Spectroscopy and Chemometric Analyses

Latest papers

• Dimensionality reduction for AI based hyperspectral image classification based on XAI

  Vladimir Zeljkovi\'c, Branka Stojanovi\'c, Harald Ganster, Aleksandar Ne\v{s}kovi\'c · 22 September 2026

  This research addresses the challenge of limited material recycling in wood recycling processes by leveraging artificial intelligence (AI)-based dimensionality reduction. Our study explores the application of convolutional neural networks (CNNs) in multi-channel hyperspectral imaging (HSI), extendin…

• Towards Reasonable Molecular Structure Elucidation from Infrared Spectroscopy with Chemical Feedback

  Yusen Tan, Hongyu Zhan, Hai-tao Yu, Changxi Chi, Wenjie Du, Jun Xia · 18 August 2026

  Infrared (IR) spectra provide characteristic signals of molecular structure, which are often interpreted by experts via functional-group identification or library matching, making the process time-consuming and ambiguous. Recent machine learning methods have made progress in molecular structure eluc…

• Attributing Preprocessing Invariance in Spectral Foundation Models

  Dongjun Wei, Hongyi Wu, Yinuo Zou · 17 August 2026

  Preprocessing invariance is an appealing goal for spectral foundation models: a frozen model should remain useful when laboratories preprocess spectra differently. It is usually measured by training a classifier under one preprocessing pipeline and testing it under another, with preserved accuracy r…

• Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples

  Yusen Tan, Yixuan Chen, Zheng Fang, Pan Liu, Yifan Li, Qinyu Guo, Zhedong Lin, Yuqiang Li, Xiangxiang Zeng, Tong Wang, Jun Xia · 14 August 2026

  Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging. Conventional interpretation is labor-intensive, relies on prior knowledge and reference spectra, and is difficult to scale, whereas most machine-learning met…

• A Multispectral Framework for the Detection of Calcium Carbide-Induced Ripening and Shelf-Life Estimation in Climacteric Fruits

  Gurbhit Chaurakoti, Harshit Kumar, Hani Kumar, Anurag Singh, Ram Asrey · 14 August 2026

  Significant health risks are associated with the illegal, yet commonly practiced use of industrial-grade Calcium Carbide (CaC2) for ripening climacteric fruits like mango and banana, which leaves behind trace residues of arsenic and phosphorus. To address this, the proposed study explores a novel, n…

• Few-Shot Ordinal Learning for Day-Wise Freshness Estimation with Hyperspectral Fish Images

  Kazi Nabiul Alam, Pooneh Bagheri Zadeh, Akbar Sheikh-Akbari · 13 August 2026

  Non-destructive food quality assessment has increasingly benefited from hyperspectral imaging (HSI), which captures spectral signatures linked to biochemical changes during storage. Estimating day-wise freshness, however, remains challenging owing to strong inter-fillet variability and scarce labell…

• Domain-Aware Lightweight Spectral-Grouped Convolutions for Hyperspectral Fish Freshness Classification

  Kazi Nabiul Alam, Pooneh Bagheri Zadeh, Akbar Sheikh-Akbari · 13 August 2026

  Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands. However, conventional deep learning approaches do not fully address the particular characteristics of HSI data, such as spectral dominance over spatial textures,…

• Exploiting chemical shift variability enables recovery of overlapping metabolites from 1H nuclear magnetic resonance spectra

  Jesper L{\o}ve Hinrich, Pia Susan Mayer, Bekzod Khakimov, S{\o}ren Balling Engelsen, Morten M{\o}rup · 11 August 2026

  Overlapping peaks and sample-dependent chemical shift variability prevent reliable metabolite recovery from complex biological spectra. This problem is critical in one-dimensional proton (1D 1H) NMR which has become the standard method providing fast acquisition and information-rich spectra in metab…

• Non-Destructive Quantification of Urea Adulteration in Bovine Milk Using Transmittance Multispectral Imaging

