Detection of freezing of Thomson variety orange fruit using Fourier transform-infrared spectroscopy and hyperspectral imaging methods

Document Type : Research Article

Authors

1 Member of the academic staff of the Agricultural Engineering and Technical Research Department - Agricultural and Natural Resources Research and Education Center of West Azarbaijan - Urmia - Iran

2 Assistant Professor of Department of Biosystem Engineering- Faculty of Agriculture- University of Tabriz- Tabriz-Iran

3 Associate Professor, Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran

4 Associate Professor of Agricultural Engineering and Technical Research Department - Center for Research and Education of Agriculture and Natural Resources of West Azarbaijan - Urmia - Iran

Abstract

Detection of oranges freezing when entering the consumer market, both from the point of view of freshness and processing, can play a very important role in the marketability of the final product.

In this study, The freezing and non-freezing of orange fruit has been investigated using Fourier transform-infrared (FT-IR) spectroscopy and hyperspectral imaging First, the FT-IR spectrum of the skin of 20 healthy and freezing orange samples was obtained. Also, in the hyperspectral imaging method, images and spectra were obtained from 18 healthy and frozen orange samples using the system made by the company of the Physic Technologists. In order to further investigate the FT-IR and hyperspectral imaging data, linear discriminant analysis (LDA) was performed after applying different pre-processing methods to classify healthy and frozen oranges. The results of this research showed that after freezing, the peaks in the FT-IR spectrum, in the regions of 400 cm-1 to approximately 1500 cm-1, have undergone a fundamental change, and the intensity of these peaks has been greatly reduced. This shows the fundamental changes in orange peel samples due to freezing. The results showed that by applying the MF+SNV preprocessing method on spectroscopic data, it is possible to detect the freezing and non-freezing of orange fruit with high accuracy (accuracy for classification 92%). Moreover, the results for the hyperspectral imaging method showed that by applying the smoothing pre-processing methods, it is possible to detect the freezing and non-freezing oranges with good accuracy (accuracy for classification 75%).In general, the results showed that the FT-IR spectroscopic method has a higher accuracy and can detect freezing and non-freezing in orange peel samples. However, it is recommended to use the hyperspectral imaging to detect orange freezing if the non-destructive assessment of the sample is considered.

Graphical Abstract

Detection of freezing of Thomson variety orange fruit using Fourier transform-infrared spectroscopy and hyperspectral imaging methods

Highlights

  • With MF+SNV pre-processing on the Fourier transform-infrared spectroscopic data and LDA modeling method, it is possible to predict the freezing of oranges with 92% accuracy.
  • In hyperspectral imaging with LDA modeling based on smoothing preprocessing, orange freezing was detected with 75% accuracy.
  • The results showed that the infrared Fourier transform spectroscopy method for detecting frozen oranges is more accurate than the hyperspectral imaging method.

