10.46243/jst.2022.v7.i02.pp315-321 registered
DEVELOPMENT OF A MACHINE AND DETECTION OF GLYCOALKALOIDS IN POTATO USING IMAGE PROCESSING TECHNIQUE
Resolves to https://www.jst.org.in/index.php/pub/article/view/491
Held by Longman Publishers (India) · prefix 10.46243 live · DOI address https://doi.org/10.46243/jst.2022.v7.i02.pp315-321
Registered 29 Sep 2026 via crossref · record version 2 · last change 29 Sep 2026, 11:59 PM · record sha256 a9208f78f6a25020…
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JournalArticle — an article in a journal · Digital · Visual · en
DEVELOPMENT OF A MACHINE AND DETECTION OF GLYCOALKALOIDS IN POTATO USING IMAGE PROCESSING TECHNIQUE (PrincipalTitle)
Published 2022-07-03
Part of Journal of Science & Technology · ISSN 2456-5660 · volume 7 · issue 2 · pages 315–321
Agents
- A. Caroline A. Caroline (author)
- Longman Publishers (publisher)
Identifiers DOI 10.46243/jst.2022.v7.i02.pp315-321
Abstract
Glycoalkaloids are secondary natural poisonous metabolites produced by plants of the Solanaceae family. Glycoalkaloids from solanaceous plants vary depending on species. The two major glycoalkaloids found in potatoes are Solanine and chaconine. The average potato contains 0.075 mg of solanine and chaconine. The doses of 200–400 mg for adult humans and 20–40 mg for children can cause toxic symptoms to human health. Commercial potatoes have GA content of less than 0.2 mg. This research aimed at determining the total glycoalkaloid content present in potatoes using image processing technique. It is one of the non-destructive method and images can be stored and retrieved easily. Potatoes were collected from local markets in Coimbatore. The major components used in this machine are Raspberry Pi, web camera, Lcd module, memory card. The Raspberry pi is powered up with 5V power supply through USB cable. The Button interfaced with raspberry pi is triggered to capture the image from camera to classify the level of glycoalkaloids and display the result in LCD module. Through this glycoalkaloid detector the amount of glycoalkaloid present in the potatoes can be determined by both milligram and percentage values. It is a cost-effective method and ensures food safety. Published by: Longman Publishers www.jst.org.in P age 315 | 7 www.jst.org.in DOI: https://doi.org/10.46243/jst.2022.v7.i02.pp315-321
System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1
Each element by the standard's name (Annex B: reference elements, then administrative) and the Handbook's (in grey), read off the record above.
| Element | Value | In the record |
|---|---|---|
| DOI Name DOI name | 10.46243/jst.2022.v7.i02.pp315-321 | doi |
| Referent Type referentType | Creation | referent |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal | type |
| Referent Name(s) referentName(s) | DEVELOPMENT OF A MACHINE AND DETECTION OF GLYCOALKALOIDS IN POTATO USING IMAGE PROCESSING TECHNIQUE (PrincipalTitle, en) | titles |
| Basic Metadata basicMetadata | author: A. Caroline A. Caroline publisher: Longman Publishers published: 2022-07-03 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 7 · no. 2 · pp. 315–321 language: en form: Digital · Visual · Language | agents, dates, container, language, structural_type, modes, characters |
| Referent Identifier(s) alternateIdentifier(s) | none besides the DOI | identifiers, relations (IsSameAs) |
| Registration Authority registrationAuthorityCode | Crossref — issued by Crossref (member 25296); held here as a copy | record.source_agency (our code, ra_doi_name, for names issued here once appointed) |
| Created Date issueDate | 2024-02-16 | record.registered (when the DOI name was first registered) |
| relatedIdentifiers | none needed — the descriptive metadata is in this record | container, relations (only where the descriptive metadata lives at another identifier) |
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History — the ledger
Every change to this DOI, in order, as it was recorded. Entries are only ever added, never changed or removed.
| # | When | What | By | Changes |
|---|---|---|---|---|
| 1 | 29 Sep 2026, 10:00 PM | register registered at Crossref; record read from api.crossref.org | Administrator (admin) | 69 fields set · sha256 99bbf11395ff… |
| 2 | 29 Sep 2026, 11:59 PM | update record re-read from api.crossref.org | Administrator (admin) | agents.0.name.family: agents.0.name.given: container.titles.0.value: |
Machine-readable: the history as JSON, with the full record after each change.
