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.
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.
, C., & Li,
Y. (2020). Classifying maize kernels naturally infected by fungi using near-infrared hyperspectral imaging.Infrared Physics & Technology
., 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.
, 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
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.
, 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.
., 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.