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Wine Characterisation with Spectral Information and Predictive Artificial Intelligence

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Neural Information Processing (ICONIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1794))

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Abstract

The purpose of this paper is to use absorbance data obtained by human tasting and an ultraviolet-visible (UV-Vis) scanning spectrophotometer to predict the attributes of grape juice (GJ) and to classify the wine’s origin, respectively. The approach combined machine learning (ML) techniques with spectroscopy to find a relatively simple way to apply them in two stages of winemaking and help improve the traditional wine analysis methods regarding sensory data and wine’s origins. This new technique has overcome the disadvantages of the complex sensors by taking advantage of spectral fingerprinting technology and forming a comprehensive study of the employment of AI in the wine analysis domain. In the results, Support Vector Machine (SVM) was the most efficient and robust in both attributes and origin prediction tasks. Both the accuracy and F1 score of the origin prediction exceed 91%. The feature ranking approach found that the more influential wavelengths usually appear at the lower end of the scan range, 250 nm (nanometers) to 420 nm, which is believed to be of great help for selecting appropriate validation methods and sensors to extract wine data in future research. The knowledge of this research provides new ideas and early solutions for the wine industry or other beverage industries to integrate big data and IoT in the future, which significantly promotes the development of ’Smart Wineries’.

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Author information

Authors and Affiliations

  1. School of Information and Technology, University of Tasmania, 7249, Hobart, Australia

    Jianping Yao, Son N. Tran & Hieu Nguyen

  2. Tasmania Institute of Agriculture. Mt Pleasant, 7250, Hobart, Australia

    Samantha Sawyer & Rocco Longo

  3. Winequip, Dudley Park, 5008, Adelaide, Australia

    Rocco Longo

Authors
  1. Jianping Yao
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  2. Son N. Tran
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  3. Hieu Nguyen
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  4. Samantha Sawyer
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  5. Rocco Longo
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Corresponding author

Correspondence to Son N. Tran .

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Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

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Yao, J., Tran, S.N., Nguyen, H., Sawyer, S., Longo, R. (2023). Wine Characterisation with Spectral Information and Predictive Artificial Intelligence. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1794. Springer, Singapore. https://doi.org/10.1007/978-981-99-1648-1_21

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  • DOI: https://doi.org/10.1007/978-981-99-1648-1_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1647-4

  • Online ISBN: 978-981-99-1648-1

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