Distinguishing plant biophysical stress responses from temporal variability using vis–NIR spectroscopy
Abstract
Background: Visible-near infrared (Vis-NIR) spectroscopy is widely used for non-destructive plant monitoring; however, in time-series experiments, signals of interest are frequently masked by diurnal rhythms and systematic measurement drift. This limits the ability to clearly detect plant responses to stress in spectral datasets. Results: This study presents a multi-sensor approach to monitor the onset and recovery dynamics of polyethylene glycol (PEG)-induced osmotic stress in sunflower (Helianthus annuus L.). Initial multivariate exploration using principal component analysis (PCA) indicated that temporal and environmental variability dominated the dataset, more than treatment-related effects. The application of REP-ASCA (Reduction of Repeatability Error for ANOVA-Simultaneous Component Analysis), a design-driven multivariate approach, allowed the separation of stress-related spectral variation from temporal and measurement-related effects. This revealed a rapid divergence between control and PEG-treated plants following PEG application. The REP-ASCA scores associated with the PEG factor showed a reversible trajectory, returning towards control levels after PEG removal, consistent with the transient nature of the applied stress. This behaviour contrasted with the progressive changes observed in pigment-related indices, suggesting that the spectral components extracted by REP-ASCA primarily capture fast and reversible biophysical responses. Conclusions: These results highlight the potential of combining high-resolution Vis-NIR spectroscopy with design-driven multivariate analysis to disentangle treatment effects from strong temporal variability in plant time-series data.
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BibTeX
@article{abdelmeguid2026distinguishing,
title = {{Distinguishing plant biophysical stress responses from temporal variability using vis–NIR spectroscopy}},
author = {Ingi Abdelmeguid and Daphné Héran and Maxime Ryckewaert and David Bastidon and Ryad Bendoula},
journal = {Discover Applied Sciences},
year = {2026},
month = sep,
doi = {10.1007/s42452-026-09299-1},
url = {https://hal.inrae.fr/hal-05855090v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Distinguishing plant biophysical stress responses from temporal variability using vis–NIR spectroscopy
AU - Ingi Abdelmeguid
AU - Daphné Héran
AU - Maxime Ryckewaert
AU - David Bastidon
AU - Ryad Bendoula
PY - 2026
DA - 2026/09/16
JO - Discover Applied Sciences
DO - 10.1007/s42452-026-09299-1
UR - https://hal.inrae.fr/hal-05855090v1
AB - Background: Visible-near infrared (Vis-NIR) spectroscopy is widely used for non-destructive plant monitoring; however, in time-series experiments, signals of interest are frequently masked by diurnal rhythms and systematic measurement drift. This limits the ability to clearly detect plant responses to stress in spectral datasets. Results: This study presents a multi-sensor approach to monitor the onset and recovery dynamics of polyethylene glycol (PEG)-induced osmotic stress in sunflower (Helianthus annuus L.). Initial multivariate exploration using principal component analysis (PCA) indicated that temporal and environmental variability dominated the dataset, more than treatment-related effects. The application of REP-ASCA (Reduction of Repeatability Error for ANOVA-Simultaneous Component Analysis), a design-driven multivariate approach, allowed the separation of stress-related spectral variation from temporal and measurement-related effects. This revealed a rapid divergence between control and PEG-treated plants following PEG application. The REP-ASCA scores associated with the PEG factor showed a reversible trajectory, returning towards control levels after PEG removal, consistent with the transient nature of the applied stress. This behaviour contrasted with the progressive changes observed in pigment-related indices, suggesting that the spectral components extracted by REP-ASCA primarily capture fast and reversible biophysical responses. Conclusions: These results highlight the potential of combining high-resolution Vis-NIR spectroscopy with design-driven multivariate analysis to disentangle treatment effects from strong temporal variability in plant time-series data.
SN - 3004-9261
ER -