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Multi-sensor integration approach based on hyperspectral imaging and electronic nose for quantitation of fat and peroxide value of pork meat

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Abstract

The study assessed the feasibility of merging data acquired from hyperspectral imaging (HSI) and electronic nose (e-nose) to develop a robust method for the rapid prediction of intramuscular fat (IMF) and peroxide value (PV) of pork meat affected by temperature and NaCl treatments. Multivariate calibration models for prediction of IMF and PV using median spectra features (MSF) and image texture features (ITF) from HSI data and mean signal values (MSV) from e-nose signals were established based on support vector machine regression (SVMR). Optimum wavelengths highly related to IMF and PV were selected from the MSF and ITF. Next, recurring optimum wavelengths from the two feature groups were manually obtained and merged to constitute “combined attribute features” (CAF) which yielded acceptable results with (Rc2 = 0.877, 0.891; RMSEC = 2.410, 1.109; Rp2 = 0.790, 0.858; RMSEP = 3.611, 2.013) respectively for IMF and PV. MSV yielded relatively low results with (Rc2 = 0.783, 0.877; RMSEC = 4.591, 0.653; Rp2 = 0.704, 0.797; RMSEP = 3.991, 0.760) respectively for IMF and PV. Finally, data fusion of CAF and MSV was performed which yielded relatively improved prediction results with (Rc2 = 0.936, 0.955; RMSEC = 1.209, 0.997; Rp2 = 0.895, 0.901; RMSEP = 2.099, 1.008) respectively for IMF and PV. The results obtained demonstrate that it is feasible to mutually integrate spectral and image features with volatile information to quantitatively monitor IMF and PV in processed pork meat.

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References

  1. Hocquette JF, Gondret F, Baéza E, Médale F, Jurie C, Pethick DW. Intramuscular fat content in meat-producing animals:development, genetic and nutritional control, and identificationof putative markers. Animal. 2010;4(2):303–19.

    CAS  PubMed  Google Scholar 

  2. Huang H, Liu L, Ngadi MO. Assessment of intramuscular fat content of pork using NIR hyperspectral images of rib end. J Food Eng. 2017;193:29–41.

    Google Scholar 

  3. Mariutti LR, Bragagnolo N. Influence of salt on lipid oxidation in meat and seafood products: a review. Food Res Int. 2017;94:90–100.

    CAS  PubMed  Google Scholar 

  4. Veiga A, Cobos Á, Ros C. Dı́az O. chemical and fatty acid composition of “Lacón gallego” (dry-cured pork foreleg): differences between external and internal muscles. J Food Compost Anal. 2003;16(2):121–32.

    CAS  Google Scholar 

  5. Jin G, Zhang J, Yu X, Zhang Y, Lei Y, Wang J. Lipolysis and lipid oxidation in bacon during curing and drying–ripening. Food Chem. 2010;123(2):465–71.

    CAS  Google Scholar 

  6. Timón ML, Ventanas J, Carrapiso AI, Jurado A. Garcı́a C. subcutaneous and intermuscular fat characterisation of dry-cured Iberian hams. Meat Sci. 2001;58(1):85–91.

    PubMed  Google Scholar 

  7. Papastergiadis A, Mubiru E, Van Langenhove H, De Meulenaer B. Malondialdehyde measurement in oxidized foods: evaluation of the spectrophotometric Thiobarbituric acid reactive substances (TBARS) test in various foods. J Agric Food Chem. 2012;60(38):9589–94.

    CAS  PubMed  Google Scholar 

  8. Wenjiao F, Yongkui Z, Yunchuan C, Junxiu S, Yuwen Y. TBARS predictive models of pork sausages stored at different temperatures. Meat Sci. 2014;96(1):1–4.

    PubMed  Google Scholar 

  9. Jiang X, Li S, Xiang G, Li Q, Fan L, He L, et al. Determination of the acid values of edible oils via FTIR spectroscopy based on the OH stretching band. Food Chem. 2016;212:585–9.

    CAS  PubMed  Google Scholar 

  10. Song J, Kim MJ, Kim YJ, Lee J. Monitoring changes in acid value, total polar material, and antioxidant capacity of oils used for frying chicken. Food Chem. 2017;220:306–12.

    CAS  PubMed  Google Scholar 

  11. Yang Y, Li Q, Yu X, Chen X, Wang Y. A novel method for determining peroxide value of edible oils using electrical conductivity. Food Control. 2014;39:198–203.

