{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T13:04:33Z","timestamp":1789650273913,"version":"4.0.0"},"reference-count":230,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Development Plan Project of Henan Province","award":["252103810384"],"award-info":[{"award-number":["252103810384"]}]},{"name":"Project of Innovation and Entrepreneurship Training for College Students in Henan Province","award":["202610478013"],"award-info":[{"award-number":["202610478013"]}]},{"name":"Natural Science Foundation of Zhenjiang City","award":["JC2025003"],"award-info":[{"award-number":["JC2025003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Foods"],"abstract":"<jats:p>Food quality and safety have become increasingly critical public health concerns, while traditional detection approaches are constrained by laborious sample preparation, lengthy analysis times, and destructive procedures. Hyperspectral imaging (HSI) has emerged as a rapid, non-destructive method that synergistically fuses spatial and spectral information for comprehensive food quality and safety assessment. However, the high dimensionality and nonlinear features of HSI data pose substantial challenges for traditional chemometric models. This review provides a comprehensive overview of recent advances in machine learning (ML) and deep learning (DL) algorithm-assisted HSI food quality and safety detection. We first introduce the fundamentals of HSI technology and its data processing workflow. We then present a comprehensive and non-mathematical introduction to typical ML and DL algorithms, highlighting their respective strengths and trade-offs. Then, we summarize typical applications across various areas, such as fruit and vegetable quality assessment, meat quality evaluation, egg and tea quality detection, moisture content quantification, variety and origin identification, adulteration and additive detection, heavy metal contamination assessment, mold detection, and plant growth monitoring. Finally, we discuss current challenges, such as data bottlenecks, limited model interpretability and generalizability, and barriers to practical applications, and outline future directions toward intelligent, systematic, and practical HSI systems for food quality and safety detection.<\/jats:p>","DOI":"10.3390\/foods15183287","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T11:41:35Z","timestamp":1789645295000},"page":"3287","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advances in Machine Learning and Deep Learning Algorithm-Assisted Hyperspectral Imaging for Food Quality and Safety Assessment"],"prefix":"10.3390","volume":"15","author":[{"given":"Lingwei","family":"Hu","sequence":"first","affiliation":[{"name":"College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou 466001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou 466001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhong","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou 466001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5352-8189","authenticated-orcid":false,"given":"Mingming","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Microelectronics, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jitao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Physics and Telecommunications Engineering, Zhoukou Normal University, Zhoukou 466001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2026, July 22). Unsafe Food Causes 866 Million Illnesses and 1.5 Million Deaths Annually, Young Children at Highest Risk. Available online: https:\/\/www.who.int\/news\/item\/04-06-2026-unsafe-food-causes-866-million-illnesses-and-1.5-million-deaths-annually--young-children-at-highest-risk."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100184","DOI":"10.1016\/j.jfp.2023.100184","article-title":"A Review of Food Contaminants and Their Pathways Within Food Processing Facilities Using Open Food Processing Equipment","volume":"86","author":"Pakdel","year":"2023","journal-title":"J. Food Prot."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"143757","DOI":"10.1016\/j.foodchem.2025.143757","article-title":"New biological strategies for preventing and controlling food contaminants in the supply chain: Smart use of common plant-derived substances","volume":"479","author":"Liu","year":"2025","journal-title":"Food Chem."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105214","DOI":"10.1016\/j.tifs.2025.105214","article-title":"Contaminant prevention framework of the global food supply chain: Food security solutions for zero hunger","volume":"164","author":"Krisnanmoorthy","year":"2025","journal-title":"Trends Food Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"101325","DOI":"10.1016\/j.foohum.2026.101325","article-title":"Food fraud in global supply chains: Challenges, frameworks and data-driven solutions","volume":"7","author":"Maharaj","year":"2026","journal-title":"Food Humanit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.foodchem.2015.06.086","article-title":"Separation, concentration and determination of trace chloramphenicol in shrimp from different waters by using polyoxyethylene lauryl ether-salt aqueous two-phase system coupled with high-performance liquid chromatography","volume":"192","author":"Lu","year":"2016","journal-title":"Food Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s00217-021-03862-3","article-title":"High-performance liquid chromatography for the sensitive zearalenone determination by the automated immunomagnetic beads purifier for one-step sample pre-treatment","volume":"248","author":"Zhang","year":"2022","journal-title":"Eur. Food Res. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Boateng, I.D., Li, F., and Yang, X.-M. (2024). Development, Validation, and Application of High-Performance Liquid Chromatography with Diode-Array Detection Method for Simultaneous Determination of Ginkgolic Acids and Ginkgols in Ginkgo biloba. Foods, 13.","DOI":"10.3390\/foods13081250"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e12557","DOI":"10.1111\/jfs.12557","article-title":"Analysis of fish spoilage by gas chromatography\u2014Mass spectrometry and electronic olfaction bionic system","volume":"38","author":"Lv","year":"2018","journal-title":"J. Food Saf."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jayan, H., Zhou, R., Sun, C., Wang, C., Yin, L., Zou, X., and Guo, Z. (2025). Intelligent Gas Sensors for Food Safety and Quality Monitoring: Advances, Applications, and Future Directions. Foods, 14.","DOI":"10.3390\/foods14152706"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jfutfo.2023.05.010","article-title":"Establishment of enzyme-linked immunosorbent assay for beef and lamb contents in cooked meat","volume":"4","author":"Li","year":"2024","journal-title":"J. Future Foods"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"132937","DOI":"10.1016\/j.foodchem.2022.132937","article-title":"Effect-directed analysis in food by thin-layer chromatography assays","volume":"390","author":"Cabezudo","year":"2022","journal-title":"Food Chem."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"131443","DOI":"10.1016\/j.foodchem.2021.131443","article-title":"Label-free ratiometric homogeneous electrochemical aptasensor based on hybridization chain reaction for facile and rapid detection of aflatoxin B1 in cereal crops","volume":"373","author":"Zhu","year":"2022","journal-title":"Food Chem."