Enhancing PQQ production in Acinetobacter calcoaceticus through uniform design and support vector regression
A novel machine learning-assisted approach for formula optimization, termed UD-SVR, is introduced by combining uniform design with support vector regression. This method was employed to optimize both the formulation and fermentation conditions for pyrroloquinoline quinone (PQQ) production by Acineto...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2025.1556322/full |
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| author | Yu-han Li Yu-han Li Su-hang Yao Su-hang Yao Yan Zhou Xiu-lan He Xiu-lan He Zhe-ming Yuan Zhe-ming Yuan Qiu-long Hu Cheng-wen Shen Xin Li Xin Li Yuan Chen |
| author_facet | Yu-han Li Yu-han Li Su-hang Yao Su-hang Yao Yan Zhou Xiu-lan He Xiu-lan He Zhe-ming Yuan Zhe-ming Yuan Qiu-long Hu Cheng-wen Shen Xin Li Xin Li Yuan Chen |
| author_sort | Yu-han Li |
| collection | DOAJ |
| description | A novel machine learning-assisted approach for formula optimization, termed UD-SVR, is introduced by combining uniform design with support vector regression. This method was employed to optimize both the formulation and fermentation conditions for pyrroloquinoline quinone (PQQ) production by Acinetobacter calcoaceticus. In just two rounds of 66 experimental treatments, UD-SVR effectively optimized a formulation involving eight factors at the shake-out level scale, enhancing PQQ production from 43.65 mg/L to 73.40 mg/L—an impressive 68.15% increase. Notably, the optimized formulation is also cost-effective, featuring minimized consumption of pivotal elements like carbon and nitrogen sources. The machine learning-supported UD-SVR method presents an inclusive resolution for optimizing experimental designs and analyses in multi-factor, multi-level formulations, characterized by robust guidance, lucid interpretability, and heightened efficiency in optimization. |
| format | Article |
| id | doaj-art-a0fab0c4560b4e188a40ca9bd998243c |
| institution | Kabale University |
| issn | 1664-302X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj-art-a0fab0c4560b4e188a40ca9bd998243c2025-08-20T03:40:31ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2025-08-011610.3389/fmicb.2025.15563221556322Enhancing PQQ production in Acinetobacter calcoaceticus through uniform design and support vector regressionYu-han Li0Yu-han Li1Su-hang Yao2Su-hang Yao3Yan Zhou4Xiu-lan He5Xiu-lan He6Zhe-ming Yuan7Zhe-ming Yuan8Qiu-long Hu9Cheng-wen Shen10Xin Li11Xin Li12Yuan Chen13National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha, ChinaHunan Province Microbiology Research Institute, Changsha, ChinaNational Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha, ChinaHunan Province Microbiology Research Institute, Changsha, ChinaHunan Province Microbiology Research Institute, Changsha, ChinaHunan Province Microbiology Research Institute, Changsha, ChinaCentral South University Graduate School Long Ping Branch, Changsha, ChinaYuelushan Laboratory of Hunan Province, Changsha, ChinaHunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-Making, Hunan Agricultural University, Changsha, ChinaNational Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha, ChinaNational Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha, ChinaHunan Province Microbiology Research Institute, Changsha, ChinaHunan Engineering Research Center for Endophytic Microbial Resources Mining and Utilization, Changsha, ChinaHunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-Making, Hunan Agricultural University, Changsha, ChinaA novel machine learning-assisted approach for formula optimization, termed UD-SVR, is introduced by combining uniform design with support vector regression. This method was employed to optimize both the formulation and fermentation conditions for pyrroloquinoline quinone (PQQ) production by Acinetobacter calcoaceticus. In just two rounds of 66 experimental treatments, UD-SVR effectively optimized a formulation involving eight factors at the shake-out level scale, enhancing PQQ production from 43.65 mg/L to 73.40 mg/L—an impressive 68.15% increase. Notably, the optimized formulation is also cost-effective, featuring minimized consumption of pivotal elements like carbon and nitrogen sources. The machine learning-supported UD-SVR method presents an inclusive resolution for optimizing experimental designs and analyses in multi-factor, multi-level formulations, characterized by robust guidance, lucid interpretability, and heightened efficiency in optimization.https://www.frontiersin.org/articles/10.3389/fmicb.2025.1556322/fullAcinetobacter calcoaceticusPQQuniform designsupport vector regressionformulation optimization |
| spellingShingle | Yu-han Li Yu-han Li Su-hang Yao Su-hang Yao Yan Zhou Xiu-lan He Xiu-lan He Zhe-ming Yuan Zhe-ming Yuan Qiu-long Hu Cheng-wen Shen Xin Li Xin Li Yuan Chen Enhancing PQQ production in Acinetobacter calcoaceticus through uniform design and support vector regression Frontiers in Microbiology Acinetobacter calcoaceticus PQQ uniform design support vector regression formulation optimization |
| title | Enhancing PQQ production in Acinetobacter calcoaceticus through uniform design and support vector regression |
| title_full | Enhancing PQQ production in Acinetobacter calcoaceticus through uniform design and support vector regression |
| title_fullStr | Enhancing PQQ production in Acinetobacter calcoaceticus through uniform design and support vector regression |
| title_full_unstemmed | Enhancing PQQ production in Acinetobacter calcoaceticus through uniform design and support vector regression |
| title_short | Enhancing PQQ production in Acinetobacter calcoaceticus through uniform design and support vector regression |
| title_sort | enhancing pqq production in acinetobacter calcoaceticus through uniform design and support vector regression |
| topic | Acinetobacter calcoaceticus PQQ uniform design support vector regression formulation optimization |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2025.1556322/full |
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