Gıda Analizlerinde Kullanılan Spektroskopik Teknikler

Year 2019, Volume: 17 Issue: 1, 121 - 130, 26.03.2019

Muhammed Yusuf Çağlar , Mehmet Demirci , Abdulkadir Şahiner Bilal Çakır Ahmet Furkan Çağlar

https://doi.org/10.24323/akademik-gida.544975

Abstract

Gıdalar,
hammaddenin tedarik edilmesinden son ürünün eldesine kadar geçen süreçte, raf
ömrünün uzatılması, tekstür, tat veya aroma iyileştirilmesi ya da maliyetin
düşürülmesi gibi çeşitli istemlerle veya istem dışı; fiziksel ya da kimyasal
müdahalelere maruz kalmaktadır. Bu müdahalelerin tüketicinin sağlığı ve refahı
açısından İslami boyutlarda takibi ve denetimi, helal gıda konsepti kapsamına
girmektedir. Günümüzde gelişen teknolojiye paralel olarak üreticilerin haksız
kazanç elde etme istekleri gibi sebeplerden ötürü gıda üretiminde taklit ve
tağşiş oranları giderek artmaktadır. Bu derlemede, helal gıda üretimi ve
takibinde önem arz eden ve doğrulama ve tağşiş belirlenmesi amacıyla kullanılan
vibrasyonel spektroskopik yöntemlerden yakın kızılötesi spektroskopisi (NIR),
Fourier dönüşümlü kızıl ötesi spektroskopi (FTIR), Raman spektroskopisi (RS) ve
üstün uzaysal görüntüleme (HSI) metotları çalışma prensipleri ve gıda grupları
bazında ele alınmıştır.

Keywords

Spektroskopi, Tağşiş, Taklit, Gıda

Spectroscopic Techniques Used in Food Analyses

Abstract

Foods
are exposed to physical or chemical processes from the period of supplying raw
materials until manufacturing the final product for a variety of purposes such
as extending shelf life, improving texture, taste and aroma or lowering the
production cost. Regarding the Islamic faith, pursuance and control of these
treatments/processes in terms of human health and well-being are a part of
halal food production concept. Recently, in parallel with developing
technology, imitation and fraud in food production have been extensively
increased due to wishes of producers to acquire unfair earnings. In this study,
near infrared spectroscopy (NIR), Fourier transform infrared spectroscopy
(FTIR), Raman spectroscopy (RS) and hyperspectral imaging (HSI), which are
sub-categories of vibrational spectroscopic methods, are reviewed in terms of
their working principles and food groups.

