Green chemistry and an ocean based biorefinery approach for the valorization of Newfoundland and Labrador snow crab (Chionoecetes opilio) processing discards
Notice bibliographique
Résumé
Snow crab (Chionoecetes opilio) is the most important commercial species to the NL fishery and NL's rural economy. According to industry stakeholders, it has replaced cod as "King". In 2020, the Government of Newfoundland and Labrador reported an export value of $648 million from annual landings averaging 30,000 t of snow crab. The NL snow crab industry generates ~30% waste each year (~10,000 t), which typically is landfilled or dumped at sea. These discards contain valuable bioproducts such as pigments, proteins, chitin, and lipids, which could be recovered for use in a wide range of fields from agriculture and aquaculture to biomedical. However, many of the processes used for snow crab valorization require hazardous chemical treatments, such as acids, bases, and flammable solvents, creating environmental concerns such as air and water pollution, and health and safety concerns. In addition, environmental requirements are becoming stricter, making traditional disposal options for crab processing discards more difficult and costly. To address these challenges, I evaluated a combined green chemistry-ocean based biorefinery approach for the valorization of NL's snow crab processing discards. Four research studies were conducted using a range of methods: semi-structured interviews, analysis of fisheries and aquaculture statistics, evaluation of raw material pre-treatment and collection methods, scientific studies to characterize and stabilize crab discards, as well as comparisons of chemically extracted vs "green" extracted crab bioproducts. (1) An inventory assessment of available marine feedstocks showed that crustaceans generate the largest wastes, which in 2015 could theoretically support regional by-product processing facilities on the Northern Peninsula, Northeast Coast, and Avalon Peninsula. (2) Characterization and stabilization studies showed that seasonality and pre-treatment method had the greatest impact on quality, and that crab by-products have unique intrinsic characteristics that influence quality. (3) Purity and safety of crab bioproducts were evaluated by measuring quantities of trace metal contaminants. Two metals of concern were identified: arsenic, which causes acute toxicity; and aluminum, which may be covertly toxic over time. (4) Sequential extraction of carotenoid pigments, pigmented protein powder, and chitin from crab processing by-products using vegetable oils, citric acid, proteases, and hydrogen peroxide to replace traditional organic (e.g., acetone, ethanol) and inorganic (e.g., HCl, NaOH) reagents was evaluated and demonstrated that these chemical reagents can be replaced with green alternatives. The findings from these studies were incorporated into a green chemistry-biorefinery model that, with optimizations, could be adopted by industry and the province to address current challenges related to snow crab waste disposal and valorization. The proposed model allows for the extraction of multiple higher value crab bioproducts that are produced using more environmentally friendly and potentially lower cost alternatives, to more traditional chemically intensive and expensive techniques. It is anticipated that this model will provide the groundwork for the development of a provincial crustacean waste disposal and by-product utilization strategy.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».