Food Analytical Methods

, Volume 12, Issue 9, pp 2078–2085 | Cite as

Influences of Detection Position and Double Detection Regions on Determining Soluble Solids Content (SSC) for Apples Using On-line Visible/Near-Infrared (Vis/NIR) Spectroscopy

  • Xiao Xu
  • Jiancan Mo
  • Lijuan XieEmail author
  • Yibin Ying


With increasing living standards, more attention has been drawn to the eating quality of fruits. To meet the standards of market and consumers, on-line fruits grading in terms of the internal qualities, such as soluble solids content (SSC), is necessary in the industrial scale. Near-infrared (NIR) spectroscopy technology has advantages of being rapid, safe, non-destructive, and environmentally friendly; thus, it was widely applied in this area. Therefore, in the present work, an on-line near-infrared SSC detection system for apples based on bicone roller transportation was used to compare the influences of detection positions and double detection regions with the help of mono-branch optical fiber and binary-branch optical fiber on the performance of the prediction models. Better prediction models were obtained at the specific position than those at the random one. The system with the binary-branch optical fiber proved superior robustness, while that with the mono-branch optical fiber proved superior accuracy. The optimal model, when considering robustness and accuracy, had a root mean square error of calibration (RMSEC), determination coefficient of calibration (\( {R}_C^2 \)), root mean square error of validation (RMSEP), and determination coefficient of validation (\( {R}_P^2 \)) of 0.568%, 0.7319, 0.610%, and 0.6295, respectively. It was established by data simultaneously detected in two different regions utilizing the binary-branch optical fiber at the specific position. This work provided a possible solution on improving the model robustness in the practical application of on-line NIR detection for the SSC measurement of apples.


Near-infrared On-line Apple Detection position Detection region Optical fiber 



This study was funded by the National Key Research and Development Project (grant number 2017YFC1600805).

Compliance with Ethical Standards

Conflict of Interest

Xiao Xu declares that she has no conflict of interest. Jiancan Mo declares that she has no conflict of interest. Lijuan Xie declares that she has no conflict of interest. Yibin Ying declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.


