangbanna, Yunnan Province to prepare palm leaf samples. After grouping and rapid aging treatment, multiple standard samples with different aging degrees are obtained. Obtain the bending strength before aging, bending strength after aging, and near-infrared spectrum of each standard sample.

[0019] Palm leaves are the raw material for making palm leaf scriptures. When making palm leaf scripture leaf samples, multiple palm leaves can be collected, and each palm leaf leaf can be cut into multiple palm leaf scripture leaf sample groups. One palm leaf scripture leaf sample is selected from each palm leaf scripture leaf sample group for bending strength testing. Then, the palm leaf scripture leaf sample groups with the same bending strength are retained. This bending strength is the bending strength of all palm leaf scripture leaf sample groups before aging, thus obtaining multiple identical palm leaf scripture leaf sample groups.

[0020] Then, each group of leaf samples was subjected to different degrees of aging treatment, and the aging bending strength and near-infrared spectrum of each aging leaf sample group were obtained.

[0021] The bending strength can be obtained by performing a bending test on the Betelgeuse leaf sample using thermomechanical analysis. The bending data is then calculated using a three-point bending formula to obtain the bending strength before and after aging. Near-infrared spectral data can be obtained by collecting spectral information from the Betelgeuse leaf surface using a near-infrared fiber optic probe, yielding the near-infrared spectral data of the aged Betelgeuse leaf sample.

[0022] S102. Calculate the bending index retention rate based on the bending strength before aging and the bending strength after aging, and construct a near-infrared data model based on the bending index retention rate and the near-infrared spectrum after aging.

[0023] Bending strength retention rate is the ratio of bending strength after aging to bending strength before aging, and this ratio can be considered as a reflection of the degree of aging. Under alkaline aging conditions, the bending strength of Betelgeuse leaves tends to decrease; therefore, the bending strength retention rate and the degree of aging of Betelgeuse leaves show a certain positive correlation. While non-destructive near-infrared spectroscopy of Betelgeuse leaves can also detect the degree of aging, comparative and quantitative analysis methods are lacking.

[0024] Therefore, after calculating the bending index retention rate of the leaf sample groups of Betelgeuse with different aging degrees, a near-infrared data model can be established based on the bending index retention rate and the near-infrared spectrum after aging, so as to realize the mutual verification between the bending index retention rate and the near-infrared spectrum.

[0025] S103. If the error between the actual aging degree of the standard sample and the predicted bending index retention rate is less than the preset error, then the near-infrared data model is used as the Bayeux aging degree prediction model.

[0026] By analyzing and comparing the error between the actual aging degree and the predicted flexural index retention rate, and confirming that the error is less than the preset error, the near-infrared data model can be used as the Bayeux aging degree prediction model.

[0027] S104. Obtain the actual near-infrared spectrum of the leaf blade to be tested, input the actual near-infrared spectrum into the aging degree prediction model of the leaf blade, and output the predicted bending index retention rate corresponding to the actual near-infrared spectrum.

[0028] Real near-infrared spectral data of bay leaves are collected under non-destructive conditions using fiber optic probes. After optimization and processing, the actual near-infrared spectrum is input into the near-infrared data model. Based on the correspondence between the near-infrared spectrum and the bending index retention rate, the predicted bending index retention rate corresponding to the actual near-infrared spectrum can be output.

[0029] This embodiment involves aging palm leaf samples used to make palm leaf scriptures to different degrees, obtaining the bending strength of the leaf samples before aging, the bending strength after aging, and the near-infrared spectrum after aging. The bending index retention rate is calculated based on the bending strength before and after aging. A near-infrared data model is constructed based on the correspondence between the bending index retention rate and the near-infrared spectrum after aging. The near-infrared data model is verified by comparing the actual bending index retention rate of the leaf samples with the predicted bending index retention rate output by the model. Finally, it is determined that the error between the degree of aging and the bending index retention rate is within an acceptable range. Then, the near-infrared spectrum of the actual palm leaf scripture to be tested is obtained non-destructively, and the predicted bending index retention rate is obtained using the near-infrared data model, thereby achieving non-destructive and quantitative aging detection of palm leaf scripture.

[0030] In one embodiment, the rapid aging process includes: Obtain multiple identical Betulacean blade sample groups and the pre-aging bending strength of each Betulacean blade sample group, wherein the pre-aging bending strength of each Betulacean blade sample group is the same; Each of the aforementioned Betelgeuse leaf sample groups was subjected to different degrees of aging treatment to obtain multiple Betelgeuse leaf aged sample groups.

[0031] The leaf samples of Betel leaves can be aged by immersing them in an aging solution, which can be a 5% NaOH solution.

