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Enregistrement W7065430383

Development of an Elisa to Detect the Incipient Stages of Tribolium Castaneum in Food Commodities
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2006· dissertation· en· W7065430383 sur OpenAlexaboutno aff

Notice bibliographique

RevueCFTRI Institutional Repository · 2006
Typedissertation
Langueen
DomainePhysics and Astronomy
ThématiqueAstrophysical Phenomena and Observations
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInfestationInsectAgricultureChristian ministryCropRed flour beetlePEST analysisFood productsLarva
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In India, every year nearly 10 per cent of the food grains are lost during post-harvest \nprocessing and storage due to insect infestation. Increased production would hardly have any \nsignificance unless protected from post harvest loss. A recent estimate by the Ministry of \nFood and Civil supplies put the total preventable post-harvest losses of food grains due to \ninsect infestation at about 20 million tons per year, which was nearly 10 per cent of the total \nproduction that could have fed upto 117 million people for a year. \nInsect pest activity in agricultural produce may start at any stage from harvest to consumption. Insect infestation causes qualitative and quantitative losses of food \ncommodities and changes the chemical composition affecting the nutritive value of the produce. Insect infestation in food commodities has health implications as well. Insects also play a significant role in the dissemination and proliferation of microorganisms including \nmycotoxigenic fungi in food commodities. \nIn national and international trade, cash value and marketability of different commodities are affected by insect infestation as are the processing and end-use qualities of food commodities. Quality maintenance by way of reduction in insect contaminants to meet \nthe requirements of International Standards Organisation (ISO) and Hazard Analysis Critical \nControl Points (HACCP) is important for marketing the produce. Food and Drug Administration (FDA) has established Defect Action levels for live insects at two insects per \nkilogram and insect damaged grains at 32 kernels/100g in food grains; in wheat flour, there \nis a limit of 75 insect fragments/50g; and in macaroni and noodle products it is 225 fragments in a 225g sample. In India, according to the Prevention of Food Adulteration Act, \nthe uric acid level in food commodities should not exceed 100 mg/kg and the number of weevil-damaged grains should not exceed 10% by count. In countries like Canada and \nAustralia, there is zero tolerance for insects in food grains and a similar standard is followed \nin international trade for grains. \nInsect infestation detection methods, in samples and storage facilities, play a significant role as an indicator and also an effective infestation management tool in the food industry. The prominence of current methods used for detection of stored product insect pest \nsuch as fragment count, X-ray method, uric acid determination, carbon dioxide analysis, etc. \nare for the detection of adult or the visible life stages of the insect pest. Nevertheless, insect \npest eggs have a key role in spread of infestation. Due to the small size of the eggs, they \noften go unnoticed and there are not many sufficiently sensitive methods to detect insect \npest eggs. Currently, only few methods are available for detection of insect pest eggs like the \negg staining techniques and breeding out method. These methods are not sufficiently sensitive; are exclusive in their application; or are time consuming. Sensitive insect pest egg detection technique would be advantageous especially for the milling industry, wherein the \nmilled products of cereals get infested by eggs of insect pests such as Tribolium castaneum,Oryzaephilus surinamensis and Corcyra cephalonica, which gets transferred to the final \nproduct thereby reducing the quality of the product and also aiding in the spread of infestation. Therefore, development of sensitive, easy and quick infestation detection methods is imperative. Currently, immunoassays due to their enormous specificity, resolution, rapidity, cost effectiveness and efficiency have gained importance in the field of insect pest detection systems. In view of this, the present work was aimed at the development of a Enzyme Linked Immunosorbent Assay (ELISA) for the detection of incipient stages of the incipient stages of the red flour beetle -Tribolium castaneum, with special reference to eggs. \nObjectives of the study: \n1. Development of ELISA for the detection of incipient and other developmental stages of Tribolium castaneum. \na. Purification of the antigen i.e. the major egg protein of Tribolium castaneum Herbst. \nb. Production of antibodies against the purified antigen in rabbit and chicken. \nc. Development of the standard ELISA based on the rabbit and chicken egg yolk antibodies. \n2. Application of the ELISA developed to food commodities like whole wheat flour and rice flour and testing of market samples. \n3. Comparison of the ELISA developed with the current insect pest egg detection methods \ni.e. AACC approved iodine method and bromocresol green staining method. \n

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,002

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.

Tête enseignante Opus0,014
Tête enseignante GPT0,245
Écart entre enseignants0,230 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2006
Routes d'admission1
Résumé présentoui

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