The DOI matches its Crossref record; 124 of the 124 references it cites were found in a registry or an index.
Bioreactor monitoring with spectroscopy and chemometrics: a review
Abstract
Biotechnological processes are crucial to the development of any economy striving to ensure a relevant position in future markets. The cultivation of microorganisms in bioreactors is one of the most important unit operations of biotechnological processes, and real-time monitoring of bioreactors is essential for effective bioprocess control. In this review, published material on the potential application of different spectroscopic techniques for bioreactor monitoring is critically discussed, with particular emphasis on optical fiber technology, reported for in situ bioprocess monitoring. Application examples are presented by spectroscopy type, specifically focusing on ultraviolet-visible, near-infrared, mid-infrared, Raman, and fluorescence spectroscopy. The spectra acquisition devices available and the major advantages and disadvantages of each spectroscopy are discussed. The type of information contained in the spectra and the available chemometric methods for extracting that information are also addressed, including wavelength selection, spectra pre-processing, principal component analysis, and partial least-squares. Sample handling techniques (flow and sequential injection analysis) that include transport to spectroscopic sensors for ex-situ on-line monitoring are not covered in this review.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“the monitoring of intracellular and extracellular macromolecules in bioprocesses share many intrinsic similarities to yeast and bacteria bioprocesses, which have been extensively reviewed together due to these similarities [20,21,29].”
From Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives (2018) · cites this paper as [21]
In the citing paper
“Currently, there are few reviews of vibrational spectroscopy as it pertains to the microalgae industry [9,17-19]; however, these reviews do not take into account the technological progress and development of on-line/in-line vibrational spectroscopy implemented in bioprocess, pharmaceutical, and liquid and solid food industries [20-28]”
From Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives (2018) · cites this paper as [21]
In the citing paper
“There are numerous cases of in situ MIR applications, all of which use ATR-FTIR probes to acquire quantitative MIR spectra of a multitude of media components and bioproducts [20,21].”
From Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives (2018) · cites this paper as [21]
In the citing paper
“MIR spectra relevant to the microalgal industry obtained in situ in yeast and bacterial bioreaction suspensions in 5 L to 15 L reactors include optical density, ammonium, phosphates, acetate, glucose, fructose, organic acids as well as several bioproducts [20,21].”
From Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives (2018) · cites this paper as [21]
In the citing paper
“However, a promising mode of MIR for wet applications is attenuated total reflectance Fourier transform infrared (ATR-FTR), which has had growing applications in pharmaceutical, agro-food, and environmental sectors and can be used on-line/in-line in liquid media (see reviews by [20] and [21]).”
From Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives (2018) · cites this paper as [21]
In the citing paper
“NIR in vivo analysis of microalgae has only yet investigated biomass, lipids, and carotenoids (Fig. 2) but the potential use of high-selectivity on-line/in-line NIR in microalgae bioreactions is evidenced by its prolific use in other bioprocesses [20,21].”
From Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives (2018) · cites this paper as [21]
In the citing paper
“Most applications of vibrational spectroscopy PAT require calibration and validation of chemometric models [21, 48,102] with the exception of some analytes, for example carotenoids measured through Raman [94].”
From Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives (2018) · cites this paper as [21]
In the citing paper
“Another drawback of ATR-FTIR is that fiber optic materials with sufficient MIR transmission are limited and costly [80] and require very short fiber optic cables and may not be suitable for multiplexing [21] to monitor inhomogeneities across large scale photobioreactors.”
From Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives (2018) · cites this paper as [21]
In the citing paper
“On the other hand, Raman and NIR are more likely to be multiplexed with fiber optic conduits, each which can be up to a few meters long [21].”
From Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives (2018) · cites this paper as [21]
Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
PLS-regression: a basic tool of chemometrics
Wold, S., Sjöström, M., & Eriksson, L. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00155-1 · Cited by 10,084
Encyclopedia of Analytical Chemistry
Grochocki, W., Markuszewski, M., & Quirino, J. P. (2006) · Wiley eBooks · doi:10.1002/9780470027318 · Cited by 7,503
Infrared Spectroscopy: Fundamentals and Applications
Stuart, B. H. (2004) · Analytical Techniques in the Sciences · doi:10.1002/0470011149 · Cited by 4,427
Fuzzy clustering with a fuzzy covariance matrix
Gustafson, D. E., & Kessel, W. (1978) · IEEE Conference on Decision and Control including the Symposium on Adaptive Processes (CDC) · doi:10.1109/cdc.1978.268028 · Cited by 1,981
Fiber-Optic Chemical Sensors and Biosensors
Wolfbeis, O. S. (2006) · Analytical Chemistry · doi:10.1021/ac060490z · Cited by 1,635
Handbook of Near-Infrared Analysis
Burns, D. A., & Ciurczak, E. W. (2001) · CRC Press eBooks · doi:10.1201/9781003042204 · Cited by 1,472
Multivariate SPC Charts for Monitoring Batch Processes
Nomikos, P., & MacGregor, J. F. (1995) · Technometrics · doi:10.1080/00401706.1995.10485888 · Cited by 1,430
A user-friendly guide to Multivariate Calibration and Classification
Næs, T., Isaksson, T., Fearn, T., & Davies, T. (2017) · doi:10.1255/978-1-906715-25-0 · Cited by 1,401
A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies
Roggo, Y., Chalus, P., Maurer, L., Lema-Martinez, C., Edmond, A., & Jent, N. (2007) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2007.03.023 · Cited by 1,136
Research works printed but not linked: 6
- Morris R (2010) How optical advances helped deliver the promise of miniature spectrometers. Spectroscopy 25:30–35
- Mark H (2000) In: Meyers RA (ed) Encyclopedia of analytical chemistry. John Wiley and Sons, Chichester
- Kourti T (2004) Process analytical technology and multivariate statistical process control. PAT J Proc Anal Technol 1:13–19
- Baylor LC, O’Rourke PE (2005) In: Bakeev KA (ed) Process analytical technology – spectroscopic tools and implementation strategies for the chemical and pharmaceutical industries. Blackwell, Oxford
- Bakeev KA (ed) (2005) Process analytical technology – spectroscopy tools and implementation strategies for the chemical and pharmaceutical industries. Blackwell, Oxford
- Hart JR, Golumbic C, Norris KH (1962) Determination of moisture content if seeds by near-infrared spectrophotometry of their methanol extracts. Cereal Chem 39:94–99
Cited by
Sorted by citations
Items 201–275 of 275
Application of a Novel Disposable Flow Cell for Spectroscopic Bioprocess Monitoring
Steinwedel, T., Raithel, P., Schellenberg, J., Kortmann, C., Gellermann, P., Belz, M., & Solle, D. (2024) · Chemosensors · doi:10.3390/chemosensors12100202 · Cited by 3
Liang, Z., Zhang, C., Hu, L., Cai, Y., & Deng, M. (2024) · Vibrational Spectroscopy · doi:10.1016/j.vibspec.2024.103760 · Cited by 3
Leitão, C., Fernandes, L., Ribeiro, R. A., Almeida, M. D. C., Pinheiro, C. I. C., & Pinheiro, H. M. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-3-319-43671-5_28 · Cited by 3
Self-citationBourakadi, K. E., Bouhfid, R., & Qaiss, A. E. K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822906-4.00010-4 · Cited by 3
Berg, C., Herbst, L., Gremm, L., Ihling, N., Paquet‐Durand, O., Hitzmann, B., & Büchs, J. (2023) · Journal of Biological Engineering · doi:10.1186/s13036-023-00332-0 · Cited by 3
Li, Z., Zhang, X., & Karanassios, V. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2177516 · Cited by 3
Lopes, M. B., Calado, C. R. C., Figueiredo, M. A. T., & Bioucas‐Dias, J. M. (2016) · Applied Spectroscopy · doi:10.1177/0003702816670913 · Cited by 3
High-throughput bioassay for mechanism of action determination of antibacterial drugs
da Cunha, B. R., Fonseca, L. P., & Calado, C. R. C. (2017) · IEEE Portuguese Meeting on Bioengineering · doi:10.1109/enbeng.2017.7889478 · Cited by 3
In vitro dynamic culture of cell-biomaterial constructs
Riboldi, S. A., Bertoldi, S., & Mantero, S. (2017) · Elsevier eBooks · doi:10.1016/b978-0-08-100737-2.00014-5 · Cited by 3