[0032] For example, there are 8 groups of *Bretschneidera sinensis* leaf samples, each group containing 10 samples. Each group of *Bretschneidera sinensis* leaf samples is soaked for a preset time, which is 10 min, 20 min, 30 min, 40 min, 50 min, 60 min, 90 min, and 120 min. After each group of *Bretschneidera sinensis* leaf samples has been soaked for the preset time, it is washed with distilled water until the pH is neutral, and then dried to obtain multiple groups of aged *Bretschneidera sinensis* leaf samples.

[0033] This embodiment obtains multiple different aged sample groups of Betelgeuse leaf blades by aging the newly prepared Betelgeuse leaf blade sample groups to different degrees, thus providing sample support for the subsequent calculation of the bending index retention rate.

[0034] In one embodiment, the step of calculating the bending index retention rate based on the bending strength of the Betelgeuse leaf before aging and the bending strength after aging includes: Obtain the bending strength before aging and the bending strength after aging for each of the aging sample groups of the Betelgeuse leaf; Calculate the ratio of the bending strength of the leaf sample after aging to the bending strength of the leaf sample before aging for each of the aging sample groups of the Betelgeuse leaf, and obtain the bending index retention rate for each of the aging sample groups of the Betelgeuse leaf.

[0035] The bending strength of Beaux-Art blades can be tested using a thermomechanical analyzer. Specifically, a TMA7100 thermomechanical analyzer can be used with the following parameters: EMA mode, span 5 mm, loading rate 30 mN / min, maintenance for 50 min, and ambient temperature 25℃. Width measurement: VHX 6000 ultra-depth-of-field 3D video microscope. Thickness measurement: digital micrometer thickness gauge.

[0036] Then, the bending strength is calculated using the three-point bending strength calculation formula:

[0037] in: : Bending strength (MPa); F: Bending failure load (mN); L: Span (mm); b : Sample width (mm); h : Sample thickness (mm).

[0038] Please refer to Table 1. Table 1 Blade Bending Strength Test

[0039] Table 1 shows the bending strength of the Betula palm leaf sample group before aging, and the bending strength after different degrees of aging treatment.

[0040] Then, according to the formula: Bending index retention rate R = Bending strength after aging / Bending strength before aging, the bending index retention rate corresponding to samples after different degrees of aging can be calculated.

[0041] Please refer to Figure 2 , Figure 2 This is a schematic diagram illustrating the retention rate of bending strength of blades at different aging levels, as provided in this embodiment.

[0042] according to Figure 2It can be seen that under alkaline aging conditions, the retention rate of bending strength of Betel leaves decreases, and after aging for 60 minutes, the retention rate of bending strength tends to level off and then remains stable. The alkaline environment causes rapid degradation of cellulose and lignin in the leaves, resulting in a significant decrease in the mechanical strength of the leaves in the early stage of aging. After the easily degradable components in the leaves are consumed, the framework structure formed by the remaining components allows the bending strength of the leaves to remain at a stable level.

[0043] This embodiment calculates the flexural strength of samples after aging at different aging levels, and then calculates the corresponding flexural strength retention rate based on the flexural strength after aging and the flexural strength before aging, providing a data basis for subsequent verification of the aging level.

[0044] Please refer to Figure 3 In one embodiment, step S102 further includes: step S1021-step S1022.

[0045] S1021. Based on the bending index retention rate and the near-infrared spectrum after aging corresponding to the Betula leaf sample group, determine the correspondence between the bending index retention rate and the near-infrared spectrum after aging. S1022. Construct an initial data model. Adjust the parameters of the initial data model according to the bending index retention rate, the near-infrared spectrum after aging, and the corresponding relationship to obtain the near-infrared data model.

[0046] Each aging level corresponds to a flexural strength and a flexural strength retention rate, and each aging level also corresponds to a near-infrared spectrum. Therefore, there is a correspondence between each flexural strength retention rate and the near-infrared spectrum after aging. A near-infrared data model can be constructed based on the flexural strength retention rate, the near-infrared spectrum after aging, and this correspondence. By using either the flexural strength retention rate or the near-infrared spectrum after aging, the corresponding data can be inferred. The initial data model can be built using The UnscramblerX 10.4 software.

[0047] Please refer to Figure 4 In one embodiment, step S103 further includes: step S1031-step S1032.

[0048] S1031. Perform a difference analysis on the actual flexural strength retention rate and the predicted flexural strength retention rate of the standard sample to obtain the analysis results and evaluate the model's predictive ability; S1032. Collect near-infrared spectral data of real palm leaf manuscript samples, predict the corresponding bending strength retention rate through data model, and compare it with the actual bending strength retention rate to verify the actual application effect of the prediction model.

[0049] The predictive ability of a model can be evaluated by cross-validation root mean square error (RMSECV). The smaller the error value, the better the predictive ability of the model.

