Diagnosis of disease in tomato paste by Bacillus subtilis bacteria, Penicillium fungi and Aspergillus fungi with the help of electronic nose

Document Type : Research Paper

Authors

1 Department of Biosystem Mechanical Engineering, Faculty of Agriculture, Razi University, Kermanshah, Iran

2 Department of Agricultural Machinery Engineering, Sonqor Faculty of Agriculture, Razi University, , Kermanshah, Iran

3 Department of Plant Protection, Faculty of Agriculture, Razi University, Kermanshah, Iran

Abstract

 
Maintaining the quality of tomato paste is very important for processing industry. Bacteria, fungal toxins and molds are factors that can cause food contamination and spoilage. The purpose of this research was to investigate the performance of electronic nose in detecting spoilage in tomato paste and also to investigate the changes of some important physicochemical properties due to spoilage in tomato paste. Bacillus subtilis bacteria and Penicillium and Aspergillus fungi were used to spoil tomato paste. Sampling for samples infected with bacteria was carried out in 4-hour intervals for 24 hours, and for samples infected with fungi, it was taken daily for one week. . Quadratic Discretion Analysis (QDA), Artificial Neural Network (ANN), Supppport Vector Regression (SVR), were the methods used to achieve this goal. The QDA method showed a good performance in the classification of bacteria and fungi and was able to detect bacterial growth and spoilage of tomato paste in 6 sampling times with 100% accuracy. Classification accuracy with the help of neural network for bacteria based on sampling time was 86.7% and for samples infected with fungi based on the type of fungus was 90%. The best prediction of the physicochemical properties of the sample infected with bacteria and fungi by the ANN model related to the properties of sediment weight percentage and acidity, respectively, and in the SVR model, it related to the properties of pH and acidity, respectively.

Keywords

Main Subjects

Diagnosis of spoilage in tomato paste by Bacillus subtilis bacteria, Penicillium and Aspergillus fungies using electronic nose

Extended Abstract

Goal

The goal of this research was to investigate the performance and ability of the electronic nose as a new, fast and low-cost tool in detecting spoilage caused by Bacillus subtilis bacteria and Penicillium and Aspergillus fungi in tomato paste, as well as to investigate the changes in some of the physicochemical properties of tomato paste.

Research method

In order to perform tomato paste spoilage tests, two strains of fungies, named Penicillium and Aspergillus, and one strain of bacteria named Bacillus subtilis were used. Fungi and bacteria were cultured in solid agar medium. After the cultivation,bacteria and fungies, they were placed in an incubator for 72 hours so that the microorganisms grow in the culture medium. After 72 hours, the microorganisms were removed from the incubator and a suspension with a concentration of 107 cfu/ml was prepared from them and was added to the tomato paste samples free of microorganisms. The incubation period for the growth of microorganisms in tomato paste was carried out as follows.

For samples containing bacteria, 48 hours

For samples containing fungi, one week

Sampling were performed after the incubation period for bacteria-infected samples at 4-hour intervals for 24 hours and for fungies-infected samples at daily intervals for one week. Counting the number of bacteria and fungies in the initial and final sampling was done by the hemocytometer method. And finally, the data were analyzed using QDA, ANN, SVR .

Findings

The QDA method has been able to classify the paste samples infected with bacteria based on different sampling times with 100% accuracy. Also, this method was able to classify samples infected with fungies based on different sampling days and type of fungi with 98.57% and 77.86% accuracy, respectively. In general, it can be concluded that the electronic nose with the help of QDA method was able to detect the growth of fungies on different days of sampling, while the electronic nose with the help of QDA method could not perform very well in detecting the type of fungi. The artificial ANN network has been able to classify the paste sample infected with bacteria based on the sampling time with an accuracy of 86.7%. Also, the artificial ANN network was able to classify with 76.4% accuracy the paste sample infected with fungi based on the sampling time. Also, the ANN  was able to classify with 90% accuracy the sample of paste infected with fungi based on the type of fungi. The results obtained by ANN for predicting parameters of pH, Brix, acidity and sediment weight percentage of the samples infected with bacteria showed that the highest and lowest  value of R2 belonged to the sediment weight percentage and acidity parameters, respectively. Also the highest and lowest value R2 obtained from the SVR method related to the pH and sediment weight percentage parameters.  Also, in the samples infected with fungus, the highest value of R2 obtained by ANN and SVR methods belonged to the acidity parameter and the lowest related to the sediment weight percentage parameter.