  1. Ariana D, Lu R (2002) A near-infrared sensing technique for measuring internal quality of apple fruit. Appl Eng Agric 18(5):585–592Google Scholar
  2. Amodio ML, Ceglie F, Chaudhry MMA, Piazzolla F, Colelli G (2017) Potential of NIR spectroscopy for predicting internal quality and discriminating among strawberry fruits from different production systems. Postharvest Biol Technol 125:112–121Google Scholar
  3. Chia KS, Rahim HA, Rahim RA (2013) Evaluation of common pre-processing approaches for visible (VIS) and shortwave near infrared (SWNIR) spectroscopy in soluble solids content (SSC) assessment. Biosyst Eng 115(1):82–88CrossRefGoogle Scholar
  4. Escribano S, Biasi WV, Lerud R, Slaughter DC, Mitcham EJ (2017) Non-destructive prediction of soluble solids and dry matter content using NIR spectroscopy and its relationship with sensory quality in sweet cherries. Postharvest Biol Technol 128:112–120Google Scholar
  5. Fu X, Ying Y, Xu H, Qi B, Xie L (2012) On-line detection of orange soluble solid content using visible and near infrared transmission measurements. Sensing for agriculture and food quality and safety IV. International Society for Optics and PhotonicsGoogle Scholar
  6. Fan S (2016) Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data. Postharvest Biol Technol 121:51–61CrossRefGoogle Scholar
  7. Fan S, Zhang B, Li J, Huang W, Wang C (2016) Effect of spectrum measurement position variation on the robustness of NIR spectroscopy models for soluble solids content of apple. Biosyst Eng 143(45):9–19CrossRefGoogle Scholar
  8. Golic M, Walsh KB (2006) Robustness of calibration models based on near infrared spectroscopy for the in-line grading of stonefruit for total soluble solids content. Analytica Chimica Acta 555(2):286–291Google Scholar
  9. Guo Z, Huang W, Peng Y, Chen Q, Ouyang Q, Zhao J (2016) 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 115:81–90Google Scholar
  10. Jie D, Xie L, Rao X, Ying Y (2014) 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):1–6CrossRefGoogle Scholar
  11. Jamshidi B, Minaei S, Mohajerani E, Ghassemian H (2014) Prediction of soluble solids in oranges using visible/near-infrared spectroscopy: effect of peel. Int J Food Eng 17(7):1460–1468Google Scholar
  12. Kumar S, Mcglone A, Whitworth C, Volz R (2015) Postharvest performance of apple phenotypes predicted by near-infrared (NIR) spectral analysis. Postharvest Biol Technol 100:16–22Google Scholar
  13. Luo X, Ye Z, Xu H, Zhang D, Bai S, Ying Y (2018) Robustness improvement of NIR-based determination of soluble solids in apple fruit by local calibration. Postharvest Biol Technol 139:82–90Google Scholar
  14. Liu Y, Sun X, Ouyang A (2010) Nondestructive measurement of soluble solid content of navel orange fruit by visible–NIR spectrometric technique with PLSR and PCA-BPNN. LWT-Food Sci Technol 43(4):602–607CrossRefGoogle Scholar
  15. Liu D, Li Q, Li W, Yang B, Guo W (2017) Discriminating forchlorfenuron-treated kiwifruits using a portable spectrometer and VIS/NIR diffuse transmittance spectroscopy technology. Anal Methods 9(28):4207–4214Google Scholar
  16. Liu Y, Liu S, Wang Y, Feng G, Zhu J, Zhao L (2008) Broad band enhanced infrared light absorption of a femtosecond laser microstructured silicon. Laser Phys 18(10):1148–1152Google Scholar
  17. Mendoza F, Lu R, Cen H (2014) Grading of apples based on firmness and soluble solids content using VIS/SWNIR spectroscopy and spectral scattering techniques. J Food Eng 125(1):59–68Google Scholar
  18. Mcglone VA, Jordan RB, Seelye R, Martinsen PJ (2002) Comparing density and NIR methods for measurement of kiwifruit dry matter and soluble solids content. Postharvest Biol Technol 26(2):191–198CrossRefGoogle Scholar
  19. Merzlyak MN, Solovchenko AE, Gitelson AA (2003) Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit. Postharvest Biol Technol 27(2):197–211CrossRefGoogle Scholar
  20. Mo C, Kim MS, Kim G, Lim J, Delwiche SR, Chao K (2017) Spatial assessment of soluble solid contents on apple slices using hyperspectral imaging. Biosyst Eng 159:10–21CrossRefGoogle Scholar
  21. Ma T, Li X, Inagaki T, Yang H, Tsuchikawa S (2018) Noncontact evaluation of soluble solids content in apples by near-infrared hyperspectral imaging. J Food Eng 224:53–61CrossRefGoogle Scholar
  22. Neto JPDS, Assis MWDD, Casagrande IP, Júnior LCC (2017) Determination of ‘Palmer’ mango maturity indices using portable near infrared (VIS-NIR) spectrometer. Postharvest Biol Technol 130:75–80CrossRefGoogle Scholar