    CAS  Google Scholar 

  12. Cebi N, Yilmaz MT, Sagdic O, Yuce H, Yelboga E. Prediction of peroxide value in omega-3 rich microalgae oil by ATR-FTIR spectroscopy combined with chemometrics. Food Chem. 2017;225:188–96.

    CAS  PubMed  Google Scholar 

  13. Sides A, Robards K, Helliwell S. Developments in extraction techniques and their application to analysis of volatiles in foods. Trends Analyt Chem. 2000;19(5):322–9.

    CAS  Google Scholar 

  14. Torkamani AE, Juliano P, Ajlouni S, Singh TK. Impact of ultrasound treatment on lipid oxidation of Cheddar cheese whey. Ultrason Sonochem. 2014;21(3):951–7.

    CAS  PubMed  Google Scholar 

  15. Arslan M, Xiaobo Z, Tahir HE, Xuetao H, Rakha A, Zareef M, et al. NIR Spectroscopy Coupled Chemometric Algorithms for Rapid Antioxidants Activity Assessment of Chinese Dates (Zizyphus Jujuba Mill.). Int J Food Eng. 2019;15.

  16. Arslan M, Xiaobo Z, Tahir HE, Zareef M, Xuetao H, Rakha A. Total polyphenol quantitation using integrated NIR and MIR spectroscopy: a case study of Chinese dates (Ziziphus jujuba). Phytochem Anal. 2019.

  17. Zhang G, Li P, Zhang W, Zhao J. Analysis of multiple soybean phytonutrients by near-infrared reflectance spectroscopy. Anal Bioanal Chem. 2017;409(14):3515–25.

    CAS  PubMed  Google Scholar 

  18. Rubert-Nason KF, Holeski LM, Couture JJ, Gusse A, Undersander DJ, Lindroth RL. Rapid phytochemical analysis of birch (Betula) and poplar (Populus) foliage by near-infrared reflectance spectroscopy. Anal Bioanal Chem. 2013;405(4):1333–44.

    CAS  PubMed  Google Scholar 

  19. Teye E, Uhomoibhi J, Wang H. Nondestructive authentication of cocoa bean cultivars by FT-NIR spectroscopy and multivariate techniques. Foc Sci. 2016;2:1–10.

    CAS  Google Scholar 

  20. Xu Y, Hassan MM, Kutsanedzie FYH, Li HH, Chen QS. Evaluation of extra-virgin olive oil adulteration using FTIR spectroscopy combined with multivariate algorithms. Qual assur saf crop. 2018;10:1–12.

    CAS  Google Scholar 

  21. Teye E, Huang X, Han F, Botchway F. Discrimination of cocoa beans according to geographical origin by electronic tongue and multivariate algorithms. Food Anal. 2014;7(2):360–5.

    Google Scholar 

  22. Peris M, Escuder-Gilabert L. Electronic noses and tongues to assess food authenticity and adulteration. Trends Food Sci Technol. 2016;58:40–54.

    CAS  Google Scholar 

  23. Lv R, Huang X, Ye W, Aheto JH, Xu H, Dai C, et al. Research on the reaction mechanism of colorimetric sensor array with characteristic volatile gases-TMA during fish storage. J Food Process Eng. 2019;42(1):1–9.

    Google Scholar 

  24. Chen Q, Hassan MM, Xu J, Zareef M, Li H, Xu Y, et al. Fast sensing of imidacloprid residue in tea using surface-enhanced Raman scattering by comparative multivariate calibration. Spectrochim Acta A Mol Biomol Spectrosc. 2019;211:86–93.

    CAS  PubMed  Google Scholar 

  25. Xu Y, Kutsanedzie FYH, Hassan MM, Li H, Chen Q. Synthesized au NPs@silica composite as surface-enhanced Raman spectroscopy (SERS) substrate for fast sensing trace contaminant in milk. Spectrochim Acta A Mol Biomol Spectrosc. 2019;206:405–12.

    CAS  PubMed  Google Scholar 

  26. Huang X, Lv R, Wang S, Aheto JH, Dai C. Integration of computer vision and colorimetric sensor array for nondestructive detection of mango quality. J Food Process Eng. 2018;41(8):e12873.

    Google Scholar 

  27. Dai C, Huang X, Lv R, Zhang Z, Sun J, Aheto JH. Analysis of volatile compounds of Tremella aurantialba fermentation via electronic nose and HS-SPME-GC-MS. J Food Safety. 2018;38(6):e12555.

