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Open research questions in Spectroscopy and Chemometric Analyses

129 gap statements mined from Spectroscopy and Chemometric Analyses papers in our 4.5M-paper local library, which holds 1,570 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • Although standard RGB imaging can distinguish red/yellow marrow and muscle from other whitish tissues, it provides limited information for interclass discrimination for a number of categories. Although the reddish appearance of muscle and marrow allows them to be visually separated from the other tissue types, this colour-based distinction was insufficient to discriminate between muscle and marrow themselves.

    Advancing bovine tissue discrimination with Vis–NIR spectroscopy coupled with machine learning methods · 2026 · DOI

  • Limited comparisons between grain protein measurement methods. Need for improved accuracy and robustness in NIR based instruments for grain protein determination.

    Comparative Analysis of Wheat Grain Protein Content: Wet Chemistry and NIR Based Methods · 2026 · DOI

  • The use of PCR for predicting albumen quality remains limited. Most existing studies focus on different species or are based on relatively small sample sizes.

    Prediction of Albumen Index in Tinted Coral Chickens Eggs Using Principal Component Regression · 2026 · DOI

  • One challenge is the need for improvement in prediction accuracy and robustness. Another challenge is the integration of multiple modalities through multimodal frameworks. The paper also identifies the challenge of deploying portable, real-time sensing systems for in-field applications.

    Comparative Evaluation of Hyperspectral, Thermal, and Near-Infrared Imaging Systems for Non-Destructive Prediction of Fruit and Vegetable Quality: A Review · 2026 · DOI

  • The performance of miniaturised sensors for biologically meaningful markers remains underexplored. There is a need for a robust, cost-effective pathway for on-farm monitoring of milk components.

    Miniaturised near-infrared spectroscopy for non-invasive assessment of de novo fatty acid concentrations in milk from individual cows · 2026 · DOI

  • The traditional methods for food quality determination have limitations in terms of efficiency, non-destructiveness, and accuracy. There is a need for rapid, non-destructive, and accurate methods for food quality determination.

    Spectroscopic Methods Applied in Food Quality Determination · 2026 · DOI

  • Existing methods for sugarcane extraneous matter prediction have limitations in terms of accuracy and computational efficiency. The application of functional regression to sugarcane extraneous matter prediction is limited.

    Improving Sugarcane Extraneous Matter Prediction Using Transformed Near-Infrared Spectral Data and Functional Regression · 2026 · DOI

  • The paper identifies a need for a comprehensive guide for using ASCA in experimental science. The tutorial review aims to fill this gap by recommending best practices for using ASCA, building upon the long-established combination of ANOVA and DoE theory.

    From Design of Experiments to Analysis of Variance of Multivariate Data: A Tutorial Review on ANOVA Simultaneous Component Analysis · 2026 · DOI

  • The dataset used is limited in size. The model is evaluated using simulation analysis only. The paper does not discuss the potential limitations of the proposed model.

    Effective Neural Network Model with Multispectral Imaging for Food Contamination Detection · 2026 · DOI

  • Food industry regulators have not yet established clear guidelines for approving AI-driven monitoring systems, creating uncertainty in compliance.

    Multimodal AI for Real‐Time Food Safety and Quality: From Sensors to Foundation Models, Edge Deployment, and Regulation · 2026 · DOI

  • Future work should focus on external validation using multi-location and multi-season datasets, improving computational efficiency for embedded implementation, and exploring hybrid deep learning–fuzzy ensemble architectures to enhance robustness, scalability, and field applicability.

    Metaheuristic Optimized Fuzzy Ensemble for Maize Seed Quality Prediction Using Vis/NIR Spectroscopy · 2026 · DOI

  • There is a need for a non-destructive, low-cost, and efficient device for predicting corn moisture content. The current methods for determining harvest time are not accurate.

    Comparative Study of Spectrometer Sensors for Corn Moisture Content Prediction and Machine Learning Models · 2026 · DOI

  • A greater number of samples are needed to confirm feasibility and robustness for FP models. Specifically, the predictive capacity of the current PLS model is constrained by two primary limitations: narrow sample composition distribution and the nature of colligative properties in complex matrices.

    Predicting Freezing Point of Ice Cream Mix with Infrared Spectroscopy and Chemometrics · 2026 · DOI

  • Meanwhile, future work will focus on lightweight backbone design, feature dimensionality reduction, and more efficient hypergraph construction to further reduce computational costs and improve the applicability of SSD-HGNN in resource-constrained and real-time quality-control scenarios. In addition, spectral feature transformation methods and multimodal spectral representations should be further explored.