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1007\/s12161-022-02419-8","article-title":"A Sensitive Nucleic Acid Detection Platform for Foodborne Pathogens Based on CRISPR-Cas13a System Combined with Polymerase Chain Reaction","volume":"16","author":"Liu","year":"2023","journal-title":"Food Anal. Methods"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108355","DOI":"10.1016\/j.jfca.2025.108355","article-title":"Mass spectrometry imaging: Applications and advances in analyzing bioactive components in foods","volume":"148","author":"Zhao","year":"2025","journal-title":"J. Food Compos. Anal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107350","DOI":"10.1016\/j.jfca.2025.107350","article-title":"Deep eutectic solvent-modified multi-walled carbon nanotubes as QuEChERS adsorbents coupled with gas chromatography-mass spectrometry for the determination of pesticide residues in Lycium barbarum","volume":"141","author":"Liu","year":"2025","journal-title":"J. Food Compos. Anal."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, H., Li, C., Shoaib, M., Zhang, W., and Murugesan, A. (2025). Advances in Non-Thermal Processing of Meat and Monitoring Meat Protein Gels Through Vibrational Spectroscopy. Foods, 14.","DOI":"10.3390\/foods14111929"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"25756","DOI":"10.1021\/acs.jafc.5c06839","article-title":"Advances in Molecular Vibrational Spectroscopy for Foodborne Pathogen Detection","volume":"73","author":"Zhou","year":"2025","journal-title":"J. Agric. Food Chem."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, L., Wu, Y., Yang, N., and Sun, Z. (2025). Advances in Hyperspectral and Diffraction Imaging for Agricultural Applications. Agriculture, 15.","DOI":"10.3390\/agriculture15161775"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"10019","DOI":"10.1021\/acs.jafc.4c11492","article-title":"Hyperspectral Imaging and Deep Learning for Quality and Safety Inspection of Fruits and Vegetables: A Review","volume":"73","author":"Yang","year":"2025","journal-title":"J. Agric. Food Chem."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liang, Y., Li, Z., Shi, J., Zhang, N., Qin, Z., Du, L., Zhai, X., Shen, T., Zhang, R., and Zou, X. (2025). Advances in Hyperspectral Imaging Technology for Grain Quality and Safety Detection: A Review. Foods, 14.","DOI":"10.3390\/foods14172977"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xing, F., and Chen, M. (2026). Advances in Hyperspectral Imaging for Nondestructive Food Quality and Safety Detection. Foods, 15.","DOI":"10.3390\/foods15101631"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112788","DOI":"10.1016\/j.postharvbio.2024.112788","article-title":"Developing universal classification models for the detection of early decayed citrus by structured-illumination reflectance imaging coupling with deep learning methods","volume":"210","author":"Cai","year":"2024","journal-title":"Postharvest Biol. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"114044","DOI":"10.1016\/j.postharvbio.2025.114044","article-title":"Detection of early decay in dekopon fruit based on structured-illumination reflectance imaging combined with a comparison between traditional machine learning and deep learning models","volume":"233","author":"Luo","year":"2026","journal-title":"Postharvest Biol. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"135431","DOI":"10.1016\/j.foodchem.2023.135431","article-title":"Nondestructive discrimination of analogous density foreign matter inside soy protein meat semi-finished products based on transmission hyperspectral imaging","volume":"411","author":"Shi","year":"2023","journal-title":"Food Chem."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"143239","DOI":"10.1016\/j.foodchem.2025.143239","article-title":"Determination of the SSC in oranges using Vis-NIR full transmittance hyperspectral imaging and spectral visual coding: A practical solution to the scattering problem of inhomogeneous mixtures","volume":"474","author":"Cai","year":"2025","journal-title":"Food Chem."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e15241","DOI":"10.1111\/jfpp.15241","article-title":"Classification of tea varieties based on fluorescence hyperspectral image technology and ABC-SVM algorithm","volume":"45","author":"Ahmad","year":"2021","journal-title":"J. Food Process. Preserv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"144055","DOI":"10.1016\/j.foodchem.2025.144055","article-title":"Fluorescence hyperspectral imaging for detection of selenium content in lettuce leaves under cadmium-free and cadmium environments","volume":"481","author":"Shi","year":"2025","journal-title":"Food Chem."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"113453","DOI":"10.1016\/j.postharvbio.2025.113453","article-title":"Hyperspectral imaging with multivariate analysis for detection of exterior flaws for quality evaluation of apples and pears","volume":"223","author":"Akter","year":"2025","journal-title":"Postharvest Biol. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106321","DOI":"10.1016\/j.infrared.2025.106321","article-title":"Simultaneous detection of surface defects and prediction of internal SSC of kumquats based on hyperspectral imaging technology","volume":"153","author":"Li","year":"2026","journal-title":"Infrared Phys. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6039","DOI":"10.1007\/s11694-023-02044-x","article-title":"Wavelength and texture feature selection for hyperspectral imaging: A systematic literature review","volume":"17","author":"Rogers","year":"2023","journal-title":"J. Food Meas. Charact."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cherifi, M., Mesloub, A., Korso, M.N.E., Touhami, T., and Gharbi, A.H. (2024, January 21\u201322). Dimensionality Reduction for Hyperspectral Image Classification. Proceedings of the 2024 8th International Conference on Image and Signal Processing and Their Applications (ISPA), Biskra, Algeria.","DOI":"10.1109\/ISPA59904.2024.10536775"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103804","DOI":"10.1016\/j.vibspec.2025.103804","article-title":"A variable selection method based on multicollinearity reduction for food origin traceability identification","volume":"138","author":"Tian","year":"2025","journal-title":"Vib. Spectrosc."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ayikpa, K.J., Mamadou, D., Ballo, A.B., and Gouton, P. (2025). Intelligent Selection of Spectral Bands from High-Precision Spectroradiometer Measurements for Optimizing Cocoa Bean Classification. Electronics, 14.","DOI":"10.3390\/electronics14101976"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"148909","DOI":"10.1016\/j.foodchem.2026.148909","article-title":"Hybrid wavelength selection technique and spectral binning for wheat protein estimation using hyperspectral imaging","volume":"512","author":"Sharma","year":"2026","journal-title":"Food Chem."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111782","DOI":"10.1016\/j.foodcont.2025.111782","article-title":"Improving SSC detection accuracy of hanging-transported strawberries through different correction methods and 1D-CNN","volume":"181","author":"Wang","year":"2026","journal-title":"Food Control"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"147323","DOI":"10.1016\/j.foodchem.2025.147323","article-title":"Nondestructive assessment of refrigerated quality attributes of kiwifruit via hyperspectral imaging coupled with a point-to-interval prediction model","volume":"499","author":"Cong","year":"2026","journal-title":"Food Chem."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e14304","DOI":"10.1111\/jfpe.14304","article-title":"Detection of starch in minced chicken meat based on hyperspectral imaging technique and transfer learning","volume":"46","author":"Yang","year":"2023","journal-title":"J. Food Process Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105713","DOI":"10.1016\/j.jfca.2023.105713","article-title":"Nondestructive identification of soybean protein in minced chicken meat based on hyperspectral imaging and VGG16-SVM","volume":"125","author":"Sun","year":"2024","journal-title":"J. Food Compos. Anal."