The Effect of Post-harvest Conditions in Narcissus sp. Cut Flowers Scent Profile

Exploring Plants with Flowers: From Therapeutic Nutritional Benefits to Innovative Sustainable Uses

Flowers have played a significant role in society, focusing on their aesthetic value rather than their food potential. This study's goal was to look into flowering plants for everything from health benefits to other possible applications. This review presents detailed information on 119 species of flowers with agri-food and health relevance. Data were collected on their family, species, common name, commonly used plant part, bioremediation applications, main chemical compounds, medicinal and gastronomic uses, and concentration of bioactive compounds such as carotenoids and phenolic compounds. In this respect, 87% of the floral species studied contain some toxic compounds, sometimes making them inedible, but specific molecules from these species have been used in medicine. Seventy-six percent can be consumed in low doses by infusion. In addition, 97% of the species studied are reported to have medicinal uses (32% immune system), and 63% could be used in the bioremediation of contaminated environments. Significantly, more than 50% of the species were only analysed for total concentrations of carotenoids and phenolic compounds, indicating a significant gap in identifying specific molecules of these bioactive compounds. These potential sources of bioactive compounds could transform the health and nutraceutical industries, offering innovative approaches to combat oxidative stress and promote optimal well-being.

Aroma Components in Horticultural Crops: Chemical Diversity and Usage of Metabolic Engineering for Industrial Applications

Plants produce an incredible variety of volatile organic compounds (VOCs) that assist the interactions with their environment, such as attracting pollinating insects and seed dispersers and defense against herbivores, pathogens, and parasites. Furthermore, VOCs have a significant economic impact on crop quality, as well as the beverage, food, perfume, cosmetics and pharmaceuticals industries. These VOCs are mainly classified as terpenoids, benzenoids/phenylpropanes, and fatty acid derivates. Fruits and vegetables are rich in minerals, vitamins, antioxidants, and dietary fiber, while aroma compounds play a major role in flavor and quality management of these horticultural commodities. Subtle shifts in aroma compounds can dramatically alter the flavor and texture of fruits and vegetables, altering their consumer appeal. Rapid innovations in -omics techniques have led to the isolation of genes encoding enzymes involved in the biosynthesis of several volatiles, which has aided to our comprehension of the regulatory molecular pathways involved in VOC production. The present review focuses on the significance of aroma volatiles to the flavor and aroma profile of horticultural crops and addresses the industrial applications of plant-derived volatile terpenoids, particularly in food and beverages, pharmaceuticals, cosmetics, and biofuel industries. Additionally, the methodological constraints and complexities that limit the transition from gene selection to host organisms and from laboratories to practical implementation are discussed, along with metabolic engineering's potential for enhancing terpenoids volatile production at the industrial level.

New Insights into the Mechanism of Spatiotemporal Scent Accumulation in Orchid Flowers

Orchid flowers have a unique structure that consists of three sepals and three petals, with one of the petals forming the labellum (lip) that can be differentiated into the hypochile and epichile. In orchids, the emission of floral scent is specific and spatially complex. Little is understood about the molecular and biochemical mechanisms of the differing scent emissions between the parts of orchid flowers. Here, we investigated this in the Cattleya hybrid KOVA, and our study showed that monoterpenes, including linalool and geraniol, are the main components responsible for the KOVA floral scent. The KOVA flower was scentless to the human nose before it reached full bloom, potentially because the 1-deoxy-d-xylulose 5-phosphate synthases (RcDXSs) and 4-hydroxy-3-methylbut-2-enyl diphosphate synthases (RcHDSs) that biosynthesize monoterpenes were highly expressed in flowers only when it reached full flowering. Additionally, the spatial expression profile of the monoterpene synthases (RcMTPSs), which were highly expressed in the basal region of the lip (hypochile), contributed to the highest monoterpene emissions from this part of the flower. This might have caused the hypochile to be more fragrant than the other parts of the flower. These findings enrich our understanding of the difference in scents between different flower parts in plants and provide information to breed novel orchid cultivars with special floral scents.

Evaluation of floral volatile patterns in the genus Narcissus using gas chromatography-coupled ion mobility spectrometry

PREMISE

Daffodils (Narcissus, Amaryllidaceae) are iconic ornamentals with a complex floral biology and many fragrant species; however, little is known about floral plant volatile organic compounds (pVOCs) across the genus and additional sampling is desirable. The present study investigates whether the floral scent of 20 species of Narcissus can be characterized using gas chromatography-coupled ion mobility spectrometry (GC-IMS), with the aim of building a comparative pVOC data set for ecological and evolutionary studies.

METHODS

We used a commercial GC-IMS equipped with an integrated in-line enrichment system for a fast, sensitive, and automated pVOC analysis. This facilitates qualitative and (semi)-quantitative measurements without sample preparation.

RESULTS

The GC-IMS provided detailed data on floral pVOCs in Narcissus with very short sampling times and without floral enclosure. A wide range of compounds was recorded and partially identified. The retrieved pVOC patterns showed a good agreement with published data, and five "chemotypes" were characterized as characteristic combinations of floral volatiles.

DISCUSSION

The GC-IMS setup can be applied to rapidly generate large amounts of pVOC data with high sensitivity and selectivity. The preliminary data on Narcissus obtained here indicate both considerable pVOC variability and a good correspondence of the pVOC patterns with infrageneric classification, supporting the hypothesis that floral scent could represent a considerable phylogenetic signal.

