Evaluation of Polysaccharide Content in Shiitake Culinary-Medicinal Mushroom, Lentinula edodes (Agaricomycetes), via Near-Infrared Spectroscopy Integrated with Deep Learning
Bibliographic record
Abstract
Polysaccharide is one of the bioactive ingredients extracted from the fruiting body of Lentinula edodes (=L. edodes), which has many medicinal functions. While the content of polysaccharide can be measured by near-infrared (NIR) spectroscopy, the NIR analytical models established previously only covered L. edodes from very limited sources, and thus could not achieve high accuracy for large samples from more varied sources. Strictly, there is a nonlinear relationship between NIR spectral data and chemical label values, and traditional modeling methods for NIR data analysis have problems such as insufficient feature learning ability and difficulty in training. The deep learning model has excellent nonlinear modeling ability and generalization capacity, which is very suitable for analyzing larger samples. In this study, we constructed a novel framework with deep learning techniques on the NIR analysis of the content of polysaccharide in L. edodes. The siPLS model was established based on the combination of the bands 4797-3995 cm-1 and 6401-5600 cm-1, while the one-dimensional convolutional neural network (1D-CNN) model was established with improved feature in the treatment of the spectral data. The comparative experimental results showed that the 1D-CNN model (R2pre = 95.50%; RMSEP =0.1875) outperformed the siPLS model (R2pre = 87.89%, RMSEP = 0.6221). As such, this work has demonstrated that NIR spectroscopy with the integration of deep learning can provide more accurate quantification of polysaccharide in L. edodes. Such method can be very useful for nutritional grading and quality control of diverse L. edodes in the market.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".