ArticleMolecules (Basel, Switzerland)2021
Characterization of Basil Volatile Fraction and Study of Its Agronomic Variation by ASCA.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Development of Pesto Sauce with Moringa Leaves and Baru Almonds: A Strategy to Incorporate Underutilized Ingredients with Nutritional and Sensory Viability.Foods (Basel, Switzerland) · 2025Article
- Morphological and Phytochemical Characterization of Old Ligurian Basil Accessions: Recovery of Old Biodiversity for Future Exploitation.Plants (Basel, Switzerland) · 2025Article
- Light Flux Density and Photoperiod Affect Growth and Secondary Metabolism in Fully Expanded Basil Plants.Foods (Basel, Switzerland) · 2024Article
- A Multivariate Analysis-Driven Workflow to Tackle Uncertainties in Miniaturized NIR Data.Molecules (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
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Abstract
Basil is a plant known worldwide for its culinary and health attributes. It counts more than a hundred and fifty species and many more chemo-types due to its easy cross-breeds. Each species and each chemo-type have a typical aroma pattern and selecting the proper one is crucial for the food industry. Twelve basil varieties have been studied over three years (2018-2020), as have four different cuts. To characterize the aroma profile, nine typical basil flavour molecules have been selected using a gas chromatography-mass spectrometry coupled with an olfactometer (GC-MS/O). The concentrations of the nine selected molecules were measured by an ultra-fast CG e-nose and Principal Component Analysis (PCA) was applied to detect possible differences among the samples. The PCA results highlighted differences between harvesting years, mainly for 2018, whereas no observable clusters were found concerning varieties and cuts, probably due to the combined effects of the investigated factors. For this reason, the ANOVA Simultaneous Component Analysis (ASCA) methodology was applied on a balanced a posteriori designed dataset. All the considered factors and interactions were statistically significant (
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