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Identification and quantification of adulterated collagen powder by fluorescence hyperspectral technology

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Abstract

As a popular nutraceutical, collagen powder is the target of adulteration like other high-value food products, therefore detection of adulteration in collagen powder is of great practical significance to ensure market order and the health of the people. To achieve accurate detection of adulterants in collagen powder, a modeling method of fluorescence hyperspectral technology combined with machine learning algorithm was proposed. This study employed various preprocessing methods for denoising and spectral correction to enhance the effective spectral data. Principal component analysis was used to visualize the spectral data and initially revealed the spatial distribution of adulterated collagen powder. Genetic algorithm-k nearest neighbor, particle swarm optimization-support vector machine, and gradient boosting decision tree were used to construct classification models to identify adulterated collagen powder, with the best 2-class discriminant model accuracy, 4-class discriminant model accuracy, and 5-class discriminant model accuracy reaching 99%, 94%, and 98%, respectively. In the quantitative detection models of adulteration level, the random forest model performed best with correlation coefficient (R2) of 0.95 to 0.99. These results suggested that fluorescence hyperspectral technology combined with machine learning algorithm can be effectively used to detect adulterated collagen powder.

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Data will be made available on request.

Abbreviations

FTIR:

Fourier transform infrared spectroscopy

GA:

Genetic algorithm

GA-KNN:

Genetic algorithm-k nearest neighbor

GBDT:

Gradient boosting decision tree

KNN:

K-nearest neighbor

MA:

Moving average

MSC:

Multivariate scatter correction

NIR:

Near-infrared spectroscopy

PCA:

Principal component analysis

PSO:

Particle swarm optimization

PSO-SVM:

Particle swarm optimization-support vector machine

RF:

Random forest

SG:

Savitzky-golay

SVM:

Support vector machine

References

  1. S. Ricard-Blum, The collagen family. Cold Spring Harb. Perspect. Biol. 3, a004978 (2011). https://doi.org/10.1101/cshperspect.a004978

    Article  PubMed  PubMed Central  Google Scholar 

  2. L. Dias Campos, A.T.S. de Almeida Pereira, C.B.B. Cazarin, The collagenmarket and knowledge, attitudes, and practices of Brazilian consumers regarding collagen ingestion. Food Res. Int. 170, 112951 (2023). https://doi.org/10.1016/j.foodres.2023.112951

    Article  CAS  Google Scholar 

  3. S. Medina, R. Perestrelo, P. Silva, J.A.M. Pereira, J.S. Câmara, Current trends and recent ad-vances on food authenticity technologies and chemometric approaches. Trends Food Sci. Technol. 85, 163–176 (2019). https://doi.org/10.1016/j.tifs.2019.01.017

    Article  CAS  Google Scholar 

  4. M.S. Martins, M.H. Nascimento, L.L. Barbosa, L.C.G. Campos, M.N. Singh, F.L. Martin, W. Romão, P.R. Filgueiras, V.G. Barauna, Detection and quantification using ATR-FTIR spectroscopy of whey protein concentrate adulteration with wheat flour. LWT 172, 114161 (2022). https://doi.org/10.1016/j.lwt.2022.114161

    Article  CAS  Google Scholar 

  5. J. Andrade, C. Guimarães Pereira, J.C. de Almeida Junior, C.C.R. Viana, L.N. de Oliveira Neves, P.H.F. da Silva, M.J.V. Bell, V. de Carvalhodos Anjos, FTIR-ATR determination of protein content to evaluate whey protein concentrate adulteration. LWT 99, 166–172 (2019). https://doi.org/10.1016/j.lwt.2018.09.079

    Article  CAS  Google Scholar 

  6. J. Müller-Maatsch, M. Alewijn, M. Wijtten, Y. Weesepoel, Detecting fraudulent additions in skimmed milk powder using a portble, hyphenated, optical multi-sensor approach in combination with one-class classification. Food Control 121, 107744 (2021). https://doi.org/10.1016/j.foodcont.2020.107744

    Article  CAS  Google Scholar 

  7. M. Masci, C. Zoani, T. Nevigato, A. Turrini, R. Jasionowska, R. Caproni, P. Ratini, Authenticity assessment of dairy products by capillary electrophoresis. Electrophoresis 43, 340–354 (2022). https://doi.org/10.1002/elps.202100154

    Article  CAS  PubMed  Google Scholar 

  8. M. Esteki, Z. Shahsavari, J. Simal-Gandara, Food identification by high performance liquid chromatography fingerprinting and mathematical processing. Food Res. Int. 122, 303–317 (2019). https://doi.org/10.1016/j.foodres.2019.04.025

    Article  CAS  PubMed  Google Scholar 

  9. N.A. van Huizen, J.N.M. Ijzermans, P.C. Burgers, T.M. Luider, Collagen analysis with mass spectrometry. Mass Spectrom. Rev. 39, 309–335 (2020). https://doi.org/10.1002/mas.21600

