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Powdery Food Identification Using NIR Spectroscopy and Extensible Deep Learning Model

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Abstract

Spectroscopy coupled with deep learning is widely studied for powdered food materials authentication, such as quality ranking and powder identification. However, the models need retraining when new categories are expected to be covered. We proposed a portable device for powder identification using metric learning. Six powders were used to train a deep learning feature extractor and establish a reference library. Then, a few samples of another six new categories were processed for reference library extension. Without retraining, the device discriminated samples of 12 categories using the cosine similarity values between the test samples and the reference records in the library, which achieved an accuracy of 97.86%, outperforming the K-nearest-neighbor and partial least squares method. The proposed method also showed stronger adaptability to the variation of the number of reference samples. The device could cover more powders by adding very few samples of each new category, showing its strong advantages in practical applications.

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Data Availability

The data used in this study are available from the corresponding author upon reasonable request.

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Funding

This research has been supported by the Zhejiang Province Key Research and Development Program (2022C02056).

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Authors and Affiliations

  1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China

    Lei Zhou, Xuefei Wang, Nan Zhao, Mohamed Farag Taha, Yong He & Zhengjun Qiu

  2. College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China

    Lei Zhou

  3. Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China

    Lei Zhou, Xuefei Wang, Nan Zhao, Mohamed Farag Taha, Yong He & Zhengjun Qiu

  4. School of Information Engineering, Huzhou University, Huzhou, 313000, China

    Chu Zhang

  5. Department of Soil and Water Sciences, Faculty of Environmental Agricultural Sciences, Arish University, Arish, 45516, North Sinai, Egypt

    Mohamed Farag Taha

Authors
  1. Lei Zhou
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  3. Chu Zhang
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  4. Nan Zhao
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Contributions

Lei Zhou: conceptualization, data curation, methodology, software, writing—original draft. Xuefei Wang: investigation, data curation, writing—review and editing. Chu Zhang: investigation, conceptualization, writing—review and editing. Nan Zhao: investigation, writing—review and editing. Mohamed Farag Taha: sample collection, hardware and software testing. Yong He: project administration, writing—review and editing. Zhengjun Qiu: funding acquisition, supervision, writing—review and editing.

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Correspondence to Zhengjun Qiu.

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Zhou, L., Wang, X., Zhang, C. et al. Powdery Food Identification Using NIR Spectroscopy and Extensible Deep Learning Model. Food Bioprocess Technol 15, 2354–2362 (2022). https://doi.org/10.1007/s11947-022-02866-5

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