skip to main content
10.1145/3495018.3501077acmotherconferencesArticle/Chapter ViewAbstractPublication PagesaiamConference Proceedingsconference-collections
research-article

Computer Mathematical Statistics and Intelligent Evaluation System Construction for Cut Tobacco Quality Using Principal Component Analysis

Authors Info & Claims
Published:14 March 2022Publication History

ABSTRACT

Principal component analysis was used to evaluate the structure of diamond (hard red) cut tobacco and the physical indexes of cigarette, and the standard deviation of rolling quality was calculated. Also, the sensory evaluation was carried out, and a new cut tobacco quality comprehensive evaluation system model of the silk flavoring process was established, thus optimizing its cut tobacco quality evaluation system in the silk flavoring process. The results showed that the eigenvalues of the first four principal components were greater than 1, and the cumulative contribution rate of variance was 70.87%. The cut tobacco quality comprehensive evaluation model and smoking verification experiment results were: test III > test IV > test I > test II > test V. The rolling quality results indicated that the deviation of test III and test IV batches was low, which was consistent with the model results. The intelligent evaluation system of cut tobacco quality established by information technology can automatically collect data, calculate data, determine results and early warning. The intelligent evaluation system is suitable for the current production situation in silk processing, and also offers a new idea for the quality evaluation of cut tobacco in the silk production process.

References

  1. Li Zhaobo. Discussion on Stability Control of Cigarette Silk Making, Flavoring and Blending Process [J]. Technology Wind, 2018 (16): 234 + 237.Google ScholarGoogle Scholar
  2. Guo Huacheng, Wu Yanyan, Zhang Junsong, Effect of Cutting Width on Fine Cigarette Making Quality, Mainstream Flue Gas and Sensory Quality [J]. Food and Machinery, 2021, 37 (02): 194-198.Google ScholarGoogle Scholar
  3. Tao Ying, Dang Lizhi, Liu Juan, Application of Near Infrared Spectroscopy in Quality Stability Control of Cigarette Silk [J]. Spectrum Laboratory, 2013, 30 (01): 27-32.Google ScholarGoogle Scholar
  4. Xiong Anyan, Li Shanlian, Ding Meizhou, Design and Application of Quality Stability Evaluation Method in Cigarette Production Process [J]. Food and Machinery, 2017, 33 (02): 183-188.Google ScholarGoogle Scholar
  5. Du Yunpeng, Shu Jiang, Hou Xiaobo. Prediction Model of Cut Tobacco Moisture in the Whole Process before Drying Based on Normal Distribution Statistical Analysis [J]. Industrial Technology Innovation, 2021, 08 (02): 119-124.Google ScholarGoogle Scholar
  6. Xue Xunming, Xu Yonghu, Xu Mowei, Moisture Analysis and Prediction of Ambient Temperature and Humidity in Cut Tobacco Process Based on LSTM [J]. Light Industry Science and Technology, 2021, 37 (01): 3-5.Google ScholarGoogle Scholar
  7. Fan Yi, Wang Xiying, He Xiaoying, Study on the Change Trend of Moisture Content in Cut Tobacco Air Conveying Process [J]. Yunnan Chemical Industry, 2020, 47 (08): 74-76.Google ScholarGoogle Scholar
  8. Gu Xi, Qian Jichun. Application Research on Real-time Data of Silk Making Based on Time Series Database and Deep Learning [J/OL]. Acta Tabacaria Sinica, 2021, 06 (13): 1-14.Google ScholarGoogle Scholar
  9. Chen Deli, Gao Lixiu, Sun Chengshun, Application of AHP Fuzzy Algorithm in Quality Evaluation of Cigarette Spinning Process [J]. Packaging Engineering, 2020, 41 (23): 195-203.Google ScholarGoogle Scholar
  10. Zhou Guanghui, Bao Liwei. Application of Principal Component Analysis in Cigarette Brand Sales System [J]. Computer Application and Software, 2005 (05): 57-59.Google ScholarGoogle Scholar
  11. Zhao Qiang. Overview on Principal Component Analysis Methods [J]. Software Engineering, 2016, 19 (06): 1-3.Google ScholarGoogle Scholar
  12. Sun Liuping, Qian Wuyong. Improvement of Comprehensive Evaluation Method Based on Principal Component Analysis [J]. Practice and Understanding of Mathematics, 2009, 39 (18): 15-20.Google ScholarGoogle Scholar
  13. Li Xiaosheng, Chen Zhenzhen. How to Correctly Apply SPSS Software for Principal Component Analysis [J].Statistical Research, 2010 (08): 105-108.Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture
    October 2021
    3136 pages
    ISBN:9781450385046
    DOI:10.1145/3495018

    Copyright © 2021 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 14 March 2022

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate100of285submissions,35%
  • Article Metrics

    • Downloads (Last 12 months)8
    • Downloads (Last 6 weeks)0

    Other Metrics

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format .

View HTML Format