Modelling biophoton emission kinetics based on the initial intensity value in Helianthus annuus plants exposed to different types of stress

Abstract

Biophoton radiation also referred to as ultra-weak photon emission (UPE) is used to denote a spontaneous and permanent photon emission associated with oxidative processes in cells and seems to universally occur in all living systems as a result of the generation of reactive oxygen species (ROS) that are produced under stress conditions. The measurement of this biophoton emission allows for a non-invasive approach in monitoring phenological stages throughout plant development which has direct relevance in agriculture research. In this study, the emission of photons emanating from sunflower (Helianthus annuus, L.) plants exposed to biotic and abiotic stress has been investigated. In healthy plants raised under controlled growth conditions UPE was low whereas in stressed individuals it considerably increased; particularly upon water stress. The kinetics of the signal is shown to reveal an exponential decay with characteristic dynamics, which appears to reflect different physiological states concomitantly setting in upon stress. The dynamics of the signal decay is shown to vary according to the type of stress applied (biotic vs. abiotic) hence suggesting a putative relationship between the kinetic traits of change in the signal intensity-decay and stress. Intriguingly, the determination of the change in the intensity of biophoton emission that ensued in a short time course was possible by using the initial biophoton emission intensity. The predictability level of the equations demonstrated the applicability of the model in a corroborative manner when employing it in independent UPE-measurements, thus permitting to forecast the intensity change in a very accurate way over a short time course. Our findings allow the notion that albeit stress confers complex and complicated changes on oxidative metabolism in biological systems, the employment of biophoton imaging offers a feasible method making it possible to monitor oxidative processes triggered by stress in a non-invasive and label-free way which has versatile applications especially in precision agriculture.

Figure 1

Representative 2 D-overlay images of black and white photos acquired by using the NightOWLcam ultra-sensitive CCD-camera (Berthold Technologies, Germany) mounted onto a dark, light-tight chamber of the NightShade LB 985 instrument and the pseudo colours-coded pixel intensity values visualising the spatial distribution and intensity of UPE (ultra-weak photon emission) on a leaf of a sample Helianthus annuus plant grown under ideal conditions and incubated in the dark chamber for 10 min prior to taking the image (A); on a leaf of a pierced (for mimicking herbivory attack) H. annuus plant (B) and on one of the leaves of a sunflower plant exposed to drought (C). The intensity colour bars on the right side of the images show signal intensities of pixels detected by the CCD-sensor and converted into colour-codes via the analysis software, according the scale established through the manufacturer’s calibration procedure ensuring traceability to a standard certified by PTB (Braunschweig, Germany).

Figure 2

Some randomly selected examples of biophoton emission intensity change over time in different experimental groups (see detailed results later). (A) Blue lines refer to biophoton emission intensity of water-stressed plants, green refers to plants in the biotic-stressed group, black refers to measurements in the control group. (B) dynamics of biophoton emission intensity in the control group which is not visible in Fig. 1A due to the O (10³–10⁴) difference in biophoton emission intensities.

Figure 3

Relationship between the exponential regression coefficient (i.e. the initial biophoton emission intensity [count/min]) and the dynamics of the change in biophoton emission intensity over time (i.e. the slope of the fitted exponential regression model) on Leaf A. The experimental groups (Control, Biotic stress, Water stress) can be clearly distinguished.

Figure 4

Logarithmic regression model was used to determine the relationship between initial biophoton emission intensity (count/min) and the dynamics of the change in biophoton emission intensity over time (i.e. slope of the fitted exponential regression model) in two experimental groups (Control and Biotic-stressed groups) on Leaf A.

Figure 5

Examples of measured and estimated exponential models from (A) Control group, (B) Biotic stress group with high correlation coefficient (R² = 0.9912 and R² = 0.9996, respectively). Higher initial biophoton emission intensities indicate more intensive decay of biophoton emission intensity in time.

Figure 6

Examples of typical changes of biophoton emission intensity (count/min) over time in the case of water-stressed group with the fitted exponential regression models. (A) Near-constant after a slightly increasing tendency, (B) moderately increasing tendency after a sudden initial decay, (C) trigonometric (“cosine-like”) wave, (D) near-constant after an initial decay.

Figure 7

Scatterplot of measured biophoton emission intensity and the bias of measurement and estimation in all experimental groups in each time step on Leaf A. There is a systematic error that can be estimated by linear regression models.

Figure 8

Scatterplot of measured biophoton emission intensity and the bias of measurement and estimation in the water-stressed group on Leaf B. There is a systematic error in the 2nd, the 4th and 5th time step. The systematic error can be estimated by linear regression models.

Figure 9

example of a biophoton emission intensity decay with fitted exponential regression curve from the control group, leaf A, R² = 0.9912.