Keywords

Spectroscopy, Adulteration, Imitatiton, Food

References

• [1] Nakyinsige, K., Man, Y.B.C., Sazili, A.Q. (2012). Halal authenticity issues in meat and meat products. Meat Science, 91(3), 207-214.
• [2] Vandendriessche, F. (2008). Meat products in the past, today and in the future. Meat Science, 78(1), 104-113.
• [3] Hargin, K.D. (1996). Authenticity issues in meat and meat products. Meat Science, 43(1), 277-289.
• [4] Lakshmi, V. (2012). Food adulteration. International Journal of Science Inventions Today, 1(2), 101-113.
• [5] Cserháti, T., Forgács, E., Deyl, Z., Miksik, I. (2005). Chromatography in authenticity and traceability tests of vegetable oils and dairy products: a review. Biomedical Chromatography, 19(3), 183-190.
• [6] Lohumi, S., Lee, S., Lee, H., Cho, B.K. (2015). A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration. Trends in Food Science & Technology, 46(1), 85-98.
• [7] Rushworth, M.F. (2009). Melamine and food safety in China. The Lancet 373, (9661), 353.
• [8] Wu, D., Shi, H.,Wang, S., He, Y., Bao, Y., Liu, K. (2012). Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system. Analytica Chemica Acta, 726, 57-66.
• [9] Baeten, V., Dardenne, P. (2002). Spectroscopy: developments in instrumentation and analysis. Grasas y Aceites, 53, 45-63.
• [10] Luypaert, J., Massart, D.L., Heyden, Y.V. (2007). Near-infrared spectroscopy applications in pharmaceutical analysis. Talanta, 72, 865-883.
• [11] Andre, M. (2003). Multivariate analysis and classification of the chemical quality of 7-Aminocephalosporanic acid using near-infrared reflectance spectroscopy. Analytical Chemistry, 75, 3460-3467.
• [12] Osborne, B.G., Fearn, T., Hindle, P.T. (1993). Practical NIR spectroscopy with applications in food and beverage analysis (2nd ed.). Singapore: Longman Scientific and Technical.
• [13] Newgard E.C. (2004). Near-Infrared Spectroscopy for Analysis of Agricultural Material. Final Reports for Physics Optical Spectroscopy 1-11.
• [14] Blanco, M., Villarroya, I.N.I.R. (2002). NIR spectroscopy: a rapid-response analytical tool. TrAC Trends in Analytical Chemistry, 21(4), 240-250.
• [15] Bajcsy, R., Lee, S.W., Leonardis, A. (1996). Detection of diffuse and specular interface reflections and inter-reflectance by color image segmentation. International Journal of Computer Vision, 17(3), 241-272.
• [16] Cozzolino, D., Murray, I. (2003). Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. Lebensmittel Wissenschaft und Technologie, 37(4), 447-452.
• [17] Ding, H.B., Xu, R.J. (2000). Near-Infrared spectroscopic technique for detection of beef hamburger adulteration. Journal of Agricultural and Food Chemistry, 48(6), 2193-2198.
• [18] Gayo, J., Hale, S.A., Blanchard, S.M. (2006). Quantitative analysis and detection of adulteration in crab meat using visible and near-infrared spectroscopy. Journal of Agricultural and Food Chemistry, 54(4), 1130-1136.
• [19] Gayo, J., Hale, S.A. (2007). Detection and quantification of species authenticity and adulteration in crabmeat using visible and near-infrared spectroscopy. Journal of Agricultural and Food Chemistry, 55(3), 585-592.
• [20] Alander, J.T., Bochko, V., Martinkauppi, B., Saranwong, S., Mantere, T. (2013). A review on optical nondestructive visual and near-infrared methods for food quality and safety. International Journal of Spectroscopy http://dx.doi.org/10.1155/2013/341402 Article ID 341402, 1-36.
• [21] Twomey, M., Doweny, G., McNulty, P.B. (1995). The potential of NIR spectroscopy for the detection of the adulteration of orange juice. Journal of the Science of Food and Agriculture, 67(1), 77-84.
• [22] Contal, L., Leon, V., Downey, G. (2002). Detection and quantification of apple adulteration in strawberry and raspberry purees using visible and near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 10(4), 289-299.
• [23] Sinelli, N., Casale, M., Egidio, V.D., Oliveri, P., Bassi, D., Tura, D., Casiraghi, E. (2010). Varietal discrimination of extra virgin olive oils by near and mid infrared spectroscopy. Food Research International, 43(8), 2126-2131.
