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NIRS as an alternative method for table grapes Seedlessness sorting.

Chaorai Kanchanomai1,2,3, Parichat Theanjumpol4,5, Phonkrit Maniwara4,5

  • 1Graduate School, Chiang Mai University, Chiang Mai 50200, Thailand.

Methodsx
|February 6, 2025
PubMed
Summary

Shortwave-near infrared spectroscopy (SW-NIRS) offers an efficient, non-destructive method for predicting seedlessness in table grapes. This technique accurately sorts grapes, improving upon traditional methods by reducing time and waste.

Keywords:
ChemometricsNon-destructive technologyNondestructive method for seedlessness sortingSeedlessnessSelf-organizing mapsTable grapes

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Area of Science:

  • Agricultural Science
  • Spectroscopy
  • Chemometrics

Background:

  • Seedlessness is a key desirable trait in table grapes, often induced using plant growth regulators (PGRs).
  • Current methods for detecting seedlessness, like cutting and counting, are destructive and inefficient.
  • The efficacy of PGRs varies, and reliable seedlessness detection is crucial for quality control.

Purpose of the Study:

  • To develop and validate a non-destructive method for predicting seedlessness in table grapes using SW-NIRS.
  • To compare the efficiency of SW-NIRS with traditional seed detection techniques.
  • To apply chemometric analysis for accurate classification of seedless grapes.

Main Methods:

  • Acquired SW-NIR spectra (3996–12,489 cm⁻¹) from 240 grape berries.
  • Determined seed presence/absence through destructive cutting and counting for ground truth.
  • Analyzed spectral data using chemometric methods, including Principal Component Analysis (PCA), Supervised Self-Organizing Map (SSOM), and Quadratic Discriminant Analysis (QDA).

Main Results:

  • PCA showed a negative tendency for classifying seedlessness.
  • SSOM provided clear classification of seedless grapes.
  • SSOM achieved high classification accuracy: 97.14% for training and 94.64% for test sets.

Conclusions:

  • SW-NIRS coupled with chemometrics, particularly SSOM, is a highly accurate and efficient non-destructive method for predicting table grape seedlessness.
  • This spectroscopic approach offers significant advantages over traditional methods in terms of accuracy, speed, and waste reduction.
  • The findings support the adoption of SW-NIRS for quality control and sorting in the table grape industry.