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.jfoodeng.2015.08.003","article-title":"Nondestructively sensing of total viable count (TVC) in chicken using an artificial olfaction system based colorimetric sensor array","volume":"168","author":"Chen","year":"2016","journal-title":"J. Food Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1007\/s10586-026-06045-5","article-title":"Deep learning approaches in computer vision: A comprehensive survey","volume":"29","author":"Reshadi","year":"2026","journal-title":"Clust. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"144","DOI":"10.54254\/2755-2721\/77\/20240674","article-title":"Evolution and advancements in deep learning models for Natural Language Processing","volume":"77","author":"Chai","year":"2024","journal-title":"Appl. Comput. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"104117","DOI":"10.1016\/j.aei.2025.104117","article-title":"A review of multi-modal deep learning towards agentic smart manufacturing","volume":"70","author":"Leng","year":"2026","journal-title":"Adv. Eng. Inform."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e40836","DOI":"10.1016\/j.heliyon.2024.e40836","article-title":"Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability","volume":"10","author":"Jabed","year":"2024","journal-title":"Heliyon"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"110401","DOI":"10.1016\/j.engappai.2025.110401","article-title":"Systematic review on machine learning and computer vision in precision agriculture: Applications, trends, and emerging techniques","volume":"148","author":"Kuan","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_46","first-page":"75","article-title":"Fusion of the deep networks for rapid detection of branch-infected aeroponically cultivated mulberries using multimodal traits","volume":"18","author":"Elsherbiny","year":"2025","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wang, Y., Zhou, Z., Zhang, Y., and Wang, X. (2023). Detection Method for Tomato Leaf Mildew Based on Hyperspectral Fusion Terahertz Technology. Foods, 12.","DOI":"10.3390\/foods12030535"},{"key":"ref_48","first-page":"202","article-title":"Multi-scale monitoring for hazard level classification of brown planthopper damage in rice using hyperspectral technique","volume":"17","author":"Liao","year":"2024","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e13584","DOI":"10.1111\/jfpe.13584","article-title":"Identification of tea white star disease and anthrax based on hyperspectral image information","volume":"44","author":"Lu","year":"2021","journal-title":"J. Food Process Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.biosystemseng.2021.11.001","article-title":"Simultaneous and nondestructive diagnostics of nitrogen\/magnesium\/potassium-deficient cucumber leaf based on chlorophyll density distribution features","volume":"212","author":"Shi","year":"2021","journal-title":"Biosyst. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Fernandes, F.A.N., and Rodrigues, S. (2026). The Digital Transformation of Food Systems: A Review of Artificial Intelligence in Food Technology. Processes, 14.","DOI":"10.3390\/pr14111789"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Varela, L., Putnik, G.D., Ferreira, L., Kumar Manupati, V., Pinheiro, P., Alves, C., Avila, P., and Castro, H. (2026). A Review of Applied Artificial Intelligence in Manufacturing: Emergent AI Models in Cyber\u2013Physical Systems for Manufacturing. Future Internet, 18.","DOI":"10.3390\/fi18050253"},{"key":"ref_53","first-page":"1954","article-title":"Deep learning based agricultural remote sensing image segmentation: A review","volume":"57","author":"Ren","year":"2026","journal-title":"J. Agric. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"105814","DOI":"10.1016\/j.chemolab.2026.105814","article-title":"Recent advances in AI-coupled spectroscopy and hyperspectral imaging for food safety, quality, and authenticity","volume":"277","author":"Rahman","year":"2026","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wan, L., Li, H., Li, C., Wang, A., Yang, Y., and Wang, P. (2022). Hyperspectral Sensing of Plant Diseases: Principle and Methods. Agronomy, 12.","DOI":"10.3390\/agronomy12061451"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, J., Wu, Z., Jia, Y., and Dai, C. (2025). Application of Non-Destructive Technology in Plant Disease Detection: Review. Agriculture, 15.","DOI":"10.3390\/agriculture15151670"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, W., Wu, Y., Du, L., Shang, X., and Shi, J. (2025). Hyperspectral Imaging for Foreign Matter Detection in Foods: Advances, Challenges, and Future Directions. Foods, 14.","DOI":"10.3390\/foods14173026"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1089\/fpd.2018.2617","article-title":"Application of Hyperspectral Imaging as a Nondestructive Technique for Foodborne Pathogen Detection and Characterization","volume":"16","author":"Ernest","year":"2019","journal-title":"Foodborne Pathog. Dis."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Jin, X., Li, G., Ge, H., Yin, Y., Zheng, H., Li, X., and Li, P. (2026). From Fingerprint Spectra to Intelligent Perception: Research Advances in Spectral Techniques for Ginseng Species Identification. Foods, 15.","DOI":"10.3390\/foods15040684"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/B978-0-444-63977-6.00002-X","article-title":"Chapter 1.2\u2014Configuration of hyperspectral and multispectral imaging systems","volume":"Volume 32","author":"Amigo","year":"2019","journal-title":"Data Handling in Science and Technology"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"110531","DOI":"10.1016\/j.foodcont.2024.110531","article-title":"Quantitative prediction and visualization of matcha color physicochemical indicators using hyperspectral microscope imaging technology","volume":"163","author":"Li","year":"2024","journal-title":"Food Control"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1002\/jsfa.10899","article-title":"Monitoring microstructural changes and moisture distribution of dry-cured pork: A combined confocal laser scanning microscopy and hyperspectral imaging study","volume":"101","author":"Tian","year":"2021","journal-title":"J. Sci. Food Agric."},{"key":"ref_63","first-page":"213","article-title":"Detection of spores using polarization image features and BP neural network","volume":"17","author":"Wang","year":"2024","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_64","first-page":"2988","article-title":"The impact of high-quality data on the assessment results of visible\/near-infrared hyperspectral imaging and development direction in the food fields: A review","volume":"17","author":"Xu","year":"2023","journal-title":"J. Food Meas. Charact."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"e16320","DOI":"10.1111\/jfpp.16320","article-title":"A comparative analysis of hybrid SVM and LS-SVM classification algorithms to identify dried wolfberry fruits quality based on hyperspectral imaging technology","volume":"46","author":"Nirere","year":"2022","journal-title":"J. Food Process. Preserv."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"107625","DOI":"10.1016\/j.jfca.2025.107625","article-title":"Application of hyperspectral imaging technology combined with ECA-MobileNetV3 in identifying different processing methods of Yunnan coffee beans","volume":"143","author":"Xin","year":"2025","journal-title":"J. Food Compos. Anal."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"e13092","DOI":"10.1111\/jfpe.13092","article-title":"Evaluation of lipid oxidation and volatile compounds of traditional dry-cured pork belly: The hyperspectral imaging and multi-gas-sensory approaches","volume":"43","author":"Aheto","year":"2020","journal-title":"J. Food Process Eng."