Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Browser

Application of Chemometrics and Machine Learning in Cultural Heritage Analysis

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 4768

Share This Special Issue

Special Issue Editors

Dr. Giulia Festa

E-Mail Website
Guest Editor

CREF - Museo Storico della Fisica e Centro Studi e Ricerche “Enrico Fermi”, Via Panisperna 89 a, c/o Piazza del Viminale 1, 00189 Rome, Italy
Interests: neutron and X-ray techniques for cultural heritage; imaging; diffraction; gamma spectroscopy; FTIR spectroscopy; Raman spectroscopy; XRF spectroscopy; chemometrics; machine learning; instrument development
Special Issues, Collections and Topics in MDPI journals

Dr. Claudia Scatigno

E-Mail Website
Guest Editor

CREF—Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Via Panisperna 89a, 00189 Rome, Italy
Interests: data analysis (machine learning, computing, modelling); physics applied: spectroscopy (optical and neutron); analytical and environmental chemistry (air quality, sensors, environmental monitoring); molecular science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cultural Heritage comprises artwork and manufacts often made of multi-component materials in which several variables relating to interlinked chemical, physical, and biological processes are combined. Studying all these parameters in a synergic way provides new perspectives for understanding raw materials and all the phenomena linked with the history and current state of conservation of the objects.

The use of Chemometrics and Machine Learning (ML) techniques opens up new opportunities and challenges in Cultural Heritage (CH) Analysis, allowing us to classify materials, analyse manufacturing processes, and predict damages. To this end, it is key to identify markers or benchmarks able to discriminate between those fingerprints that describe a certain object or phenomenon in its uniqueness while also finding distinct ways to optimize data acquisition and improve the data processing phase.

Supervised learning techniques such as Support Vector Machines (SVM) within CH are still limited, nonetheless, classification and regression techniques have shown unique potential in this domain. Recent developments and research approaches are looking into the identification and study of handcrafted features, detection and recognition of iconographic artworks, pigments classification, determination of maximum firing temperatures of ancient ceramics, optimisation of iterative procedures for large datasets.

We are pleased to invite you to contribute to the present Special Issue on “Application of Chemometrics and Machine Learning in Cultural Heritage Analysis” which aims to offer researchers an opportunity to share findings and new developments in the field of heritage science as well as to present statistical approaches. This Special issue, in particular, also aims to address specific challenges related to data analysis such as pre-processing approaches, new insights in the specific frameworks for CH, application of chemometrics and Machine Learning tools on different matrices/objects as well as to assess the development of new methods and benchmarks in the identification and classification of CH assets.

Dr. Giulia Festa
Dr. Claudia Scatigno
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

chemometrics
machine learning
data science for cultural heritage

Published Papers (3 papers)

Download All Papers

Research

14 pages, 3530 KiB

Open AccessArticle

Infrared Spectroscopy to Assess Manufacturing Procedures of Bone Artefacts from the Chalcolithic Settlement of Vila Nova de São Pedro (Portugal)

by David Gonçalves, Joana Rosa, Ana L. Brandão, Andrea Martins, César Neves, Mariana Diniz, José M. Arnaud, Maria Paula M. Marques and Luís A. E. Batista de Carvalho

Appl. Sci. 2023, 13(14), 8280; https://doi.org/10.3390/app13148280 - 18 Jul 2023

Viewed by 1007

Abstract

Vibrational spectroscopy was applied to study cylindrical engraved bone boxes from the Chalcolithic settlement of Vila Nova de São Pedro (VNSP, Azambuja, Portugal) which has the largest and richest artefact assemblage of Copper Age Western Iberia. The objectives were to reconstitute manufacturing techniques, determine the role of pyrotechnology in the production of cylindrical engraved bone boxes and assess oxygen conditions during burning. Four fragments of cylindrical engraved bone “boxes” from VNSP were used in this research. Anaerobic experimental burn conditions were recreated by using a home-made steel airtight chamber under vacuum. Human bone fragments were burnt at 400–1000 °C for 120–211 min. Fourier-transform infrared spectroscopy analyses were performed on bone powder samples. The resulting spectra and chemometric indices were used as a reference to establish comparisons with the archaeological artefacts. None of these presented spectral features compatible with anaerobic burning. Therefore, aerobic burns were used to achieve the whitish look and were most probably used to attain the darker shade displayed by the artefacts. Artefact manufacturing appears to have relied on bone cutting, bone engraving and maybe polishing, followed by heat treatment. The population from VNSP appears to have been highly specialized in the use of fire to work different raw materials. Full article