    Google Scholar 

  28. Ezhilan M, Nesakumar N, Babu KJ, Srinandan CS, Rayappan JBB. An electronic nose for Royal Delicious Apple Quality Assessment – a tri-layer approach. Food Res Int. 2018;109:44–51.

    CAS  PubMed  Google Scholar 

  29. Huang L, Zhao J, Chen Q, Zhang Y. Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. Food Chem. 2014;145:228–36.

    CAS  PubMed  Google Scholar 

  30. Gromski PS, Correa E, Vaughan AA, Wedge DC, Turner ML, Goodacre R. A comparison of different chemometrics approaches for the robust classification of electronic nose data. Anal Bioanal Chem. 2014;406(29):7581–90.

    CAS  PubMed  Google Scholar 

  31. Strike DJ, Meijerink MGH, Koudelka-Hep M. Electronic noses – a mini-review. Fresenius J Anal Chem. 1999;364(6):499–505.

    CAS  Google Scholar 

  32. Aheto JH, Huang X, Tian X, Lv R, Dai C, Bonah E, et al. Evaluation of lipid oxidation and volatile compounds of traditional dry-cured pork belly: the hyperspectral imaging and multi-gas-sensory approaches. J Food Process Eng. 2019;42(5):1–10.

    Google Scholar 

  33. Fan S, Zhang B, Li J, Liu C, Huang W, Tian X. Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data. Postharvest Biol Technol. 2016;121:51–61.

    Google Scholar 

  34. Huang L, Zhao J, Chen Q, Zhang Y. Rapid detection of total viable count (TVC) in pork meat by hyperspectral imaging. Food Res Int. 2013;54(1):821–8.

    CAS  Google Scholar 

  35. Calvini R, Foca G, Ulrici A. Data dimensionality reduction and data fusion for fast characterization of green coffee samples using hyperspectral sensors. Anal Bioanal Chem. 2016;408(26):7351–66.

    CAS  PubMed  Google Scholar 

  36. Vermeulen P, Fernández Pierna JA, van Egmond HP, Zegers J, Dardenne P, Baeten V. Validation and transferability study of a method based on near-infrared hyperspectral imaging for the detection and quantification of ergot bodies in cereals. Anal Bioanal Chem. 2013;405(24):7765–72.

    CAS  PubMed  Google Scholar 

  37. Wei M, Geladi P, Xiong S. NIR hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary study. Anal Bioanal Chem. 2017;409(9):2449–60.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Barriuso B, Astiasarán I, Ansorena D. A review of analytical methods measuring lipid oxidation status in foods: a challenging task. Eur Food Res Technol. 2013;236(1):1–15.

    CAS  Google Scholar 

  39. Khaleghi B, Khamis A, Karray FO, Razavi SN. Multisensor data fusion: a review of the state-of-the-art. Inf Fusion. 2013;14(1):28–44.

    Google Scholar 

  40. Aheto JH, Huang X, Tian X, Ren Y, Bonah E, Alenyorege E, et al. Combination of spectra and image information of hyperspectral imaging data for fast prediction of lipid oxidation attributes in pork meat. J Food Process Eng. 2019;42(6):1–11.

    Google Scholar 

  41. Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern Syst. 1973;SMC-3(6):610–21.

    Google Scholar 

  42. Liu Y, Xiao H, Xu H, Rao Y, Jiang X, Sun X. Visual discrimination of citrus HLB based on image features. Vib Spectrosc. 2019;102:103–11.

    CAS  Google Scholar 

  43. Li H, Liang Y, Xu Q, Cao D. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Anal Chim Acta. 2009;648(1):77–84.

    CAS  PubMed  Google Scholar 

  44. Galvão RKH, Araujo MCU, José GE, Pontes MJC, Silva EC, Saldanha TCB. A method for calibration and validation subset partitioning. Talanta. 2005;67(4):736–40.

    PubMed  Google Scholar 

  45. Liu L, Zhang D, You J. Detecting wide lines using isotropic nonlinear filtering. IEEE Trans Image Process. 2007;16(6):1584–95.

    PubMed  Google Scholar 

  46. Bonah E, Huang X, Yi R, Aheto JH, Osae R, Golly M. Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm. J Food Process Eng. 2019;5(0):e13236.

    Google Scholar 

  47. Bauer R, Nieuwoudt H, Bauer FF, Kossmann J, Koch KR, Esbensen KH. FTIR spectroscopy for grape and wine analysis. Anal Chem. 2008;80(5):1371–9.