    Article  CAS  PubMed  Google Scholar 

  10. L. Yuan, B. Liu, K. Yin, Z.-L. Xu, Development of an enzyme-linked immunosorbent assay for quantification of estriol in milk. Food Agric. Immunol. 30, 817–828 (2019). https://doi.org/10.1080/09540105.2019.1637824

    Article  CAS  Google Scholar 

  11. R. Sun, J.-Y. Zhou, D. Yu, Nondestructive prediction model of internal hardness attribute of fig fruit using NIR spectroscopy and RF. Multimed. Tools Appl. 80, 21579–21594 (2021). https://doi.org/10.1007/s11042-021-10777-4

    Article  Google Scholar 

  12. Y. Wu, X. Li, L. Xu, R. Fan, Y. Lin, C. Zhan, Z. Kang, Counterfeit detection of bulk Baijiu based on fluorescence hyperspectral technology and machine learning. J. Food Meas. Charact. (2024). https://doi.org/10.1007/s11694-024-02384-2

    Article  Google Scholar 

  13. H. Yan, S. Jie, Z. Chunyi, H. Peng, K. Zhiliang, Identification and quantification of adulterated Tieguanyin based on the fluorescence hyperspectral image technique. J. Food Compos. Anal. 120, 105343 (2023). https://doi.org/10.1016/j.jfca.2023.105343

    Article  CAS  Google Scholar 

  14. Z. Zou, Q. Wu, T. Long, B. Zou, M. Zhou, Y. Wang, B. Liu, J. Luo, S. Yin, Y. Zhao, L. Xu, Classification and adulteration of mengding mountain green tea varieties based on fluorescence hyperspectral image method. J. Food Compos. Anal. 117, 105141 (2023). https://doi.org/10.1016/j.jfca.2023.105141

    Article  CAS  Google Scholar 

  15. J. Hao, F. Dong, Y. Li, S. Wang, J. Cui, S. Liu, Y. Lv, Quantification of polycyclic arom-atic hydrocarbons in roasted Tan lamb using fluorescence hyperspectral imaging technology. J. Food Compos. Anal. 124, 105646 (2023). https://doi.org/10.1016/j.jfca.2023.105646

    Article  CAS  Google Scholar 

  16. H. Jiang, X. Jiang, Y. Ru, Q. Chen, J. Wang, L. Xu, H. Zhou, Detection and visualizationof soybean protein powder in ground beef using visible and near-infrared hyperspectral imaging. Infrared Phys. Technol. 127, 104401 (2022). https://doi.org/10.1016/j.infrared.2022.104401

    Article  CAS  Google Scholar 

  17. M. De Géa Neves, R.J. Poppi, M.C. Breitkreitz, Authentication of plant-based protein powders and classification of adulterants as whey, soy protein, and wheat using FT-NIR in tandem with OC-PLS and PLS-DA models. Food Control 132, 108489 (2022). https://doi.org/10.1016/j.foodcont.2021.108489

    Article  CAS  Google Scholar 

  18. Y. Hu, L. Xu, P. Huang, J. Sun, Y. Wu, J. Geng, R. Fan, Z. Kang, Non-destructive detection of Tieguanyin adulteration based on fluorescence hyperspectral technique. J. Food Meas. Charact. 17, 2614–2622 (2023). https://doi.org/10.1007/s11694-023-01817-8

    Article  Google Scholar 

  19. M. Landauskas, Z. Navickas, A. Vainoras, M. Ragulskis, Weighted moving averaging revisited: an algebraic approach. Comput. Appl. Math. 36, 1545–1558 (2017). https://doi.org/10.1007/s40314-016-0309-9

    Article  Google Scholar 

  20. J. Steinier, Y. Termonia, J. Deltour, Smoothing and differentiation of data by simplified least square procedure. Anal. Chem. 44, 1906–1909 (1972). https://doi.org/10.1021/ac60319a045

    Article  CAS  PubMed  Google Scholar 

  21. J.L. Ilari, H. Martens, T. Isaksson, Determination of particle size in powders by scatter correction in diffuse near-infrared reflectance. Appl. Spectrosc. 42, 722–728 (1988). https://doi.org/10.1366/0003702884429058

    Article  CAS  Google Scholar 

  22. S. Marukatat, Tutorial on PCA and approximate PCA and approximate kernel PCA. Artif. Intell. Rev. 56, 5445–5477 (2023). https://doi.org/10.1007/s10462-022-10297-z

    Article  Google Scholar 

  23. Z. Zou, Q. Wu, J. Wang, I. Xu, M. Zhou, Z. Lu, Y. He, Y. Wang, B. Liu, Y. Zhao, Research on non-destructive testing of hotpot oil quality by fluorescence hyperspectral techn-ology combined with machine learning. Spectroc. Acta Pt. A-Molec. Biomolec. Spectr. 284, 121785 (2023). https://doi.org/10.1016/j.saa.2022.121785

