PRINCIPAL COMPONENT ANALYSIS FOR TEXTURAL PROPERTIES OF SELECTED BLOOD CURD
Bibliographic record
Abstract
ABSTRACT Mechanical tests, sensory evaluation and chemical analysis were performed to assess textural characteristics of blood curd (Zisheokwai, a popular Chinese animal blood food; five short shelf life products and two long shelf life products). It indicated that texture profile analysis displayed greater variability than chemical composition and sensory assessment of the samples. Moreover, long shelf life products showed higher hardness, protein and moisture contents than the short ones but lower juicy and tenderness compared with short shelf life products. Observed associations from principal component analysis (PCA) showed that moisture was positively related to cooking loss, total expressible fluid and juice, and high hardness, chewiness and gumminess correlated with low juice and tenderness. PCA proved to be a very effective procedure to obtain a comprehensive judgment of blood curd quality. The experiments suggested that water and protein content affected the textural properties of blood curd. PRACTICAL APPLICATIONS Through investigating of mechanical, sensory and chemical characteristics of blood curd, this traditional Chinese food can be better described and characterized. Principal component analysis used in this article may provide a good statistical method in reducing and explaining textural parameters. The identification of the most important principal components, regarding the product quality, was important for the manufacturer to control and optimizing quality of their products. In addition, such information can be used for quality evaluation of other blood foods.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".