Abstract
The improvement of quality of life and human activity has many directions. One of them is providing high-quality and safe food. Advancements in sensor technologies, data mining and processing algorithms have provided technical capabilities for development of innovative engineering methods that guarantee certainty regarding the quality control of food and public health. The potential of Near Infrared Spectral Analysis and Aquaphotomics as non-destructive and rapid methods for monitoring food quality through observation of water absorbance bands is presented.
References
Atanassova, S. (2015). Near Infrared Spectroscopy and aquaphotomics for monitoring changes during yellow cheese ripening. Agricultural Science and Technology, 7 (2), 269–272.
View in Google Scholar
Atanassova, S., Veleva, P., Stoyanchev, T. (2017). Near-infrared spectral informative indicators for meat and dairy products bacterial contamination and freshness evaluation. In: A. M. Holban, A. M. Grumezescu (eds). Microbial Contamination and Food Degradation (pp. 315–340). Amsterdam: Elsevier Academic Press. ISBN 9780128112632.
View in Google Scholar
Balage, J.M., da Luz, E., Silva, S., Gomide, C.A., Bonin, M. de N., Figueira, A.C. (2015). Predicting pork quality using Vis/NIR spectroscopy. Meat Science, 108, 37–43.
View in Google Scholar
Bázár, G., Kovács, Z., Tanaka, M., Tsenkova, R. (2014). Aquaphotomics and its extended water mirror concept explain why NIRS can measure low concentration aqueous solutions [online, accessed: 2017-10-20]. In: Aquaphotomics, ‘Understanding Water in Biological World’. The 5th Kobe University Brussels European Centre Symposium ‘Innovation, Environment, and Globalisation’, Brussels, Belgium. Retrieved from: http://www.aquaphotomics.com/pdf-posters/nr2.pdf.
View in Google Scholar
Cattaneo, T., Vanoli, M., Grassi, M., Rizzolo, A., Barzaghi, S. (2016). The aquaphotomics approach as a tool for studying the influence of food coating materials on cheese and winter melon samples. Journal of Near Infrared Spectroscopy, 24, 381–390.
View in Google Scholar
Cheng, J.H., Dai, Q., Sun, D.W., Zeng, X.A., Liu, D., Pu, H.B. (2013). Applications of non-destructive spectroscopic techniques for fish quality and safety evaluation and inspection. Trends in Food Science & Technology, 34, 18–31.
View in Google Scholar
Collell, C., Gou, P., Arnau, J., Muñoz, I., Comaposada, J. (2012). NIR technology for on-line determination of superficial a(w) and moisture content during the drying process of fermented sausages. Food Chemistry, 135 (3), pp. 1750–1755.
View in Google Scholar
Cozzolino, D., Murray, I. (2012). A review on the application of infrared technologies to determine and monitor composition and other quality characteristics in raw fish, fish products, and seafood. Applied Spectroscopy Reviews, 47 (3), 207–218.
View in Google Scholar
Feng, Y.Z., Sun, D.W. (2013). Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of Pseudomonas loads in chicken fillets. Talanta, 109, 74–83.
View in Google Scholar
Gowen, A.A., Marini, F., Tsuchisaka, Y., De Luca, S., Bevilacqua, M., O’Donnell, C., Downey, G., Tsenkova, R. (2015). On the feasibility of near infrared spectroscopy to detect contaminants in water using single salt solutions as model systems. Talanta, 131, 609–618.
View in Google Scholar
Horvat, K., Seregely, Zs., Andrassy, E., Dalmadi, I., Farkas, J. (2008). A preliminary study using near infrared spectroscopy to evaluate freshness and detect spoilage in sliced pork meat. Acta Alimentaria, 37, 93–102.
View in Google Scholar
Huang, L., Zhao, J., Chen, Q., Zhang, Y. (2014). Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. Food Chemistry, 145, 228–236.
View in Google Scholar
Huang, H., Liu, L., Ngadi, M.O. (2016). Prediction of pork fat attributes using NIR images of frozen and thawed pork. Meat Science, 119, 51–61.