  23. Nicolaï BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46(2):99–118Google Scholar
  24. Paz P, Sánchez M-T, Pérez-Marín D, Guerrero J-E, Garrido-Varo A (2008) Nondestructive determination of total soluble solid content and firmness in plums using near-infrared reflectance spectroscopy. J Agric Food Chem 56(8):2565–2570CrossRefGoogle Scholar
  25. Savenije B, Geesink GH, Jg VDP, Hemke G (2006) Prediction of pork quality using visible/near-infrared reflectance spectroscopy. Meat Sci 73(1):181–184CrossRefGoogle Scholar
  26. Schaare PN, Fraser DG (2000) Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidiachinensis). Postharvest Biol Technol 20(2):175–184CrossRefGoogle Scholar
  27. Schmutzler M, Huck CW (2014) Automatic sample rotation for simultaneous determination of geographical origin and quality characteristics of apples based on near infrared spectroscopy (NIRS). Vib Spectrosc 72(20):97–104CrossRefGoogle Scholar
  28. Sun X (2009) Nondestructive measurement soluble solids content of apple by portable and online near infrared spectroscopy. Proc SPIE - Int Soc Opt Eng 7514:24Google Scholar
  29. Sun T, Lin H, Xu H, Ying Y (2009) Effect of fruit moving speed on predicting soluble solids content of ‘Cuiguan’ pears (Pomaceaepyrifolia Nakai cv. Cuiguan) using PLS and LS-SVM regression. Postharvest Biol Technol 51(1):86–90Google Scholar
  30. Vega MVM, Sara S, Dvoralai W, Thomas S, Line Harder C, Toldam-Andersen TB (2014) A sampling approach for predicting the eating quality of apples using visible-near infrared spectroscopy. J Sci Food & Agric 93(15):3710–3719CrossRefGoogle Scholar
  31. Varmuza K, Filzmoser P (2009) Comparison of some linear regression methods—available in R—for a QSPR problem. Chem Cent J 3(Suppl 1):1–1CrossRefGoogle Scholar
  32. Vaudelle F, L’Huillier JP (2015) Influence of the size and skin thickness of apple varieties on the retrieval of internal optical properties using VIS/NIR spectroscopy: a Monte Carlo-based study. Comput Electron Agric 116(C):137–149CrossRefGoogle Scholar
  33. Wang J, Wang J, Chen Z, Han D (2017) Development of multi-cultivar models for predicting the soluble solid content and firmness of European pear (Pyruscommunis L.) using portable VIS–NIR spectroscopy. Postharvest Biol Technol 129:143–151Google Scholar
  34. Wang A, Hu D, Xie L (2014) 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):126–132CrossRefGoogle Scholar
  35. Williams PC, Sobering DC (1993) Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. J Near Infrared Spectrosc 1(1):25–32CrossRefGoogle Scholar
  36. Xu H, Qi B, Sun T, Fu X, Ying Y (2012) Variable selection in visible and near-infrared spectra: application to on-line determination of sugar content in pears. J Food Eng 109(1):142–147Google Scholar
  37. Xu H (2003) Research on fruit feeding and rolling installation with bicone rollers. Trans CSAM 34(6):100–103Google Scholar
  38. Xu X, Xu H, Xie L, Ying Y (2018) Effect of measurement position on prediction of apple soluble solids content (SSC) by an on-line near-infrared (NIR) system. J Food Meas Charact:1–7Google Scholar
  39. Xie L, Ye X, Liu D, Ying Y (2011) Prediction of titratable acidity, malic acid, and citric acid in bayberry fruit by near-infrared spectroscopy. Food Res Int 44(7):2198–2204Google Scholar
  40. Xie L, Wang A, Xu H, Fu X, Ying Y (2016) Applications of near-infrared systems for quality evaluation of fruits: a review. Trans ASABE 59(2):399–419CrossRefGoogle Scholar
  41. Yao Y, Chen H, Xie L, Rao X (2013) 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):22–27Google Scholar
  42. Yan Y (2005) Analytical Basis and Application of Near Infrared Spectroscopy. China light industry press, BeijingGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Xiao Xu
    • 1
    • 2
  • Jiancan Mo
    • 3
  • Lijuan Xie
    • 1
    • 2
    Email author
  • Yibin Ying
    • 1
    • 2
    • 4
  1. 1.College of Biosystems Engineering and Food ScienceZhejiang UniversityZhejiangPR China
  2. 2.Key Laboratory of On Site Processing Equipment for Agricultural ProductsMinistry of AgricultureHangzhouPR China
  3. 3.Zhejiang Zhongduo Smart Manufacturing Co., Ltd.ZhejiangPR China
  4. 4.Faculty of Agricultural and Food ScienceZhejiang A&F UniversityZhejiangPR China

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