Rapid detection of microplastics in chicken feed based on near infrared spectroscopy and machine learning algorithm.

Journal: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
PMID: 39733534

Abstract

The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MPs) in chicken feed. The application of machine learning techniques in building optimal classification models for MPs-contaminated chicken feeds was explored. 80 chicken feed samples with non-contaminated and 240 MPs-contaminated chicken feed samples including polypropylene (PP), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) were prepared, and the NIR diffuse reflectance spectra of all the samples were collected. NIR spectral properties of chicken feeds, three MPs of PP, PVC and PET, MPs-contaminated chicken feeds were firstly investigated, and principal component analysis was carried out to reveal the effect of MPs on spectra of chicken feed. Moreover, the raw spectral data were pre-processed by multiplicative scattering correction (MSC) and standard normal variate (SNV), and the characteristic variables were selected using the competitive adaptive re-weighted sampling (CARS) algorithm and the successive projections algorithm (SPA), respectively. On this basis, four machine learning methods, namely partial least squares discriminant analysis (PLSDA), back propagation neural network (BPNN), support vector machine (SVM) and random forest (RF), were used to establish discriminant models for MPs-contaminated chicken feed, respectively. The overall results indicated that SPA was a powerful tool to select the characteristic wavelength. SPA-SVM model was proved to be optimal in all constructed models, with a classification accuracy of 96.26% for unknow samples in test set. The results show that it is not only feasible to combine NIR spectroscopy with machine learning for rapid detection of microplastics in chicken feed, but also achieves excellent analysis results.

Authors

  • Yinuo Liu
    College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China.
  • Zhengting Huo
    College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China.
  • Mingyue Huang
    College of Artificial Intelligence, Nankai University, Tianjin 300350, China.
  • Renjie Yang
    State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Eastern Clinic, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
  • Guimei Dong
    College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China.
  • Yaping Yu
    College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China.
  • Xiaohui Lin
    School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China. datas@dlut.edu.cn.
  • Hao Liang
    a Marine College Shandong University (weihai) , Shandong , China .
  • Bin Wang
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.

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