View in Google Scholar
ISO 4833–1:2013. Microbiology of food and animal feeding stuffs: Horizontal method for the enumeration of microorganisms. Colony-count technique at 30 degrees C.
View in Google Scholar
Jia, B., Yoon, S., Zhuang, H., Wang, W., Li, Ch. (2017). Prediction of pH of fresh chicken breast fillets by VNIR hyperspectral imaging. Journal of Food Engineering, 208, 57–65.
View in Google Scholar
Kitamoto, M., Kito, K., Niimi, Y., Shoda, S., Takamura, A., Hiramatsu, T., Akashi, T., Yokoi, Y., Hirano, H., Hosokawa, M., Yamamoto, A., Agata, N., Hamajima, N. (2009). Food poisoning by Staphylococcus aureus at a university festival. Japanese Journal of Infectious Diseases, 62 (3), 242–243.
View in Google Scholar
Kovacs, Z., Bázár, G., Oshima, M., Shigeokad, S., Tanaka, M., Furukawa, A., Nagai, A., Osawa, M., Itakura, Y., Tsenkova, R. (2016). Water spectral pattern as holistic marker for water quality monitoring. Talanta, 147, 598–608.
View in Google Scholar
Kumar, N., Bansal, A., Sarma, G.S., Rawal, R.K. (2014). Chemometrics tools used in analytical chemistry: An overview. Talanta, 123, 186–199.
View in Google Scholar
Li, X., Feng, F., Gao, R., Wang, L., Qian, Y., Li, C., Zhou, G. (2016). Application of near infrared reflectance (NIR) spectroscopy to identify potential PSE meat. Journal of the Science and Food Agriculture, 96 (9), 3148–3156.
View in Google Scholar
Lu, W., Sun, D.W., Pu, H., Cheng, J.H. (2016). Quality analysis and classification and authentication of liquid foods by near-infrared spectroscopy: A review of recent research developments. Critical Reviews in Food Science and Nutrition, 57 (7) 1524–1538. DOI:10.1080/10408398.2015.1115954.
View in Google Scholar
Matija, L., Tsenkova, R. (2011). Aquaphotomics of hydrogenated fullerenes. In: Book of abstracts of the Second Scientific International Conference on Water and Nanomedicine (pp. 30–31). Banja Luka: Academy of Sciences and Arts of Republic of Srpska.
View in Google Scholar
Munćan, J. (2011). A comparative study of structure and properties of water by IR and opto-magnetic spectroscopy. In: Book of abstracts of the Second Scientific International Conference on Water and Nanomedicine (pp. 56–57). Banja Luka: Academy of Sciences and Arts of Republic of Srpska.
View in Google Scholar
Niu, X.Y., Shao, L.M., Dong, F., Zhao, Z., Zhu, Y. (2014). Discrimination of donkey meat by NIR and chemometrics (article in Chinese). Guang Pu Xue Yu Guang Pu Fen Xi, 34 (10), 2737–2742.
View in Google Scholar
Official Methods of Analysis (2000). 17th ed., AOAC International, Gaithersburg, MD, Method 983.18.
View in Google Scholar
Reis, M.M., Rosenvold, K. (2014). Early on-line classification of beef carcasses based on ultimate pH by near infrared spectroscopy. Meat Science, 96, 862–869.
View in Google Scholar
Rouger, A., Remenant, B., Prévost, H., Zagorec, M. (2017). A method to isolate bacterial communities and characterize ecosystems from food products: Validation and utilization as a reproducible chicken meat model. International Journal of Food Microbiology, 247, 38–47.
View in Google Scholar
Rukchon, C., Nopwinyuwong, A., Trevanich, S., Jinkarn, T., Suppakul P. (2014). Development of a food spoilage indicator for monitoring freshness of skinless chicken breast. Talanta, 130, 547–554.
View in Google Scholar
Sandorfy, C., Buchet, R., Lachenal, G. (2006). Principles in molecular vibrations for near-infrared spectroscopy. In: Y. Ozaki, W.F. McClure, A.A. Christy (eds). Near infrared spectroscopy in food science and technology (pp. 11–48). Hoboken, NJ: John Wiley & Sons. ISBN 9780471672012.
