RESEARCH PAPER
Methodology for preparing a cosmetic sample for the development of Microorganism Detection System (SDM) software and artificial intelligence learning to recognize specific microbial species
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1
Institute of Rural Health, Lublin, Poland
2
Department of Occupational Medicine, Medical University, Lublin, Poland
3
NVT Limited Liability Company, Warsaw, Poland
4
Institute of Theory of Electrical Engineering, Measurement and Information Systems, University of Technology, Warsaw, Poland
Ann Agric Environ Med. 2021;28(4):681-685
KEYWORDS
TOPICS
ABSTRACT
Introduction and objective:
The article presents the methodology of preparing a cosmetic sample for analysi, and the creation of a dataset for teaching artificial intelligence to recognize specific species of microorganisms in cosmetic samples in terms of compliance with the ISO standard document, to develop of the Microorganism Detection System (SDM).
Material and methods:
Methodology of preparation a cosmetic sample for testing covers the steps from taking a cosmetic sample to obtaining separated living microorganisms through staining to photos, which in the final stage are used for analysis of the purity of cosmetics by SDM, as well as for learning and testing of the deep convolutional neural network (CNN) for detecting and classifying cells of specific species of bacteria, fungi and yeast in cosmetics, according to the document of standard PN-EN ISO 17516–2014:11.
Results:
A new techique was devised for preparing a cosmetic sample for the development of Microorganism Detection System (SDM) software, and artificial intelligence learning to recognize specific microbial species. Based on metod demonstrated, the Intelligent algorithms of SDM proved to be effective in counting and recognizing specific microorganisms (average accuracy for Candida albicans – 97%, Escherichia coli – 76%, Pseudomonas aeruginosa – 70%, Staphylococcus aureus – 85%), which are the most important species for the assessment of the purity of cosmetics. In addition, the reproducibility of the developed method was verified, and the results obtained were comparable to the breeding methods currently used, based on specific standards.
Conclusions:
The experiments confirmed the high sensitivity and specificity of the SDM method, its repeatability and, above all, the comparability of the results with clasic methods of European standards.
ACKNOWLEDGEMENTS
The research was co-financed by the European Union
from the European Regional Development Fund under the
Intelligent Development programme, implemented under
the competition 1 / 4.1.4 / 2018 / POIR: ‘Microorganism
detection system – SDM’ – National Centre for Research
and Development.
REFERENCES (32)
1.
Norma PN-EN ISO 17516:2014-11: Kosmetyki–Mikrobiologia–Limity mikrobiologiczne.
2.
Regulation (EC) No 1223/2009 of the European Parliament and of the Council of 30 November 2009 on cosmetic products.
https://eur-lex.europa.eu/eli/... (access: 2021.11.07).
3.
Halla N, Fernandes IP, Heleno SA, Costa P, Boucherit-Otmani Z, Boucherit K, et al. Cosmetics preservation: a review on present strategies. Molecules. 2018; 23(7): 1571. doi: 10.3390/molecules23071571.
4.
Campana R, Scesa C, Patrone V, Vittoria E, Baffone W. Microbiological study of cosmetic products during their use by consumers: health risk and efficacy of preservative systems. Lett Appl Microbiol. 2006; 43(3): 301–6. doi: 10.1111/j.1472-765X.2006.01952.x.
5.
Bilal M, Mehmood S, Iqbal Hafiz MN. The Beast of Beauty: Environmental and Health Concerns of Toxic Components in Cosmetics. Cosmetics 2020; 7: 13. doi: 10.3390/cosmetics7010013.
6.
Findley K, Grice EA. The skin microbiome: a focus on pathogens and their association with skin disease. PLoS Pathog. 2014; 10(10): e1004436-e. doi: 10.1371/journal.ppat.1004436.
7.
Bashir A, Lambert P. Microbiological study of used cosmetic products: highlighting possible impact on consumer health. J Appl Microbiol. 2020; 128(2): 598–605. doi: 10.1111/jam.14479.
8.
Skowron K, Jakubicz A, Budzyńska A, Kaczmarek A, Grudlewska K, Reśliński A, Gospodarek-Komkowska E. Microbiological purity assessment of cosmetics used by one and several persons and cosmetics after their expiry date. Rocz Panstw Zakl Hig. 2017; 68(2): 191–197.
9.
Russell AD. Challenge testing: principles and practice. Int J Cosmet Sci. 2003; 25(3): 147–53. doi: 10.1046/j.1467-2494.2003.00179.x.
10.
Nemati M, Hamidi A, Maleki Dizaj S, Javaherzadeh V, Lotfipour F. An Overview on Novel Microbial Determination Methods in Pharmaceutical and Food Quality Control. Adv Pharm Bull. 2016; 6(3): 301–308. doi: 10.15171/apb.2016.042.
11.
Huang F, Zhang Y, Lin J, Liu Y. Biosensors Coupled with Signal Amplification Technology for the Detection of Pathogenic Bacteria: A Review. Biosensors (Basel). 2021 Jun 9; 11(6): 190. doi: 10.3390/bios11060190.