  Sharukshan Niranjan, Iresha Ranaweera, Tharindu Chandrarathne, Kalana Dissanayaka, Roshan Godaliyadda, Vijitha Herath, Parakrama Ekanayake, Janak Vidanarachchi · 5 August 2026

  Adulteration of bovine milk using urea remains a major food quality and health concern, motivating the development of rapid and quantitative screening tools. Conventional approaches, including laboratory-based analytical methods and spectroscopic techniques, have been used for urea detection; howeve…

• Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction

  Ahmed Baha Ben Jmaa, Faten Chaieb, Anna Fabija\'nska · 4 August 2026

  Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that has attracted much attention, thanks to its wide-ranging advantages in agriculture field for both pre-harvest and post-harvest management. Accurate and timely FRP can be achieved using machine/deep learn…

• Using Data-Derived Priors to Guide CNN Architecture Design for NIR Chemometrics

  D\'ario Passos · 29 July 2026

  Convolutional neural networks (CNN) for near-infrared (NIR) chemometrics are often designed using generic architectural rules, although spectral datasets differ in sampling, smoothness, redundancy, and sample size. We tested whether these properties can provide empirical priors for CNN design. Acros…

• Improving Improved Kernel PLS

  Ole-Christian Galbo Engstr{\o}m · 20 July 2026

  Improved Kernel Partial Least Squares (IKPLS) algorithms 1 and 2 are among the fastest PLS calibration algorithms. This article focuses on two shared steps, the computation of the $\mathbf{X}$ rotations, $\mathbf{R}$, and the $\mathbf{Y}$ loadings, $\mathbf{Q}$, and accelerates both. For $\mathbf{R}…

• Reconstructing Randomly Masked Spectra Helps DNNs Identify Discriminant Wavenumbers

  Yingying Wu, Jinchao Liu, Yan Wang, Stuart Gibson, Margarita Osadchy, Yongchun Fang · 23 June 2026

  Nondestructive detection methods, based on vibrational spectroscopy, are vitally important in a wide range of applications including industrial chemistry, pharmacy and national defense. Recently, deep learning has been introduced into vibrational spectroscopy showing great potential. Different from …

• Spectral Analysis of Molecular Features: When Richer Features Do Not Guarantee Better Generalization

  Asma Jamali, Tin Sum Cheng, Rodrigo A. Vargas-Hern\'andez · 16 June 2026

  The spectral properties of feature embeddings offer critical insights into model generalization and representation quality. While deep learning models are widely used for molecular property prediction, kernel methods remain competitive in low-data regimes, yet their spectral behavior is largely unex…

• Non-destructive Identification of Oyster Species is possible from Hyperspectral Images with Machine Learning

  Ethan Kane Waters, Max Wingfield, Aiden Mellor, Paul Stewart, Iman Tahmasbian · 1 June 2026

  Differentiating between oyster species is important for developing new commercial oyster species suited to production systems and is critical for traceability in seafood supply chains. Common methods, such as DNA profiling, are destructive and time consuming. The possibility of using hyperspectral i…

• A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection

  Randolph W. Linderman (Electrical and Computer Engineering Department, Duke University, Durham, NC, USA), Noah Cowan (Statistics Department, Stanford University, Stanford, CA, USA), Yiran Chen (Electrical and Computer Engineering Department, Duke University, Durham, NC, USA), Scott W. Linderman (Statistics Department, Stanford University, Stanford, CA, USA, The Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA) · 28 May 2026

  Bayesian nonparametric methods are naturally suited to the problem of out-of-distribution (OOD) detection. However, these techniques have largely been eschewed in favor of simpler methods based on distances between pre-trained or learned embeddings of data points. Here we show a formal relationship …

• Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models

  Gregory Beurier, Robin Reiter, Camille No\^us, Lauriane Rouan, Denis Cornet · 14 May 2026

  Near-infrared spectroscopy (NIRS) is rapid and non-destructive, but reliable calibration still depends heavily on spectral preprocessing. In routine practice, preprocessing is often selected by large external pipeline searches that are costly, unstable on small calibration sets, and difficult to aud…