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"142593","DOI":"10.1016\/j.foodchem.2024.142593","article-title":"Nondestructive detection of pungent and numbing compounds in spicy hotpot seasoning with hyperspectral imaging and machine learning","volume":"469","author":"Zhang","year":"2025","journal-title":"Food Chem."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"e13085","DOI":"10.1111\/jfpe.13085","article-title":"Spectral classification of lettuce cadmium stress based on information fusion and VISSA-GOA-SVM algorithm","volume":"42","author":"Zhou","year":"2019","journal-title":"J. Food Process Eng."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Liu, C., Yu, H., Liu, Y., Zhang, L., Li, D., Zhang, J., Li, X., and Sui, Y. (2024). Prediction of Anthocyanin Content in Purple-Leaf Lettuce Based on Spectral Features and Optimized Extreme Learning Machine Algorithm. Agronomy, 14.","DOI":"10.3390\/agronomy14122915"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Nie, R., Huang, X., Tian, X., Yu, S., Dai, C., Zhang, X., and Fang, Q. (2025). Non-Destructive Detection of Pomegranate Blackheart Disease via Near-Infrared Spectroscopy and Soft X-ray Imaging Systems. Foods, 14.","DOI":"10.3390\/foods14142454"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"125767","DOI":"10.1016\/j.saa.2025.125767","article-title":"Comprehensive comparison on different wavelength selection methods using several near-infrared spectral datasets with different dimensionalities","volume":"331","author":"Wang","year":"2025","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Masithoh, R.E., Hernanda, R.A., Pahlawan, M.F., Kim, J., Amanah, H.Z., and Cho, B.-K. (2025). Shortwave near Infrared\u2013Hyperspectral Imaging Spectra to Detect Pork Adulteration in Beef Using Partial Least Square Regression Coupled with VIP Wavelength Selections Method. Optics, 6.","DOI":"10.3390\/opt6010001"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"111615","DOI":"10.1016\/j.foodcont.2025.111615","article-title":"Predicting sensory characteristics of pork using physicochemical and VIS\/NIR hyperspectral imaging data with machine learning","volume":"180","author":"Choi","year":"2026","journal-title":"Food Control"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Wang, A., Song, Z., Xie, Y., Hu, J., Zhang, L., and Zhu, Q. (2024). Detection of Rice Leaf SPAD and Blast Disease Using Integrated Aerial and Ground Multiscale Canopy Reflectance Spectroscopy. Agriculture, 14.","DOI":"10.3390\/agriculture14091471"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"e13718","DOI":"10.1111\/jfpe.13718","article-title":"Nondestructive detection for Panax notoginseng powder grades based on hyperspectral imaging technology combined with CARS-PCA and MPA-LSSVM","volume":"44","author":"Yao","year":"2021","journal-title":"J. Food Process Eng."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"e13386","DOI":"10.1111\/jfpe.13386","article-title":"Detection of mites Tyrophagus putrescentiae and Cheyletus eruditus in flour using hyperspectral imaging system coupled with chemometrics","volume":"43","author":"He","year":"2020","journal-title":"J. Food Process Eng."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"e12654","DOI":"10.1111\/jfpe.12654","article-title":"Quantitative detection of mixed pesticide residue of lettuce leaves based on hyperspectral technique","volume":"41","author":"Sun","year":"2018","journal-title":"J. Food Process Eng."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Dai, L. (2025). Application of Hyperspectral Imaging and Deep Convolutional Neural Network for Freezing Damage Identification on Embryo and Endosperm Side of Single Corn Seed. Foods, 14.","DOI":"10.3390\/foods14040659"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"S1037","DOI":"10.1080\/10942912.2017.1326058","article-title":"Quantitative determination of rice starch based on hyperspectral imaging technology","volume":"20","author":"Lu","year":"2017","journal-title":"Int. J. Food Prop."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.biosystemseng.2024.01.013","article-title":"Rapid identification of greenhouse tomato senescent leaves based on the sucrose-spectral quantitative prediction model","volume":"238","author":"Ni","year":"2024","journal-title":"Biosyst. Eng."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"e13052","DOI":"10.1111\/jfpe.13052","article-title":"Grading bunch tightness for grape by multiperspective imaging approach coupled with multivariate classification methods","volume":"42","author":"Feng","year":"2019","journal-title":"J. Food Process Eng."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"e13793","DOI":"10.1111\/jfpe.13793","article-title":"Nondestructive detection of lead content in oilseed rape leaves based on MRF-HHO-SVR and hyperspectral technology","volume":"44","author":"Cao","year":"2021","journal-title":"J. Food Process Eng."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"111258","DOI":"10.1016\/j.foodcont.2025.111258","article-title":"Physicochemical properties and gel quality monitoring of surimi during thermal processing using hyperspectral imaging combined with deep learning","volume":"175","author":"Xia","year":"2025","journal-title":"Food Control"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"112158","DOI":"10.1016\/j.foodcont.2026.112158","article-title":"Non-destructive inspection and explainability analysis of freeze-thaw cycled chicken breast using multi-indicator fusion based on hyperspectral imaging and attention mechanisms","volume":"187","author":"Li","year":"2026","journal-title":"Food Control"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Taha, M.F., Mao, H., Mousa, S., Zhou, L., Wang, Y., Elmasry, G., Al-Rejaie, S., Elwakeel, A.E., Wei, Y., and Qiu, Z. (2024). Deep Learning-Enabled Dynamic Model for Nutrient Status Detection of Aquaponically Grown Plants. Agronomy, 14.","DOI":"10.3390\/agronomy14102290"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"143997","DOI":"10.1016\/j.foodchem.2025.143997","article-title":"Quantitative analysis and visualization of chemical compositions during shrimp flesh deterioration using hyperspectral imaging: A comparative study of machine learning and deep learning models","volume":"481","author":"Xi","year":"2025","journal-title":"Food Chem."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"445","DOI":"10.17221\/129\/2022-CJFS","article-title":"Rapid non-destructive identification of selenium-enriched millet based on hyperspectral imaging technology","volume":"40","author":"Zhang","year":"2022","journal-title":"Czech J. Food Sci."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1111\/ijfs.16173","article-title":"Non-destructive prediction of total soluble solids and titratable acidity in Kyoho grape using hyperspectral imaging and deep learning algorithm","volume":"58","author":"Xu","year":"2023","journal-title":"Int. J. Food Sci. Technol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"144454","DOI":"10.1016\/j.foodchem.2025.144454","article-title":"Integration of multispectral imaging with colorimetric sensor arrays for rapid grading and volatile component prediction of Zhenjiang aromatic vinegar","volume":"487","author":"Li","year":"2025","journal-title":"Food Chem."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.biosystemseng.2022.04.018","article-title":"Detection of citrus Huanglongbing (HLB) based on the HLB-induced leaf starch accumulation using a home-made computer vision system","volume":"218","author":"Xu","year":"2022","journal-title":"Biosyst. Eng."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Wu, Y., Yuan, S., Zhu, J., Tang, Y., and Tang, L. (2025). Estimation of Wheat Leaf Water Content Based on UAV Hyper-Spectral Remote Sensing and Machine Learning. Agriculture, 15.","DOI":"10.3390\/agriculture15171898"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s10651-021-00496-2","article-title":"Analysing spectroscopy data using two-step group penalized partial least squares regression","volume":"28","author":"Chang","year":"2021","journal-title":"Environ. Ecol. Stat."