• [24] Xie, L.J., Ye, X.Q., Liu, D.H., Ying, Y.B. (2008). Application of principal component radial basis function neural networks (PC-RBFNN) for the detection of water adulterated bayberry juice by near-infrared spectroscopy. Journal of Zhejiang University Science B, 9(12), 982-989.
• [25] Riovanto, R., Marchi, M. D., Cassandro, M., Penasa, M. (2012). Use of near infrared transmittance spectroscopy to predict fatty acid composition of chicken meat. Food Chemistry, 134(4), 2459-2464.
• [26] Sierra, V., Aldai, N., Castro, P., Osoro, K., Montes, A.C., Olivan, M. (2008). Prediction of the fatty acid composition of beef by near infrared spectroscopy. Meat Science, 78(3), 248-255.
• [27] Pasquini, C. (2003). Near Infrared Spectroscopy: fundamentals, practical aspects and analytical applications. Journal of Brazil Chemistry Society, 14(2), 198-219.
• [28] Garcia-Alvarez, M., Huidobro, J.F., Hermida, M., Rodriguez-Otero, J.L. (2000). Major components of honey analysis by near-infrared transflactance spectroscopy. Journal of Agricultural and Food Chemistry, 48(11), 5154-5158.
• [29] Kelly, J. D., Petisco, C., Doweny, G. (2006). Application of Fourier transform midinfrared spectroscopy to the discrimination between Irish artisanal honey and such honey adulterated with various sugar syrups. Journal of Agricultural and Food Chemistry, 54(17), 6166-6171.
• [30] Ruoff, K., Luginbuhl, W., Bogdanov, S., Bosset, J.O., Estermann, B., Ziolko, T., Amado, R. (2006a). Authentication of botanical origin of honey by near-infrared spectroscopy. Journal of Agricultural and Food Chemistry, 54(18), 6867-6872.
• [31] Downey, G., Mclntyre, P., Davies, A.N. (2002). Detecting and quantifying sunflower oil adulteration in extra virgin olive oils from the eastern Mediterranean by visible and near-infrared spectroscopy. Journal of Agricultural and Food Chemistry, 50(20), 5520-5525.
• [32] Zhang, L.G., Zhang, X., Ni, L.J., Xue, Z.B., Gu, X., Huang, S.H. (2014). Rapid identification of adulterated cow milk by non-linear pattern recognition methods based on near infrared spectroscopy. Food Chemistry, 145, 342-348.
• [33] Mamani-Linares, L.W., Gallo, C., Alomar, D. (2012). Identification of cattle, ilama and horse meat by near infrared reflectance or transflactance spectroscopy. Meat Science, 90(2), 378-385.
• [34] Liu, L. (2006). Geographical classification of wines using Vis-NIR spectroscopy (pp.1-78). China: School of Chemical Engineering, Shenyang Pharmaceutical University. Master thesis.
• [35] Downey, G. (1998). Food and food ingredient authentication by mid-infrared spectroscopy and chemometrics. TrAC Trends in Analytical Chemistry, 17(7), 418-424.
• [36] Sun, D.W. (2009). Infrared spectroscopy for food quality analysis and control (1st ed., pp. 146-173). Elsevier Inc.
• [37] Hsu, C.P.S. (1997). Handbook of instrumental techniques for analytical chemistry (pp. 247-282). Separation Sciences Research and Product Development Mallinckrodt, Inc.
• [38] Polshin, E., Aernouts, B., Saeys,W., Delvaux, F., Delvaux, F.R., Saison, D., Hertog, M., Nicolai, B.M., Lammertyn, J. (2011). Beer quality screening by FT-IR spectrometery: Impact of measurement strategies, data pre-processings and variable selection algorithms. Journal of Food Engineering, 106(3), 188-198.
• [39] Brindet, R., Kemsley, E.K., Wilson, R.H. (1996). Approaches to adulteration detection in instant coffees using infrared spectroscopy and chemomatrix. Journal of the Science of Food and Agriculture, 71(3), 359-366.
• [40] Herringshaw, S. (2009). Application of infrared spectroscopy and chemometrics for the authentication of organic butter and determination of sugar in tomatoes (Solanum lycopersicum). Master thesis (pp. 1-54). The Ohio State University.
• [41] Santos, P.M., Pereira-Filho, E.R., Rodriguez-Saona, L.E. (2013). Rapid detection and quantification of milk adulteration using infrared microspectroscopy and chemometrics analysis. Food chemistry, 138(1), 19-24.
• [42] Halim, Y., Schwartz, S., Baldauf, N., Rodriquez-Saona, L.E. (2006). Direct determination of lycopene content in tomatoes (Lycopersicon esculentum) by attenuated total reflectance infrared spectroscopy and multivariate analysis. Journal of AOAC International, 89(5), 1257-1262.
