Plant cell wall enzymatic hydrolysis: Predicting yield dynamics from autofluorescence and morphological temporal changes Article Swipe
Solmaz Hossein Khani
,
Khadidja Ould Amer
,
Fatemeh Shiasi Ghalemaleki
,
Maxime Corre
,
Noah Remy
,
Anouck Habrant
,
Berangère Lebas
,
Jafar Massah
,
Ali Faraj
,
Gabriel Paës
,
Yassin Refahi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.biortech.2025.133642
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.biortech.2025.133642
Enzymatic hydrolysis of plant cell walls into fermentable sugars is a critical step in biotechnological conversion, yet efficiency is limited by cell wall recalcitrance. Predicting conversion yields of cell wall-derived sugars during hydrolysis is challenging due to the complex underlying mechanisms and the labor-intensive nature of conventional methods. This study introduces an innovative pipeline that accurately quantifies cell wall autofluorescence intensity and morphological descriptors during enzymatic hydrolysis. The pipeline incorporates a novel adaptive drift compensation strategy that dynamically adjusts to the progression and extent of deconstruction, ensuring robust analysis. Applied to time-lapse images of spruce wood enzymatic deconstruction, the pipeline revealed strong negative correlations between conversion yields during hydrolysis and both the dynamics of cell wall autofluorescence intensity and morphological descriptors. Phase-specific analysis uncovered distinct correlation patterns dependent on hydrolysis stage and sugar type. This nondestructive pipeline eliminates the need for extensive sampling and time-consuming chemical assays, establishing plant cell wall autofluorescence and morphological descriptors as accurate predictive real-time biomarkers of dynamics of sugar conversion yields. The findings provide a framework for accelerating the development of optimized biotechnological conversion processes.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.biortech.2025.133642
- OA Status
- hybrid
- References
- 43
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W7105806582Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.biortech.2025.133642Digital Object Identifier
- Title
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Plant cell wall enzymatic hydrolysis: Predicting yield dynamics from autofluorescence and morphological temporal changesWork title
- Type
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
- Publication date
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2025-11-16Full publication date if available
- Authors
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Solmaz Hossein Khani, Khadidja Ould Amer, Fatemeh Shiasi Ghalemaleki, Maxime Corre, Noah Remy, Anouck Habrant, Berangère Lebas, Jafar Massah, Ali Faraj, Gabriel Paës, Yassin RefahiList of authors in order
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https://doi.org/10.1016/j.biortech.2025.133642Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.biortech.2025.133642Direct OA link when available
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Autofluorescence, Cell wall, Hydrolysis, Chemistry, Pipeline (software), Sugar, Biological system, Enzymatic hydrolysis, Yield (engineering), Biochemistry, Biophysics, Lysis, Enzyme, Cell, Dynamics (music), Plant cell, Intensity (physics), Light intensityTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.This | 48, 134 |
| abstract_inverted_index.both | 110 |
| abstract_inverted_index.cell | 4, 21, 28, 57, 114, 149 |
| abstract_inverted_index.into | 6 |
| abstract_inverted_index.need | 139 |
| abstract_inverted_index.step | 12 |
| abstract_inverted_index.that | 54, 76 |
| abstract_inverted_index.wall | 22, 58, 115, 150 |
| abstract_inverted_index.wood | 95 |
| abstract_inverted_index.drift | 73 |
| abstract_inverted_index.novel | 71 |
| abstract_inverted_index.plant | 3, 148 |
| abstract_inverted_index.stage | 130 |
| abstract_inverted_index.study | 49 |
| abstract_inverted_index.sugar | 132, 163 |