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"e70769","DOI":"10.1111\/1750-3841.70769","article-title":"Prediction of Apple Quality Indicators Under Different Bagging Treatments Using Hyperspectral Imaging Integrated with a Stacking SDAE-PLSR-RR Deep Learning Model","volume":"90","author":"Zhu","year":"2025","journal-title":"J. Food Sci."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"e12647","DOI":"10.1111\/jfpe.12647","article-title":"Visualization research of moisture content in leaf lettuce leaves based on WT-PLSR and hyperspectral imaging technology","volume":"41","author":"Zhou","year":"2018","journal-title":"J. Food Process Eng."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Sidaoui, B., and Abdelmoumene, H. (2025, January 15\u201318). Kernel functions for Support Vector Machines: A Survey. Proceedings of the 2025 11th International Conference on Control, Decision and Information Technologies (CoDIT), Split, Croatia.","DOI":"10.1109\/CoDIT66093.2025.11321616"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"e14120","DOI":"10.1111\/jfpe.14120","article-title":"Identification of maize seed varieties based on stacked sparse autoencoder and near-infrared hyperspectral imaging technology","volume":"45","author":"Fu","year":"2022","journal-title":"J. Food Process Eng."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"e13422","DOI":"10.1111\/jfpe.13422","article-title":"Nondestructive detection for egg freshness grade based on hyperspectral imaging technology","volume":"43","author":"Yao","year":"2020","journal-title":"J. Food Process Eng."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"e14591","DOI":"10.1111\/jfpp.14591","article-title":"Classification detection of saccharin jujube based on hyperspectral imaging technology","volume":"44","author":"Zhang","year":"2020","journal-title":"J. Food Process. Preserv."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"e13950","DOI":"10.1111\/jfpe.13950","article-title":"Identification of fumigated and dyed Lycium barbarum by hyperspectral imaging technology","volume":"45","author":"Ningqiu","year":"2022","journal-title":"J. Food Process Eng."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Yaxuan, H., Jingli, M., and Yong, L. (2011, January 28\u201330). An improved ensemble learning method based on SVR. Proceedings of the 2011 International Conference on Advanced Intelligence and Awareness Internet (AIAI 2011), Shenzhen, China.","DOI":"10.1049\/cp.2011.1453"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"e16414","DOI":"10.1111\/jfpp.16414","article-title":"Detection of soluble solid content in apples based on hyperspectral technology combined with deep learning algorithm","volume":"46","author":"Tian","year":"2022","journal-title":"J. Food Process. Preserv."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"117367","DOI":"10.1016\/j.microc.2026.117367","article-title":"Index Reinforcement Integration System (IRIS): A dual-loop analytical framework for multi-index fusion and prediction optimisation","volume":"223","author":"Qiu","year":"2026","journal-title":"Microchem. J."},{"key":"ref_104","first-page":"335","article-title":"Rapid and Non-Destructive Determination of the Protein and Fat Contents in Wheat by Hyperspectral Imaging Combined with AdaBoost-SVR Modeling","volume":"82","author":"He","year":"2024","journal-title":"J. Am. Soc. Brew. Chem."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least Squares Support Vector Machine Classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process. Lett."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1111\/1750-3841.16004","article-title":"Nondestructive detection of total soluble solids in grapes using VMD-RC and hyperspectral imaging","volume":"87","author":"Xu","year":"2022","journal-title":"J. Food Sci."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1186\/s40537-025-01137-2","article-title":"Effective k-nearest neighbor models for data classification enhancement","volume":"12","author":"Amer","year":"2025","journal-title":"J. Big Data"},{"key":"ref_108","first-page":"12521","article-title":"On Convergence of Nearest Neighbor Classifiers over Feature Transformations","volume":"33","author":"Rimanic","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Jaiswal, J.K., and Samikannu, R. (2017, January 2\u20134). Application of Random Forest Algorithm on Feature Subset Selection and Classification and Regression. Proceedings of the 2017 World Congress on Computing and Communication Technologies (WCCCT), Tiruchirappalli, India.","DOI":"10.1109\/WCCCT.2016.25"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"2801","DOI":"10.1021\/jasms.4c00324","article-title":"The Application of a Random Forest Classifier to ToF-SIMS Imaging Data","volume":"35","author":"Shamraeva","year":"2024","journal-title":"J. Am. Soc. Mass Spectrom."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Pajila, P.J.B., Sheena, B.G., Gayathri, A., Aswini, J., Nalini, M., and Siva, S.R. (2023, January 20\u201322). A Comprehensive Survey on Naive Bayes Algorithm: Advantages, Limitations and Applications. Proceedings of the 2023 4th International Conference on Smart Electronics and Communication (ICOSEC), Tiruchirappalli, India.","DOI":"10.1109\/ICOSEC58147.2023.10276274"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Melnykov, Y., and Li, J. (2026). On transformation discriminant analysis. Commun. Stat.-Simul. Comput., 1\u201316.","DOI":"10.1080\/03610918.2026.2621895"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liu, Z., Zhao, Q., Tan, K., and Fang, J. (2025). Grading and Detecting of Organic Matter in Phaeozem Based on LSVM-Stacking Model Using Hyperspectral Reflectance Data. Agriculture, 15.","DOI":"10.3390\/agriculture15181979"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"110627","DOI":"10.1016\/j.foodcont.2024.110627","article-title":"Prediction of prunoideae fruit quality characteristics based on machine learning and spectral characteristic acquisition optimization","volume":"165","author":"Liu","year":"2024","journal-title":"Food Control"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Jia, Y., Fei, S., Li, L., Yang, S., Song, Q., Hassan, M.A., and Rasheed, A. (2026). Estimation of Relative Chlorophyll Content in Winter Wheat Employing Hyperspectral Reflectance: A Comprehensive Analysis of Data-Driven Ensemble Learning Methods. Inf. Process. Agric., in press.","DOI":"10.1016\/j.inpa.2026.06.001"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1109\/ACCESS.2015.2450498","article-title":"High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications","volume":"3","author":"Akusok","year":"2015","journal-title":"IEEE Access"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"126766","DOI":"10.1016\/j.saa.2025.126766","article-title":"Rapid detection of food quality indicators using ELM and near-infrared spectroscopy","volume":"345","author":"Shi","year":"2026","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_118","unstructured":"Koumoutsou, D., Charou, E., Siolas, G., and Stamou, G. (2021). Class-Wise Principal Component Analysis for hyperspectral image feature extraction. arXiv."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"21434","DOI":"10.1038\/s41598-026-60975-z","article-title":"A two-stage hierarchical support vector machine framework detects roasted coffee adulterants through principal component analysis of hyperspectral imaging data","volume":"16","author":"Ebrahem","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_120","unstructured":"Liang, T., Singh, E., Parhi, R., Cloninger, A., and Wang, Y.