Correlation between chemical characteristics and optical spectra of Spirulina commercially available on the Bulgarian market
Bibliographic record
Abstract
The aggregate of various chemical substances useful for the functioning of the human body are known as nutrients. Spirulina has been present in human nutrition since ancient times, but in recent years the interest in it has been particularly increased due to the emergence of numerous alternative methods of nutrition. This study aimed to compare the functional and elemental composition as well as the optical properties of commercial Spirulina products available on the Bulgarian market. For this purpose, fluorescence spectroscopy in the ultraviolet and visible range, fourier transform infrared spectroscopy and inductively coupled plasma optical emission spectroscopy were used. The basic components of the analyzed Spirulina samples are proteins (1657 and 1537 cm-1) and carbohydrates (1069 and 1054 cm-1) and no meaningful differences between the IR spectra of the samples. Concentrations of important microelements Mg, Fe, Cu, Zn, and Mn varies with the manufacturer. The highest levels for Mg (6.69 g kg-1) were measured in samples from USA, while the Spirulina fabricated in Bulgaria exhibits the highest contents of Zn (242 mg kg-1) and Cu (25.4 mg kg-1). All samples followed the tendency Mg>Fe>Mn>Zn>Cu. Making use of a fiber optic spectrometer the fluorescence spectra of the studied samples of Spirulina platensis for an excitation wavelength of 380 nm were measured. In these spectra we observe three fluorescence maxima: at 465 nm – nicotinamide dinucleotide phosphate, 640 nm chlorophyll a, and 736 nm due to similar to chlorophyll pigments. A strong positive correlation between the contents of Zn and Cu on the one side and the second fluorescence peak (λ=640 nm) for excitation wavelength at 380 nm. In contrast, a high negative correlation for Fe and the third fluorescence maximum (λ= 736nm) is observed for all excitation wavelengths. The correlation dependencies were obtained with the least squares method with a significance level of p ≤ 0.05.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".