فناوری تصویربرداری ابرطیفی فروسرخ نزدیک برای شناسایی آلودگی میکروبی: مطالعه موردی اشریشیاکلی در کاهو

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی ماشینهای کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

2 عضو هیات علمی/ دانشکده مهندسی و فناوری کشاورزی دانشگاه تهران، کرج، ایران

3 )، موسسه تحقیقات فنی و مهندسی کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران

4 موسسه تحقیقات فنی و مهندسی کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران

5 عضو هیات علمی دانشکده مهندسی و فناوری کشاورزی دانشگاه تهران، تهران، کرج، ایران

چکیده

با افزایش تقاضا برای محصولات کشاورزی سالم، توسعه روش‌های تشخیص غیر مخرب برای ارزیابی سریع ایمنی محصولات کشاورزی از نظر آلودگی میکروبی بسیار حائز اهمیت است. هدف از این پژوهش توسعه یک روش اپتیکی غیر مخرب مبتنی بر تصویربرداری ابرطیفی در ناحیه فروسرخ نزدیک به همراه روش‌ تفکیک کمترین مربعات جزئی برای تشخیص سریع کاهوی برگی آلوده به میکروب اشرشیاکلی از نمونه‌های کنترل (فاقد آلودگی) بود. برای این منظور، پیش‌پردازش مکانی با روش تجزیه مولفه­های اصلی و پیش پردازش طیفی بر پایه توزیع نرمال استاندارد به همراه میانگین‌گیری مرکزی انجام شدند. نتایج تحلیل با روش تفکیک کمترین مربعات جزئی نشان داد که 4 گروه متفاوت با دقت بیش از 90 درصد و خطای کمتر از 008/0 قابل طبقه‌بندی هستند. هم­چنین، با استفاده از بردار اهمیت متغیر، ناحیه طیفی 1400 تا 1500 و 1200 نانومتر به عنوان  طول موج‌هایی که بیشترین اطلاعات را برای تشخیص هدف در اختیار می­گذارند، انتخاب شدند.

کلیدواژه‌ها


عنوان مقاله [English]

Microbial Contamination Assessment of Lettuce using NIR Hyperspectral Imaging: Case Study on Escherichia coli

نویسندگان [English]

  • Hossein Mobli 2
  • Bahareh Jamshidi 3
  • Aslan Azizi 4
  • Mohammad Sharifi 5
1 Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
2 Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
3 Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
4 Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
5 Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
چکیده [English]

Development of non-destructive detection methods to rapidly assess the safety of agricultural products in terms of microbial contamination due to the increasing demand for safe ones is very important. In this research, a non-destructive optical method based on NIR hyperspectral imaging with (PLS-DA) method for rapid detection of microbial contaminated lettuce class (contaminated with Escherichia coli) from control class (with no contamination) was developed. To this end, dimensionality reduction (Spatial preprocessing) and spectral preprocessing were performed using (PCA) and Standard Normal Variate (SNV) with Mean Centering (MC) methods. The results of PLS-DA analysis showed the good ability in classification of control class and contaminated ones with different microbial population with high accuracy of 90% and class error of 0.008. Besides, the spectral region of 1400 to 1500 nm and the wavelength of 1200 nm were selected as the most important wavelengths that provide the most information for target identification and group classification using Variable Importance in Projection (VIP) score of the loading plot for PLS-DA. In general, the result showed that the NIR hyperspectral imaging method with PLS-DA analysis could be a fast and accurate method for real-time and non-destructive detection of microbial contamination in lettuce samples and classification of different classes (safe and contaminated).

