Home Food Science and Human Wellness Article
PDF (2.4 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Article | Open Access

Structured-illumination reflectance imaging for the evaluation of microorganism contamination in pork: effects of spectral and imaging features on its prediction performance

Binjing Zhoua,1, Xiaohua Liua,1, Yan Geb,c, Kang Tua, Jing Penga, Juan Francisco García-Martínd, Jie Wue, Weijie Lana(), Leiqing Pana()
College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
College of Engineering, Nanjing Agricultural University, Nanjing 210095, China
The Academy of Science, Nanjing Agricultural University, Nanjing 210095, China
Departamento de Ingeniería Química, Facultad de Química, Universidad de Sevilla, Sevilla 41012, Spain
School of Food and Biological Engineering, Bengbu University, Bengbu 233030, China

1 These authors contributed equally.

Peer review under responsibility of Beijing Academy of Food Sciences.

Show Author Information

Abstract

Structured-illumination reflectance imaging (SIRI) provides a new means for food quality detection. This original work investigated the capability of (SIRI) technique coupled with multivariate chemometrics to evaluate the microbial contamination in pork inoculated with Pseudomonas fluorescens and Brochothrix thermosphacta during storage at different temperatures. The prediction performances based on different spectrum and the textural features of direct component and amplitude component images demodulated from the SIRI pattern, as well as their data fusion were comprehensively compared. Based on the full wavelength spectrum (420–700 nm) of amplitude component images, the orthogonal signal correction coupled with support vector machine regression provided the best predictions of the number of P. fluorescens and B. thermosphacta in pork, with the determination coefficients of prediction (Rp2) values of 0.870 and 0.906, respectively. Besides, the prediction models based on the amplitude component or direct component image textural features and the data fusion models using spectrum and textural features from direct component and amplitude component images cannot significantly improve their prediction accuracy. Consequently, SIRI can be further considered as a potential technique for the rapid evaluation of microbial contaminations in pork meat.

Keywords

Pseudomonas fluorescensBrochothrix thermosphactaPorkStructured-illumination reflectance imagingData fusion

Electronic Supplementary Material

Download File(s)
fshw-14-2-9250104_ESM.docx (12.5 KB)

References

[1]

I. Baek, H. Lee, B. Cho, et al., Shortwave infrared hyperspectral imaging system coupled with multivariable method for TVB-N measurement in pork, Food Contr. 124 (2021) 107854. https://doi.org/10.1016/j.foodcont.2020.107854.

Crossref Google Scholar
[2]

V. Pothakos, F. Devlieghere, F. Villani, et al., Lactic acid bacteria and their controversial role in fresh meat spoilage, Meat Sci. 109 (2015) 66-74. https://doi.org/10.1016/j.meatsci.2015.04.014.

Crossref Google Scholar
[3]

H.X. Li, R.R. Tang, W.A.W. Mustapha, et al., Application of gelatin composite coating in pork quality preservation during storage and mechanism of gelatin composite coating on pork flavor, Gels. 8 (2021) 21. https://doi.org/10.3390/gels8010021.

Crossref Google Scholar
[4]

C. Adley, Past, present and future of sensors in food production, Foods 3 (2014) 491-510. https://doi.org/10.3390/foods3030491.

Crossref Google Scholar
[5]

E. Bahlinger, S. Dorn-In, P.M. Beindorf, et al., Development of two specific multiplex qPCRs to determine amounts of Pseudomonas, Enterobacteriaceae, Brochothrix thermosphacta and Staphylococcus in meat and heat-treated meat products, Int. J. Food Microbiol. 337 (2021) 108932. https://doi.org/10.1016/j.ijfoodmicro.2020.108932.

Crossref Google Scholar
[6]

A. von Groll, A. Martin, F. Portaels, et al., Growth kinetics of Mycobacterium tuberculosis measured by quantitative resazurin reduction assay: a tool for fitness studies, Braz. J. Microbiol. 41 (2010) 300-303. https://doi.org/10.1590/S1517-83822010000200006.

Crossref Google Scholar
[7]

E. Cauchie, L. Delhalle, G. Baré, et al., Modeling the growth and interaction between Brochothrix thermosphacta, Pseudomonas spp., and Leuconostoc gelidum in minced pork samples, Front. Microbiol. 11 (2020) 639. https://doi.org/10.3389/fmicb.2020.00639.

Crossref Google Scholar
[8]

H.J. He, D.W. Sun, Toward enhancement in prediction of Pseudomonas counts distribution in salmon fillets using NIR hyperspectral imaging, LWT-Food Sci. Technol. 62 (2015) 11-18. https://doi.org/10.1016/j.lwt.2015.01.036.

Crossref Google Scholar
[9]

J.Y. An, Y.L. Li, C.Z. Zhang, et al., Rapid nondestructive prediction of multiple quality attributes for different commercial meat cut types using optical system, Food Sci. Anim. Resour. 42 (2022) 655-671. https://doi.org/10.5851/kosfa.2022.e28.

