Development of an Elisa to Detect the Incipient Stages of Tribolium Castaneum in Food Commodities \n
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".