Abstract
Near infrared spectroscopy has been widely applied in the area of rapid determination for fruits’ internal qualities. Therefore, an on-line near-infrared detection system was established to predict the SSC of apples in this study. Due to random measurement positions of apples, negative influences will be exerted on the performance of prediction models. With the aim of learning more about these influences and also compensating for them, spectra were taken at different measurement positions in the present work, including six fixed positions and a random position. Besides, the relations between these positions were investigated as well. It is also found that when the concave surfaces at apple’s calyx and stem interfered with the light path in the detection system, model’s robustness and accuracy would be deteriorated. At last, average and global spectra were used to build prediction models with comparison purpose. The optimal prediction model was established by the average spectra of seven measurement positions (RMSEC 0.356%; rc 0.947; RMSEP 0.370%; rp 0.906), which was superior to results in many previous studies. Last but not least, several suggestions on compensation for these influences were made to improve the model performance in some practical applications.
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References
FAOSTAT, Production of Apples, in the World. Food and Agriculture Organization of the United Nations (2016)
D. Ariana, R. Lu, A near-infrared sensing technique for measuring internal quality of apple fruit. Appl. Eng. Agric. 18(5), 585–592 (2002)
F. Mendoza, R. Lu, H. Cen, Grading of apples based on firmness and soluble solids content using VIS/SWNIR spectroscopy and spectral scattering techniques. J. Food Eng. 125(1), 59–68 (2014)
J.U. Porep, D.R. Kammerer, R. Carle, On-line application of near infrared (NIR) spectroscopy in food production. Trends Food Sci. Technol. 46(2), 211–230 (2015)
X. Luo, Z. Ye, H. Xu, D. Zhang, S. Bai, Y. Ying, Robustness improvement of NIR-based determination of soluble solids in apple fruit by local calibration. Postharvest Biol. Technol. 139, 82–90 (2018)
J. Wang, J. Wang, Z. Chen, D. Han, Development of multi-cultivar models for predicting the soluble solid content and firmness of European pear (Pyrus communis L.) using portable VIS-NIR spectroscopy. Postharvest Biol. Technol. 129, 143–151 (2017)
X. Sun, Nondestructive measurement soluble solids content of apple by portable and online near infrared spectroscopy. Proc. SPIE - Int. Soc. Opt. Eng. 7514, 24 (2009)
D. Jie, L. Xie, X. Rao, Y. Ying, Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system. Postharvest Biol. Technol. 90(3), 1–6 (2014)
A. Peirs, J. Tirry, B. Verlinden, P. Darius, B.M. Nicolaı̈, Effect of biological variability on the robustness of NIR models for soluble solids content of apples. Postharvest Biol. Technol. 28(2), 269–280 (2003)
E. Bobelyn, A.S. Serban, M. Nicu, J. Lammertyn, B.M. Nicolai, W. Saeys, Postharvest quality of apple predicted by NIR-spectroscopy: study of the effect of biological variability on spectra and model performance. Postharvest Biol. Technol. 55(3), 133–143 (2010)
J. Guthrie, B. Wedding, K. Walsh, Robustness of NIR calibrations for soluble solids in intact melon and pineapple. J. Near Infrared Spectrosc. 6(1–4), 259–265 (1998)
L. León, A. Garrido-Varo, G. Downey, Parent and harvest year effects on near-infrared reflectance spectroscopic analysis of olive (Olea europaea L.) fruit traits. J. Agric. Food Chem 52(16), 4957–4962 (2004)
M.V.M. Vega, S. Sara, W. Dvoralai, S. Thomas, C. Line Harder, T.B. Toldam-Andersen, A sampling approach for predicting the eating quality of apples using visible-near infrared spectroscopy. J. Sci. Food Agric. 93(15), 3710–3719 (2014)
S. Kumar, A. Mcglone, C. Whitworth, R. Volz, Postharvest performance of apple phenotypes predicted by near-infrared (NIR) spectral analysis. Postharvest Biol. Technol. 100, 16–22 (2015)
S. Fan, B. Zhang, J. Li, W. Huang, C. Wang, Effect of spectrum measurement position variation on the robustness of NIR spectroscopy models for soluble solids content of apple. Biosyst. Eng. 143(45), 9–19 (2016)
D.C. Slaughter, D. Barrett, M. Boersig, Nondestructive determination of soluble solids in tomatoes using near infrared spectroscopy. J. Food Sci. 61(4), 695–697 (1996)
H. Xu, Research on fruit feeding and rolling installation with bicone rollers. Trans. CSAM. 34(6), 100–103 (2003)
Z. Guo, W. Huang, Y. Peng, Q. Chen, Q. Ouyang, J. Zhao, Color compensation and comparison of shortwave near infrared and long wave near infrared spectroscopy for determination of soluble solids content of ‘Fuji’ apple. Postharvest Biol. Technol. 115, 81–90 (2016)
Y. Yao, H. Chen, L. Xie, X. Rao, Assessing the temperature influence on the soluble solids content of watermelon juice as measured by visible and near-infrared spectroscopy and chemometrics. J. Food Eng. 119(1), 22–27 (2013)
B.M. Nicolaï, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K.I. Theron, Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol. Technol. 46(2), 99–118 (2007)
L. Xie, X. Ye, D. Liu, Y. Ying, Prediction of titratable acidity, malic acid, and citric acid in bayberry fruit by near-infrared spectroscopy. Food Res. Int. 44(7), 2198–2204 (2011)
Y. Yan, Analytical Basis and Application of Near Infrared Spectroscopy (China light industry press, Beijing, 2005)
A. Wang, D. Hu, L.Xie, Comparison of detection modes in terms of the necessity of visible region (VIS) and influence of the peel on soluble solids content (SSC) determination of navel orange using VIS–SWNIR spectroscopy. J. Food Eng. 126(4), 126–132 (2014)
B. Savenije, G.H. Geesink, J.G.P.V.D. Palen, G. Hemke, Prediction of pork quality using visible/near-infrared reflectance spectroscopy. Meat Sci. 73(1), 181–184 (2006)
M.N. Merzlyak, A.E. Solovchenko, A.A. Gitelson, Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit. Postharvest Biol. Technol. 27(2), 197–211 (2003)
M. Golic, K.B. Walsh, Robustness of calibration models based on near infrared spectroscopy for the in-line grading of stonefruit for total soluble solids content. Anal. Chim. Acta 555(2), 286–291 (2006)
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The authors gratefully acknowledge the financial support provided by the Natural Science Foundation of Zhejiang Province for Distinguished Young Scholars (Grant No. LR18C130001).
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Xu, X., Xu, H., Xie, L. et al. Effect of measurement position on prediction of apple soluble solids content (SSC) by an on-line near-infrared (NIR) system. Food Measure 13, 506–512 (2019). https://doi.org/10.1007/s11694-018-9964-4
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DOI: https://doi.org/10.1007/s11694-018-9964-4