View in Google Scholar
Schmid, D., Gschiel, E., Mann, M., Huhulescu, S., Ruppitsch, W., Bohm, G., Pichler, J., Lederer, I., Hoger, G., Heuberger, S., Allerberger, F. (2007). Outbreak of acute gastroenteritis in an Austrian boarding school. Euro Surveillance, 12 (3), 224.
View in Google Scholar
Teixeira dos Santos, C.A., Lopo, M., Pascoa, R., Lopes, J.A. (2013). A review on the applications of portable near-infrared spectrometers in the agro-food industry. Applied Spectroscopy, 67, 1215–1233.
View in Google Scholar
Tito, N.B., Rodemann, T., Powell, S.M. (2012). Use of near infrared spectroscopy to predict microbial numbers on Atlantic salmon. Food Microbiology, 32 (2), 431–436.
View in Google Scholar
Trocino, A., Xiccato, G., Majolini, D., Tazzoli, M., Bertotto, D., Pascoli, F., Palazzi, R. (2012). Assessing the quality of organic and conventionally-farmed European sea bass (Dicentrarchus labrax). Food Chemistry, 131, 427–433.
View in Google Scholar
Tsenkova, R., 2009. Introduction to Aquaphotomics, dynamic spectroscopy of aqueous and biological systems describes peculiarities of water. Journal of Near Infrared Spectroscopy, 17, 303–314.
View in Google Scholar
Veleva-Doneva, P. (2017). Аdvanced computer-based approaches for food quality evaluation: updated review. Trakia Journal of Sciences, series Social Sciences, 15 (1), 413–418.
View in Google Scholar
Veleva-Doneva, P., Draganova, T., Atanassova, S., Tsenkova, R. (2010). Detection of bacterial contamination in milk using NIR spectroscopy and two classification methods—SIMCA and Neuro-Fuzzy classifier. IFAC Proceedings Volumes, 43 (26), 225–229.
View in Google Scholar
Veleva-Doneva, P., Stoyanchev, T., Daskalov, H., Draganova, T., Atanassova, S. (2012). Informative indicators used for bacterial presence determination in yellow cheese by near-infrared spectral data. Workshop on Dynamics and Control in Agriculture and Food Processing, IFAC 2012 (pp. 55–59).
View in Google Scholar
Vieira, C., Diaz, M.T., Martínez, B., García-Cachán, M.D. (2009). Effect of frozen storage conditions (temperature and length of storage) on microbiological and sensory quality of rustic crossbred beef at different states of ageing. Meat Science, 83 (3), 398–404.
View in Google Scholar
Weeranantanaphan, J., Downey, G., Allen, P., Sun, D.W. (2011). A review of near infrared spectroscopy on muscle food analysis: 2005–2010. Journal of Near Infrared Spectroscopy, 19, 61–104.
View in Google Scholar
Workman, J., Weyer, L. (2008). Practical guide of interpretative near-infrared spectroscopy. Boca Raton, FL: Taylor & Francis. ISBN 1420018310.
View in Google Scholar
Wu X., Song, X., Qiu, Z., He, Y. (2016). Mapping of TBARS distribution in frozen-thawed pork using NIR hyperspectral imaging. Meat Science, 113, 92–96.
View in Google Scholar
Xia, X., Kong, B., Liu, Q., Liu, J. (2009). Physicochemical change and protein oxidation in porcine longissimus dorsi as influenced by different freeze-thaw cycles. Meat Science, 83, 239–245.
View in Google Scholar
Xiong, Z., Sun, D.W., Pu, H., Xie, A., Han, Z., Luo, M. (2015). Non-destructive prediction of thiobarbituricacid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chemistry, 179, 175–181.
View in Google Scholar
Zhang, Q.Q., Han, Y. Q., Cao, J.X., Xu, X.L., Zhou, G.H., Zhang, W.Y. (2012). The spoilage of air-packaged broiler meat during storage at normal and fluctuating storage temperatures. Poultry Science, 91 (1), 208–214.
View in Google Scholar
© Copyright by Małopolska School of Economics in Tarnów. The articles are available under the Creative Commons Attribution NonCommercial-NoDerivatives 4.0 International License