Data-Driven Optimization of Cellulase-Assisted Extraction of Flavonoids and Chlorophyll from Silkworm Excrement

Qian, Feng; Lu, Jie-Juan; Guan, De-Long; Song, Jing; Lu, Fuzhi

doi:10.3390/pr14030495

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Data-Driven Optimization of Cellulase-Assisted Extraction of Flavonoids and Chlorophyll from Silkworm Excrement

by

Feng Qian

,

Jie-Juan Lu

,

De-Long Guan

,

Jing Song

and

Fuzhi Lu

^*

Guangxi Key Laboratory of Sericulture Ecology and Intelligent Technology Application, Guangxi Collaborative Innovation Center of Modern Sericulture and Silk, Guangxi Colleges Universities Key Laboratory of Exploitation and Utilization of Microbial and Botanical Resources, School of Chemistry and Bioengineering, Hechi University, Hechi 546300, China

^*

Author to whom correspondence should be addressed.

Processes 2026, 14(3), 495; https://doi.org/10.3390/pr14030495

Submission received: 8 January 2026 / Revised: 27 January 2026 / Accepted: 28 January 2026 / Published: 30 January 2026

(This article belongs to the Special Issue Recent Advances in Bioprocess Engineering and Fermentation Technology)

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Abstract

Guangxi, one of China’s dominant sericulture regions, generates substantial silkworm excrement (SE) annually, yet most remains underutilized. This study optimized cellulase-assisted ethanol extraction of flavonoids and chlorophyll from silkworm frass. Systematic experimentation (n = 31) revealed that extraction temperature exerted dominant influence on both contents (r = 0.54 and 0.37 for chlorophyll and flavonoids, respectively), while the two contents exhibited near-zero correlation (r = 0.06). An XGBoost model achieved R² = 0.9146 for flavonoid prediction; SHAP analysis identified a critical temperature threshold (~40 °C). Monte Carlo simulation (n = 10,000) constructed a Pareto frontier for multi-objective optimization. The optimized condition (9% enzyme, 50 °C) achieved chlorophyll and flavonoid contents of 1.13 and 6.42 mg/g, respectively. These findings demonstrate that sericulture waste can serve as a biorefinery feedstock and that interpretable machine learning can navigate multi-objective extraction challenges under data constraints.

Keywords: silkworm excrement; cellulase-assisted extraction; machine learning; waste valorization

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MDPI and ACS Style

Qian, F.; Lu, J.-J.; Guan, D.-L.; Song, J.; Lu, F. Data-Driven Optimization of Cellulase-Assisted Extraction of Flavonoids and Chlorophyll from Silkworm Excrement. Processes 2026, 14, 495. https://doi.org/10.3390/pr14030495

AMA Style

Qian F, Lu J-J, Guan D-L, Song J, Lu F. Data-Driven Optimization of Cellulase-Assisted Extraction of Flavonoids and Chlorophyll from Silkworm Excrement. Processes. 2026; 14(3):495. https://doi.org/10.3390/pr14030495

Chicago/Turabian Style

Qian, Feng, Jie-Juan Lu, De-Long Guan, Jing Song, and Fuzhi Lu. 2026. "Data-Driven Optimization of Cellulase-Assisted Extraction of Flavonoids and Chlorophyll from Silkworm Excrement" Processes 14, no. 3: 495. https://doi.org/10.3390/pr14030495

APA Style

Qian, F., Lu, J.-J., Guan, D.-L., Song, J., & Lu, F. (2026). Data-Driven Optimization of Cellulase-Assisted Extraction of Flavonoids and Chlorophyll from Silkworm Excrement. Processes, 14(3), 495. https://doi.org/10.3390/pr14030495

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Data-Driven Optimization of Cellulase-Assisted Extraction of Flavonoids and Chlorophyll from Silkworm Excrement

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