    CAS  PubMed  Google Scholar 

  48. Bligh EG, Dyer WJ. A rapid method of total lipid extraction and purification. Can J Biochem Physiol. 1959;37:911–7.

    CAS  PubMed  Google Scholar 

  49. Velásquez L, Cruz-Tirado JP, Siche R, Quevedo R. An application based on the decision tree to classify the marbling of beef by hyperspectral imaging. Meat Sci. 2017;133:43–50.

    PubMed  Google Scholar 

  50. Mamani-Linares LW, Gallo C, Alomar D. Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy. Meat Sci. 2012;90(2):378–85.

    CAS  PubMed  Google Scholar 

  51. Kobayashi K-I, Matsui Y, Maebuchi Y, Toyota T, Nakauchi S. Near infrared spectroscopy and Hyperspectral imaging for prediction and visualisation of fat and fatty acid content in intact raw beef cuts. JNIRS. 2010;18(5):301–15.

    CAS  Google Scholar 

  52. Mendoza F, Lu R, Ariana D, Cen H, Bailey B. Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biol Technol. 2011;62(2):149–60.

    Google Scholar 

  53. Mohanaiah P, Sathyanarayana P, GuruKumar L. Image texture feature extraction using GLCM approach. IJSRP. 2013;3(5):1–5.

    Google Scholar 

  54. Jin G, He L, Zhang J, Yu X, Wang J, Huang F. Effects of temperature and NaCl percentage on lipid oxidation in pork muscle and exploration of the controlling method using response surface methodology (RSM). Food Chem. 2012;131(3):817–25.

    CAS  Google Scholar 

  55. Aidos I, Lourenclo S, Van Der Padt A, Luten JB, Boom RM. Stability of crude herring oil produced from fresh byproducts: influence of temperature during storage. J Food Sci. 2002;67(9):3314–20.

    CAS  Google Scholar 

  56. Xiong Z, Sun D-W, Pu H, Zhu Z, Luo M. Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats. Lebenson Wiss Technol. 2015;60(2, Part 1):649–55.

    CAS  Google Scholar 

  57. Ma J, Sun D-W, Qu J-H, Pu H. Prediction of textural changes in grass carp fillets as affected by vacuum freeze drying using hyperspectral imaging based on integrated group wavelengths. Lebenson Wiss Technol. 2017;82:377–85.

    CAS  Google Scholar 

  58. Tamanna N, Mahmood N. Food processing and Maillard reaction products: effect on human health and nutrition. Int J Food Sci. 2015;2015:1–6.

    Google Scholar 

  59. Pan L, Zhang W, Zhu N, Mao S, Tu K. Early detection and classification of pathogenic fungal disease in post-harvest strawberry fruit by electronic nose and gas chromatography–mass spectrometry. Food Res Int. 2014;62:162–8.

    CAS  Google Scholar 

  60. Shi Y, Li X, Huang A. A metabolomics-based approach investigates volatile flavor formation and characteristic compounds of the Dahe black pig dry-cured ham. Meat Sci. 2019;158:107904.

    CAS  PubMed  Google Scholar 

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Acknowledgments

This work was sponsored by the National Key Research and Development Program of China (Grant/Award number: 2017YFD0400100)

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Authors and Affiliations

  1. School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013, Jiangsu, China

    Joshua Harrington Aheto, Xingyi Huang, Xiaoyu Tian, Yi Ren, Bonah Ernest, Evans Adingba Alenyorege, Chunxia Dai, Tu Hongyang, Zhang Xiaorui & Peichang Wang

  2. Suzhou Polytechnic Institute of Agriculture, School of Smart Agriculture, No.279 Xiyuan Road, Suzhou, 215008, China

    Yi Ren

  3. Food and Drugs Authority, Laboratory Services Department, P. O. Box CT 2783, Cantonments, Accra, Ghana

    Bonah Ernest

  4. Faculty of Agriculture, University for Development Studies, Tamale, Ghana

    Evans Adingba Alenyorege

  5. School of Electrical and Information Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013, Jiangsu, China

    Chunxia Dai

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  1. Joshua Harrington Aheto
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Aheto, J.H., Huang, X., Tian, X. et al. Multi-sensor integration approach based on hyperspectral imaging and electronic nose for quantitation of fat and peroxide value of pork meat. Anal Bioanal Chem 412, 1169–1179 (2020). https://doi.org/10.1007/s00216-019-02345-5

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