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Updated: Aug 13, 2025

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Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy.

Michael Pérez-Rodríguez1, Alberto Mendoza1, Lucy T González1

  • 1Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, N.L., Mexico.

Foods (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

Laser-induced breakdown spectroscopy (LIBS) offers a rapid, objective method for rice quality inspection. This technique accurately classifies rice grain quality, achieving 94% prediction accuracy, surpassing traditional visual methods.

Keywords:
LIBSgrain quality featuresk-nearest neighborsricespectral processing

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Agricultural Science

Background:

  • Rice quality inspection is vital for consumer protection and market value.
  • Current visual and image-based methods for rice quality assessment are subjective and inefficient for large-scale analysis.
  • Laser-induced breakdown spectroscopy (LIBS) provides rapid elemental composition analysis, offering a potential objective alternative.

Purpose of the Study:

  • To evaluate the performance of Laser-induced breakdown spectroscopy (LIBS) for objective rice grain quality labeling.
  • To develop and optimize a LIBS-based classification model for different rice quality types.
  • To compare the accuracy and efficiency of LIBS against traditional quality inspection methods.

Main Methods:

  • Collected LIBS spectra from rice samples numerically classified as Type 1, 2, and 3.
  • Applied various spectral processing techniques, including smoothing and normalization.
  • Utilized k-nearest neighbors (k-NN) classification, incorporating principal component analysis (PCA) for variable selection and optimal k-value determination.

Main Results:

  • The optimized LIBS method, using spectrum smoothing, normalization, and the first fifteen principal components (PCs) with k=9, achieved 94% overall prediction accuracy.
  • The classification model demonstrated high performance with sensitivities ranging from 90% to 100% and specificities from 92% to 100%.
  • The LIBS approach proved to be objective, avoiding the subjectivity inherent in visual inspection methods.

Conclusions:

  • LIBS is a highly effective and objective analytical technique for classifying rice grain quality.
  • The developed LIBS-based method offers significant advantages in terms of speed and accuracy over conventional inspection techniques.
  • This rapid, chemical-response-guided analysis enhances the reliability of rice quality assessment in the food industry.