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| Measurement of Tomato Quality Attributes Based on Wavelength Ratio and Near-Infrared Spectroscopy |
| HUANG Yu-ping1, Renfu Lu2, QI Chao3, CHEN Kun-jie3* |
1. College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037，China
2. United States Department of Agriculture Agricultural Research Service (USDA/ARS), Michigan State University, East Lansing, MI 48824, USA
3. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China |
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Abstract The soluble solids content (SSC), pH and firmness (Firmness) of tomato are the key factors that determine the taste and post harvest quality of tomato. A new method for detecting tomato SSC, pH and firmness based on wavelength ratio and near infrared spectroscopy is proposed in this paper. Thespectra of six hundreds tomato samples with different maturity were collected with the portable Vis/NIR spectrometer (wavelength: 400～1 100 nm) and the portablenear infrared spectrometer (wavelength: 900～1 683 nm) in the interaction mode, respectively. After these spectra were pretreated with the wavelength ratio method are as follows：automatic scaling one and the wavelength ratio+automatic scaling one, the prediction models for SSC, pH and firmness of tomatowere developed, respectively, and then the prediction results of the four methods: are as follows automatic scaling, wavelength ratio, wavelength ratio + automatic scaling and no preprocessing were compared and analyzed. The experimental results showed that the prediction accuracy of the visible/near infrared spectra for SSC, pH and firmnesscouldbe visibly improvedby the wavelength ratio combined with the automatic scaling pretreatment, with rp=0.779, 0.796 and 0.917, respectively. The wavelength ratio combined with the automatic scaling also could enhance the prediction ability of the Near infrared spectroscopy for SSC with rp=0.818, which suggests that the proposed wavelength ratio method in this paper had considerable potential in optimizing and processing the spectral information of tomato.
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Received: 2017-09-13
Accepted: 2018-01-08
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Corresponding Authors:
CHEN Kun-jie
E-mail: kunjiechen@njau.edu.cn
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[1] Zhu Q, He C, Lu R，et al. Postharvest Biology and Technology, 2015，103：27.
[2] Xie L, Ying Y, Ying T，et al. Analytica Chimica Acta, 2017, 584(2)：379.
[3] Dorais M, Ehret D L，Papadopoulos A P. Phytochemistry Reviews, 2008, 7(2)：231.
[4] Cen H, Lu R, Mendoza F，et al. Transactions of the ASABE, 2012, 55(2)：647.
[5] Gómez A H, Wang J, Hu G，et al. Journal of Food Engineering, 2008, 85(4)：625.
[6] Nicolai B M, Beullens K, Bobelyn E, et al. Postharvest Biology and Technology, 2007, 46(2)：99.
[7] Pan L, Lu R, Zhu Q, et al. Postharvest Biology and Technology, 2015，102: 42.
[8] Guo Y, Ni Y，Kokot S. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2016，153: 79.
[9] He Y, Zhang Y, Pereira A G，et al. International Journal of Information Technology, 2005, 11(11)：97.
[10] Shao Y, He Y, Gómez A H，et al. Journal of Food Engineering, 2007, 81(4)：672.
[11] Clément A, Dorais M，Vernon M. Journal of Agricultural and Food Chemistry, 2008, 56(21)：9813.
[12] Xie L, Ying Y, Lin H，et al. Sensing and Instrumentation for Food Quality and Safety, 2008, 2(2)：111.
[13] Song M, Tao D, Liu Z，et al. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010, 40(3)：779.
[14] Kim M S, Chen Y，Mehl P. Transactions of the ASAE, 2001, 44(3)：721.
[15] Gil M, Conesa M A，Artes F. Postharvest Biology and Technology, 2002, 25(2)：199.
[16] Qin J，Lu R. Postharvest Biology and Technology, 2008, 49(3)：355.
[17] Ecarnot M, Bczyk P, Tessarotto L，et al. Plant Physiology and Biochemistry, 2013，70: 159.
[18] Szuvandzsiev P, Helyes L, Lugasi A，et al. International Agrophysics, 2014, 28(4)：521. |
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