Conclusion

In this research, the use of an electronic nose system based on ten metal oxide semiconductor (MOS) sensors to detect the spoilage caused by Bacillus subtilis bacteria and Aspergillus and Penicillium fungies in tomato paste was investigated. In order to classify the samples, QDA and ANN methods were used. QDA method with 100% accuracy and ANN with 87.6% accuracy classified the tomato paste sample infected with bacteria based on 6 different sampling times. The accuracy of classifying samples infected with fungies based on the type of fungi by ANN was 90%. According to this research, it can be concluded that the electronic nose is a suitable tool for detecting spoilage caused by Bacillus subtilis bacteria and Aspergillus and Penicillium fungies in tomato paste and it can detect spoilage in tomato paste with less time and cost.

References

Astuti, S. D., Al Isyrofie, A. I. F., Nashichah, R., Kashif, M., Mujiwati, T., Susilo, Y., & Syahrom, A. (2022). Gas Array Sensors based on Electronic Nose for Detection of Tuna (Euthynnus Affinis) Contaminated by Pseudomonas Aeruginosa. Journal of Medical Signals & Sensors, 12(4), 306.
Alizadeh behbahani, B., Noshad, M., & Sahraiyan, B. (2021). Investigation of the minimum inhibitory concentration and the minimum bactericidal concentration of Eucalyptus globulus essential oil on a number of pathogenic bacteria and the cause of food spoilage. FSCT, 18 (110), 49-57. URL: http://fsct.modares.ac.ir/article-7-43058-fa.html.(In Persian).
Aghili Nategh, N., Anvar, A., & Dalvand, M. J. (2020). Detection of Ripeness Grades of Strawberry Using an Electronic Nose. Journal of Researches in Mechanics of Agricultural Machinery, 9(2), 71-80. magiran.com/p2199818
Adab, H., Farajzadeh, M., Filekash, A., & Esmaili, R. (2015). preparation of autumn rapeseed yield map using perceptron neural network, case study: Sabzevar city. Quarterly scientific research journal of geographical space, 13(41), 171-180.( In Persian)
Asogwa, O. C., & Oladugba, A. V. (2015). On the comparison of artificial neural network (ANN) and multinomial logistic regression (MLR). West African Journal of Industrial and Academic Research, 13(1), 3-9.
Al-Maskari, S., Li, X., & Liu Q. (2014). An effective approach to handling noise and drift in electronic noses. In: Wang H, Sharaf MA (eds) Databases theory and applications. Lecture Notes in Computer Science, Springer, Cham, pp, 223–230
Balasubramanian, S., Panigrahi, S., Logue, C., Marchello, M., Doetkott, C., Gu, H., Sherwood, J. & Nolan, L. (2009). Spoilage identification of beef using an electronic nosesystem. Transactions of the American Society of Agricultural Engineering, 47(5), 1625–1633.
Chinipardaz, R., Rekabdar, Q., & Yousefi Hajiabad, R. (2015). Studying the human development of countries using mixed audit analysis methods. Quarterly Journal of Economic Studies,3(3), 5-20.( In Persian).
Concina, I., Falasconi, M., Gobbi, E., Bianchi, F., Musci, M., Mattarozzi, M., Pardo, M., Mangia, A., Careri M., & Sberveglieri, G. (2009). Early detection of microbial contamination in processed tomatoes by electronic nose. Food Control, 20(10), 873-880.
Ghiami Rad, M., Akbari, R., & Safari, A. (2017). Effect of alcoholic extract of garlic (Allium sativum) on some food born pathogenic bacteria. Journal of Food Hygiene, 6(4), 53-102.(In Persian).
Guil-Guerrero, J. L., Ramos, L., Moreno, C., Zúñiga-Paredes, J. C., Carlosama-Yépez, M., & Ruales, P. (2016). Plant-food by-products to improve farm-animal health. Animal Feed Science and Technology, 220, 121-135.
Hassen, Y., Gebre, H., & Haile, A. (2019). Effects of Pre-Heating and concentration temperatures on Physico-Chemical quality of semi concentrated tomato (Solanum lycopersicum) paste. J. Food Process. Technol, 10(2). doi: 10.4172/2157-7110.1000795
Hawksworth, D. L. (2015). Naming fungi involved in spoilage of food, drink, and water. Current Opinion in Food Science, 5, 23-28.
Heidarbeigi, K., Mohtasebi, S. S., Foroughirad, A., Ghasemi-Varnamkhasti, M., Rafiee, S., & Rezaei, K. (2015). Detection of adulteration in saffron samples using electronic nose. International Journal of Food Properties, 18(7), 1391-1401.