| abstract_inverted_index.type. | 133 |
| abstract_inverted_index.walls | 5 |
| abstract_inverted_index.during | 31, 64, 107 |
| abstract_inverted_index.extent | 83 |
| abstract_inverted_index.images | 92 |
| abstract_inverted_index.nature | 44 |
| abstract_inverted_index.robust | 87 |
| abstract_inverted_index.spruce | 94 |
| abstract_inverted_index.strong | 101 |
| abstract_inverted_index.sugars | 8, 30 |
| abstract_inverted_index.yields | 26, 106 |
| abstract_inverted_index.Applied | 89 |
| abstract_inverted_index.adjusts | 78 |
| abstract_inverted_index.assays, | 146 |
| abstract_inverted_index.between | 104 |
| abstract_inverted_index.complex | 38 |
| abstract_inverted_index.limited | 19 |
| abstract_inverted_index.provide | 168 |
| abstract_inverted_index.yields. | 165 |
| abstract_inverted_index.accurate | 156 |
| abstract_inverted_index.adaptive | 72 |
| abstract_inverted_index.analysis | 122 |
| abstract_inverted_index.chemical | 145 |
| abstract_inverted_index.critical | 11 |
| abstract_inverted_index.distinct | 124 |
| abstract_inverted_index.dynamics | 112, 161 |
| abstract_inverted_index.ensuring | 86 |
| abstract_inverted_index.findings | 167 |
| abstract_inverted_index.methods. | 47 |
| abstract_inverted_index.negative | 102 |
| abstract_inverted_index.patterns | 126 |
| abstract_inverted_index.pipeline | 53, 68, 99, 136 |
| abstract_inverted_index.revealed | 100 |
| abstract_inverted_index.sampling | 142 |
| abstract_inverted_index.strategy | 75 |
| abstract_inverted_index.Enzymatic | 0 |
| abstract_inverted_index.analysis. | 88 |
| abstract_inverted_index.dependent | 127 |
| abstract_inverted_index.enzymatic | 65, 96 |
| abstract_inverted_index.extensive | 141 |
| abstract_inverted_index.framework | 170 |
| abstract_inverted_index.intensity | 60, 117 |
| abstract_inverted_index.optimized | 176 |
| abstract_inverted_index.real-time | 158 |
| abstract_inverted_index.uncovered | 123 |
| abstract_inverted_index.Predicting | 24 |
| abstract_inverted_index.accurately | 55 |
| abstract_inverted_index.biomarkers | 159 |
| abstract_inverted_index.conversion | 25, 105, 164, 178 |
| abstract_inverted_index.efficiency | 17 |
| abstract_inverted_index.eliminates | 137 |
| abstract_inverted_index.hydrolysis | 1, 32, 108, 129 |
| abstract_inverted_index.innovative | 52 |
| abstract_inverted_index.introduces | 50 |
| abstract_inverted_index.mechanisms | 40 |
| abstract_inverted_index.predictive | 157 |
| abstract_inverted_index.processes. | 179 |
| abstract_inverted_index.quantifies | 56 |
| abstract_inverted_index.time-lapse | 91 |
| abstract_inverted_index.underlying | 39 |
| abstract_inverted_index.challenging | 34 |
| abstract_inverted_index.conversion, | 15 |
| abstract_inverted_index.correlation | 125 |
| abstract_inverted_index.descriptors | 63, 154 |
| abstract_inverted_index.development | 174 |
| abstract_inverted_index.dynamically | 77 |
| abstract_inverted_index.fermentable | 7 |
| abstract_inverted_index.hydrolysis. | 66 |
| abstract_inverted_index.progression | 81 |
| abstract_inverted_index.accelerating | 172 |
| abstract_inverted_index.compensation | 74 |
| abstract_inverted_index.conventional | 46 |
| abstract_inverted_index.correlations | 103 |
| abstract_inverted_index.descriptors. | 120 |
| abstract_inverted_index.establishing | 147 |
| abstract_inverted_index.incorporates | 69 |
| abstract_inverted_index.wall-derived | 29 |
| abstract_inverted_index.morphological | 62, 119, 153 |
| abstract_inverted_index.Phase-specific | 121 |
| abstract_inverted_index.nondestructive | 135 |
| abstract_inverted_index.recalcitrance. | 23 |
| abstract_inverted_index.time-consuming | 144 |
| abstract_inverted_index.deconstruction, | 85, 97 |
| abstract_inverted_index.labor-intensive | 43 |
| abstract_inverted_index.autofluorescence | 59, 116, 151 |
| abstract_inverted_index.biotechnological | 14, 177 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile.value | 0.85851309 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |