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Prediction of Soluble Solids Content and Firmness of Pears Using Hyperspectral Reflectance Imaging

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Abstract

Hyperspectral imaging technique was investigated to determine the soluble solids content (SSC) and firmness of pears. A total of 160 pear samples were prepared for the calibration (n = 120) and prediction (n = 40) sets. A hyperspectral imaging system was used to acquire hyperspectral reflectance image from each pear in visible and near infrared (400–1000 nm) regions. Mean spectra were extracted from the regions of interest for the hyperspectral image of each pear. Spectral data were first pretreated with different preprocessing techniques and analyzed using partial least square (PLS) to establish calibration models. However, the large size of spectral data contains a large number of redundant variables that lead to complexity and poor predicting ability of calibration models. Several variable selection methods were investigated to select effective wavelength variables for the determination of SSC and firmness of pear. In this study, the variables selected by successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS) and the combination of CARS and SPA were used for PLS regression. The CARS-SPA-PLS models based on 25 and 22 variables achieved the optimal performance for two internal quality indices compared with full-spectrum PLS, CARS-PLS, and SPA-PLS models. The correlation coefficient (r pre) and root mean square error of prediction (RMSEP) by CARS-SPA-PLS were 0.876, 0.491 for SSC and 0.867, 0.721 for firmness, respectively. The overall results indicated that the CARS-SPA was a powerful way for the selection of effective variables and the hyperspectral imaging system together with CARS-SPA-PLS model could be applied as a fast and potential method for the determination of SSC and firmness of pear.

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References

  • Andersen CM, Bro R (2010) Variable selection in regression—a tutorial. J Chemom 24:728–737

    Article  CAS  Google Scholar 

  • Araújo MCU, Saldanha TCB, Galvã RKH, Yoneyama T, Chame HC, Visani V (2001) The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemom Intell Lab Syst 57:65–73

    Article  Google Scholar 

  • Baiano A, Terracone C, Peri G, Romaniello R (2012) Application of hyperspectral imaging for prediction of physico-chemical and sensory characteristics of table grapes. Comput Electron Agric 87:142–151

    Article  Google Scholar 

  • Barnes RJ, Dhanoa MS, Lister SJ (1989) Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl Spectrosc 43:772–777

    Article  CAS  Google Scholar 

  • Burger J, Gowen A (2011) Data handling in hyperspectral image analysis. Chemometrics Intellig Lab Syst 108:13–22

    Article  CAS  Google Scholar 

  • ElMasry G, Wang N, ElSayed A, Ngadi M (2007) Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. J Food Eng 81:98–107

    Article  CAS  Google Scholar 

  • ElMasry G, Wang N, Vigneault C, Qiao J, ElSayed A (2008) Early detection of apple bruises on different background colors using hyperspectral imaging. LWT-Food Sci Technol 41:337–345

    Article  CAS  Google Scholar 

  • Fan S, Huang W, Li J, Zhao C, Zhang B (2014) Characteristic wavelengths selection of soluble solids content of pear based on NIR spectral and LS-SVM. Spectrosc Spectr Anal 34(8):2089–2093

    CAS  Google Scholar 

  • Fu X, Ying Y (2014) Food safety evaluation based on near infrared spectroscopy and imaging: a review. Crit Rev Food Sci Nutr. doi:10.1080/10408398.2013.807418

    Google Scholar 

  • Galvão RKH, Araújo MCU, Fragoso WD, Silva EC, José GE, Soares SFC, Paiva HM (2008) A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm. Chemom Intell Lab Syst 92:83–91

    Article  Google Scholar 

  • Helland IS, Nas T, Isaksson T (1995) Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data. Chemom Intell Lab Syst 1995:233–241

    Article  Google Scholar 

  • Huang H, Yu H, Xu H, Ying Y (2008) Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review. J Food Eng 87:303–313

    Article  CAS  Google Scholar 

  • Huang M, Wang Q, Zhang M, Zhu Q (2014) Prediction of color and moisture content for vegetable soybean during drying using hyperspectral imaging technology. J Food Eng 128:24–30

    Article  Google Scholar 

  • Jiang H, Zhu W (2013) Determination of pear internal quality attributes by Fourier transform near infrared (FT-NIR) spectroscopy and multivariate analysis. Food Anal Methods 6:569–577

    Article  Google Scholar 

  • Jie D, Xie L, Fu X, Rao X, Ying Y (2013) Variable selection for partial least squares analysis of soluble solids content in watermelon using near-infrared diffuse transmission technique. J Food Eng 118:387–392

    Article  Google Scholar 

  • Lee W-H, Kim MS, Lee H, Delwiche SR, Bae H, Kim D-Y, Cho B-K (2014) Hyperspectral near-infrared imaging for the detection of physical damages of pear. J Food Eng 130:1–7

    Article  Google Scholar 

  • Leiva-Valenzuela GA, Lu R, Aguilera JM (2013) Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. J Food Eng 115:91–98

    Article  Google Scholar 

  • Li H, Liang Y, Xu Q, Cao D (2009) Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Anal Chim Acta 648:77–84

    Article  CAS  Google Scholar 

  • Li J, Huang W, Zhao C, Zhang B (2013) A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. J Food Eng 116:324–332

    Article  CAS  Google Scholar 

  • Liu F, Jiang Y, He Y (2009) Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: a case study to determine soluble solids content of beer. Anal Chim Acta 635:45–52