• [43] Martin, A.P., Palmer, W.M., Byrt, C.S., Furbank, R.T., Grof, C.P. (2013). A holistic high-throughput screening framework for biofuel feedstock assessment that characterises variations in soluble sugars and cell wall composition in sorghum bicolor. Biotechnology for Biofuels, 6(1), 1-13.
• [44] Shapaval, V., Moretro, T., Suso, H.P., Asli, A.W., Schmitt, J., Lillehaug, D., Martens, H., Böcker, U., Kohler, A. (2010). A high throughput multicultivation protocol for FTIR spectroscopic characterization and identification of fungi. Journal of Biophotonics, 3(8-9), 1-10.
• [45] Cebi, N., Durak, M.Z., Toker, O.S., Sagdic, O., Arici, M. (2016). An evaluation of Fourier transforms infrared spectroscopy method for the classification and discrimination of bovine, porcine and fish gelatins. Food Chemistry, 190, 1109-1115.
• [46] Kuswandi, B., Putri, F.K., Gani, A.A., Ahmad, M. (2015). Application of class-modelling techniques to infrared spectra for analysis of pork adulteration in beef jerkys. Journal of Food science and Technology 52(12): 7655-7668.
• [47] Dominguez-Vidal, A., Pantoja-de la Rosa, J., Cuadros-Rodríguez, L., Ayora-Cañada, M.J. (2016). Authentication of canned fish packing oils by means of Fourier transform infrared spectroscopy. Food Chemistry, 190, 122-127.
• [48] Rohman, A., Che Man, Y.B., Nurrulhidayah, A.F. (2015). Fourier-Transform Infrared Spectra Combined with Chemometrics and Fatty Acid Composition for Analysis of Pumpkin Seed Oil Blended Into Olive Oil. International Journal of Food Properties, 18(5), 1086-1096.
• [49] Rohman, A., Man, Y.B.C. (2011). The use of Fourier transform mid infrared (FT-MIR) spectroscopy for detection and quantification of adulteration in virgin coconut oil. Food Chemistry, 129(2), 583-588.
• [50] Zhong, J., Qin, X. (2016). Rapid quantitative analysis of corn starch adulteration in Konjac Glucomannan by chemometrics-assisted FT-NIR spectroscopy. Food Analytical Methods, 9(1), 61-67.
• [51] Jha, S.N., Jaiswal, P., Borah, A., Gautam, A.K., Srivastava, N. (2015). Detection and quantification of urea in milk using attenuated total reflectance-Fourier transform infrared spectroscopy. Food and Bioprocess Technology, 8(4), 926-933.
• [52] Hernández, K.U., Velázquez, T.G., Revilla, G.O., Abarca, N.A., Martínez, M.H. (2015). Development of chemometric models using infrared spectroscopy (MID-FTIR) for detection of sulfathiazole and oxytetracycline residues in honey. Food Science and Biotechnology, 24(4), 1219-1226.
• [53] Argyri, A.A., Jarvis, R.M., Wedge, D., Xu, Y., Panagou, E.Z., Goodacre, R., Nychas, G.J.E. (2013). A comparison of Raman and FT-IR spectroscopy for the prediction of meat spoilage. Food Control, 29(2), 461-470.
• [54] McCreery, R.L. (2001). Raman spectroscopy for chemical analysis. Measurement science and technology, 12(5), 653. John Wiley & Sons.
• [55] Li, L., Wang, H., Cheng, J.X. (2005). Quantitative coherent anti-Stokes Raman scattering imaging of lipid distribution in coexisting domains. Biophysical journal, 89(5), 3480-3490.
• [56] Freudiger, C.W., Min, W., Saar, B.G., Lu, S., Holtom, G. R., He, C., Tsai, J.C., Kang, J.X., Xie, X.S. (2008). Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy. Science, 322(5909), 1857-1861.
• [57] Roeffaers, M. B., Zhang, X., Freudiger, C.W., Saar, B.G., van Ruijven, M., van Dalen, G., Xiao, C., Xie, X.S. (2011). Label-free imaging of biomolecules in food products using stimulated Raman microscopy. Journal of Biomedical Optics, 16(2), 1-6.
• [58] Ozaki, Y., Cho, R., Ikegaya, K., Muraishi, S., Kawauchi, K. (1992). Potential of near-infrared Fourier transform Raman spectroscopy in food analysis. Applied Spectroscopy 46(10): 1503-1507.
• [59] Zou, M.Q., Zhang, X.F., Qi, X.H., Ma, H.L., Dong, Y., Liu, C.W., Guo, X., Wang, H. (2009). Rapid authentication of olive oil adulteration by Raman spectrometry. Journal of Agricultural and Food Chemistry, 57(14), 6001-6006.
• [60] Carmona, M.A., Lafont, F., Jiménez-Sanchidrián, C., Ruiz, J.R. (2015). Characterization of macadamia and pecan oils and detection of mixtures with other edible seed oils by Raman spectroscopy. Grasas y Aceites, 66(3), 1-9.
• [61] Beattie, J.R., Bell, S.E., Borgaard, C., Fearon, A., Moss, B.W. (2006). Prediction of adipose tissue composition using Raman spectroscopy: average properties and individual fatty acids. Lipids, 41(3), 287-294.
