Effect of plant biostimulants on nutritional and chemical profiles of Corylus avellana L. (hazelnut) and potential application in functional foods
Notice bibliographique
Résumé
The interest in the functional characteristics of nuts has been increasing due to their high content in bioactive constituents. Hazelnut (Corylus avellana L.) is the most important cultivated species in the Corylus genus (Betulaceae), and it is widely spread from the Himalayas to the far north of Canada1. The inclusion of nuts in the human diet can bring benefits that are partially related to the high percentage of monounsaturated fatty acids (MUFA), particularly oleic acid, and polyunsaturated fatty acids (PUFA), particularly linoleic acid, tocopherols (for example, α-tocopherol), and phytosterols (for instance, β-sitosterol)2–4. With the increase in food production, there is an orientation towards more sustainable agriculture, free of pesticides and fertilizers harmful to the environment. Plant biostimulants, a class of bio-based agriculture products designed to improve crop development, represent a feasible alternative to chemical fertilizers or, at least, an effective way of reducing the applied quantities. In the present work, different types of plant biostimulants compatible with organic farming (NPK, Fitoalgas Green® and Sprint Plus®) were tested in one of the most popular nut products worldwide: hazelnut. Furthermore, the samples were tested for nutritional parameters, fatty acids profiles and tocopherols contents. The nutritional evaluation of hazelnuts showed that this species is mainly composed of fat (around 55% on a fresh weight basis). The highest fat content was detected in the control line (samples grown in soils without any biostimulant), with no significant differences in result of the type of soil supplementation. Protein levels were also high (16.8 g/100 g fw), particularly in hazelnuts treated with NPK (12% higher than the control), but all plant biostimulants (except phytoalgae) induced a positive effect in this macronutrient. Ash and water with the minor components showed minimal variations. The maximal caloric value (675 kcal/100 g fw) was obtained in the control line. Regarding soluble sugars, only sucrose was identified with an average value of 16g /100g fw. Oleic acid (C18:1n9c) was the predominant fatty acid, and a noticeable decrease was observed in hazelnut, independently of the plant biostimulant, compared with the control (76%). Linoleic acid (C18:2n6c), contrarily to oleic acid, showed a significant increase in hazelnut samples grown in soils treated with plant biostimulants, reaching the maximum value when using NPK (15.1%). Palmitic acid, likewise, was affected in hazelnut samples, reaching the highest percentage with Sprint Plus (9.6%). A very similar result was observed for stearic acid (C18:0). Other fatty acids were detected in trace percentages (total sum less than 2%): myristic acid (C14:0), palmitoleic acid (C16:1), marginal acid (C17:0), α-linolenic (C18:3n3), eicosanoic acid (C20:0) and eicosenoic acid (C20:1). Overall, the concentration of tocopherols was elevated: average values of 25 mg/100 g fw. The most notorious effects were obtained with NPK+phytoalgae, characterized by an increase of almost 18% in tocopherols levels (23 to 28 mg/100 g fw). In comparison, treatment with NPK alone induced a 15.1% higher percentage of linoleic acid. The obtained values were lower than those reported in different hazelnut varieties5, which might be related to genetic factors (different cultivars), climatic variation6,7, soil type8, or analytical methodology9. In general, the tested plant biostimulants induced increased levels of important bioactive compounds, particularly in what concerns linoleic acid (mainly using NPK) and tocopherols levels (with best results using NPK + phytoalgae) in hazelnuts. These results can be important to select the best plant biostimulant to be applied and, thus, enable the increase in the amount of a specific bioactive compound, interesting for a potential application for functional foods.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».