-X.J.A. (2026). The Inductive Bias of Convolutional Neural Networks: Locality and Weight Sharing Reshape Implicit Regularization. arXiv."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1038\/s41467-024-55229-3","article-title":"Local kernel renormalization as a mechanism for feature learning in overparametrized convolutional neural networks","volume":"16","author":"Aiudi","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"115960","DOI":"10.1016\/j.foodres.2025.115960","article-title":"Exploring the impact of lenticels on the detection of soluble solids content in apples and pears using hyperspectral imaging and one-dimensional convolutional neural networks","volume":"205","author":"Wang","year":"2025","journal-title":"Food Res. Int."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"116436","DOI":"10.1016\/j.lwt.2024.116436","article-title":"Chemical composition prediction in goji (Lycium barbarum) using hyperspectral imaging and multi-task 1DCNN with attention mechanism","volume":"204","author":"Hu","year":"2024","journal-title":"LWT Food Sci. Technol."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"112837","DOI":"10.1016\/j.postharvbio.2024.112837","article-title":"Almond defect and freshness inspection system using hyperspectral imaging and deep learning techniques","volume":"211","author":"Chen","year":"2024","journal-title":"Postharvest Biol. Technol."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"2971","DOI":"10.1007\/s11694-025-03888-1","article-title":"The multilayer perceptron recognition model for imperfect maize kernels based on hyperspectral imaging technology","volume":"20","author":"Dong","year":"2026","journal-title":"J. Food Meas. Charact."},{"key":"ref_126","first-page":"102945","article-title":"Revealing hidden insect damage in sunflower seeds using near-infrared hyperspectral imaging and machine learning","volume":"28","author":"Kakkar","year":"2026","journal-title":"J. Agric. Food Res."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Shi, Z.Y., Chen, M., and Wu, Z.G. (2023). Hyperspectral Image Classification Based on Dual-Scale Dense Network with Efficient Channel Attentional Feature Fusion. Electronics, 12.","DOI":"10.3390\/electronics12132991"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Zhang, Y.C., Huan, K.W., Liu, X.X., Fan, Y.Q., Cao, X.W., and Han, X.Y. (2025). Multi-Scale Feature Attention Network for Rapid and Non-Destructive Quantification of Aflatoxin B1 in Maize Using Hyperspectral Imaging. Foods, 14.","DOI":"10.3390\/foods14213769"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"128424","DOI":"10.1016\/j.saa.2026.128424","article-title":"Defective wheat kernel classification using dual-range hyperspectral imaging and an interpretable spectral-spatial fusion convolutional neural network","volume":"363","author":"Sun","year":"2026","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"125357","DOI":"10.1016\/j.saa.2024.125357","article-title":"An improved deep convolutional generative adversarial network for quantification of catechins in fermented black tea","volume":"327","author":"Zhu","year":"2025","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"104428","DOI":"10.1016\/j.fbio.2024.104428","article-title":"Advancing fraud detection in New Zealand M\u0101nuka honey: Integrating hyperspectral imaging and GANomaly-based one-class classification","volume":"60","author":"Cheng","year":"2024","journal-title":"Food Biosci."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.55730\/1300-011X.3326","article-title":"JujubeNet: A dual-stage spectral reconstruction network for rapid jujube moisture content estimation","volume":"49","author":"Xu","year":"2025","journal-title":"Turk. J. Agric. For."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"115933","DOI":"10.1016\/j.asoc.2026.115933","article-title":"A review of generative autoencoder frameworks and their latent modeling strategies","volume":"202","author":"Behera","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"118163","DOI":"10.1016\/j.lwt.2025.118163","article-title":"Firmness classification in \u2018Rojo Brillante\u2019 persimmon fruit after cold storage using hyperspectral imaging and deep learning with variational autoencoders","volume":"229","author":"Gonzalez","year":"2025","journal-title":"LWT Food Sci. Technol."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Del Bue, A., Canton, C., Pont-Tuset, J., and Tommasi, T. (2025). Unsupervised Tomato Split Anomaly Detection Using Hyperspectral Imaging and Variational Autoencoders. Computer Vision\u2014ECCV 2024 Workshops, Springer Nature.","DOI":"10.1007\/978-3-031-91838-4"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1007\/s10462-025-11169-y","article-title":"A review of hyperspectral image classification based on graph neural networks","volume":"58","author":"Zhao","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"108909","DOI":"10.1016\/j.engappai.2024.108909","article-title":"A fine-grained grading network for natural products based on dynamic association inference","volume":"136","author":"Cen","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"122560","DOI":"10.1016\/j.ins.2025.122560","article-title":"Adaptive recognition of flexible objects via hierarchical deformable graph network","volume":"721","author":"Yang","year":"2025","journal-title":"Inf. Sci."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"107360","DOI":"10.1016\/j.jfca.2025.107360","article-title":"Beef quality dual-label classification incorporating texture and multihead map attention mechanisms","volume":"141","author":"Zhang","year":"2025","journal-title":"J. Food Compos. Anal."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.patrec.2026.05.017","article-title":"Hyperspectral image classification using feature-weighted fusion with Kolmogorov-Arnold network and graph attention network","volume":"206","author":"Wang","year":"2026","journal-title":"Pattern Recognit. Lett."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1007\/s10115-024-02234-z","article-title":"LmGa: Combining label mapping method with graph attention network for agricultural recognition","volume":"67","author":"Vu","year":"2025","journal-title":"Knowl. Inf. Syst."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"111740","DOI":"10.1016\/j.foodcont.2025.111740","article-title":"Task-driven and interpretable hyperspectral band selection via deep sequential modeling: A case study on apple bruise detection","volume":"181","author":"Chen","year":"2026","journal-title":"Food Control"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"105816","DOI":"10.1016\/j.tifs.2026.105816","article-title":"Transfer learning and few-shot learning for breaking the small-sample bottleneck in food hyperspectral analysis","volume":"174","author":"Li","year":"2026","journal-title":"Trends Food Sci. Technol."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"111942","DOI":"10.1016\/j.foodcont.2025.111942","article-title":"Non-destructive visualization and prediction of surimi gel quality under atmospheric cold plasma via hyperspectral imaging and deep learning","volume":"183","author":"Li","year":"2026","journal-title":"Food Control"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Shen, L., Wang, H., Liu, Y., Liu, Y., Zhang, X., and Fei, Y. (2020). Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder. Appl. Sci., 10.","DOI":"10.3390\/app10113769"},{"key":"ref_146","first-page":"184701","article-title":"Autoencoder-CatBoost Model for Accurate Hyperspectral Quality Assessment of Yunling Snowflake Beef","volume":"12","author":"Gao","year":"2024","journal-title":"IEEE Access"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"113752","DOI":"10.1016\/j.postharvbio.2025.113752","article-title":"Hyperspectral imaging combined with a universal hybrid deep network for identifying early chilling injury in kiwifruit across varieties","volume":"230","author":"Cong","year":"2025","journal-title":"Postharvest Biol. Technol."