Karazhyan, R., Habibi-najafi, M. H., Yavarmanesh, M., Edalatian dovom, M. R., & Pourianfar, H. (2017). Comparison of optimized DNA extraction from mold Aspergillus, Penicillium and Rhizopus of tomato paste. FSCT, 14(67), 1-9 .(In Persian).
Lianou, A., Panagou, E. Z., & Nychas, G. J. E. (2016). Microbiological spoilage of foods and beverages. Stab Shelf Life Food: 3–42.
Lashgari, M., & MohammadiGol, R. (2016). Discrimination of Golab apple storage time using acoustic impulse response and LDA and QDA discriminant analysis techniques. Iran Agricultural Research, 35(2), 65-70.
Modupalli, N., Naik, M., Sunil, C. K., & Natarajan, V. (2021). Emerging non-destructive methods for quality and safety monitoring of spices. Trends in Food Science & Technology, 108, 133-147.
Mu, F., Gu, Y., Zhang, J., & Zhang, L. (2020). Milk source identification and milk quality estimation using an electronic nose and machine learning techniques. Sensors, 20(15), 4238.
Mabood, F., Hussain, J., Jabeen, F., Abbas, G., Allaham, B. A., Albroumi, M., & Haq, Q. M. (2018). Applications of FT-NIRS combined with PLS multivariate methods for the detection & quantification of saccharin adulteration incommercial fruit juices. Food Addit Contam, 35(6), 1052-1060.
Møretrø, T., Moen, B., Heir, E., Hansen, A. A., & Langsrud, S. (2016). Contamination of salmon fillets and processing plants with spoilage bacteria. International Journal of Food Microbiology, 237, 98-108
Omondi, S. (2018). The most popular vegetables in the world. WorldAtlas.
Pajohi, M. R., Tajik, H., & Farshid, A. A. (2010). Antimicrobial activity of Nisin on Bacillus cereus and Bacillus subtilis in a food model and their ultra structural investigation. Iranian Journal of Medical Microbiology, 4(3), 45-52.(In Persian).
Pearce, T. C., Schiffman, S. S., Nagle, H. T., & Gardner, J. W. (Eds.). (2006). Handbook of machine olfaction: electronic nose technology. John Wiley & Sons.
Roy, M., & Yadav, B. K. (2021). Electronic nose for detection of food adulteration: A review. Journal of Food Science and Technology, 1-13.
Rodriguez-Perez, R., Vogt, M., & Bajorath, J. (2017). Support vector machine classification and regression prioritize different structural features for binary compound activity and potency value prediction. ACS omega, 2(10), 6371-6379.
Rad, A. E., & Shahidi, F. (2004). Evaluation of physicochemical and microbial changes of bulk tomato paste in cold storage. Isfahan University of Technology-Journal of Crop Production and Processing, 8(1), 171-181.
Sanaeifar, A., Mohtasebi, S. S., Ghasemi-Varnamkhasti, M., & Ahmadi, H. (2016). Application of MOS based electronic nose for the prediction of banana quality properties. Measurement, 82, 105-114.
Triyana, K., Taukhid Subekti, M., Aji, P., Nur Hidayat, S., & Rohman, A. (2015). Development of electronic nose with low‑cost dynamic headspace for classifying vegetable oils and animal fats. In Applied Mechanics and Materials (Vol. 771, pp. 50‑54). Trans Tech Publications Ltd.
Vitalis, F., Zaukuu, J. L. Z., Bodor, Z., Aouadi, B., Hitka, G., Kaszab, T., Zsom-Muha, V., Gillay, Z., & Kovacs, Z. (2020). Detection and quantification of tomato paste adulteration using conventional and rapid analytical methods. Sensors, 20(21), 6059. doi:10.3390/s20216059.
Varidi, M. J., Varidi, M., Vajdi, M., & Sharifpour, A. (2018). Design, development and application of electronic nose instrument to rapidly detect spoilage of air, vacuum and modified atmosphere packaged camel minced meat. FSCT, 15 (74), 254-243. URL: http://fsct.modares.ac.ir/article-7-2048-fa.html. .(In Persian).
Zaki Dizaji, H., Adibzadeh, A., & Aghili Nategh, N. (2021). Application of E-nose technique to predict sugarcane syrup quality based on purity and refined sugar percentage. Journal of Food Science and Technology, 58, 4149-4156.
Iranian Journal of Biosystems Engineering
Volume 54, Issue 2
November 2023
Pages 33-47

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History

  • Receive Date: 18 June 2023
  • Revise Date: 25 November 2023
  • Accept Date: 27 November 2023

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