Prediction of essential oil content of oregano by hand-held and Fourier transform NIR spectroscopy.

BACKGROUND: In the framework of a breeding programme, the analysis of hundreds of oregano samples to determine their essential oil content (EOC) is time-consuming and expensive in terms of labour. Therefore developing a new method that is rapid, accurate and less expensive to use would be an asset to breeders. The aim of the present study was to develop a method based on near-inrared (NIR) spectroscopy to determine the EOC of oregano dried powder. Two spectroscopic approaches were compared, the first using a hand-held NIR device and the second a Fourier transform (FT) NIR spectrometer. RESULTS: Hand-held NIR (1000-1800 nm) measurements and partial least squares regression allowed the determination of EOC with R² and SEP values of 0.58 and 0.81 mL per 100 g dry matter (DM) respectively. Measurements with FT-NIR (1000-2500 nm) allowed the determination of EOC with R² and SEP values of 0.91 and 0.68 mL per 100 g DM respectively. RPD, RER and RPIQ values for the model implemented with FT-NIR data were satisfactory for screening application, while those obtained with hand-held NIR data were below the level required to consider the model as enough accurate for screening application. CONCLUSION: The FT-NIR approach allowed the development of an accurate model for EOC prediction. Although the hand-held NIR approach is promising, it needs additional development before it can be used in practice.

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy.

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.

Leaf economics and slow-fast adaptation across the geographic range of Arabidopsis thaliana.

Life history strategies of most organisms are constrained by resource allocation patterns that follow a 'slow-fast continuum'. It opposes slow growing and long-lived organisms with late investment in reproduction to those that grow faster, have earlier and larger reproductive effort and a short longevity. In plants, the Leaf Economics Spectrum (LES) depicts a leaf-level trade-off between the rate of carbon assimilation and leaf lifespan, as stressed in functional ecology from interspecific comparative studies. However, it is still unclear how the LES is connected to the slow-fast syndrome. Interspecific comparisons also impede a deep exploration of the linkage between LES variation and adaptation to climate. Here, we measured growth, morpho-physiological and life-history traits, at both the leaf and whole-plant levels, in 378 natural accessions of Arabidopsis thaliana. We found that the LES is tightly linked to variation in whole-plant functioning, and aligns with the slow-fast continuum. A genetic analysis further suggested that phenotypic differentiation results from the selection of different slow-fast strategies in contrasted climates. Slow growing and long-lived plants were preferentially found in cold and arid habitats while fast growing and short-lived ones in more favorable habitats. Our findings shed light on the role of the slow-fast continuum for plant adaptation to climate. More broadly, they encourage future studies to bridge functional ecology, genetics and evolutionary biology to improve our understanding of plant adaptation to environmental changes.