Crossref Google Scholar
[10]

A. Soni, M. Al-Sarayreh, M.M. Reis, et al., Hyperspectral imaging and deep learning for quantification of Clostridium sporogenes spores in food products using 1D-convolutional neural networks and random forest model, Food Res. Int. 147 (2021) 110577. https://doi.org/10.1016/j.foodres.2021.110577.

Crossref Google Scholar
[11]

R.D.D. Riggs, I.H. Chen, O. Pustovyy, Hyperspectral imaging of a single bacterial cell, Food Public Health (2020). https://doi.org/10.5923/j.fph.20201001.03.

Crossref Google Scholar
[12]

J. Geng, Structured-light 3D surface imaging: a tutorial, Adv. Opt. Photonics. 3 (2011) 128. https://doi.org/10.1364/AOP.3.000128.

Crossref Google Scholar
[13]

M.A.A. Neil, R. Juškaitis, T. Wilson, Method of obtaining optical sectioning by using structured light in a conventional microscope, Opt. Lett. 22 (1997) 1905. https://doi.org/10.1364/OL.22.001905.

Crossref Google Scholar
[14]

A. Bassi, C. D’Andrea, G. Valentini, et al., Detection of inhomogeneities in diffusive media using spatially modulated light, Opt. Lett. 34 (2009) 2156. https://doi.org/10.1364/OL.34.002156.

Crossref Google Scholar
[15]

Y.Z. Lu, R.F. Lu, Development of a multispectral structured illumination reflectance imaging (SIRI) system and its application to bruise detection of apples, Trans. ASABE 60 (2017) 1379-1389. https://doi.org/10.13031/trans.12158.

Crossref Google Scholar
[16]

Y. Sun, R.F. Lu, Y.Z. Lu, et al., Detection of early decay in peaches by structured-illumination reflectance imaging, Postharvest Biol. Technol. 151 (2019) 68-78. https://doi.org/10.1016/j.postharvbio.2019.01.011.

Crossref Google Scholar
[17]

Y.Z. Lu, R. Li, R.F. Lu, Structured-illumination reflectance imaging (SIRI) for enhanced detection of fresh bruises in apples, Postharvest Biol. Technol. 117 (2016) 89-93. https://doi.org/10.1016/j.postharvbio.2016.02.005.

Crossref Google Scholar
[18]

X.Z. Gu, L. Feng, J.Y. Zhu, et al., Application of gas sensors for modelling the dynamic growth of Pseudomonas in pork stored at different temperatures, Meat Sci. 171 (2021) 108282. https://doi.org/10.1016/j.meatsci.2020.108282.

Crossref Google Scholar
[19]

A. Bassi, D.J. Cuccia, A.J. Durkin, et al., Spatial shift of spatially modulated light projected on turbid media, J. Opt. Soc. Am. A. 25 (2008) 2833. https://doi.org/10.1364/JOSAA.25.002833.

Crossref Google Scholar
[20]

Rachmawati, E. Rohaeti, M. Rafi, Combination of near infrared spectroscopy and chemometrics for authentication of taro flour from wheat and sago flour, J. Phys. Conf. Ser. 835 (2017) 012011. https://doi.org/10.1088/1742-6596/835/1/012011.

Crossref Google Scholar
[21]

L. Huang, J.W. Zhao, Q.S. Chen, et al., Rapid detection of total viable count (TVC) in pork meat by hyperspectral imaging, Food Res. Int. 54 (2013) 821-828. https://doi.org/10.1016/j.foodres.2013.08.011.

Crossref Google Scholar
[22]

E.O. Olaniyi, A.A. Adekunle, T. Odekuoye, et al., Automatic system for grading banana using GLCM texture feature extraction and neural network arbitrations, J. Food Process Eng. 40 (2017) e12575. https://doi.org/10.1111/jfpe.12575.

Crossref Google Scholar
[23]

H. Jiang, S.C. Yoon, H. Zhuang, et al., Integration of spectral and textural features of visible and near-infrared hyperspectral imaging for differentiating between normal and white striping broiler breast meat, Spectrochim. Acta. A. Mol. Biomol. Spectrosc. 213 (2019) 118-126. https://doi.org/10.1016/j.saa.2019.01.052.

Crossref Google Scholar
[24]

M. Kamruzzaman, G. ElMasry, D.W. Sun, et al., Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis, Anal. Chim. Acta. 714 (2012) 57-67. https://doi.org/10.1016/j.aca.2011.11.037.

Crossref Google Scholar
[25]

X.Y. Liu, H. Fu, PSO-based support vector machine with cuckoo search technique for clinical disease diagnoses, Sci. World J. 2014 (2014) 1-7. https://doi.org/10.1155/2014/548483.