    Spectral-Structural Decoupled Hypergraph Neural Network for Fritillaria Species Identification Using Hyperspectral Imaging · 2026 · DOI

  • The quality assessment of supramolecular complexes remains challenging. Techniques such as scanning electron microscopy and HPLC are typically cumbersome and time-consuming.

    Rapid evaluation of the comprehensive quality of paeonol/cyclodextrin supramolecular complexes using CASSA based on near-infrared spectroscopy combined with artificial intelligence · 2026 · DOI

  • Existing methods may not effectively reduce dimensionality while preserving predictive performance.

    Reconstruction-Assisted Band Selection for Non-Destructive Prediction of Citrus Soluble Solids Content from VNIR Hyperspectral Images · 2026 · DOI

  • Future developments are expected to focus on multimodal data fusion frameworks. Deep learning–based feature extraction and transfer learning–enabled model generalization are proposed for improving prediction accuracy and robustness.

    Comparative Evaluation of Hyperspectral, Thermal, and Near-Infrared Imaging Systems for Non-Destructive Prediction of Fruit and Vegetable Quality: A Review · 2026 · DOI

  • Intelligent biosensors are innovative devices that combine biosensing technologies with digital systems and other advanced functionalities. Artificial intelligence and machine learning will enable predictive modeling for non-thermal processes, analyzing sensor data from parameters like ROS/RNS ratios in CP or pressure profiles in HPP to dynamically adjust treatments and minimize quality losses.

    Advances in non-thermal processing techniques and sensor-integration: real-time monitoring and control for food quality and safety · 2026 · DOI

  • Expansion of the system to include more types of products and additives. Integration of the system with other technologies, such as mobile apps or websites.

    Method for searching and analyzing e-additives and other components in food products of the population · 2026 · DOI

  • To explore the use of other machine learning algorithms and techniques for food contamination detection.

    Effective Neural Network Model with Multispectral Imaging for Food Contamination Detection · 2026 · DOI

  • More samples, especially more high-lignin references, would likely raise the scores. The study's results can be improved by using a larger and more independent dataset.

    Machine-learning prediction of biomass chemical composition using derivative thermogravimetric data of different biomass feedstocks · 2026 · DOI

  • Further comparison of grain protein measurement methods. Development of more accurate and robust NIR based instruments for grain protein determination. Evaluation of the applicability of NIR based methods for different types of wheat and growing conditions.

    Comparative Analysis of Wheat Grain Protein Content: Wet Chemistry and NIR Based Methods · 2026 · DOI

  • Existing analytical methods encounter difficulties due to excessive time required to analyze complicated data - Need for improved operational efficiency and analytical precision in pharmaceutical…

    ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN PHARMACEUTICAL ANALYSIS: RECENT ADVANCES IN METHOD DEVELOPMENT AND DATA INTERPRETATION · 2026 · DOI

  • Future compact citrus-quality sensing systems may utilize the selected bands, - Further research could investigate the application of this framework to other fruit types or quality…

    Reconstruction-Assisted Band Selection for Non-Destructive Prediction of Citrus Soluble Solids Content from VNIR Hyperspectral Images · 2026 · DOI

  • Extensions using B-splines with non-equispaced knots to emphasize local spectral features. Further comparison of the proposed method to other machine learning models.

    Improving Sugarcane Extraneous Matter Prediction Using Transformed Near-Infrared Spectral Data and Functional Regression · 2026 · DOI

Most-cited papers in Spectroscopy and Chemometric Analyses

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Open questions raised by papers listed on this page include: (1) The use of PCR for predicting albumen quality remains limited (“Prediction of Albumen Index in Tinted Coral Chickens Eggs Using Principal…”, 2026); (2) The performance of miniaturised sensors for biologically meaningful markers remains underexplored (“Miniaturised near-infrared spectroscopy for non-invasive assessment of de novo…”, 2026); (3) There is a need for rapid, non-destructive, and accurate methods for food quality determination (“Spectroscopic Methods Applied in Food Quality Determination”, 2026); (4) Food industry regulators have not yet established clear guidelines for approving AI-driven monitoring systems, creating uncertainty in compliance (“Multimodal AI for Real‐Time Food Safety and Quality: From Sensors to Foundation…”, 2026); (5) There is a need for a non-destructive, low-cost, and efficient device for predicting corn moisture content (“Comparative Study of Spectrometer Sensors for Corn Moisture Content Prediction…”, 2026). 129 gap statements have been mined from Spectroscopy and Chemometric Analyses papers in our 4.5M-paper local library, which holds 1,570 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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