• Machine Learning Enhanced Laser Spectroscopy for Multi-Species Gas Detection in Complex and Harsh Environments

  Mohamed Sy · 5 May 2026

  Laser absorption spectroscopy (LAS) is a well-established technique for non-intrusive measurement of gas species in combustion and atmospheric environments, but conventional methods struggle with multi-species mixtures under dynamic or interference-laden conditions. Overlapping spectral features, no…

• CNNs for Vis-NIR Chemometrics: From Contradiction to Conditional Design

  D\'ario Passos · 5 May 2026

  Near-infrared (NIR; a.k.a.\ NIRS) deep-learning studies in chemometrics increasingly report mutually inconsistent conclusions regarding convolutional neural network (CNN) design, including small versus large kernels, shallow versus deep architectures, raw spectra versus preprocessing, and single-dom…

• Non-Destructive Prediction of Fruit Ripeness and Firmness Using Hyperspectral Imaging and Lightweight Machine Learning Models

  Phongsakon Mark Konrad, Casper Kunstmann-Olsen, Jacek Fiutowski, Serkan Ayvaz · 28 April 2026

  Post-harvest fruit quality assessment is essential for reducing food waste, yet reliable non-destructive methods typically depend on expensive hyperspectral cameras and computationally intensive deep learning models. These systems typically require GPU resources, large-scale training data, and domai…

• Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review

  Leonardo Arrighi, Ingrid Alves de Moraes, Marco Zullich, Michele Simonato, Douglas Fernandes Barbin, Sylvio Barbon Junior · 28 April 2026

  Artificial Intelligence (AI) has become essential for analyzing complex data and solving highly-challenging tasks. It is being applied across numerous disciplines beyond computer science, including Food Engineering, where there is a growing demand for accurate and reliable predictions to meet string…

• Integrating Feature Selection and Machine Learning for Nitrogen Assessment in Grapevine Leaves using In-Field Hyperspectral Imaging

  Atif Bilal Asad, Achyut Paudel, Safal Kshetri, Chenchen Kang, Salik Ram Khanal, Nataliya Shcherbatyuk, Pierre Davadant, R. Paul Schreiner, Santosh Kalauni, Manoj Karkee, Markus Keller · 21 April 2026

  Nitrogen (N) is one of the most critical nutrients in winegrape production, influencing vine vigor, fruit composition, and wine quality. Because soil N availability varies spatially and temporally, accurate estimation of leaf N concentration is essential for optimizing fertilization at the individua…

• Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting

  Ebrahim Farahmand, Shovito Barua Soumma, Nooshin Taheri Chatrudi, Hassan Ghasemzadeh · 16 April 2026

  The availability of continuous glucose monitors as over-the-counter commodities have created a unique opportunity to monitor a person's blood glucose levels, forecast blood glucose trajectories and provide automated interventions to prevent devastating chronic complications that arise from poor gluc…

• MyoVision: A Mobile Research Tool and NEATBoost-Attention Ensemble Framework for Real Time Chicken Breast Myopathy Detection

  Chaitanya Pallerla, Siavash Mahmoudi, Dongyi Wang · 16 April 2026

  Woody Breast (WB) and Spaghetti Meat (SM) myopathies significantly impact poultry meat quality, yet current detection methods rely either on subjective manual evaluation or costly laboratory-grade imaging systems. We address the problem of low-cost, non-destructive multi-class myopathy classificatio…

• SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Data

  Mingxing Zhang, Nicola Rossberg, Simone Innocente, Katarzyna Komolibus, Rekha Gautam, Barry O'Sullivan, Luca Longo, Andrea Visentin · 20 March 2026

  In recent years, machine learning models have been increasingly applied to spectroscopic datasets for chemical and biomedical analysis. For their successful adoption, particularly in clinical and safety-critical settings, professionals and researchers must be able to understand and trust the reasoni…