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Soluble Solids Content and pH Prediction and Maturity Discrimination of Lychee Fruits Using Visible and Near Infrared Hyperspectral Imaging

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Abstract

Hyperspectral imaging (HSI) technique has shown promise as a rapid and nondestructive tool to evaluate various internal quality attributes of fruits and vegetables. The objective of this study was to investigate the nondestructive prediction of soluble solids content (SSC) and pH of lychees and maturity discrimination. Two hyperspectral imaging systems of visible/short-wave near infrared range (600–1000 nm, Spectral Set I) and long-wave near infrared range (1000–2500 nm, Spectral Set II) were employed. Results showed that Spectral Set II (SSC: r p  = 0.877, RMSEP = 0.911 °Brix; pH: r p  = 0.745, RMSEP = 0.291) performed better than Spectral Set I (SSC: r p  = 0.790, RMSEP = 1.279 °Brix; pH: r p  = 0.701, RMSEP = 0.308) for the internal quality prediction of litchi and maturity discrimination. The partial least square discriminant analysis (PSL-DA) model had a discrimination rate of 90.63 % for Spectral Set I and 96.88 % for Spectral Set II. β-Coefficients of partial least squares regression (PLSR) models were used to choose optimal wavelengths for quality predictions. The performance of optimized PLSR in both spectral sets were comparable to the models developed using the whole spectral range.

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Acknowledgments

The authors gratefully acknowledge the Guangdong Province Government (China) for its support through the program “Leading Talent of Guangdong Province (Da-Wen Sun).” This research was also supported by the National Key Technologies R&D Program (2014BAD08B09) and the International S&T Cooperation Projects of Guangdong Province (2013B051000010).

Conflict of Interest

Hongbin Pu declares that he has no conflict of interest. Dan Liu declares that she has no conflict of interest, Lu Wang declares that she has no conflict of interest. Da-Wen Sun declares that he has no conflict of interest. This article does not contain any studies with human or animal subjects.

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Authors and Affiliations

  1. College of Light Industry and Food Sciences, South China University of Technology, Guangzhou, 510641, People’s Republic of China

    Hongbin Pu, Dan Liu, Lu Wang & Da-Wen Sun

  2. Food Refrigeration and Computerised Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland

    Da-Wen Sun

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  1. Hongbin Pu
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Correspondence to Da-Wen Sun.

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Pu, H., Liu, D., Wang, L. et al. Soluble Solids Content and pH Prediction and Maturity Discrimination of Lychee Fruits Using Visible and Near Infrared Hyperspectral Imaging. Food Anal. Methods 9, 235–244 (2016). https://doi.org/10.1007/s12161-015-0186-7

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