کلیدواژه‌ها [English]

  • Microbial contamination
  • Partial Least Square Discriminant Analysis
  • Hyperspectral imaging
  • Chemometrics
Ammor, M. S., Argyri, A., & Nychas, G.J.E. (2009). Rapid monitoring of the spoilage of minced beef stored under   conventionally and active packaging conditions using Fourier transform infrared spectroscopy in tandem with chemometrics. Journal of Meat Science, 81, 507-514
Aït-Kaddour, A., Boubellouta, T., & Chevallier, I. (2011). Development of a portable spectrofluorimeter for measuring the microbial spoilage of minced beef. Journal of Meat Science, 88, 675-681.
Argyri, A.A., Panagou, E.Z., Tarantilis, P.A., Polysiou, M., & Nychas, G.J.E. (2010). Rapid qualitative and quantitative detection of beef fillets spoilage based on Fourier transform infrared spectroscopy data and artificial neural networks. Sensors and Actuators B-Chemical, 145, 146-154.
Argyri, A.A., Jarvis, R.M., Wedge, D., Xu, Y., Panagou, E.Z., & Goodacre, R. (2013). A comparison of Raman and FT-IR spectroscopy for the prediction of meat spoilage. Food Control, 29, 461-470.
Beuchat, L.R. (1996). Pathogenic microorganism associated with fresh product. Journal of Food Protection, 59, 204-216.
Bonah, E., Huang, X., Aheto, J.H., Yi, R., Yu, SH., & Tu, H. (2020). 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 Physics & Technology, In Press.
Chu, X., Wang, W., Ni, X., Li, C., & Li, Y. (2020). Classifying maize kernels naturally infected by fungi using near-infrared hyperspectral imaging.Infrared Physics & Technology, 105, 103242.
Foca, G., Ferrari, C., Ulrici, A., Sciutto, G., Prati, S., Morandi, S., Brasca, M., Lavermicocca, P., Lanteri, S., & Oliveri, P. (2016). The potential of spectral and hyperspectral-imaging techniques for bacterial detection in food: A case study on lactic acid bacteria. Journal of Talanta, 153, 111-9.
Jamshidi, B. (2018). Rapid detection of pesticide-contaminated product using novel methods of NDT technology. Journal of nondestructive technology. 2(2), 58-65. (In Farsi)
Jackson, C.R., Randolph, K.C., Osborn, S.L., & Tyler, H.L. (2013). Culture dependent and independent analysis of bacterial communities associated with commercial salad leaf vegetables. BMC Microbiology, 13, 274.
Jung, Y., Jang, H., & Matthews, K.R. (2014). Effect of the food production chain from farm practices to vegetable processing on outbreak incidence. Microb Biotechnol, 7:517-27.
Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., Javan-Nikkhah, M., Jayas, D.S., & Siliveu, K. (2015). Detection of fungal infection in pistachio kernel by long-wave near-infrared hyperspectral imaging technique. Quality Assurance and Safety of Crops & Foods,  8 (1), 129 – 135.
Kodogiannis, V.S., & Alshejari, A. (2014). An adaptive neuro-fuzzy identification model for the detection of meat spoilage. Applied Soft Computing, 23, 483-497.
Kamruzzaman, M., ElMasry, G., Sun, D.W., & Allen, P. (2012). Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Journal of Innovative Food Science & Emerging Technologies, 16, 218-226
Lee, H., Everard, C.D., Kang, S., Cho, B.K., Chao, K., Chan, D.E., & Kim, M.S. (2014). Multispectral fluorescence imaging for detection of bovine faeces on Romaine lettuce and baby spinach leaves. Journal of Biosystems Engineering, 127, 125-134
Lara. M.A., Le. L., Diezma-Iglesias, B., Roger, J.M., & Ruiz-Altsent, M. (2013). Monitoring spinach shelf-life with hyperspectral image through packaging films. Journal of Food Engineering, 119, 353- 361.
Lundaei, L., Diezma, B., Lie, L., Ruiz-Garcia, L., Cantalapiedra, S., & Ruiz-Altisent, M. (2012). Monitoring of fresh-cut spinach leaves through a multispectral vision system. Postharvest Biology and Technology, 63, 74-84.
Mahesh, S., Jayas, D.S., Paliwal, J., & White, N.D.G. (2015). Hyperspectral imaging to classify and monitor quality of agricultural materials. Journal of Stored Products Research, 61, 17-26
