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CHANGES IN SOYMILK QUALITY AS A FUNCTION OF COMPOSITION AND STORAGE

2007· article· en· W2021717161 on OpenAlex

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

VenueJournal of Food Quality · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSensory Analysis and Statistical Methods
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsHexanalFood scienceChemistryFlavorNonanalOctanalSugarAroma

Abstract

fetched live from OpenAlex

ABSTRACT Three soymilk products formulated with different concentrations of fat, sugar and starch were evaluated for changes in their physical properties and volatiles profile over time (3 months) under different temperatures (4, 22 and 38C) of storage. Samples were tested for pH, color, viscosity and volatile flavor changes. The pH and color of the soymilks decreased significantly during the first month of storage and then remained stable over time. Color and viscosity of the soymilk products were affected by both the soymilk composition and storage treatment. The high‐fat soymilk sample (product C) had the whitest color (lower Δ E ) and the lowest viscosity. Storage at 38C negatively affected the color. The viscosities of the soymilk products stored at 4C were the lowest among the treatments. The major volatiles identified in all soymilk products were hexanal, heptanal, octanal, nonanal, hexanol, 1‐octen‐3‐ol, benzaldehyde, 2‐pentyl furan and 2‐ethyl furan. The intensities of the volatile compounds in the soymilk products increased during the first weeks of storage, particularly when stored at 38C. The intensities, however, decreased gradually over time. Among the three formulated soymilk products, the sweetened sample (product B) gave the lowest flavor intensities under all three temperatures of storage. Overall, storage at 4C and addition of sugar preserve best the soymilk quality. PRACTICAL APPLICATIONS Soy products are well appreciated for their nutritional and potential health benefits. Soy beverage consumption is increasing among North American consumers because of improvements in soy beverage quality and processing technologies. There is, however, a demand for new value‐added soy‐based drinks with improved “functional” (health‐benefiting) properties. Soy beverage could be an excellent carrier for “functional” or “nutritive” ingredients such as minerals, vitamins and omega 3 oils; however, addition of such ingredients may affect the stability of the product and requires the development appropriate of technologies for their incorporation. Results from this project provide new knowledge on the storage stability and quality of three different soy product formulations. The information could be useful in the establishment of optimal conditions for processing of functional soy beverages, for use by the food industry.

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)0.005
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.165

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 head0.121
GPT teacher head0.381
Teacher spread0.260 · 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