[0050] In practical applications, when near-infrared technology is used to calibrate palm-leaf manuscripts of different types and aging degrees, the near-infrared spectral reflectance will be between ~4400-4200 cm⁻¹. -1 The range of aging intervals varies, therefore, the actual aging of the beech leaf can be determined based on its actual near-infrared spectrum. However, this method lacks quantitative indicators. This embodiment constructs a near-infrared data model, enabling correspondence and cross-referencing between the bending index retention rate and the near-infrared spectrum, thus achieving quantitative differentiation of the degree of aging of the beech leaf.

[0051] Please refer to Table 2. Table 2. Predicted and Actual Values ​​of Bending Strength and Bending Index of Palm-Leaf Manuscript Samples

[0052] By comparing the actual aging degree of two cultural relic samples B1 and B2 with the predicted bending index retention rate, that is, comparing the actual value with the predicted value, it can be determined that the average error of the accurate value is 0.025%. Therefore, the bending index retention rate is strongly correlated with the aging degree and can be used to quantitatively distinguish the aging degree of palm-leaf manuscripts.

[0053] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0054] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0055] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A non-destructive testing method for the degree of aging of palm leaves, characterized in that, include: Leaf samples of *Begonia spp.* were prepared by selecting leaves from *Begonia spp.* in Xishuangbanna, Yunnan Province. After grouping and rapid aging treatment, multiple standard samples with different aging degrees were obtained. The bending strength before aging, bending strength after aging, and near-infrared spectrum of each standard sample were obtained. The flexural index retention rate is calculated based on the flexural strength before aging and the flexural strength after aging, and a near-infrared data model is constructed based on the flexural index retention rate and the near-infrared spectrum after aging. If the error between the actual aging degree of the standard sample and the predicted flexural index retention rate is less than the preset error, then the near-infrared data model will be used as the Bayeux aging degree prediction model. The actual near-infrared spectrum of the leaf blade to be tested is obtained, and the actual near-infrared spectrum is input into the aging degree prediction model of the leaf blade. The predicted bending index retention rate corresponding to the actual near-infrared spectrum is output.

2. The non-destructive testing method for the degree of aging of palm leaves according to claim 1, characterized in that, The rapid aging process includes the following steps: Obtain multiple groups of Betelgeuse blade samples and the pre-aging bending strength of each group of Betelgeuse blade samples; Each of the aforementioned Betelgeuse leaf sample groups was subjected to different degrees of aging treatment to obtain multiple Betelgeuse leaf aged sample groups.

3. The non-destructive testing method for the degree of aging of palm leaves according to claim 2, characterized in that, The step of subjecting each of the *Begonia spp.* leaf sample groups to different degrees of aging treatment to obtain multiple *Begonia spp.* leaf aging standard sample groups includes: Each of the aforementioned beech leaf sample groups was immersed in an alkaline aging solution of the same concentration. When the soaking time of each of the Betel leaf sample groups reaches the preset time, the sample groups are washed and dried to obtain multiple aged Betel leaf sample groups, each with a different preset time.

4. The non-destructive testing method for the degree of aging of palm leaves according to claim 2, characterized in that, The step of calculating the flexural index retention rate based on the flexural strength before aging and the flexural strength after aging includes: Obtain the flexural strength before aging and the flexural strength after aging for each of the standard samples; Calculate the ratio of the flexural strength after aging to the flexural strength before aging for each standard sample to obtain the flexural index retention rate for each standard sample.

5. The non-destructive testing method for the degree of aging of palm leaves according to claim 1, characterized in that, The step of constructing a near-infrared data model based on the flexural index retention rate and the near-infrared spectrum after aging includes: Based on the flexural index retention rate and the near-infrared spectrum after aging corresponding to the standard sample, the correspondence between the flexural index retention rate and the near-infrared spectrum after aging is determined. An initial data model is constructed, and the parameters of the initial data model are adjusted according to the flexural index retention rate, the near-infrared spectrum after aging, and the corresponding relationship to obtain the near-infrared data model.

6. The non-destructive testing method for the degree of aging of palm leaves according to claim 1, characterized in that, The step of determining the actual aging degree of the palm leaf to be tested based on the actual near-infrared spectrum includes: The actual near-infrared spectrum of the standard sample is input into the near-infrared data model, and the predicted flexural index retention rate corresponding to the actual near-infrared spectrum is output and compared with the actual flexural index retention rate of the standard sample for verification. If the error between the actual aging degree and the predicted bending index retention rate is less than a preset error, then the near-infrared data model will be used as the Bayeux aging degree prediction model.

7. The non-destructive testing method for the degree of aging of palm leaves according to claim 1, characterized in that, The method further includes: Near-infrared spectral data of real palm leaf manuscripts were collected non-destructively using fiber optic probes. The corresponding bending strength retention rate was predicted by the palm leaf manuscript aging degree prediction model, which served as an indicator for quantitatively evaluating the aging degree of real manuscripts.

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