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Tian, Y., Sun, J., Zhou, X., Cong, S., Dai, C., and Shi, L. (2025). Nondestructive Detection of Soluble Solids Content in Apples Based on Multi-Attention Convolutional Neural Network and Hyperspectral Imaging Technology. Foods, 14.","DOI":"10.3390\/foods14223832"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"118463","DOI":"10.1016\/j.foodres.2026.118463","article-title":"Simultaneous detection and visualization of lipid and protein oxidation in frozen-thawed chicken meat using hyperspectral imaging","volume":"229","author":"Yao","year":"2026","journal-title":"Food Res. Int."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Dai, C., Sun, J., Huang, X., Zhang, X., Tian, X., Wang, W., Sun, J., and Luan, Y. (2023). Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content. Foods, 12.","DOI":"10.3390\/foods12152957"},{"key":"ref_151","first-page":"599","article-title":"DETECTION OF THE NUTRITIONAL STATUS OF PHOSPHORUS IN LETTUCE USING THZ TIME-DOMAIN SPECTROSCOPY","volume":"41","author":"Zhang","year":"2021","journal-title":"Eng. Agric."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"113966","DOI":"10.1016\/j.postharvbio.2025.113966","article-title":"Automatic early bruise detection in strawberry fruit by hyperspectral imaging and deep learning techniques","volume":"232","author":"Sun","year":"2026","journal-title":"Postharvest Biol. Technol."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"e70378","DOI":"10.1111\/jfpe.70378","article-title":"Hyperspectral Imaging for Identification of Early Freeze Damage in Oranges","volume":"49","author":"Shi","year":"2026","journal-title":"J. Food Process Eng."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"106647","DOI":"10.1016\/j.jfca.2024.106647","article-title":"Simultaneous detection for storage condition and storage time of yellow peach under different storage conditions using hyperspectral imaging with multi-target characteristic selection and multi-task model","volume":"135","author":"Shi","year":"2024","journal-title":"J. Food Compos. Anal."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"108975","DOI":"10.1016\/j.meatsci.2022.108975","article-title":"Nondestructive detection and visualization of protein oxidation degree of frozen-thawed pork using fluorescence hyperspectral imaging","volume":"194","author":"Cheng","year":"2022","journal-title":"Meat Sci."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"113778","DOI":"10.1016\/j.lwt.2022.113778","article-title":"A decision fusion method based on hyperspectral imaging and electronic nose techniques for moisture content prediction in frozen-thawed pork","volume":"165","author":"Cheng","year":"2022","journal-title":"LWT"},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"105497","DOI":"10.1016\/j.jfca.2023.105497","article-title":"Nondestructive detection of lipid oxidation in frozen pork using hyperspectral imaging technology","volume":"123","author":"Cheng","year":"2023","journal-title":"J. Food Compos. Anal."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"e13225","DOI":"10.1111\/jfpe.13225","article-title":"Combination of spectra and image information of hyperspectral imaging data for fast prediction of lipid oxidation attributes in pork meat","volume":"42","author":"Aheto","year":"2019","journal-title":"J. Food Process Eng."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/j.foodchem.2015.11.084","article-title":"Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms","volume":"197","author":"Khulal","year":"2016","journal-title":"Food Chem."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"109196","DOI":"10.1016\/j.meatsci.2023.109196","article-title":"Multi-task convolutional neural network for simultaneous monitoring of lipid and protein oxidative damage in frozen-thawed pork using hyperspectral imaging","volume":"201","author":"Cheng","year":"2023","journal-title":"Meat Sci."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"103880","DOI":"10.1016\/j.fbio.2024.103880","article-title":"An effective method fusing electronic nose and fluorescence hyperspectral imaging for the detection of pork freshness","volume":"59","author":"Cheng","year":"2024","journal-title":"Food Biosci."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.meatsci.2016.04.031","article-title":"Feasibility study on nondestructively sensing meat\u2019s freshness using light scattering imaging technique","volume":"119","author":"Li","year":"2016","journal-title":"Meat Sci."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"109002","DOI":"10.1016\/j.meatsci.2022.109002","article-title":"Quantitative characterization of the diffusion behavior of sucrose in marinated beef by HSI and FEA","volume":"195","author":"Shi","year":"2023","journal-title":"Meat Sci."},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Li, W., Shi, Y., Huang, X., Li, Z., Zhang, X., Zou, X., Hu, X., and Shi, J. (2024). Study on the Diffusion and Optimization of Sucrose in Gaido Seak Based on Finite Element Analysis and Hyperspectral Imaging Technology. Foods, 13.","DOI":"10.3390\/foods13020249"},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"e14589","DOI":"10.1111\/jfpe.14589","article-title":"Application of hyperspectral imaging and colorimetric sensor array coupled with multivariate analysis for quality detection during salted duck eggs processing","volume":"47","author":"Xu","year":"2024","journal-title":"J. Food Process Eng."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"e12888","DOI":"10.1111\/jfs.12888","article-title":"Nondestructive detection for egg freshness based on hyperspectral imaging technology combined with harris hawks optimization support vector regression","volume":"41","author":"Yao","year":"2021","journal-title":"J. Food Saf."},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Yao, K., Sun, J., Cheng, J., Xu, M., Chen, C., Zhou, X., and Dai, C. (2022). Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage. Foods, 11.","DOI":"10.3390\/foods11142024"},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"128515","DOI":"10.1016\/j.foodchem.2020.128515","article-title":"Application of volatile and spectral profiling together with multimode data fusion strategy for the discrimination of preserved eggs","volume":"343","author":"Ren","year":"2021","journal-title":"Food Chem."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"111024","DOI":"10.1016\/j.jfoodeng.2022.111024","article-title":"Non-destructive detection of egg qualities based on hyperspectral imaging","volume":"325","author":"Yao","year":"2022","journal-title":"J. Food Eng."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"9098","DOI":"10.1007\/s11694-024-02862-7","article-title":"Classification of tea quality grades based on hyperspectral imaging spatial information and optimization models","volume":"18","author":"Ding","year":"2024","journal-title":"J. Food Meas. Charact."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"117009","DOI":"10.1016\/j.indcrop.2023.117009","article-title":"Cross-cultivar prediction of quality indicators of tea based on VIS-NIR hyperspectral imaging","volume":"202","author":"Luo","year":"2023","journal-title":"Ind. Crops Prod."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"e13570","DOI":"10.1111\/jfpe.13570","article-title":"Nondestructive determination of the total mold colony count in green tea by hyperspectral imaging technology","volume":"43","author":"Cao","year":"2020","journal-title":"J. Food Process Eng."