Pachira aquatica (Malvaceae): An unconventional food plant with food, technological, and nutritional potential to be explored

Costa, Lyvia Daim; Trindade, Renata Pereira; Cardoso, Patrick da Silva; Colauto, Nelson Barros; Linde, Giani Andréa; Otero, Deborah Murowaniecki

doi:10.1016/j.foodres.2022.112354

Cited by 3 publications

(1 citation statement)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They provide a wealth of nutritional, sensory, and bioactive compounds that deliver both prophylactic and therapeutic effects [5,6] and have been traditionally used in folk medicines and cuisines across cultures. In the modern world, the trend towards preventive healthcare and natural remedies has made medicinal and edible plants an attractive alternative for promoting health, preventing disease, and improving the quality of life [7,8]. However, the quality of these plants can vary significantly depending on factors such as growing environment, harvesting, and processing methods.…”

Section: Introductionmentioning

confidence: 99%

The Identification of Fritillaria Species Using Hyperspectral Imaging with Enhanced One-Dimensional Convolutional Neural Networks via Attention Mechanism

Hu,

Xu,

Wei

et al. 2023

Foods

View full text Add to dashboard Cite

Combining deep learning and hyperspectral imaging (HSI) has proven to be an effective approach in the quality control of medicinal and edible plants. Nonetheless, hyperspectral data contains redundant information and highly correlated characteristic bands, which can adversely impact sample identification. To address this issue, we proposed an enhanced one-dimensional convolutional neural network (1DCNN) with an attention mechanism. Given an intermediate feature map, two attention modules are constructed along two separate dimensions, channel and spectral, and then combined to enhance relevant features and to suppress irrelevant ones. Validated by Fritillaria datasets, the results demonstrate that an attention-enhanced 1DCNN model outperforms several machine learning algorithms and shows consistent improvements over a vanilla 1DCNN. Notably under VNIR and SWIR lenses, the model obtained 98.97% and 99.35% for binary classification between Fritillariae Cirrhosae Bulbus (FCB) and other non-FCB species, respectively. Additionally, it still achieved an extraordinary accuracy of 97.64% and 98.39% for eight-category classification among Fritillaria species. This study demonstrated the application of HSI with artificial intelligence can serve as a reliable, efficient, and non-destructive quality control method for authenticating Fritillaria species. Moreover, our findings also illustrated the great potential of the attention mechanism in enhancing the performance of the vanilla 1DCNN method, providing reference for other HSI-related quality controls of plants with medicinal and edible uses.

show abstract

Section: Introductionmentioning

confidence: 99%

The Identification of Fritillaria Species Using Hyperspectral Imaging with Enhanced One-Dimensional Convolutional Neural Networks via Attention Mechanism

Hu,

Xu,

Wei

et al. 2023

Foods

View full text Add to dashboard Cite

show abstract

Chemical characterization, in vivo safety profile and effects of the extract from unconventional food plant Acca sellowiana (O. BERG) Burret on endothelial cells under glucose overload

Minello,

Mattiello,

Cassini

et al. 2024

Journal of Pharmaceutical and Biomedical Analysis

View full text Add to dashboard Cite

Optimization of the extraction of bioactive compounds from Clitoria ternatea L and evaluation of encapsulation by ionotropic gelation

Santana

Ribeiro

2023

Preprint

View full text Add to dashboard Cite

The aim of this work was to optimize the extraction of bioactive compounds from Clitoria ternatea L and then to encapsulate them by ionic gelation. The extraction of the bioactive compounds, using water as solvent, was carried out the solid-liquid extractions in cold and hot, obtaining a response factor the content of total phenolic compounds (TPC). The bioactive compounds of the extracts were evaluated based on the levels of TPC and total monomeric anthocyanins. Analyzes of physical, chemical, phytochemical and antioxidant potential were performed. Encapsulation was performed by ionic gelation. The capsules were evaluated by characterization and visual aspects. Simulated gastrointestinal digestibility was determined based on TPC bioaccessibility. There was no statistical difference (p ≤ 0.05) between the extracts. The quadratic mathematical model presented a correction coefficient, R² = 0.997 and R²adjust = 0.974. Hot extraction at 80°C/5 minutes at a concentration of 0.008 g/mL showed a TPC of 23.91 ± 0.90 mg (GAE)/g and 0.97 ± 0.50 mg (MVE)/g for monomeric anthocyanins. For antioxidant potential, the values of ABTS, FRAP and β-carotene/linoleic acid were 84.86 ± 1.52 µM trolox/g, 220.83 ± 3.69 µM ferrous sulfate/g and 55, 95 ± 4.51%, respectively, for the same assay. Visually, the capsules had regular sizes and preserved color, high solubility (99%), encapsulation efficiency (76%). Bioaccessibility was 71% for lyophilized capsules, with greater intestinal absorption. Therefore, extracts and capsules of Clitoria ternatea L. have bioactive compounds with an antioxidant profile.

show abstract

Pachira aquatica (Malvaceae): An unconventional food plant with food, technological, and nutritional potential to be explored

Cited by 3 publications

References 65 publications

The Identification of Fritillaria Species Using Hyperspectral Imaging with Enhanced One-Dimensional Convolutional Neural Networks via Attention Mechanism

The Identification of Fritillaria Species Using Hyperspectral Imaging with Enhanced One-Dimensional Convolutional Neural Networks via Attention Mechanism

Chemical characterization, in vivo safety profile and effects of the extract from unconventional food plant Acca sellowiana (O. BERG) Burret on endothelial cells under glucose overload

Optimization of the extraction of bioactive compounds from Clitoria ternatea L and evaluation of encapsulation by ionotropic gelation

Contact Info

Product

Resources

About