Application of hyperspectral imaging and acoustic emission techniques for apple quality prediction

N. Ekramirad, A. Rady, A. A. Adedeji, R. Alimardani

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


There is a growing demand for developing effective non-destructive quality assessment methods with quick response, high accuracy, and low cost for fresh fruits. In this study, hyperspectral reflectance imaging (400 to 1000 nm) and acoustic emission (AE) tests were applied to 'GoldRush' apples (total number, n = 180) to predict fruit firmness, total soluble solids (TSS), and surface color parameters (L∗, a∗, b∗) during an eight-week storage period. Partial least squares (PLS) regression, least squares support vector machine (LS-SVM), and multivariate linear regression (MLR) methods were used to establish models to predict the quality attributes of the apples. The results showed that hyperspectral imaging (HSI) could accurately predict all the attributes except TSS, while the AE method was capable of predicting fruit firmness, b∗ color index, and TSS. Overall, HSI regression using PLS had better comprehensive ability for predicting firmness, TSS, and color parameters (L∗, a∗, b∗) than AE, with correlation coefficients of prediction (rp) of 0.92, 0.41, 0.83, 0.87, and 0.94 and root mean square errors of prediction (RMSEP) of 4.32 (N), 1.78 (°Brix), 3.41, 2.28, and 4.29, respectively, while AE regression using LS-SVM gave rp values of 0.88, 0.74, 0.34, 0.37, and 0.81 and RMSEP values of 4.26 (N), 0.64 (°Brix), 4.69, 1.8, and 5.17 for firmness, TSS, and color parameters (L∗, a∗, b∗), respectively. The results show the potential of these two non-destructive methods for predicting some of the quality attributes of apples.

Original languageEnglish
Pages (from-to)1391-1401
Number of pages11
JournalTransactions of the ASABE
Issue number4
StatePublished - 2017

Bibliographical note

Publisher Copyright:
© 2017 American Society of Agricultural and Biological Engineers.


  • Acoustic emission
  • Apple
  • Fruit quality
  • Hyperspectral imaging
  • Regression model

ASJC Scopus subject areas

  • Forestry
  • Food Science
  • Biomedical Engineering
  • Agronomy and Crop Science
  • Soil Science


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