(This article belongs to the Special Issue Application of Chemometrics and Machine Learning in Cultural Heritage Analysis)

► Show Figures

Figure 1

24 pages, 2548 KiB

Open AccessArticle

Analysis of the Composition of Ancient Glass and Its Identification Based on the Daen-LR, ARIMA-LSTM and MLR Combined Process

by Zhi-Xing Li, Peng-Sen Lu, Guang-Yan Wang, Jia-Hui Li, Zhen-Hao Yang, Yun-Peng Ma and Hong-Hai Wang

Appl. Sci. 2023, 13(11), 6639; https://doi.org/10.3390/app13116639 - 30 May 2023

Cited by 1 | Viewed by 1148

Abstract

The glass relics are precious material evidence of the early trade and cultural exchange between the East and the West. To explore the cultural differences and trade development between early China and foreign countries, it is extremely important to classify glass cultural relics. Despite their similar appearances, Chinese glass contains more lead, while foreign glass contains more potassium. In view of this, this paper proposes a joint Daen-LR, ARIMA-LSTM, and MLR machine learning algorithm (JMLA) for the analysis and identification of the chemical composition of ancient glass. We separate the sampling points of ancient glass into two systems: lead-barium glass and high-potassium glass. Firstly, an improved logistic regression model based on a double adaptive elastic network (Daen-LR) is used to select variables with both Oracle and adaptive classification characteristics. Secondly, the ARIMA-LSTM model was used to establish the correlation curve of chemical composition before and after weathering and to predict the change in chemical composition with weathering. Thirdly, combining the data processed by the above two methods, a multiple linear regression model (MLR) is used to classify unknown glass products. It was shown that the sample obtained by this processing method has a very good fit. In comparison with other similar types of models like Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and Random Forests based on classification and regression trees (CART-RF), the classification accuracy of JMLA is 97.9% on the train set. The accuracy rate on the test set reached 97.6%. The results of the research demonstrate that JMLA can improve the accuracy of the glass type classification problem, greatly enhance the research efficiency of archaeological staff, and gain a more reliable result. Full article

(This article belongs to the Special Issue Application of Chemometrics and Machine Learning in Cultural Heritage Analysis)

► Show Figures

Figure 1

14 pages, 2108 KiB

Open AccessArticle

Application of Support Vector Machine Algorithm Incorporating Slime Mould Algorithm Strategy in Ancient Glass Classification

by Yuheng Guo, Wei Zhan and Weihao Li

Appl. Sci. 2023, 13(6), 3718; https://doi.org/10.3390/app13063718 - 14 Mar 2023

Cited by 6 | Viewed by 1828

Abstract

Glass products are important evidence of early East–West cultural exchanges. Ancient glass in China mostly consisted of lead glass, and potassium glass is widely believed to be imported abroad. In order to figure out the origin of glass artefacts, it is crucial to define the type of glass products accurately. In contemporary research on the chemical composition of ancient glass products, potassium glass is separated from lead glass primarily by the weight ratio of oxides or the proportion of lead-containing compounds. This approach can be excessively subjective and prone to mistakes while calculating the mass fraction of compounds containing potassium. So, it is better to find out the link between the proportion of glass’s chemical composition and its classifications during the weathering process of the glass products, to develop an effective classification model using machine learning techniques. In this research, we suggest employing the slime mould approach to optimise the parameters of a support vector machine and examine a 69-group glass chemical composition dataset. In addition, the results of the proposed algorithm are compared to those of commonly used classification models: decision trees (DT), random forests (RF), support vector machines (SVM), and support vector machines optimised by genetic algorithms (GA-SVM). The results of this research indicated that the support vector machine method with the sticky slime mould algorithm strategy is the most effective. On the training set, 100% accuracy was attained, while on the test set, 97.50% accuracy was attained in this research. The research results demonstrate that the support vector machine algorithm combining the slime mould algorithm strategy is capable of providing a trustworthy classification reference for future glass artefacts. Full article

(This article belongs to the Special Issue Application of Chemometrics and Machine Learning in Cultural Heritage Analysis)

► Show Figures