ORIGINAL PAPER
Visual detection of microbial community during three bacteria mixed fermentation through hyperspectral imaging technology
| 1 | School of Agricultural Equipment Engineering, Jiangsu University, China |
| 2 | School of Food and Biological Engineering, Jiangsu University, China |
| 3 | International Research Center for Food Nutrition and Safety, Jiangsu University, China |
| 4 | Department of Analytical Chemistry and Food Science, University of Vigo - Ourense Campus, Spain |
CORRESPONDING AUTHOR
Submission date: 2021-10-15
Final revision date: 2021-11-09
Acceptance date: 2021-11-09
Online publication date: 2021-11-13
Publication date: 2022-02-01
eFood 2021;2(5):271–278
KEYWORDS
Microbial community structureVinegar fermentationHyperspectral imaging technologyStrain colony identificationStrain colony counting
TOPICS
ABSTRACT
Hyperspectral imaging technology with chemometrics was used for identifying and counting each species in microbial community during mixed fermentation. Hyperspectral images of microbial community of Enterobacter sp, Acetobacter pasteurianus, and Lactobacillus paracasei colonies were obtained and the spectra of strain colonies were extracted. Identification models were developed using linear discriminant analysis (LDA) and least-squares support vector machine (LS-SVM) by using 23 variables selected by genetic algorithm. The optimal LS-SVM model with identification rate of 96.67 % was used to identify colonies and prepare colony distribution maps in color for strains counting. The counting results by hyperspectral imaging technology agree with that of the manual counting method with average relative error of 3.70 %. The developed counting method has been successfully used to identify and count the specific strain from the mixed strains simultaneously. The hyperspectral imaging technology has a great potential to monitor changes in the microbial community structure.
REFERENCES (34)
1.
M J Bianco. “Machine learning in acoustics: Theory and applications”. The Journal of the Acoustical Society of America 146 (2019), pp. 3590–3628. DOI: https://doi.org/10.1121/1.5133....
2.
W. -B Chen and C Zhang. “An automated bacterial colony counting and classification system”. Information Systems Frontiers 11(4) (2009), pp. 349–368. DOI: https://doi.org/10.1007/s10796....
3.
Talha Demirci, Çig˘dem Konak Göktepe, and Hale ˙Inci Öztürk. “Prevalence and fingerprinting of lactic acid bacteria community during 180 days of ripening in traditional Turkish goatskin bag Tulum cheeses produced in the mountainous region of Karaman using culture-dependent and-independent methods”. International Dairy Journal 118 (2021), p. 105041. DOI: https://doi.org/10.1016/j.idai....
4.
G. -Y Fang. “Deciphering the succession patterns of bacterial community and their correlations with environmental factors and flavor compounds during the fermentation of Zhejiang rosy vinegar”. International Journal of Food Microbiology 341 (2021), p. 109070. DOI: https://doi.org/10.1016/j.ijfo....
5.
Y. -Z Feng et al. “Invasive weed optimization for optimizing one-agar-for-all classification of bacterial colonies based on hyperspectral imaging”. Sensors and Actuators B: Chemical 269 (2018), pp. 264–270. DOI: http://doi.org/10.1016/j.snb.2....
6.
Rocío Fernández-Pérez et al. “Strain typing of acetic acid bacteria responsible for vinegar production by the submerged elaboration method”. Food microbiology 27 (2010), pp. 973–978. DOI: https://doi.org/10.1016/j.fm.2....
7.
A Ferrari, S Lombardi, and A J P R Signoroni. “Bacterial colony counting with convolutional neural networks in digital microbiology imaging”. Pattern Recognition 61 (2017), pp. 629–640. DOI: https://doi.org/10.1016/j.patc....
8.
G Foca et al. “The potential of spectral and hyperspectral-imaging techniques for bacterial detection in food: A case study on lactic acid bacteria”. Talanta 153 (2016), pp. 111–119. DOI: https://doi.org/10.1016/j.tala....
9.
Gan Xing, Tang Hanlan, Ye Dongdong, Li Pan, Luo Lixin, Lin Weifeng. 2017. “Diversity and dynamics stability of bacterial community in traditional.Diversity and dynamics stability of bacterial community in traditional solid-state fermentation of Qishan vinegar 67 (2017), pp. 703–713. DOI: https://doi.org/10.1007/s13213....
10.
Ahmad Hassan at et al. “Choosing mutation and crossover ratios for genetic algorithms-a review with a new dynamic approach”. Information 10 (2019), p. 390. DOI: https://doi.org/10.3390/info10....
11.
S Jia. “Feasibility of analyzing frost-damaged and non-viable maize kernels based on near infrared spectroscopy and chemometrics”. Journal of Cereal Science 69 (2016), pp. 145–150. DOI: https://doi.org/10.1016/j.jcs.....
12.
JiyongShi et al. “A rapid and nondestructive method to determine the distribution map of protein, carbohydrate and sialic acid on Edible bird’s nest by hyper-spectral imaging and chemometrics”. Food Chemistry 229 (2017), pp. 235–241. DOI: https://doi.org/10.1016/j.food....
13.