Keywords

Main Subjects


[1] FAO (Food Agriculture Organization). Citrus Fruit Statistical Compendium, (2021). URL https://www. fao.org/3/cb6492en/.pdf
[2] Tajvar, Y., Ghasemi, Y., & Fifaei, R. (2014). Cold stress and control methods in citrus. Ramsar, Iran: Iran Citrus Res. Inst. Public. Comm., [In Persian] 214 
[3] Hashempour, A., Tajvar, Y., SheikhAshkevari, A., Ebadi, H., FatahiMoghadam, J., FaghihNasiri, M., & Golmohammadi, M. (2017). Evaluation of cold and frost damage in citrus and kiwifruit of Mazandaran province citrus. Ramsar, I.R. Iran: Iran Citrus Res. Inst. Public. Comm., [In Persian].
[4] Feridoni, H., (2016). Methods of dealing with frost in fruit tree orchards. I.R. Iran:Golestan Agri. Nat. Res. Educ. Cent., (In Persian) [5] Gambhir, P.N., Choi, Y.J., Slaughter, D.C., Thompson, J. F. & McCarthy, M.J. (2005). Proton spin–spin relaxation time of peel and flesh of navel orange varieties exposed to freezing temperature. J. Sci. Food Agric., 85, 2482-2486.
[6] Moomkesh, SH., Mireei, S.A., Sadeghi, M., & Nazeri, M. (2017). Non-destructive prediction of quality parameters of sweet lemon (Citrus limetta) by Vis/SWNIR spectroscopy. Iran. J. Biosyst. Eng., 47, 603-613. (In Persian).
[7] Rahmanian, A., Mireei, S.A., Sadri, S., Gholami, M. and Nazeri, M. (2020). Application of biospeckle laser imaging for early detection of chilling and freezing disorders in orange. Postharvest Bio. Technol., 162, 111118.
[8] Buta, J.G., Qi, L. & Wang, C.Y. (1997). Fourier transform infrared spectra of zucchini squash stored at chilling or non-chilling temperatures. Environ. Exp. Bot., 38, 1-6.
[9] Sherazi, S.T.H., Bhutto, A.A. Mahesar, S.A. & Bhanger, M.I. (2017). Application of fouriertransform infrared (ft-ir) spectroscopy for determination of total phenolics of freeze-dried lemon juices. J. Chem. Soc. Pakistan., 39(6), 955-961.
[10] Oldenhof, H., Akhoondi, M., Sieme, H. & Wolkers, W. (2013). Use of Fourier transform infrared spectroscopy to determine optimal cooling rates for cryopreservation of cells. Biomed. Spectrosc. Imaging, 2(2), 83-90.
[11] Wolkers, W.F. & Oldenhof, H. (2015). Use of in situ Fourier transform infrared spectroscopy to study freezing and drying of cells. W.F. Wolkers, & H. Oldenhof, Cryopreservation and Freeze-Drying Protocols (pp.147-161). Springer Science+Business Media New York.
[12] Rahi, S., Mobli, H., Jamshidi, B., Azizi, A., & Sharifi, M. (2020). Microbial contamination assessment of lettuce using NIR hyperspectral imaging: case study on escherichia coli. Iran J Biosyst Eng, 51(3), 599-610. (In Persian).
[13] Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., Javan-Nikkhah, M., Jayas, D., & Siliveru, K. (2016). Detection of fungal infection in pistachio kernel by long-wave near-infrared hyperspectral imaging technique. Qual. Assu.r Saf. Crop. Food, 8 (1), 129 - 135.
[14] Nunes, A., Martins, J., Barros, A.S., GalvisSánchez, A.C. & Delgadillo, I. (2009). Estimation of olive oil acidity using FT-IR and partial least squares regression. Sen. Instrum. Food Qual. Saf., 3 (3), 187- 191.
[15] Ebadi, H., gholamian, E., FatahiMoghaddam, J., Golein,B., GolMohammadi, M., & Moradi, B. ( 2019). Guide to planting, growing, harvesting and supply of citrus fruits. I.R. Iran Agric. Educ. Promote. Public., (In Persian).
[16] Jamshidi, B., Minaei, S., Mohajerani, E. & Ghassemian, H. (2014). Effect of spectral preprocessing methods on non-destructive quality assessment of oranges using NIRS. J. Agric. Eng. Res., 15(2), 44-27. (In Persian).
[17] Yang, H., Yan, R., Chen, H., Lee, D.H. & Zheng, C. (2007). Characteristics of hemicellulose, cellulose and lignin pyrolysis. Fuel, 86 (12-13), 1781-1788.
[18] McKendry, P. (2002). Energy production from biomass (part 1): overview of biomass. Bioresour.Technol., 83, 37-46.
[19] DemirbaƟ, A. (2000). Mechanisms of liquefaction and pyrolysis reactions of biomass. Energy Convers. Manag., 41, 633-646.
[20] Zapata, B., Balmaseda, J, Fregoso-Israel, E. & Torres-Garcia, E. (2009). Thermo-kinetics study of orange peel in air. J. Therm. Anal. Calorim., 98, 309- 315.
[21] Chaudhari, A. (2016). Nitrobenzene oxidation for isolation of value-added products from industrial waste lignin. J. Chem. Biolo. Physic. Sci., 6, 501-513.
[22] Mohamed, S.E., Khalifi, M.G., Sayed, S.A., Kamel, A.M., & Shalabi, M. (2009). Removal of lignin from pulp waste water's black liquor via bypass cement dust. Eurasian. Chem-Technol. J, 11, 51- 59.
[23] Rafiei, M. & Rajabi, H., (2017). Differentiation of essential oil cavities in developing lemon (Citrus limon (L.) Burm. F.) flower and fruit. IR. Iran. Iran J. Plant Biol., 3(8), 59-68. (In Persian).
Volume 10, Issue 3
May 2023
Pages 203-214
  • Receive Date: 05 February 2023
  • Revise Date: 17 April 2023
  • Accept Date: 24 April 2023
  • First Publish Date: 24 April 2023