Crossref Google Scholar
[26]

B.M. Nicolaï, K. Beullens, E. Bobelyn, et al., Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review, Postharvest Biol. Technol. 46 (2007) 99-118. https://doi.org/10.1016/j.postharvbio.2007.06.024.

Crossref Google Scholar
[27]

E.M. Achata, M. Oliveira, C.A. Esquerre, et al., Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions, LWT-Food Sci. Technol. 128 (2020) 109463. https://doi.org/10.1016/j.lwt.2020.109463.

Crossref Google Scholar
[28]

M.Y. Li, H.M. Niu, G.M. Zhao, et al., Analysis of mathematical models of Pseudomonas spp. growth in pallet-package pork stored at different temperatures, Meat Sci. 93 (2013) 855-864. https://doi.org/10.1016/j.meatsci.2012.11.048.

Crossref Google Scholar
[29]

E. Teye, X. Huang, N. Afoakwa, Review on the potential use of near infrared spectroscopy (NIRS) for the measurement of chemical residues in food, Am. J. Food Sci. Technol. 1 (2013) 1-8. https://doi.org/10.12691/ajfst-1-1-1.

Crossref Google Scholar
[30]

J.H. Aheto, X. Huang, X. Tian, 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 (2020) 1169-1179. https://doi.org/10.1007/s00216-019-02345-5.

Crossref Google Scholar
[31]

E. Bonah, X. Huang, J.H. Aheto, et al., Comparison of variable selection algorithms on vis-NIR hyperspectral imaging spectra for quantitative monitoring and visualization of bacterial foodborne pathogens in fresh pork muscles, Infrared Phys. Technol. 107 (2020) 103327. https://doi.org/10.1016/j.infrared.2020.103327.

Crossref Google Scholar
[32]

J. Ma, J.H. Cheng, D.W. Sun, et al., Mapping changes in sarcoplasmatic and myofibrillar proteins in boiled pork using hyperspectral imaging with spectral processing methods, LWT-Food Sci. Technol. 110 (2019) 338-345. https://doi.org/10.1016/j.lwt.2019.04.095.

Crossref Google Scholar
[33]

X.C. Zheng, Y.K. Peng, W.X. Wang, A Nondestructive real-time detection method of total viable count in pork by hyperspectral imaging technique, Appl. Sci. 7 (2017) 213. https://doi.org/10.3390/app7030213.

Crossref Google Scholar
[34]

A. Casaburi, P. Piombino, G.J. Nychas, et al., Bacterial populations and the volatilome associated to meat spoilage, Food Microbiol. 45 (2015) 83-102. https://doi.org/10.1016/j.fm.2014.02.002.

Crossref Google Scholar
[35]

Y. Xu, Q.S. Chen, Y. Liu, A novel hyperspectral microscopic imaging system for evaluating fresh degree of pork, Food and Public Health 38 (2020) 362-375. https://doi.org/10.5851/kosfa.2018.38.2.362.

Crossref Google Scholar
[36]

S.A.M. Abdelhameed, H.G.T. Ly, J. Moons, et al., Expanding the reactivity of inorganic clusters towards proteins: the interplay between the redox and hydrolytic activity of Ce (IV)-substituted polyoxometalates as artificial proteases, Chem. Sci. 12 (2021) 10655-10663. https://doi.org/10.1039/D1SC02760C.

Crossref Google Scholar
[37]

L.W. Mamani-Linares, C. Gallo, D. Alomar, Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy, Meat Sci. 90 (2012) 378-385. https://doi.org/10.1016/j.meatsci.2011.08.002.

Crossref Google Scholar
[38]

C.E. Alupoaei, L.H. García-Rubio, An interpretation model for the UV-vis spectra of microorganisms, Chem. Eng. Commun. 192 (2005) 198-218. https://doi.org/10.1080/00986440590473326.

Crossref Google Scholar
[39]

Y.Z. Feng, D.W. Sun, Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms, Talanta 105 (2013) 244-249. https://doi.org/10.1016/j.talanta.2012.11.042.

Crossref Google Scholar
[40]

D.F. Barbin, G. ElMasry, D.W. Sun, et al., Non-destructive assessment of microbial contamination in porcine meat using NIR hyperspectral imaging, Innov. Food Sci. Emerg. Technol. 17 (2013) 180-191. https://doi.org/10.1016/j.ifset.2012.11.001.

Crossref Google Scholar
Food Science and Human Wellness
Volume 14 Issue 2,
February 2025
Article number: 9250104
DOI: 10.26599/FSHW.2024.9250104
Cite this article:
Zhou B, Liu X, Ge Y, et al. Structured-illumination reflectance imaging for the evaluation of microorganism contamination in pork: effects of spectral and imaging features on its prediction performance. Food Science and Human Wellness, 2025, 14(2): 9250104. https://doi.org/10.26599/FSHW.2024.9250104
Metrics & Citations  
Article History
Copyright
Rights and Permissions
Return