12.
Michalek IM, John SM, Caetano dos Santos FL. Microbiological contamination of cosmetic products–observations from Europe, 2005–2018. J Eur Acad Dermatol Venereol. 2019; 33: 2151–2157.
13.
The Scientific Committee on Consumers Safety, Directorate-General for Health and Consumer Protection of the European Commission. The SSCS’s Notes of Guidance for the Testing of Cosmetic Ingredients and Their Safety Evaluation, 10th ed. Brussels, Belgium: European Comission; 2018.
14.
Szeląg B, Drewnowski J, Łagód G, Majerek D, Dacewicz E, Fatone F. Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning. Sensors 2020, 20, 1941; doi: 10.3390/s20071941.
15.
Yuanyi Z, Wang J, Peng J, Zhang L. Anchor box optimization for object detection. In The IEEE Winter Conference on Applications of Computer Vision, 2020. p. 1286–1294.
16.
Alzubaidi L, Zhang J, Amjad I, Humaidi J, Al-Dujaili A, Duan Y, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021; 8(1): 53. doi: 10.1186/s40537-021-00444-8.
17.
Zawadzki P, Adamczuk P, Jamka K, Wróblewska-Łuczka P, Bojar H, Raszewski G. The Microorganism Detection System (SDM) for microbiological control of cosmetic products. Ann Agric Environ Med. doi: 10.26444/aaem/144668.
18.
Strains specified by official microbial assays, American Type Culture Collection [ATCC].
https://www.atcc.org/~/media/P... Pharmaceutical% 20Microbiology.ashx, 2017. (access: 2021.11.07).
19.
Smith KP, Kirby JE Image analysis and artificial intelligence in infectious disease diagnostics. Clin Microbiol Infect. 2020 Oct; 26(10): 1318–1323. doi: 10.1016/j.cmi.2020.03.012.
20.
Satyanarayana KV, Rao NT, Bhattacharyya D, Hu YC. Identifying the presence of bacteria on digital images by using asymmetric distribution with k-means clustering algorithm. Multidimens Syst Signal Process. 2021 Oct 9: 1–26. doi: 10.1007/s11045-021-00800-0.
21.
Smith KP, Kang AD, Kirby JE. Automated interpretation of blood culture Gram stains by use of a deep convolutional neural network. J Clin Microbiol. 2018; 56 (e01521): 17.
22.
Stevens KA, Jaykus LA. Bacterial separation and concentration from complex sample matrices: a review. Crit Rev Microbiol. 2004; 30(1): 7–24. doi: 10.1080/10408410490266410.
23.
Fukushima H, Katsube K, Hata Y, Kishi R, Fujiwara S. Rapid separation and concentration of food-borne pathogens in food samples prior to quantification by viable-cell counting and real-time PCR. Appl Environ Microbiol. 2007; 73(1): 92–100. doi: 10.1128/AEM.01772-06.
25.
Garbacz M, Malec A, Duda-Saternus S, Suchorab Z, Guz Ł, Łagód G. Methods for Early Detection of Microbiological Infestation of Buildings Based on Gas Sensor Technologies. Chemosensors. 2020; 8: 7. doi: 10.3390/chemosensors8010007.
26.
Romero S, Schell RF, Pennell DR. Rapid method for the differentiation of gram-positive and gram-negative bacteria on membrane filters. J Clin Microbiol. 1988 Jul; 26(7): 1378–82. doi: 10.1128/jcm.26.7.1378-1382.1988.
27.
Saida H, Ytow N, Seki H. Photometric Application of the Gram Stain Method To Characterize Natural Bacterial Populations in Aquatic Environments. Appl Environ Microbiol. 1998; 64(2): 742–7. doi: 10.1128/AEM.64.2.742-747.1998.
28.
Wang H, Kodmir HC, Qiu Y, et al. Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning. Light Science Applications. 2020; 9(118): 1–17.
29.
Zieliński B, Plichta A, Misztal K, Spurek P, Brzychczy-Włoch M, Ochońska D. Deep learning approach to bacterial colony classification. PLoS ONE. 2017; 12(9): e0184554. doi: 10.1371/journal.pone.0184554.
30.
Maruthamuthu MK, Raffiee AH, De Oliveira DM, Ardekani AM, Verma MS. Raman spectra-based deep learning: A tool to identify microbial contamination. Microbiologyopen. 2020; 9(11): e1122. doi: 10.1002/mbo3.1122.
31.
Rani P, Kotwal S, Manhas J, Sharma V, Sharma S. Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments. Arch Comput Methods Eng. 2021; 1–37. doi: 10.1007/s11831-021-09639-x.
32.
Zhang Y, Jiang H, Ye T, Juhas M. Deep Learning for Imaging and Detection of Microorganisms. Trends Microbiol. 2021; 29(7): 569–572. doi: 10.1016/j.tim.2021.01.006.