Floral Scents and Fruit Aromas: Functions, Compositions, Biosynthesis, and Regulation

Floral scents and fruit aromas are crucial volatile organic compounds (VOCs) in plants. They are used in defense mechanisms, along with mechanisms to attract pollinators and seed dispersers. In addition, they are economically important for the quality of crops, as well as quality in the perfume, cosmetics, food, drink, and pharmaceutical industries. Floral scents and fruit aromas share many volatile organic compounds in flowers and fruits. Volatile compounds are classified as terpenoids, phenylpropanoids/benzenoids, fatty acid derivatives, and amino acid derivatives. Many genes and transcription factors regulating the synthesis of volatiles have been discovered. In this review, we summarize recent progress in volatile function, composition, biosynthetic pathway, and metabolism regulation. We also discuss unresolved issues and research perspectives, providing insight into improvements and applications of plant VOCs.

Transcriptome-Based WGCNA Analysis Reveals Regulated Metabolite Fluxes between Floral Color and Scent in Narcissus tazetta Flower

A link between the scent and color of Narcissus tazetta flowers can be anticipated due to their biochemical origin, as well as their similar biological role. Despite the obvious aesthetic and ecological significance of these colorful and fragrant components of the flowers and the molecular profiles of their pigments, fragrant formation has addressed in some cases. However, the regulatory mechanism of the correlation of fragrant components and color patterns is less clear. We simultaneously used one way to address how floral color and fragrant formation in different tissues are generated during the development of an individual plant by transcriptome-based weighted gene co-expression network analysis (WGCNA). A spatiotemporal pattern variation of flavonols/carotenoids/chlorophyll pigmentation and benzenoid/phenylpropanoid/ monoterpene fragrant components between the tepal and corona in the flower tissues of Narcissus tazetta, was exhibited. Several candidate transcription factors: MYB12, MYB1, AP2-ERF, bZIP, NAC, MYB, C2C2, C2H2 and GRAS are shown to be associated with metabolite flux, the phenylpropanoid pathway to the production of flavonols/anthocyanin, as well as related to one branch of the phenylpropanoid pathway to the benzenoid/phenylpropanoid component in the tepal and the metabolite flux between the monoterpene and carotenoids biosynthesis pathway in coronas. It indicates that potential competition exists between floral pigment and floral fragrance during Narcissus tazetta individual plant development and evolutionary development.

gcProfileMakeR: An R Package for Automatic Classification of Constitutive and Non-Constitutive Metabolites

Metabolomes comprise constitutive and non-constitutive metabolites produced due to physiological, genetic or environmental effects. However, finding constitutive metabolites and non-constitutive metabolites in large datasets is technically challenging. We developed gcProfileMakeR, an R package using standard Excel output files from an Agilent Chemstation GC-MS for automatic data analysis using CAS numbers. gcProfileMakeR has two filters for data preprocessing removing contaminants and low-quality peaks. The first function NormalizeWithinFiles, samples assigning retention times to CAS. The second function NormalizeBetweenFiles, reaches a consensus between files where compounds in close retention times are grouped together. The third function getGroups, establishes what is considered as Constitutive Profile, Non-constitutive by Frequency i.e., not present in all samples and Non-constitutive by Quality. Results can be plotted with the plotGroup function. We used it to analyse floral scent emissions in four snapdragon genotypes. These included a wild type, Deficiens nicotianoides and compacta affecting floral identity and RNAi:AmLHY targeting a circadian clock gene. We identified differences in scent constitutive and non-constitutive profiles as well as in timing of emission. gcProfileMakeR is a very useful tool to define constitutive and non-constitutive scent profiles. It also allows to analyse genotypes and circadian datasets to identify differing metabolites.

3DeepM: An Ad Hoc Architecture Based on Deep Learning Methods for Multispectral Image Classification

Current predefined architectures for deep learning are computationally very heavy and use tens of millions of parameters. Thus, computational costs may be prohibitive for many experimental or technological setups. We developed an ad hoc architecture for the classification of multispectral images using deep learning techniques. The architecture, called 3DeepM, is composed of 3D filter banks especially designed for the extraction of spatial-spectral features in multichannel images. The new architecture has been tested on a sample of 12210 multispectral images of seedless table grape varieties: Autumn Royal, Crimson Seedless, Itum4, Itum5 and Itum9. 3DeepM was able to classify 100% of the images and obtained the best overall results in terms of accuracy, number of classes, number of parameters and training time compared to similar work. In addition, this paper presents a flexible and reconfigurable computer vision system designed for the acquisition of multispectral images in the range of 400 nm to 1000 nm. The vision system enabled the creation of the first dataset consisting of 12210 37-channel multispectral images (12 VIS + 25 IR) of five seedless table grape varieties that have been used to validate the 3DeepM architecture. Compared to predefined classification architectures such as AlexNet, ResNet or ad hoc architectures with a very high number of parameters, 3DeepM shows the best classification performance despite using 130-fold fewer parameters than the architecture to which it was compared. 3DeepM can be used in a multitude of applications that use multispectral images, such as remote sensing or medical diagnosis. In addition, the small number of parameters of 3DeepM make it ideal for application in online classification systems aboard autonomous robots or unmanned vehicles.