ReviewFrontiers in nutrition2022
A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products.
Review in Frontiers in nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
17 citing papers in PubMed.
- Prediction of Total Anthocyanin Content in Single-Kernel Maize Using Spectral and Color Space Data Coupled with AutoML.Sensors (Basel, Switzerland) · 2026Article
- Integrative analysis of the metabolome and transcriptome reveals the mechanism of flower color and antioxidant capacity in threeFrontiers in plant science · 2026Article
- Changes in Physicochemical Characteristics of Goat Meat Emulsion-type Sausage According to the Ratio of Fat and Water Contents.Food science of animal resources · 2025Article
- Fourier Spectral Deconvolution to Describe Behaviors of pH-Dependent Monomeric and Self-Associated Anthocyanin Species.Journal of chemical information and modeling · 2025Article
- Destructive and Non-Destructive Evaluation of Anthocyanin Content and Quality Attributes in Red Kiwifruit Subjected to Plant Spray Treatment with Cis-3-Hexenyl Butyrate.Foods (Basel, Switzerland) · 2025Article
- Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity.Plant methods · 2024Article
- A Review of Quantitative and Topical Analysis of Anthocyanins in Food.Molecules (Basel, Switzerland) · 2024Review
- Prediction of cyanidin 3-rutinoside content inFrontiers in plant science · 2024Article
- Miniaturized Vis-NIR handheld spectrometer for non-invasive pigment quantification in agritech applications.Scientific reports · 2023Article
- Prediction of Anthocyanidins Content in Purple Chinese Cabbage Based on Visible/Near Infrared Spectroscopy.Foods (Basel, Switzerland) · 2023Article
- Antioxidant activity and phytochemical analysis of fennel seeds and flaxseed.Food science & nutrition · 2023Article
- High-intensity ultrasonication impact on the chlorothalonil fungicide and its reduction pathway in spinach juice.Ultrasonics sonochemistry · 2023Article
- Characterization of ultrasonically extracted flaxseed polysaccharide gum and assessing its lipid-lowering potential in a rat model.Food science & nutrition · 2023Article
- An Optimized HPLC-DAD Methodology for the Determination of Anthocyanins in Grape Skins of Red Greek Winegrape Cultivars (Molecules (Basel, Switzerland) · 2022Article
- Starch-based environment friendly, edible and antimicrobial films reinforced with medicinal plants.Frontiers in nutrition · 2022Article
- Crocin: Functional characteristics, extraction, food applications and efficacy against brain related disorders.Frontiers in nutrition · 2022Review
- Recent advances in carrageenan-based films for food packaging applications.Frontiers in nutrition · 2022Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations.
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