Komposisi Kimia, Profil Asam Amino dan Kualitas Protein Caulerpa lentillifera Hasil Budidaya pada Wadah Terkontrol
Bibliographic record
Abstract
Komposisi kimia dan asam amino C. lentillifera dipengaruhi oleh beberapa faktor eksternal, di antaranya suhu, nutrien, kualitas air, musim, geografi, cuaca, dan lingkungan atau tempat tumbuh. C. lentillifera seperti makroalga lainnya, memiliki kemampuan untuk mengakumulasi nutrien bahkan senyawa antropogenik dari lingkungan yang kemudian dimanfaatkan untuk aktivitas fisiologinya. Komposisi nutrisi rumput laut dari lingkungan yang berbeda menarik untuk dikaji. Tujuan dari penelitian adalah untuk mengevaluasi komposisi kimia dan asam amino C. lentillifera dari hasil budidaya dan mengestimasi kualitas proteinnya kemudian dibandingkan dengan C. lentillifera hasil alam. Budidaya C. lentillifera dilakukan pada wadah terkontrol selama 40 hari. Parameter yang dianalisis meliputi identifikasi morfologi, proksimat, profil asam amino menggunakan HPLC dan kualitas protein menggunakan pendekatan indeks asam amino esensial (EAAI) dan rasio efisiensi protein (P-PER). Hasil penelitian menunjukkan C. lentillifera budidaya dalam wadah terkontrol memiliki komposisi mineral yang lebih tinggi daripada C. lentillifera hasil alam, sedangkan kandungan protein, lemak, air, dan karbohirat pada C. lentillifera hasil budidaya lebih kecil dibandingkan C. lentillifera hasil alam. C. lentillifera hasil budidaya memiliki total kandungan asam amino (esensial dan non esensial) yang rendah dibandingkan hasil alam. Berdasarkan evaluasi kualitas protein, C. lentillifera hasil budidaya dalam wadah terkontrol dan hasil alam menghasilkan protein berkualitas sangat baik. Asam amino pembatas untuk C. lentillifera hasil budidaya dalam wadah terkontrol adalah triptofan sedangkan asam amino pembatas untuk C. lentillifera hasil alam adalah metionin dan sistein. AbstractThe chemical and amino acid composition of C. lentillifera is influenced by several external factors such as temperature, nutrients, water quality, season, geography, weather, and the environment or location where it grows. C. lentillifera, like other macroalgae, can accumulate nutrients and even anthropogenic compounds from environment and use them for their physiological activities. Therefore, the nutritional composition of seaweeds under different conditions is an interesting research topic. This study aimed to evaluate the chemical and amino acid composition of C. lentillifera from cultivated products, estimate the quality of the resulting protein, and compare it with that of C. lentillifera from wild-stock products. C. lentillifera was cultivated in controlled containers for 40 d. The parameters analyzed included identification of morphology, proximate amino acids using HPLC, and protein quality using the essential amino acid index (EAAI) and protein efficiency ratio (P-PER) approaches. The results of the research showed that cultivated C. lentillifera in controlled containers had a higher mineral composition than wild-stock C. lentillifera, while the protein, fat, water and carbohydrate content of cultivated C. lentillifera was lower than wild-stock C. lentillifera. C. lentillifera from cultivation has a lower total amino acid content (essential and non-essential) compared to wild-stock products. Based on protein quality evaluation, C. lentillifera cultivated in controlled containers and wild stock products produced very high-quality protein. The limiting amino acid for C. lentillifera cultivated in controlled containers is tryptophan, whereas the limiting amino acids for C. lentillifera from wild stock products are methionine and cysteine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".