• [62] Beattie, R.J., Bell, S.J., Farmer, L.J., Moss, B.W., Patterson, D. (2004). Preliminary investigation of the application of Raman spectroscopy to the prediction of the sensory quality of beef silverside. Meat Science, 66(4), 903-913.
• [63] Bocker, U., Ofstad, R., Wu, Z., Bertram, H.C., Sockalingum, G.D., Manfait, M., Egelandsdal, B., Kohler, A. (2007). Revealing covariance structures in Fourier transform infrared and Raman microspectroscopy spectra: a study on pork muscle fiber tissue subjected to different processing parameters. Applied Spectroscopy, 61(10), 1032-1039.
• [64] Herrero, A.M., Carmona, P., Careche, M. (2004). Raman spectroscopic study of structural changes in hake (Merluccius merluccius L.) muscle proteins during frozen storage. Journal of Agricultural and Food Chemistry, 52(8), 2147-2153.
• [65] Marquardt, B.J., Wold, J.P. (2004). Raman analysis of fish: a potential method for rapid quality screening. LWT-Food Science and Technology, 37(1), 1-8.
• [66] Herrero, A.M. (2008). Raman spectroscopy for monitoring protein structure in muscle food systems. Critical Reviews in Food Science and Nutrition, 48(6), 512-523.
• [67] Boyacı, I.H., Temiz, H.T., Uysal, R.S., Velioğlu, H.M., Yadegari, R.J., Rishkan, M.M. (2014). A novel method for discrimination of beef and horsemeat using Raman spectroscopy. Food Chemistry, 148, 37-41.
• [68] Wijaya, W., Pang, S., Labuza, T.P., He, L. (2014). Rapid detection of acetamiprid in foods using Surface‐Enhanced Raman Spectroscopy (SERS). Journal of Food Science, 79(4), 743-747.
• [69] Di Anibal, C.V., Marsal, L.F., Callao, M.P., Ruisánchez, I. (2012). Surface Enhanced Raman Spectroscopy (SERS) and multivariate analysis as a screening tool for detecting Sudan I dye in culinary spices. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 87, 135-141.
• [70] Kumar, C.S. (Ed.). (2012). Raman spectroscopy for nanomaterials characterization. Springer Science & Business Media.
• [71] Gowen, A.A., O'Donnell, C.P., Cullen, P. J., Downey, G., Frias, J.M. (2007). Hyperspectral imaging - an emerging process analytical tool for food quality and safety control. Trends in Food Science and Technology, 18(12), 590-598.
• [72] Ariana, D., Lu, R. (2006). Visible/near-infrared hyperspectral transmittance imaging for detection of internal mechanical injury in pickling cucumbers. In ASABE annual international.
• [73] Rocha,W. F.D.C., Sabin, G.P., Marco, P.H., Poppi, R.J. (2011). Quantitative analysis of piroxicam polymorphs pharmaceutical mixtures by hyperspectral imaging and chemometrics. Chemometrics and Intelligent Laboratory Systems, 106(2), 198-204.
• [74] Lu, G., Fei, B. (2014). Medical hyperspectral imaging: a review. Journal of Biomedical Optics, 19(1), 1-23.
• [75] Chaudhari, A.J., Darvas, F., Banding, J.R., Moats, R.A., Conti, P.S., Smith, D.J., Cherry, S.R., Leahy, R.M. (2005). Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging. Physics in Medicine and Biology, 50(23), 5421-5441.
• [76] Daeid, N.N. (2013). IFSMS review papers 2013. In 17th Interpol international forensic science managers symposium, Lyon, France, 1-923p.
• [77] Williams, D.J., Feldman, B.L., Williams, T.J., Pilant, D., Lucey, P.G., Worthy, L.D. (2005). SPIE Proceedings, 5655, 134-141.
• [78] Huang, H., Liu, L., Ngadi, M.O. (2014). Recent developments in hyperspectral imaging for assessment of food quality and safety. Sensor, 14(4), 7248-7276.
• [79] ElMasry, G., Iqbal, A., Sun, D.W., Allen, P., Ward, P. (2011a). Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system. Journal of Food Engineering, 103(3), 333-344.
• [80] ElMasry, G., Sun, D.W., Allen, P. (2011b). Non-destructive determination of waterholding capacity in fresh beef by using NIR hyperspectral imaging. Food Research International, 44(9), 2624-2633.
• [81] ElMasry, G., Barbin, D.F., Sun, D.W., Allen, P. (2012a). Meat quality evaluation by hyperspectral imaging technique: an overview. Critical Reviewers in Food Science and Nutrition, 52(8), 689-711.
• [82] ElMasry, G., Sun, D.W., Allen, P. (2012b). Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. Journal of Food Engineering, 110(1), 127-140.
