Use of machine learning to establish limits in the classification of hyperaccumulator plants growing on serpentine, gypsum and dolomite soils

Mota-Merlo, Marina; Martos, Vanessa

doi:10.5209/mbot.67609

Cited by 7 publications

(2 citation statements)

References 33 publications

(42 reference statements)

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“…Machine learning techniques have demonstrated their pivotal role in advancing the development of prediction tools and digital support systems across diverse fields, such as nutrition and agri-food research [46][47][48][49][50][51][52] . In our previous works related to the prediction of taste, a bitter/sweet predictor, named…”

Section: Discussionmentioning

confidence: 99%

VirtuousMultiTaste: Predicting Multiple Taste Sensations with a Multi- objective ML-driven Method

Androutsos,

Pallante,

Bompotas

et al. 2023

Preprint

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Taste perception plays a pivotal role in guiding nutrient intake and aiding in the avoidance of potentially harmful substances through five basic tastes - sweet, bitter, umami, salty, and sour. Taste perception originates from molecular interactions in the oral cavity between taste receptors and chemical tastants. Hence, the recognition of taste receptors and the subsequent perception of taste heavily rely on the physicochemical properties of food ingredients. In recent years, several advances have been made towards the development of machine learning-based algorithms to classify chemical compounds' tastes using their molecular structures. Despite the great efforts, there remains significant room for improvement by developing multi-class models to predict the entire spectrum of basic tastes. Here, we present a multi-class predictor aimed at distinguishing three different tastes, i.e., bitter, sweet, and umami, from other taste sensations. The developed model has been integrated into a publicly accessible web platform. This work lays the groundwork for a comprehensive understanding of the molecular features that drive the perception of tastes, paving the way towards new methodologies in the rational design of foods, such as the pre-determination of specific tastes, the engineering of complementary diets to traditional pharmacological treatments, and many others.

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Section: Discussionmentioning

confidence: 99%

VirtuousMultiTaste: Predicting Multiple Taste Sensations with a Multi- objective ML-driven Method

Androutsos,

Pallante,

Bompotas

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, depending on the values obtained, a plant can be classified as an indicator, accumulator or hyperacumulator [65,66]. In addition, new machine learning techniques can assist in proposing more objective thresholds when classifying plant species from this perspective [67]. In addition, ionomic studies may also be used to understand the properties and peculiarities of plant communities developing on particular substrates such as dolomites [14].…”

Section: Ionomic Studies In Plantsmentioning

confidence: 99%

Elementome of Endemic Dolomitic Flora: Pterocephalus spathulatus (Lag.) Coult

Merlo

Mendoza-Fernández

Salmerón-Sánchez

et al. 2021

Land

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Unusual dolomite substrates present a phenomenon known as dolomitophily, which refers to the specificity of the flora that grows on them. Within the dolomitic flora, one of the most widespread plants in Spain is the characteristic species Pterocephalus spathulatus (Lag.) Coult., which forms whitish prostrate thymes. The present study focused on the knowledge about the ionome (or elementome) of a characteristic dolomite species and some of its applications, both in terms of its nutritional behaviour and in determining the factors that favour the rich and rare flora growing on dolomitic soils. Soil, foliar, stem and root samples of the species studied were collected from different locations in the south of Spain. The samples were analysed to determine their mineral composition. The Bioconcentration Factor (BCF) and the Translocation Factor (TF) were calculated, relating the values of the mineral contents in the soil, both total and phytoavailable, to the values from the foliar, root and stem samples. It was found that this species was able to accumulate some elements, including B, Cr, Cu, Ni, Ti, Tl and Zn, which can be phytotoxic in high concentrations, and are considered to be some of the main toxic metals in soils. In addition, it was observed that the plant can accumulate metals in the stem and leaves (TF), thus proving that it is capable of transporting them from the root to the aerial part of the plant. The data obtained may indicate that other species in the plant community may possess this bioindicator or even phytoremediation capacity.

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Predicting multiple taste sensations with a multiobjective machine learning method

Androutsos,

Pallante,

Bompotas

et al. 2024

npj Sci Food

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Taste perception plays a pivotal role in guiding nutrient intake and aiding in the avoidance of potentially harmful substances through five basic tastes - sweet, bitter, umami, salty, and sour. Taste perception originates from molecular interactions in the oral cavity between taste receptors and chemical tastants. Hence, the recognition of taste receptors and the subsequent perception of taste heavily rely on the physicochemical properties of food ingredients. In recent years, several advances have been made towards the development of machine learning-based algorithms to classify chemical compounds’ tastes using their molecular structures. Despite the great efforts, there remains significant room for improvement in developing multi-class models to predict the entire spectrum of basic tastes. Here, we present a multi-class predictor aimed at distinguishing bitter, sweet, and umami, from other taste sensations. The development of a multi-class taste predictor paves the way for a comprehensive understanding of the chemical attributes associated with each fundamental taste. It also opens the potential for integration into the evolving realm of multi-sensory perception, which encompasses visual, tactile, and olfactory sensations to holistically characterize flavour perception. This concept holds promise for introducing innovative methodologies in the rational design of foods, including pre-determining specific tastes and engineering complementary diets to augment traditional pharmacological treatments.

show abstract

Use of machine learning to establish limits in the classification of hyperaccumulator plants growing on serpentine, gypsum and dolomite soils

Cited by 7 publications

References 33 publications

VirtuousMultiTaste: Predicting Multiple Taste Sensations with a Multi- objective ML-driven Method

VirtuousMultiTaste: Predicting Multiple Taste Sensations with a Multi- objective ML-driven Method

Elementome of Endemic Dolomitic Flora: Pterocephalus spathulatus (Lag.) Coult

Predicting multiple taste sensations with a multiobjective machine learning method

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