Thermal desorption-photoionization ion mobility-electronic nose (TD-PIM-Nose) with distance-probability joint decision SVM algorithm: A novel system for Daqu Grade identification.

Journal: Food chemistry
Published Date: Sep 20, 2024

Abstract

Electronic nose is a bionic technology that uses sensor arrays and pattern recognition algorithms to mimic the human olfactory system. This study developed a thermal desorption-photoionization ion mobility-electronic nose (TD-PIM-Nose) system, employing thermal desorption for direct sampling and humidity control, with a photoionization ion mobility tube as virtual sensor array for component separation and detection, and pattern recognition algorithms for signal processing to differentiate and identify samples. Furthermore, it was applied to assess four quality grades of Daqu samples ("Excellent+", "Excellent", "Grade I", and "Grade II") determined by the Check-All-That-Apply (CATA) method. Characteristic compound differences among these grades were identified using fingerprint spectra and reduced mobility values. A distance-probability joint decision support vector machine (SVM) algorithm model was established, validated against sensory CATA standards. Results showed identification accuracies: 90 %, 90 %, 96.88 %, and 100 % for respective grades. These findings demonstrated the promising potential of the TD-PIM-Nose system in Daqu quality grading.

Authors

  • Shiwen Cheng
    School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310018, China; Collaborative Innovation Center of Statistical Data Engineering Technology & Application, Zhejiang Gongshang University, Hangzhou 310018, China; China-UK Joint Research Laboratory of Eating Behaviour and Appetite, Zhejiang Gongshang University, Hangzhou 310018, China.
  • Qiang Han
    Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, P.R. China.
  • Yumei Qin
    School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310018, China; China-UK Joint Research Laboratory of Eating Behaviour and Appetite, Zhejiang Gongshang University, Hangzhou 310018, China.
  • Li Chen
    Department of Endocrinology and Metabolism, Qilu Hospital, Shandong University, Jinan, China.
  • Yuezhong Mao
    School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310018, China; China-UK Joint Research Laboratory of Eating Behaviour and Appetite, Zhejiang Gongshang University, Hangzhou 310018, China.
  • Jianmei Yang
    Jiangsu Yanghe Brewery Joint-Stock Co., Ltd, Suqian 223800, China.
  • Ruihang Zheng
    Ningbo Institute for Food Control, Ningbo 315048, China.
  • Jianzhong Han
    Coriell Institute for Medical Research, Camden, NJ, USA.
  • Zihan Qin
    School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310018, China. Electronic address: zihanqin@mail.zjgsu.edu.cn.
  • Chuang Chen
    College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China.
  • Shiyi Tian
    School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310018, China; Collaborative Innovation Center of Statistical Data Engineering Technology & Application, Zhejiang Gongshang University, Hangzhou 310018, China; China-UK Joint Research Laboratory of Eating Behaviour and Appetite, Zhejiang Gongshang University, Hangzhou 310018, China. Electronic address: tianshiyi@zjgsu.edu.cn.

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