Repository logo
Communities & Collections
Research Outputs
Fundings & Projects
People
Statistics
New user? Click here to register.Have you forgotten your password?
  1. Home
  2. KMITL
  3. Publication
  4. Predicting marian plum fruit quality without environmental condition impact by handheld visible-near-infrared spectroscopy
Loading...
Thumbnail Image

Predicting marian plum fruit quality without environmental condition impact by handheld visible-near-infrared spectroscopy

Author(s)
Posom, Jetsada
Klaprachan, Junjira
Rattanasopa, Kamonpan
Sirisomboon, Panmanas
Saengprachatanarug, Khwantri
Wongpichet, Seree
Date Issued
November 3, 2020
Type
Article
DOI
10.1021/acsomega.0c03203
Abstract
Handheld near-infrared spectroscopy was used to study the effect of integration time and wavelength selection on predicting marian plum quality including soluble solids content (SSC), the potential of hydrogen ion (pH), and titratable acidity (TA). For measurements representing actual conditions, the on-tree fruits were scanned under in-field conditions. The assumption was that the robust model might be achieved when the models were developed under actual conditions. The results of the main effect test show that the integration time did not statistically affect SSC, pH, and TA predictions (p-value > 0.05) and the wavelength range had a significant impact on prediction (p-value < 0.01). An integration time of 30 ms coupled with a wavelength range of 670-1000 nm was the optimal conditions for the SSC prediction, while an integration time of 20 ms with 670-1000 nm wavelength was optimal for pH and TA prediction because of the lowest root-mean-square error of cross-validation (RMSECV). The optimal models for SSC, pH, and TA could be improved using spectral pre-processing of multiplicative scatter correction. The effective models for SSC, pH, and TA improved and reported the coefficients of determination (r2) and root-mean-square errors of prediction (RMSEP) of 0.66 and 0.86 °Brix; 0.79 and 0.15; and 0.71 and 1.91%, respectively. The SSC, pH, and TA models could be applied for quality assurance. These models benefit the orchardist for on-tree measurement before harvesting.
Citation
ACS Omega, 5(43), 27909-27921, 2020
Metrics
Get Involved!
  • Source Code
  • Documentation
  • Slack Channel
Make it your own

DSpace-CRIS can be extensively configured to meet your needs. Decide which information need to be collected and available with fine-grained security. Start updating the theme to match your Institution's web identity.

Need professional help?

The original creators of DSpace-CRIS at 4Science can take your project to the next level, get in touch!

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Accessibility settings
  • Privacy policy
  • End User Agreement
  • Send Feedback