Hoonsoo Lee et al. “Detection of cucumber green mottle mosaic virus-infected watermelon seeds using a near-infrared (NIR) hyperspectral imaging system: Application to seeds of the “Sambok Honey” cultivar”. Biosystems Engineering 148 (2016), pp. 138–147. DOI: https://doi.org/10.1016/j.bios....
14.
H Li et al. “Quantifying total viable count in pork meat using combined hyperspectral imaging and artificial olfaction techniques”. Food Analytical Methods 9 (2016), pp. 3015–3024. DOI: https://doi.org/10.1007/s12161....
15.
S Li et al. “Microbial diversity and their roles in the vinegar fermentation process”. Applied microbiology and biotechnology 99 (2015), pp. 4997–5024. DOI: https://doi.org/10.1007/s00253....
16.
Y Li et al. “Determination of Geographical Origin and Anthocyanin Content of Black Goji Berry ( Lycium ruthenicum Murr.) Using Near-Infrared Spectroscopy and Chemometrics”. Food Analytical Methods 10(4) (2016), pp. 1–11. DOI:10.1007/s12161-016-0666-4
17.
S. -N Liu, Y Han, and Z.-J J F R I Zhou. “Lactic acid bacteria in traditional fermented Chinese foods”. Food Research International 44 (2011), pp. 643–651. DOI: https://doi.org/10.1016/j.food....
18.
Y Liu, H Pu, and D W Sun. “Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications”. Trends in Food Science & Technology 69 (2017), pp. 25–35. DOI: https://doi.org/10.1016/j.tifs....
19.
S S Patra. “Emerging Healthcare Problems in High-Dimensional Data and Dimension Reduction”. Advanced Prognostic Predictive Modelling in Healthcare Data Analytics (2021), pp. 25–49. DOI: https://doi.org/10.1007/978-98....
20.
Rizwan Qureshi et al. “Hyperspectral document image processing: Applications, challenges and future prospects”. Pattern Recognition 90 (2019), pp. 12–22. DOI: https://doi.org/10.1016/j.patc....
21.
M Rajeev, T Sushmitha, and S K Pandian. "Culture-dependent and-Independent Strategies in Bacterial Diversity Appraisal". Microbial Systematics (2020), pp. 1-27.https://doi.org/10.1201/978042...
22.
Yvonne Salfinger and Mary Lou Tortorello. Compendium of methods for the Microbiological Examination. Washington: American Public Health Association, 2018. DOI: 10.2105/MBEF.0222.
23.
T Shen, Z Xiaobo, S Jiyong, et al. “Determination Geographical Origin and Flavonoids Content of Goji Berry Using Near-Infrared Spectroscopy and Chemometrics”. Food Analytical Methods 9(1) (2016), pp. 68–79. DOI: https://doi.org/10.1007/s12161....
24.
J Shi. “Noise-free microbial colony counting method based on hyperspectral features of agar plates”. Food Chemistry 274 (2019), pp. 925–932. DOI: https://doi.org/10.1016/j.food....
25.
Jiyong Shi et al. “Application of spectral features for separating homochromatic foreign matter from mixed congee”. Food Chemistry: X 11 (2021), p. 100128. DOI: https://doi.org/10.1016/j.foch....
26.
J Shi et al. “Rapid identification of Lactobacillus species using near infrared spectral features of bacterial colonies”. Journal of Near Infrared Spectroscopy 27 (2019), pp. 302–313. DOI: https://doi.org/10.1177/096703....
27.
A Sitorus et al. “Dataset of adulteration with water in coconut milk using FTIR spectroscopy”. Dataset of adulteration with water in coconut milk using FTIR spectroscopy 36 (2021), pp. 107058–107058. DOI: https://doi.org/10.1016/j.dib.....
28.
Petter Stefansson et al. “Fast method for GA-PLS with simultaneous feature selection and identification of optimal preprocessing technique for datasets with many observations”. Journal of Chemometrics 34 (2020), p. 3195. DOI: https://doi.org/10.1002/cem.31....
29.
L Thomas, C Larroche, and A Pandey. “Current developments in solid-state fermentation”. Biochemical Engineering Journal 81 (2013), pp. 146–161. DOI: https://doi.org/10.1016/j.bej.....
30.
J W Jia et al. “Culture-dependent and culture-independent analysis of lactic acid bacteria from Shanxi aged vinegar”. Annals of Microbiology 62(4) (2012), pp. 1825–1830. DOI: https://doi.org/10.1007/s13213....
31.
X Wei. Analysis of the microbial diversity and function during acetic acid fermentation process of Zhenjiang Aromatic Vinegar. Wuxi: Jiangnan University, 2011.
32.
Yu-Hui Yeh et al. “Strawberry foliar anthracnose assessment by hyperspectral imaging”. Computers and Electronics in Agriculture 122 (2016), pp. 1–9. DOI: https://doi.org/10.1016/j.comp....
33.
C Zhang, F Liu, and H Yong. “ Identification of coffee bean varieties using hyperspectral imaging: Influence of preprocessing methods and pixel-wise spectra analysis”. Scientific Reports 8 (2018), p. 2166. DOI: https://doi.org/10.1038/s41598....
34.
Q Zhang. “Monitoring microbial succession and metabolic activity during manual and mechanical solid-state fermentation of Chinese cereal vinegar”. LWT 133 (2020), p. 109868. DOI: https://doi.org/10.1016/j.lwt.....
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.