    Article  CAS  Google Scholar 

  24. M.J. Hasan, J.-M. Kim, Fault detection of a spherical tank using a genetic algorithm-based hybrid feature pool and k-nearest neighbor algorithm. Energies (2019). https://doi.org/10.3390/en12060991

    Article  Google Scholar 

  25. X. Sun, J. Li, Y. Shen, W. Li, Non-destructive detection of insect foreign bodies in finishing tea product based on terahertz spectrum and image. Front. Nutr. (2021). https://doi.org/10.3389/fnut.2021.757491

    Article  PubMed  PubMed Central  Google Scholar 

  26. X. Zhang, J. Sun, P. Li, F. Zeng, H. Wang, Hyperspectral detection of salted sea cucumberadulteration using different spectral preprocessing techniques and SVM method. LWT 152, 112295 (2021). https://doi.org/10.1016/j.lwt.2021.112295

    Article  CAS  Google Scholar 

  27. X. Wei, D. Kong, S. Zhu, S. Li, S. Zhou, W. Wu, Rapid identification of soybean varieties by terahertz frequency-domain spectroscopy and grey wolf optimizer-support vector machine. Front. Plant Sci. (2022). https://doi.org/10.3389/fpls.2022.823865

    Article  PubMed  PubMed Central  Google Scholar 

  28. H.F. Jerome, Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001). https://doi.org/10.1214/aos/1013203451

    Article  Google Scholar 

  29. X.-Z. Wang, H.-L. Wu, T. Wang, A.-Q. Chen, H.-B. Sun, Z.-W. Ding, H.-Y. Chang, R.-Q. Yu, Rapid identification and semi-quantification of adulteration in walnut oil by using excitation–emission matrix fluorescence spectroscopy coupled with chemometrics and ensemble learning. J. Food Compos. Anal. 117, 105094 (2023). https://doi.org/10.1016/j.jfca.2022.105094

    Article  CAS  Google Scholar 

  30. A. Amjad, R. Ullah, S. Khan, M. Bilal, A. Khan, Raman spectroscopy based analysis of milk using random forest classification. Vib. Spectrosc. 99, 124–129 (2018). https://doi.org/10.1016/j.vibspec.2018.09.003

    Article  CAS  Google Scholar 

  31. J. Fernández-Habas, M. Carriere Cañada, A.M. García Moreno, J.R. Leal-Murillo, M.P. González-Dugo, B. Abellanas Oar, P.J. Gómez-Giráldez, P. Fernández-Rebollo, Estimating pasture quality of Mediterranean grasslands using hyperspectral narrow bands from field spectroscopy by Random Forest and PLS regressions. Comput. Electron. Agric. (2022). https://doi.org/10.1016/j.compag.2021.106614

    Article  Google Scholar 

  32. A.D. Vibhute, K.V. Kale, S.C. Mehrotra, R.K. Dhumal, A.D. Nagne, Determination of soil physicochemical attributes in farming sites through visible, near-infrared diffuse reflectance spectroscopy and PLSR modeling. Ecol. Process. 7, 26 (2018). https://doi.org/10.1186/s13717-018-0138-4

    Article  Google Scholar 

  33. V.G. Kelis Cardoso, R.J. Poppi, Cleaner and faster method to detect adulteration in cassavastarch using Raman spectroscopy and one-class support vector machine. Food Control 125, 107917 (2021). https://doi.org/10.1016/j.foodcont.2021.107917

    Article  CAS  Google Scholar 

  34. Ł Saletnik, W. Szczęsny, J. Szmytkowski, J.J. Fisz, On the nature of stationary and time-resolved fluorescence spectroscopy of collagen powder from bovine achilles tendon. Int. J. Mol. Sci. (2023). https://doi.org/10.3390/ijms24087631

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This work received support from the subject double support program of Sichuan Agricultural University (Grant No. 035-1921993093).

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an, 625014, China

    Yi Lin, Youli Wu, Rongsheng Fan, Chunyi Zhan & Zhiliang Kang

  2. Sichuan Intelligent Agriculture Engineering Technology Research Center, Ya’an, 625014, China

    Zhiliang Kang

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  1. Yi Lin
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Contributions

Yi Lin: Conceptualization, Resources, Formal analysis, Roles/Writing–original draft, Data curation, Project administration, Investigation, Methodology. Youli Wu: Software, Data analysis, Equipment commissioning, Supervision, Validation. Rongsheng Fan: Methodology, Supervision, Validation. Chunyi Zhan: Supervision, Validation. Zhiliang Kang: Funding acquisition, Review, Editing, Supervision, Validation.

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Correspondence to Zhiliang Kang.

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Lin, Y., Wu, Y., Fan, R. et al. Identification and quantification of adulterated collagen powder by fluorescence hyperspectral technology. Food Measure (2024). https://doi.org/10.1007/s11694-024-02577-9

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