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Record W4409170586 · doi:10.1007/s44187-025-00370-1

The benefits and processing technologies of gari, a famous indigenous food of Nigeria

2025· article· en· W4409170586 on OpenAlex
Patrick Othuke Akpoghelie, Joseph Oghenewogaga Owheruo, Great Iruoghene Edo, Emad Yousif, Khalid Zainulabdeen, Agatha Ngukuran Jikah, Athraa Abdulameer Mohammed, Winifred Ndudi, Susan Chinedu Nwachukwu, Rapheal Ajiri Opiti, Irene Ebosereme Ainyanbhor, Priscillia Nkem Onyibe, Ufuoma Ugbune, Gracious Okeoghene Ezekiel, Helen Avuokerie Ekokotu, Ephraim Evi Alex Oghroro, Lauretta Dohwodakpo Ekpekpo, Endurance Fegor Isoje, Ufuoma Augustina Igbuku, Joel Okpoghono, Arthur Efeoghene Athan Essaghah, Joy Johnson Agbo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDiscover Food · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsGeorge Brown College
Fundersnot available
KeywordsIndigenousFood processingGeographyFood scienceBiologyEcology

Abstract

fetched live from OpenAlex

Gari is a creamy, granular flour obtained from roasting fermented cassava mash. Gari is a staple food in Nigeria that is consumed by almost everybody and which can enhance food security due to it availability and affordability. The raw material for gari production is cassava which is processed either through traditional method or modern method. The traditional method involves harvesting, peeling, wet cleaning (washing), grating, adding red oil (optional), fermentation, dewatering, sieving, garifying on heated hot pan to gelatinize the starch and then cooling while the modern method involves the use of mechanized machines for the various processes involved in the gari production. Gari is rich in carbohydrates, minerals (like Ca, Mg and P) and vitamins (like vitamins A and B). Due to roasting and leaching out with water, gari processing results in severe nutritional losses. The nutritional content of gari is affected by the type of processing regime employed. Compared to gari prepared using mechanized approach, gari processed using a traditional method typically contains more nutrients and less anti-nutrients. Gari can be eaten directly or it can be soaked in water together with sugar and groundnut. It can be used to make eba by mixing the gari in hot water and stirred into a dough and the eba can be eaten with vegetable soup. Gari is rich in carbohydrates and fibre but low in protein and therefore should be eaten with food rich in protein like meat, fish, egg and beans. Inadequate processing of gari can result in excessive concentrations of anti-nutrients such as hydrogen cyanide. Soaking, grating, pressing, fermentation and oven-frying are methods which have been employed to reduce the cyanide content of gari to acceptable levels for consumption. Some microorganisms involved in cassava fermentation include Lactobacillus acidophilus , Levilactobacillus brevis among others. The consuming markets of Africa comprise, among others, the following nations Chad, Gabon, Cameroon, Niger, Burkina Faso, Ivory Coast, Guinea and Congo. In many African nations, including Nigeria, women are primarily responsible for the processing of cassava.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.147

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.227
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it