Journal article
Evaluation of Diffuse Reflectance Spectroscopy for Prediction of In Situ Phototrophic Biofilm Composition
ACS ES&T engineering
09/15/2026
DOI: 10.1021/acsestengg.6c00233
Appears in UI Libraries Support Open Access
Abstract
Biofilm-based algae cultivation is a compelling alternative to suspended growth due to high productivity and a simplified harvesting process. However, process monitoring of attached growth systems presents a challenge due to destructive, labor-intensive, and slow conventional sampling and analysis. This study evaluated a potential approach to in situ monitoring: diffuse reflectance spectroscopy (DRS). Visible-near infrared spectra were used to develop partial least squares regression (PLSR) models for the prediction of fatty acid methyl esters (FAME), volatile solids (VS), ash, and carbohydrate content in phototrophic biofilms. Previous DRS algae studies typically used dried biomass and/or pure algal cultures, whereas this study collected spectral data directly from wet, in situ biofilms grown on a demonstration-scale revolving algal biofilm (RAB) system treating real wastewater. Despite the challenging conditions of mixed-species, wet, in situ biofilms, the PLSR model for FAME achieved moderate predictive performance (NRMSE: 10%; RPD: 2.3), demonstrating potential for resolving relative differences in lipid content under similar operating conditions. Models predicting VS, ash, and carbohydrates did not achieve predictive performance sufficient for practical application (NRMSE: 13–14%; RPD: 1.1). When models were calibrated and validated using seasonally disparate datasets (trained on summer and tested on fall, and vice versa) rather than the combined, mixed-season dataset, prediction accuracy declined sharply, highlighting the influence of seasonal variability and the need for larger, more diverse calibration datasets and periodic recalibration. These results suggest that DRS is a promising tool for non-destructive compositional monitoring of FAME in wet phototrophic biofilms, but additional model development and validation across broader operating conditions are needed before reliable long-term field deployment.
Details
- Title: Subtitle
- Evaluation of Diffuse Reflectance Spectroscopy for Prediction of In Situ Phototrophic Biofilm Composition
- Creators
- Audrey E. Kocher - University of IowaMaclaine K. Putney - University of IowaCraig L. Just - University of Iowa
- Resource Type
- Journal article
- Publication Details
- ACS ES&T engineering
- DOI
- 10.1021/acsestengg.6c00233
- ISSN
- 2690-0645
- eISSN
- 2690-0645
- Publisher
- American Chemical Society
- Grant note
- Triad National Security: 89233218CNA000001 Bioenergy Innovation Fund, University of Iowa Center for Advancement: NA Graduate College, University of Iowa: NA Iowa Economic Development Authority: 20-SEPF-007 Donald E. Bently Professorship, University of Iowa: NA
We gratefully acknowledge the National Laboratory of the Rockies (previously the National Renewable Energy Laboratory) for conducting biomass composition analysis. We thank the Iowa City Water Resource Recovery Facility for housing the RAB system and we are grateful to Gross-Wen Technologies for developing the RAB system and for their valuable collaboration and technical support. Additional thanks to Zoe Kramin for sharing her DRS knowledge and undergraduate researcher Madi Beiermann for her assistance with lab work. This work was supported financially by the Bioenergy Innovation Fund at the University of Iowa Center for Advancement, the Donald E. Bently Professorship, the Iowa Economic Development Authority (contracts 21-SI-001 and 20-SEPF-007), the Waste to Jet Consortium (Triad National Security, LLC, Prime Contract No. 89233218CNA000001), and the Iowa Recruitment Fellowship from the University of Iowa Graduate College. Generative AI tools (ChatGPT, OpenAI; Claude, Anthropic) were used for coding assistance and language refinement. The authors take full responsibility for the content.
- Language
- English
- Electronic publication date
- 09/15/2026
- Academic Unit
- Civil and Environmental Engineering; IIHR--Hydroscience and Engineering
- Record Identifier
- 9985230926202771
Metrics
2 Record Views