    Article  CAS  Google Scholar 

  • Liu D, Sun D-W, Zeng X-A (2014) Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry. Food Bioprocess Technol 7:307–323

    Article  Google Scholar 

  • Lu R (2004) Multispectral imaging for predicting firmness and soluble solids content of apple fruit. Postharvest Biol Technol 31:147–157

    Article  Google Scholar 

  • McGlone VA, Kawano S (1998) Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy. Postharvest Biol Technol 13:131–141

    Article  Google Scholar 

  • Mendoza F, Lu R, Ariana D, Cen H, Bailey B (2011) Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biol Technol 62:149–160

    Google Scholar 

  • 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 125:59–68

    Article  CAS  Google Scholar 

  • 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 46:99–118

    Article  Google Scholar 

  • Nicolaï BM, Verlinden BE, Desmet M, Saevels S, Saeys W, Theron K, Cubeddu R, Pifferi A, Torricelli A (2008) Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear. Postharvest Biol Technol 47:68–74

    Article  Google Scholar 

  • Paz P, Sánchez M-T, Pérez-Marín D, Guerrero JE, Garrido-Varo A (2009) Instantaneous quantitative and qualitative assessment of pear quality using near infrared spectroscopy. Comput Electron Agric 69:24–32

    Article  Google Scholar 

  • Peng Y, Lu R (2008) Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content. Postharvest Biol Technol 48:52–62

    Article  Google Scholar 

  • Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 16:1627–1639

    Article  Google Scholar 

  • Sun T, Lin H, Xu H, Ying Y (2009) Effect of fruit moving speed on predicting soluble solids content of ‘Cuiguan’ pears (Pomaceae pyrifolia Nakai cv. Cuiguan) using PLS and LS-SVM regression. Postharvest Biol Technol 51:86–90

    Article  CAS  Google Scholar 

  • Tanvir H, Demetriades-Shah, Steven MD, Clark JA (1990) High resolution derivative spectra in remote sensing. Remote Sens Environ 33:55–64

    Article  Google Scholar 

  • Travers S, Bertelsen MG, Petersen KK, Kucheryavskiy SV (2014) Predicting pear (cv. Clara Frijs) dry matter and soluble solids content with near infrared spectroscopy. LWT-Food Sci Technol 59:1107–1113

    Article  CAS  Google Scholar 

  • Wang A, Xie L (2014) Technology using near infrared spectroscopic and multivariate analysis to determine the soluble solids content of citrus fruit. J Food Eng 143:17–24

    Article  CAS  Google Scholar 

  • Wu D, Sun D-W (2013) Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. Talanta 111:39–46

    Article  CAS  Google Scholar 

  • Wu D, He Y, Nie P, Cao F, Bao Y (2010) Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice. Anal Chim Acta 659:229–237

    Article  CAS  Google Scholar 

  • 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 44:2198–2204

    Article  CAS  Google Scholar 

  • Xing J, Saeys W, De Baerdemaeker J (2007) Combination of chemometric tools and image processing for bruise detection on apples. Comput Electron Agric 56:1–13

    Article  Google Scholar 

  • 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 109:142–147

    Article  CAS  Google Scholar 

  • Liu Y-d, Ying Y-b, Fu X, Lu H (2007) Experiments on predicting sugar content in apples by FT-NIR technique. J Food Eng 80:986–989

    Article  CAS  Google Scholar 

  • Ying Y, Liu Y (2008) Nondestructive measurement of internal quality in pear using genetic algorithms and FT-NIR spectroscopy. J Food Eng 84:206–213

    Article  Google Scholar 

  • Yu K, Zhao Y, Li X, Shao Y, Zhu F, He Y (2014) Identification of crack features in fresh jujube using Vis/NIR hyperspectral imaging combined with image processing. Comput Electron Agric 103:1–10

    Article  Google Scholar 

  • Zhang B, Huang W, Li J, Zhao C, Fan S, Wu J, Liu C (2014) Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: a review. Food Res Int 62:326–343

    Article  Google Scholar 

Download references

Acknowledgments

The authors gratefully acknowledge the financial support provided by National Key Technology R&D Program (2014BAD21B00) and Beijing Municipal Natural Science Foundation (No. 6144024).

Conflict of Interest

Shuxiang Fan, Wenqian Huang, Zhiming Guo, Baohua Zhang, and Chunjiang Zhao declare that they have no conflict of interest. This article does not contain any studies with human participants or animals performed by any of the authors.

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Authors and Affiliations

  1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China

    Shuxiang Fan, Wenqian Huang, Zhiming Guo, Baohua Zhang & Chunjiang Zhao

  2. College of Mechanical and Electronic Engineering, Northwest Agricultural and Forestry University, Yangling, Shaanxi, 712100, China

    Shuxiang Fan & Chunjiang Zhao

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  1. Shuxiang Fan
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  2. Wenqian Huang
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Correspondence to Chunjiang Zhao.

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Fan, S., Huang, W., Guo, Z. et al. Prediction of Soluble Solids Content and Firmness of Pears Using Hyperspectral Reflectance Imaging. Food Anal. Methods 8, 1936–1946 (2015). https://doi.org/10.1007/s12161-014-0079-1

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  • DOI: https://doi.org/10.1007/s12161-014-0079-1

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