The Role of Optical Techniques in Regenerative Medicine
Morgan, S. P., Wilson, B. C., Vitkin, I. A., & Rose, F. R. A. J. (2013) · Taylor & Francis eBooks · doi:10.1201/b15324-3 · Cited by 3
Dzurendová, S., Losada, C. B., Dupuy-Galet, B. X., Slaný, O., Fjær, K., Bartolomeo, F. D., Markussen, S., Wentzel, A., Várnai, A., Hansen, L. D., et al. (2025) · Microbial Cell Factories · doi:10.1186/s12934-025-02868-w · Cited by 2
Arzate-Cárdenas, M. A., Carbajal-Hernández, A. L., Ramos-Garza, J., Arreguin-Rebolledo, U., & Rico‐Martínez, R. (2024) · Environmental science and engineering · doi:10.1007/978-3-031-62898-6_4 · Cited by 3
Alemneh, S. T., Emire, S. A., Jekle, M., Paquet‐Durand, O., & Hitzmann, B. (2022) · International Electronic Conference on Processes (ECP) · doi:10.3390/ecp2022-12650 · Cited by 2
Augmenting Automated Analytics Using Fluorescent Nanosensors
Chauhan, V. M. (2018) · Cell and Gene Therapy Insights · doi:10.18609/cgti.2018.085 · Cited by 1
Industrial Monitoring of Cell Culture
Schwamb, S., Engel, M., Werner, T., & Wiedemann, P. (2021) · Cell engineering · doi:10.1007/978-3-030-79871-0_17 · Cited by 2
Freemantle, R., Butson, N., Goodfellow, J., Konzuk, J. S., & Longstaffe, J. G. (2022) · Applied Sciences · doi:10.3390/app12094620 · Cited by 2
Real‐Time Measurement and Monitoring of Bioprocesses
Sonnleitner, B. (2015) · doi:10.1002/9783527697441.ch14 · Cited by 2
Production of polyhydroxyalkanoates from oil-containing substrates
Proença, M. M. P. V. D. C. (2015) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 2
Development of Soft Sensors for Monitoring of Chinese Hamster Ovary Cell Processes
Ohadi, S. K. (2014) · UWSpace (University of Waterloo) · Cited by 2
Berg, C., Busch, S., Alawiyah, M. D., Finger, M., Ihling, N., Paquet‐Durand, O., Hitzmann, B., & Büchs, J. (2023) · Biotechnology and Bioengineering · doi:10.1002/bit.28474 · Cited by 1
Quickly tunable ultra-narrow filter via a metal film waveguide
Shan, H., Wei, Q., Dai, H., & Chen, X. (2024) · Optics Letters · doi:10.1364/ol.517815 · Cited by 1
Janssen, S. A. J., de Jong, A. M., & Prins, M. W. J. (2025) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2025.117808 · Cited by 1
Ultrasound Imaging and Machine Learning for Nondestructive Sensing in Bioreactors
Serpe, M., Lee, C., Povinelli, R., Thyden, R., Perreault, L. R., He, K., Gaudette, G. R., dos Santos, A., & Ranger, B. J. (2025) · ACS Omega · doi:10.1021/acsomega.5c06107 · Cited by 1
Striedner, G., & Bayer, K. (2013) · Pharmaceutical Bioprocessing · doi:10.4155/pbp.13.16 · Cited by 1
- The Role of Optical Techniques in Regenerative Medicine
Wilson, B. C. (2013) · CRC Press eBooks · doi:10.1201/b15324-5 · Cited by 1
Photometric and Fluorimentric Data for Pollution Monitoring in River Waters
Bondarenko, E. A., Михайлова, А. С., Lednova, J. A., & Andrianova, M. J. (2018) · IEEE International Forum on Research and Technology for Society and Industry (RTSI) · doi:10.1109/rtsi.2018.8548367 · Cited by 1
Liu, J., Ma, H. L., Yuan, G., Wang, Z., Ma, R., Zhang, S., Cao, X., Wang, Y., & Liu, Y. (2023) · Review of Scientific Instruments · doi:10.1063/5.0136099 · Cited by 1
EFPI Solution Type and Concentration Sensing System Based on ConvNeXt Network Model
Wang, M., Wan, S., & Yu, J. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3472888 · Cited by 1
Bioprocess technology for production of functional foods
de Sousa, R. D. C. P., dos Santos, D. C., Neves, L. C., & Chagas, E. A. (2013) · Revista Agro mbiente On-line · Cited by 1
Assawajaruwan, S., & Hitzmann, B. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-409547-2.11324-1 · Cited by 1
Mid-infrared spectroscopy: A groundbreaking tool for monitoring mammalian cells processes
Rosa, F., da Cunha, B. R., Carmelo, J. G., Fernandes‐Platzgummer, A., da Silva, C. L., & Calado, C. R. C. (2017) · IEEE Portuguese Meeting on Bioengineering · doi:10.1109/enbeng.2017.7889479 · Cited by 1
Matějka, P., Vlčková, B., Bednářová, L., & Maloň, P. (2014) · Natural Products Analysis · doi:10.1002/9781118876015.ch6 · Cited by 1
Zavatti, V. (2019) · UWSpace (University of Waterloo) · Cited by 1
Vishakha, V., & Jančář, J. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-c1b8w · Cited by 1
