Quality Analysis and Comprehensive Evaluation of the Fruit of Macadamia integrifolia Grown in Yunnan Province
Bibliographic record
Abstract
In order to ascertain the quality characteristics of macadamia nut from different regions and varieties in Yunnan so as to establish their comprehensive evaluation model. Thirty-five samples of macadamia nuts from 3 major production areas in Yunnan were selected to determine indicators of fruit characteristics and quality indexes including the composition and content of physicochemical, nutritional and functional components and amino acids in kernels. The quality of macadamia nuts was comprehensively evaluated by correlation analysis (FCOR), principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). The results showed that there were significant differences in the fruit characteristics of 35 samples from different regions and varieties in Yunnan (P<0.05), and the single fruit weights of nut-in-husk, nut-in-shell and kernel were 13.85 g to 30.48 g, 5.92 g to 11.68 g, and 1.87 g to 3.75 g, respectively, the large fruit rates of nut-in-shell and kernel were 38.89% to 100.00% and 10.00% to 100.00%, respectively, and the rates of seed, kernel yield and defect kernel were 37.86% to 72.41%, 25.18% to 44.61%, and 1.67% to 17.00%, respectively. The large fruit rates of nut-in-shell and kernel of macadamia nut from Lincang producing areas in Yunnan were both highest, and the defect kernel rate of macadamia nut from Pu'er producing areas in Yunnan was highest, suggesting the necessity of strengthening orchard management. The results of variation analysis showed that 35 samples of macadamia nuts were rich in variation, the coefficient of variation ranging from 4.49% to 68.01%. The variation coefficient of contents of total phenol, polysaccharide, total sugar, kernel moisture and ash were all greater than 10%, which showed that there were significant differences between macadamia nuts samples. Seventeen kinds of amino acids were all detected in 35 samples of macadamia nuts, their medicinal amino acids accounted for 63.22% to 73.36% of the total amino acids with the largest proportions. Indicators used to evaluate the quality characteristics of macadamia nut were obtained by orthogonal partial least squares-discriminant analysis method, which were contents of crude fat, seed rate, leucine, the large fruit rate of nut-in-shell, polysaccharide, histidine, total sugar, the single fruit weights of nut-in-shell, alanine, valine, threonine, tyrosine, aspartic acid, and the single fruit weights of kernel. The comprehensive quality evaluation model of macadamia nuts was established by principal component analysis, which showed that the samples with the best comprehensive quality were respectively Pu'er 'HVA4', Baoshan 'Nanya No.3', Baoshan 'HAES246', Baoshan 'HVA16' and Pu'er 'Own Choice' in 35 samples of macadamia nuts. The results of this study can provide important reference for breeding of new varieties, variety screening of processed products and quality control of macadamia nuts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".