• [83] Kandpal, L.M., Lee, H., Kim, M.S., Mo, C., Cho, B.K. (2013). Hyperspectral reflectance imaging technique for visualization of moisture distribution in cooked chicken breast. Sensors, 13(10), 13289-13300.
• [84] Williams, P., Geladi, P., Fox, G., Manley, M. (2009). Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Analytica Chimica Acta, 653(2), 121-130.
• [85] Singh, C.B., Jayas, D.S., Paliwal, J., White, N.D. (2010). Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging. Computers and Electronics in Agriculture, 73(2), 118-125.
• [86] September, D.J.F. (2011). Detection and quantification of spice adulteration by near infrared hyperspectral imaging. Graduate thesis. Stellenbosch University.
• [87] Fu, X., Kim, M. S., Chao, K., Qin, J., Lim, J., Lee, H.,Garrido-Varo, A. Perez-Marin, D., Ying, Y. (2014). Detection of melamine in milk powders based on NIR hyperspectral imaging and spectral similarity analyses. Journal of Food Engineering, 124, 97-104.
• [88] Siripatrawan, U., Makino, Y., Kawagoe, Y., Oshita, S. (2011). Rapid detection of Escherichia coli contamination in packaged fresh spinach using hyperspectral imaging. Talanta, 85(1), 276-281.
• [89] Gaston, E., Frias, J.M., Cullen, P.J., O'Connell, C.P., Gowen, A.A. (2011). Hyperspectral imaging for the detection of microbila spoilage of mushrooms. Conference paper in Dublin Institute of Technology.
• [90] Wu, D., Sun, D.W. (2013a). Potential of time series-hyperspectral imaging (TS-HSI) fornon-invasive determination of microbial spoilage of salmon flesh. Talanta, 111, 39-46.
• [91] Barbin, D.F., ElMasry, G., Sun, D.W., Allen, P., Morsy, N. (2013). Non-destructive assessment of microbial contamination in porcine meat using NIR hyperspectral imaging. Innovative Food Science & Emerging Technologies, 17, 180-191.
• [92] Wu, D., Sun, D.W. (2013b). Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review- Part II: applications. Innovative Food Science & Emerging Technologies, 19, 15-28.
• [93] Kim, M.S., Chen, Y.R., Mehl, P.M. (2001). Hyperspectral reflectance and fluorescence imaging system for food quality and safety. Transactions-American Society of Agricultural Engineers, 44(3), 721-730.
• [94] Kim, M.S., Lefcourt, A.M., Chen, Y.R., Kim, I., Chan, D.E., Chao, K. (2002). Multispectral detection of fecal contamination on apples based on hyperspectral imagery: Part II. Application of hyperspectral fluorescence imaging. Transactions-American Society of Agricultural Engineers, 45(6), 2039-2048.
• [95] Yao, H., Hruska, Z., Kincaid, R., Brown, R., Cleveland, T., Bhatnagar, D. (2010). Correlation and classification of single kernel fluorescence hyperspectral data with aflatoxin concentration in corn kernels inoculated with Aspergillus flavus spores. Food Additives and Contaminants, 27(5), 701-709.
• [96] Cho, B.K., Kim, M.S., Baek, I.S., Kim, D.Y., Lee, W.H., Kim, J., Lee, W.H., Kim, J., Bae, H., Kim, Y.S. (2013). Detection of cuticle defects on cherry tomatoes using hyperspectral fluorescence imagery. Postharvest Biology and Technology, 76, 40-49.
• [97] Timlin, J.A., Carden, A., Morris, M.D., Bonadio, J.F., Hoffler, C.E., Kozloff, K.M., Goldstein, S.A. (1999). Spatial distribution of phosphate species in mature and newly generated mammalian bone by hyperspectral Raman imaging. Journal of Biomedical Optics, 4(1), 28-34.
• [98] Fu, D., Holtom, G., Freudiger, C., Zhang, X., Xie, X.S. (2013). Hyperspectral imaging with stimulated Raman scattering by chirped femtosecond lasers. The Journal of Physical Chemistry B, 117(16), 4634-4640.
• [99] Qin, J., Chao, K., Kim, M.S. (2010). Raman chemical imaging system for food safety and quality inspection. Transactions of the ASABE, 53(6), 1873-1882.
• [100] Qin, J., Chao, K., Kim, M.S. (2014). High-throughput Raman chemical imaging for evaluating food safety and quality. In SPIE Sensing Technology+ Applications (pp. 91080F-91080F). International Society for Optics and Photonics.
• [101] Qin, J., Chao, K., Kim, M.S. (2011). Investigation of Raman chemical imaging for detection of lycopene changes in tomatoes during postharvest ripening. Journal of Food Engineering, 107(3), 277-288.