The Quality Improvement of Indonesian Konjac Chips (Amorphophallus Muelleri Blume) through Drying Methods and Sodium Metabisulphite Soaking
Bibliographic record
Abstract
Amorphophallus muelleri Blume (Indonesian konjac) is an annual herbaceous wild plant growing in Indonesia. It produces glucomannan, i.e., a polysaccharide hydrocolloid compound that has many benefits in various fields of industry and has high economic value. To obtain this compound, Indonesian konjac tuber has to be processed into chips, ground, and separated from the other components such as fiber and starch. The problem encountered in producing glucomannan is to find a drying method which may optimally decrease the water content in chips with higher drying rate to produce good quality of the chips. This paper proposes a drying method and to study its effect on the quality of Indonesian konjac chips. For this purpose, we consider these two main treatments; (i) soaking in sodium metabisulphite solution in pre-drying process, and (ii) drying using oven and direct sun light. Thus, we work with four combinations of treatments and then we compare the effect of each combination on the quality characteristics of the chips. The experiment shows that the combination of oven drying method and soaking method produces the best results. In this experiment, we use 1500 ppm of that solution and 10 minutes of soaking. According to our knowledge, these is an unprecedented experiment and thus the results will hopefully be a significant contribution to the literature of food engineering.
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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 | 0.000 | 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".