ReviewSensors (Basel, Switzerland)2022
A Review of Machine Learning for Near-Infrared Spectroscopy.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
53 citing papers in PubMed, 201 citations in OpenAlex.
- Rapid identification of feline sporotrichosis by ATR-FTIR spectroscopy coupled with machine learning.Analytical and bioanalytical chemistry · 2026Article
- Interpretable Multi-Feature Optical Analysis for Stage- and Batch-Aware Quality Assessment of Brain-Organoid Cultures.Sensors (Basel, Switzerland) · 2026Article
- The neural and physiological mechanisms of Animal-Assisted Interventions (AAI): A systematic review and integrated framework.Comprehensive psychoneuroendocrinology · 2026Review
- Validated Near-Infrared Spectroscopy and Chemometric Modelling for Rapid Quantification of Essential Oil Yield and α/β-Santalol inGlobal challenges (Hoboken, NJ) · 2026Article
- Non-Invasive Technologies in Wearable Glucose Monitoring: A Structured Overview for the Future.Biosensors · 2026Review
- Vibrational Spectroscopy Predicts Antimicrobial Activity of Orange Peels: A Case Study on Batch-to-Batch Variation in Food By-Product Valorization.Journal of food science · 2026Article
- Infrared Spectra Prediction for a Carbonyl Group Utilizing a Graph Network Approach.Precision chemistry · 2026Article
- A comparative ML approach to classify Lupinus species using VIS-NIR spectral data from whole seeds and various data transformation techniques and resampling methods.Scientific reports · 2026Article
- Rapid Determination of Soybean Protein Content by Near-Infrared Spectroscopy Coupled with Multi-Learner Ensemble Wavelength Selection.Foods (Basel, Switzerland) · 2026Article
- Machine learning enhanced optical spectroscopy for breast cancer diagnosis: A review.Lasers in medical science · 2026Review
- Hybrid near-infrared and chemical-based machine learning enhances the reliability of chili powder origin classification.Scientific reports · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Optical imaging and spectroscopic characterization of subvisible particles in protein therapeutics.Advanced drug delivery reviews · 2026Review
- Non-Destructive Assessment of Beef Freshness Using Visible and Near-Infrared Spectroscopy with Interpretable Machine Learning.Foods (Basel, Switzerland) · 2026Article
- Trends in Vibrational Spectroscopy: NIRS and Raman Techniques for Health and Food Safety Control.Sensors (Basel, Switzerland) · 2026Review
- Non-Destructive Geographical Traceability and Quality Control ofFoods (Basel, Switzerland) · 2026Article
- Phenotypic feature-based identification of tea geographical origin using lightweight deep learning.NPJ science of food · 2026Article
- Enabling rapid and accurate grand discrimination of flue-cured tobacco: a near-infrared hyperspectral and machine learning approach.Frontiers in plant science · 2026Article
- The Role of Artificial Intelligence in Modern Analytical Chemistry: Current Trends and Future Directions.International journal of analytical chemistry · 2026Review
- From optical architectures to actual deployment a review of online process spectroscopy in industrial environments.Frontiers in chemistry · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.