Optical imaging spectroscopy for plant research: more than a colorful picture

Exploring the potential of cellulose autofluorescence for optical detection of tannin in red wines

The growing demand for opto-electronic devices within an automated landscape has opened up new opportunities for harnessing sustainable cellulose materials for sensors technology. Cellulose, a versatile material, enables its combination with other materials, but in most of these applications, cellulose is typically employed as support or substrate, while its inherent autofluorescence remains largely underexplored for sensors. In light of this context, this study delves into the autofluorescence characteristics of pristine cellulose nanocrystals extracted from wood via enzymatic route for optical sensors tailored to detect tannins. By fine-tuning the experimental setup, photoluminescence (PL) emission bands were scrutinized across three distinct spectral regions, namely 300-400 nm, 400-500 nm and 550-700 nm. The proposed mechanism reveals the occurrence of dynamic fluorescence quenching, which enabled the selective monitoring of tannins in red wines across a dynamic range spanning from 10 to 1060 μg mL-1. This sensing platform provided a limit of detection (LoD) of 6.1 μg mL-1. Notably, the sensing platform's efficacy was validated with remarkable recovery rates of 99.7 % and 95.3 % when subjected to testing with cabernet sauvignon and tannat wines. These findings emphasize the sensing platform's potential for monitoring tannic acids in beverages and food products.

Detection of Apple Valsa Canker Based on Hyperspectral Imaging

Approximately half of the world’s apple production occurs in East Asia, where apple Valsa canker (AVC) is a prominent disease. This disease affects the bark of the tree, ultimately killing it and resulting in significant economic loss. Visual identification of the diseased area of the bark, particularly in the early stages, is extremely difficult. In this study, we conducted hyperspectral imaging of the trunks and branches of AVC-infected apple trees and revealed that the diseased area can be identified in the near-infrared reflectance, even when it is difficult to distinguish visually. A discriminant analysis using the Mahalanobis distance was performed on the normalized difference spectral index (NDSI) obtained from the measured spectral reflectance. A diagnostic model for discriminating between the healthy and diseased areas was created using the threshold value of NDSI. An accuracy assessment of the diagnostic model presented the overall accuracy as >0.94 for the combinations of spectral bands at 660–690 nm and 720–760 nm. This simple diagnostic model can be applied to other tree bark canker diseases.

Principles and Applications of Vibrational Spectroscopic Imaging in Plant Science: A Review

Detailed knowledge about plant chemical constituents and their distributions from organ level to sub-cellular level is of critical interest to basic and applied sciences. Spectral imaging techniques offer unparalleled advantages in that regard. The core advantage of these technologies is that they acquire spatially distributed semi-quantitative information of high specificity towards chemical constituents of plants. This forms invaluable asset in the studies on plant biochemical and structural features. In certain applications, non-invasive analysis is possible. The information harvested through spectral imaging can be used for exploration of plant biochemistry, physiology, metabolism, classification, and phenotyping among others, with significant gains for basic and applied research. This article aims to present a general perspective about vibrational spectral imaging/micro-spectroscopy in the context of plant research. Within the scope of this review are infrared (IR), near-infrared (NIR) and Raman imaging techniques. To better expose the potential and limitations of these techniques, fluorescence imaging is briefly overviewed as a method relatively less flexible but particularly powerful for the investigation of photosynthesis. Included is a brief introduction to the physical, instrumental, and data-analytical background essential for the applications of imaging techniques. The applications are discussed on the basis of recent literature.