A novel method based on infrared spectroscopic inception-resnet networks for the detection of the major fish allergen parvalbumin.

Journal: Food chemistry
PMID: 32920269

Abstract

We have developed a novel approach that involves inception-resnet network (IRN) modeling based on infrared spectroscopy (IR) for rapid and specific detection of the fish allergen parvalbumin. SDS-PAGE and ELISA were used to validate the new method. Through training and learning with parvalbumin IR spectra from 16 fish species, IRN, support vector machine (SVM), and random forest (RF) models were successfully established and compared. The IRN model extracted highly representative features from the IR spectra, leading to high accuracy in recognizing parvalbumin (up to 97.3%) in a variety of seafood matrices. The proposed infrared spectroscopic IRN (IR-IRN) method was rapid (~20 min, cf. ELISA ~4 h) and required minimal expert knowledge for application. Thus, it could be extended for large-scale field screening and identification of parvalbumin or other potential allergens in complex food matrices.

Authors

  • Xiaopeng Zhang
  • Yaru Li
    College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, Shanghai 201306, China.
  • Yan Tao
    College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
  • Yang Wang
    Department of General Surgery The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.
  • Changhua Xu
    College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, Shanghai 201306, China. Electronic address: chxu@shou.edu.cn.
  • Ying Lu
    Department of Endocrinology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China.

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