## 5 Innovative Approaches for Enzyme Immobilization
### AI-Based Enzyme Immobilization  
Artificial intelligence (AI) and machine learning (ML) have been applied in pattern recognition, chemical process optimization, and the predictive modeling of unmeasured parameters [243]. Machine learning (ML), a subset of AI, enhances performance models by analyzing sample data and optimizing biocatalytic processes, predicting reaction conditions and designing enzyme variants [244]. The spatial and chemical distribution of immobilized enzymes is critical for performance, activity, and stability [245]. Imaging techniques such as atomic force microscopy, near-infrared fluorescence imaging, and mass spectrometry provide structural insights, but they also exhibit limitations. Near-infrared imaging requires labeling, which may alter the results, while mass spectroscopy can damage the samples [246]. ML aids in analyzing complex spectroscopic data, such as that obtained from label-free Raman microscopy, which captures vibrational data regarding enzymes and their supports. Raman hyperspectral imaging allows simultaneous chemical and spatial mapping, while unsupervised ML methods like non-negative matrix factorization (NMF) can resolve mixed chemical species [246; 247]. Current techniques often fail to accurately assess the chemical and spatial distribution of immobilized enzymes, focusing only on surface visibility, stability, or activity. New analytical tools are needed, such as combining Raman hyperspectral imaging with principal component analysis (PCA) to study enzyme immobilization [248]. This approach enables the spatial and spectral resolution of enzymes on porous resins, distinguishing between enzyme, resin, and related chemical species, offering a novel approach to study immobilized biocatalysts [248]. Advances in proteomics, metabolomics, and genomics are accelerating ML integration into enzyme design. ML algorithms predict enzyme behavior, optimize enzyme immobilization conditions, and identify factors influencing enzymatic activity, aiding rational enzyme design [249; 250]. For instance, lipase immobilization in microfluidic devices enhances stability, recyclability, and biodiesel production efficiency [250]. ML has also been used to predict enzyme MOF properties, such as reusability, activity retention, and enzyme loading. However, the diversity of MOFs and enzymes pose challenges. Random forest models improve predictions for immobilization yield and enzyme loading, although activity retention remains difficult to predict [251]. Raman hyperspectral imaging, combined with advanced analysis techniques like partial least squares discriminant analysis (PLS-DA) and NMF, has been used to identify variations in enzyme immobilization systems, with high accuracy in distinguishing samples [252; 253]. ML uses sophisticated statistical techniques to refine models based on sample data; supervised, unsupervised, and reinforcement learning are among the common ML approaches [254]. Figure 8 explains how machine learning algorithms estimate the enzyme immobilization performance metrics of enzyme-nanocomposite biocatalysts. AI improves process control and automation by predicting unmeasured parameters, replacing hardware sensors, and enabling pattern recognition [243]. Supervised machine learning (ML) methods, such as artificial neural networks (ANN), help improve MOFs for adsorption and catalysis [255; 256]. ML also aids in screening high performing MOFs and predicting unknown properties. For example, [PERSON] and [PERSON] [257] applied Gaussian process regression to link MOF structures with methane uptake, creating a model to identify the best structures for methane storage. Recent progress involves automated ML tools like JAD Bio which analyze MOF structural features, such as linkers, metal clusters, and functional groups, alongside CO\({}_{2}\) and H\({}_{2}\) storage data [258]. These models predict storage capacities from structural traits, enabling efficient property estimation for new, untested MOFs. [PERSON] et al.