Mesure en ligne par spectroscopie Raman appliquée à un photobioréacteur de microalgues
Lieutaud, C. (2019) · theses.fr (ABES) · doi:10.70675/b149f935zc54az4f46z877az5412eeee4d12 · Cited by 1
Awhangbo, L. (2019) · theses.fr (ABES) · doi:10.70675/ef779563z49fcz4433za7ccze7ea0dd60f74 · Cited by 1
Flow-cytometric identification of vinegars using a multi-parameter analysis optical detection module
Verschooten, T., Ottevaere, H., Vervaeke, M., Erps, J. V., Callewaert, M., Malsche, W. D., & Hugo Thienpont, A. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2197474
Control of Microbial Processes
Shimizu, K., Shimizu, H., & Yoshida, T. (2017) · doi:10.1002/9783527800599.ch8
Adamczak, W., Adamski, M., Wojciechowski, T., & Czechlowski, M. (2016) · Journal of Research and Applications in Agricultural Engineering
Wang, M., Wang, W.-P., & Shao, L. (2018) · Chinese Journal of Chemical Physics · doi:10.1063/1674-0068/31/cjcp1805115
Handl, A., López‐Lorente, Á. I., Handrick, R., Mizaikoff, B., & Hesse, F. (2019) · The Analyst · doi:10.1039/c9an00424f
Goby, J. D., Furuya, K., Zimmermann, E., Beller, J. A., Schmitt, J., Cortese, M., Breit, J. F., & Coffman, J. (2020) · Biotechnology Progress · doi:10.1002/btpr.2988
References Part I and Recommended Textbooks and References for Further Reading
Heinzle, E., Dunn, I. J., Ingham, J., & Přenosil, J. E. (2021) · doi:10.1002/9783527685332.ref
Hybrid Modelling and Multi- Parametric Control of Bioprocesses
Herwig, C. (2018) · MDPI eBooks · doi:10.3390/books978-3-03842-746-9
Adetunji, C. O., Jeevanandam, J., Selvam, J. D., Inobeme, A., Thangadurai, D., Islam, S., Michael, O. S., Ubi, B. E., & Olaniyan, O. T. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822921-7.00006-4
Monitoring of anaerobic biological processes
Yang, X. P., Li, Z., Yan, X., Liang, T., & Chen, H. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99874-1.00007-5
Next generation techniques for anaerobic bioprocess optimization
Vyrides, I., & Kartakoullis, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91167-2.00015-0
Reza, V., Muñoz, J. A. T., & Tavares, J. N. G. (2023) · International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) · doi:10.1109/cce60043.2023.10332902
Yang, N., Guerin, C., Kokanyan, N., & Perré, P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4680879
Development of a novel disposable flowcell for spectroscopic bioprocess monitoring
Thiel, P., Steinwedel, T., Raithel, P., Belz, M., & Solle, D. (2025) · Measurement Sensors · doi:10.1016/j.measen.2025.101862
Andersen, P. V., Wubshet, S. G., Tzimorotas, D., Dankel, K. R., Afseth, N. K., Wold, J. P., & Rønning, S. B. (2025) · Research Square · doi:10.21203/rs.3.rs-6123711/v1
Deep Learning for Raman Spectroscopy: Benchmarking Models for Upstream Bioprocess MonitoringPreprint
Lange, C., Altmann, M., Stors, D., Seidel, S., Moynahan, K., Cai, L., Born, S., Neubauer, P. B., & Bournazou, M. N. C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5239248
Wet-Lab Techniques for Characterizing Bioinspired Materials and Structures for Regenerative Medicine
Joshi, S., Prajapati, S. K., Sharma, V., Sharma, D., & Gupta, M. M. (2025) · Springer eBooks · doi:10.1007/978-3-031-87744-5_22
Reza, V., Torres, J., & Guerrero, J. (2025) · Kybernetika · doi:10.14736/kyb-2025-3-0404
Offline Raman monitoring of SARS-CoV-2 VLP upstream stage using two culture media
Rabello, J. P., Fontes, I. D. S., Dias, F. M., Camalhonte, S. O., Guardalini, L. G. O., Tanaka, J. L., Leme, J., Bernardino, T. C., Sposito, F. S., Dias, E., et al. (2025) · Spectroscopy Letters · doi:10.1080/00387010.2025.2541638
Online analysis for industrial bioprocesses: Broth analysis
Veloso, A. C., & Ferreira, E. C. (2017) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
Quantum chemical and solvatochromism study of NSAIDs in neat and binary solvent systems
Prachalith, N. C., Vibha, K., Kumar, H. M. S., Thipperudrappa, J., & Khadke, U. V. (2026) · International Journal of Modern Physics B · doi:10.1142/s0217979226500402
Zhang, Q., Liu, T., Yin, Y., Zhang, Q., & Chen, J. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3669149
Near infrared spectroscopy applied to solid organic waste˸ how to avoid water effects?
Mallet, A. (2021) · theses.fr (ABES) · doi:10.70675/3340ec57zca1az49c8za61ez13e5c8dabba7
Microalgal Biofilms : An Experimental, Monitoring and Modeling Approach
Pereira, D. M. (2025) · theses.fr (ABES) · doi:10.70675/39584f0dza802z449azb515z0097f8282711
Online monitoring of bioreactors by Raman spectroscopy and machine learning
Yang, N. (2024) · theses.fr (ABES) · doi:10.70675/cfae52a0zdcd5z41a8z877bz3023f86d9a13
Design of bioreactors and factors affecting their efficiency
Abedi‐Firoozjah, R., Bahramian, B., Khezerlou, A., Sani, M. A., & Jafari, S. M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-12-819533-8.00012-9
Yang, M., Bidoia, J., Keita, E., Weng, Y., & Roussel, N. (2026) · Cement and Concrete Research · doi:10.1016/j.cemconres.2026.108331
Smarter Fermenters, Better Foods: Automation and AI in the Plant-Based Revolution
Buttrós, V. H., Dias, D. R., & Schwan, R. F. (2026) · Springer eBooks · doi:10.1007/978-3-032-29927-7_10
Miniaturized Bioreactors: Transforming Upstream Processing in Biopharmaceutical Production
Sharma, A., Syali, S., Aastha, Yadav, K., Sharma, B., Dewangan, R. P., Chauhan, V. S., Kumar, S., Kumar, L., & Kumar, K. (2026) · Springer eBooks · doi:10.1007/978-3-032-22332-6_3
Reactors for stem cell culture
Salvador, W. O. S., Nogueira, D. E. S., Prielhofer, R., Graumann, K., Rodrigues, C. A. V., & Cabral, J. M. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36330-6.00001-1
Guo, S., Wang, D., Liu, H., Ding, K., Zhang, T., Tang, H., Geng, P., Zhai, L., Li, J., Wang, M., et al. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.182263
Hong, Y., & Zhao, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7511765
Regulatory considerations for polymeric nanofibers: A global perspective
Tiwari, P., Kadiri, S. K., Gaurav, P., & Dhanawat, M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41702-3.00003-9
Metadata
- Authors
- Lourenço, N. D., Lopes, J. A., Almeida, C. F., Sarraguça, M. C., & Pinheiro, H. M.
- Affiliations
- Institute for Biotechnology and Bioengineering (PT)
- Universidade do Porto (PT)
- Corresponding author
- Lourenço, N. D.
- Venue
- Analytical and Bioanalytical Chemistry
- Published
- 2012-05-30
- Volume
- 404
- Issue
- 4
- Pages
- 1211-1237
- ISSN
- 1618-2642 (ISSN-L)
- 1618-2650
- OpenAlex
- W2057328981
- Cited by
- 275
- References
- 123
- Citations by year
- 2026: 10 · 2025: 20 · 2024: 23 · 2023: 13 · 2022: 20 · 2021: 23 · 2020: 22 · 2019: 23 · 2018: 21 · 2017: 29 · 2016: 28 · 2015: 17 · 2014: 16 · 2013: 10
- FWCI
- 13.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Analytical and Bioanalytical Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Physical Sciences › Environmental Science › Waste Management and Disposal › Water Quality Monitoring and Analysis
- Keywords
- bioreactor monitoring, bioprocess monitoring, in situ monitoring, fiber optic sensors, UV-Vis spectroscopy, near-infrared spectroscopy, mid-infrared spectroscopy, Raman spectroscopy, fluorescence spectroscopy, chemometrics, principal component analysis, partial least squares, wavelength selection, microbial cultivation, real-time process control
- MeSH terms
- Bacteria, Biotechnology, Spectrum Analysis, Bioreactors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review