Melikechi, N. Ding, H., Rock, S., Marcano A., & Connolly, D. (2008). Laser-induced breakdown spectroscopy of whole blood and liquid organic compounds. Journal of Optical Diagnostics and Sensing VIII, 68630O. Retrieved March 14, 2008, from https://doi.org/10.1117/12.761901.
Mo, C., kim, G., Kim, M., S., Lim, J., Lee, K., Lee, W.H., & Cho, B.K. (2017). On-line fresh-cut lettuce quality measurement system using hyperspectral imaging. Biosystems Engineering, 156, 38-50.
Niemira, B.A. (2007). Relative efficacy of sodium hypochlorite wash versus irradiation to inactivate Escherichia coli O157:H7 internalized in leaves of romaine lettuce and baby spinach. Journal of Food Protection, 70, 2526–2532.
Olaimat, AN., & Holley, R.A. (2012). Factors influencing the microbial safety of fresh produce: a review. Food Microbiol, 32(1), 1-19.
Panagou, E.Z., Mohareb, F.R., Argyri, A.A., Bessant, C.M., & Nychas, G.J.E. (2011). A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints. Food Microbiology, 28, 782-790.
Rahi, S., Mobli, H., & Jamshidi, B. (2018). Spectroscopy and spectral imaging techniques for non-destructive food microbial assessment. Proceedings of the 5th Iranian International NDT Conference November 4-5, Tehran IRNDT 2018. Available from: https://www.ndt.net.
Ravikanth, L., Singh, C.B., Jayas, D.S., & White, N.D.G. (2016). Performance evaluation of a model for the classification of contaminants from wheat using near-infrared hyperspectral imaging. Biosystems Engineering, 147, 248-258.
Schröder, S., Pavlov, S., Rauschenbach, I., Jessberger, E., & Hübers, H.W. (2013). Detection and identification of salts and frozen salt solutions combining laser-induced breakdown spectroscopy and multivariate analysis methods: A study for future martian exploration. Journal of Icarus, 223, 61-73.
Siripatrawan, U., Makino, Y., Kawagoe, Y., & Oshita, S. (2011). Rapid detection of Escherichia coli contamination in packaged fresh spinach using hyperspectral imaging. Talanta, 85, 276–281
Tauxe, R., Kruse, H., Hedberg, C., Potter, M., Madden, J., & Wachsmuth, K. (1997). Microbial hazards and emerging issues associated with produce. A preliminary report to the national advisory committee on microbiological criteria for foods. Journal of Food Protection, 60, 1400-1408.
Tito, N.B., Rodemann, T., & Powell, S.M. (2012). Use of near infrared spectroscopy to predict microbial numbers on Atlantic salmon. Food Microbiology, 32, 431-436.
Tao, F.F., & Peng, Y.K. (2014). A method for non-destructive prediction of pork meat quality and safety attributes by hyperspectral imaging technique. Journal of Food Engineering, 126, 98-106.
Williams, P.J., Geladi, P., Britz, T.J., & Manley, M. (2012). Near-infrared (NIR) hyperspectral imaging and multivariate image analysis to study growth characteristics and differences between species and strains of members of the genus Fusarium. Journal of Analytical and Bioanalytical Chemistry, 404(6-7), 1759-69.
Wei, M., Geladi, P., & Xiong, S. (2017). IR hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary study. Journal of analytical and bioanalytical chemistry, 409(9), 2449–2460.
Yin, J. (2011). LogP prediction for blocked tripeptides with amino acids descriptors (HMLP) by multiple linear regression and support vector regression. Procedia Environmental Sciences, 8, 173–178.
Yoshimura, M., Sugiyama, J., Tsuta, M., Fujita, K., Shibata, M., Kokawa, M., & Oto, N. (2014). Prediction of aerobic plate count on beef surface using fluorescence fingerprint. Food and Bioprocess Technology, 7, 1496-1504.
Zhang, H.Paliwal, J., Jayas, D.S., & White, N.D.G. (2007). Classification of fungal infected wheat kernels using near-infrared reflectance hyperspectral imaging and support vector machine. Journal ofTransactionsof the ASABE 50(5), 1779-1785.