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"2234","DOI":"10.1111\/1750-3841.14706","article-title":"Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms","volume":"84","author":"Li","year":"2019","journal-title":"J. Food Sci."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"129141","DOI":"10.1016\/j.foodchem.2021.129141","article-title":"Simultaneous quantification of chemical constituents in matcha with visible-near infrared hyperspectral imaging technology","volume":"350","author":"Ouyang","year":"2021","journal-title":"Food Chem."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"109782","DOI":"10.1016\/j.jfoodeng.2019.109782","article-title":"A novel hyperspectral microscope imaging technology for rapid evaluation of particle size distribution in matcha","volume":"272","author":"Ouyang","year":"2020","journal-title":"J. Food Eng."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"109254","DOI":"10.1016\/j.lwt.2020.109254","article-title":"Assessment of matcha sensory quality using hyperspectral microscope imaging technology","volume":"125","author":"Ouyang","year":"2020","journal-title":"LWT"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"140254","DOI":"10.1016\/j.foodchem.2024.140254","article-title":"Non-invasive anticipation of infusion taste in fine-manipulated green teas through hyperspectral appearance analysis guided by ECG content","volume":"458","author":"Tian","year":"2024","journal-title":"Food Chem."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1002\/jsfa.9371","article-title":"Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems","volume":"99","author":"Li","year":"2019","journal-title":"J. Sci. Food Agric."},{"key":"ref_179","doi-asserted-by":"crossref","unstructured":"Zhao, J., Li, H., Chen, C., Pang, Y., and Zhu, X. (2022). Detection of Water Content in Lettuce Canopies Based on Hyperspectral Imaging Technology under Outdoor Conditions. Agriculture, 12.","DOI":"10.3390\/agriculture12111796"},{"key":"ref_180","doi-asserted-by":"crossref","unstructured":"Tian, X., Fang, Q., Zhang, X., Yu, S., Dai, C., and Huang, X. (2024). Visualization of Moisture Content, Reducing Sugars, and Chewiness in Bread During Oral Processing Based on Hyperspectral Imaging Technology. Foods, 13.","DOI":"10.3390\/foods13223589"},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"104411","DOI":"10.1016\/j.jcs.2026.104411","article-title":"Visualizing bolus development using hyperspectral imaging: Effects of buckwheat substitution on the oral processing of Chinese steamed bread","volume":"128","author":"Tian","year":"2026","journal-title":"J. Cereal Sci."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"e12916","DOI":"10.1111\/jfpe.12916","article-title":"Quantitative detection of moisture content in rice seeds based on hyperspectral technique","volume":"41","author":"Lu","year":"2018","journal-title":"J. Food Process Eng."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"e12446","DOI":"10.1111\/jfpe.12446","article-title":"Quantitative Determination of Rice Moisture Based on Hyperspectral Imaging Technology and BCC-LS-SVR Algorithm","volume":"40","author":"Sun","year":"2017","journal-title":"J. Food Process Eng."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"e13112","DOI":"10.1111\/jfs.13112","article-title":"Detection of rice (with husk) moisture content based on hyperspectral imaging technology combined with MSLPP\u2013ESMA\u2013SVR model","volume":"44","author":"Zhong","year":"2024","journal-title":"J. Food Saf."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"e13797","DOI":"10.1111\/jfpe.13797","article-title":"A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology","volume":"44","author":"Sun","year":"2021","journal-title":"J. Food Process Eng."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"e13900","DOI":"10.1111\/jfpe.13900","article-title":"Laser light backscattering image to predict moisture content of mango slices with different ripeness during drying process","volume":"44","author":"Bai","year":"2021","journal-title":"J. Food Process Eng."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.jfoodeng.2015.11.019","article-title":"Determination of total acid content and moisture content during solid-state fermentation processes using hyperspectral imaging","volume":"174","author":"Zhu","year":"2016","journal-title":"J. Food Eng."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"e12297","DOI":"10.1111\/jfpe.12297","article-title":"A Method for Rapid Identification of Rice Origin by Hyperspectral Imaging Technology","volume":"40","author":"Sun","year":"2017","journal-title":"J. Food Process Eng."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"e13603","DOI":"10.1111\/jfpe.13603","article-title":"Identification of Lycium barbarum varieties based on hyperspectral imaging technique and competitive adaptive reweighted sampling-whale optimization algorithm-support vector machine","volume":"44","author":"Tang","year":"2021","journal-title":"J. Food Process Eng."},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"e13289","DOI":"10.1111\/jfpe.13289","article-title":"Classification of oolong tea varieties based on hyperspectral imaging technology and BOSS-LightGBM model","volume":"42","author":"Ge","year":"2019","journal-title":"J. Food Process Eng."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"e12800","DOI":"10.1111\/jfpe.12800","article-title":"Nondestructive identification of green tea varieties based on hyperspectral imaging technology","volume":"41","author":"Sun","year":"2018","journal-title":"J. Food Process Eng."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1080\/10942912.2015.1055760","article-title":"Classification of Black Beans Using Visible and Near Infrared Hyperspectral Imaging","volume":"19","author":"Sun","year":"2016","journal-title":"Int. J. Food Prop."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"e14137","DOI":"10.1111\/jfpe.14137","article-title":"Identification of red jujube varieties based on hyperspectral imaging technology combined with CARS-IRIV and SSA-SVM","volume":"45","author":"Wang","year":"2022","journal-title":"J. Food Process Eng."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"e13432","DOI":"10.1111\/jfpe.13432","article-title":"Research on apple origin classification based on variable iterative space shrinkage approach with stepwise regression\u2013support vector machine algorithm and visible-near infrared hyperspectral imaging","volume":"43","author":"Tian","year":"2020","journal-title":"J. Food Process Eng."},{"key":"ref_195","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Liu, Q., Ma, D., Zhu, Y., Zhang, L., Wang, A., and Fan, S. (2025). Maize Seed Variety Classification Based on Hyperspectral Imaging and a CNN-LSTM Learning Framework. Agronomy, 15, Correction in Agronomy\u00a02025, 15, 2255.","DOI":"10.3390\/agronomy15102255"},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"e14293","DOI":"10.1111\/jfpe.14293","article-title":"Nondestructive detection of adulterated wolfberry (Lycium Chinense) fruits based on hyperspectral imaging technology","volume":"46","author":"Nirere","year":"2023","journal-title":"J. Food Process Eng."},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"859","DOI":"10.3136\/fstr.27.859","article-title":"Quantitative detection of restructured steak adulteration based on hyperspectral technology combined with a wavelength selection algorithm cascade strategy","volume":"27","author":"Liu","year":"2021","journal-title":"Food Sci. Technol. Res."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"8854","DOI":"10.1007\/s11694-025-03576-0","article-title":"An effective method for identifying cow milk powder adulteration levels in goat milk powder using hyperspectral imaging","volume":"19","author":"Cai","year":"2025","journal-title":"J. Food Meas. Charact."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"2955","DOI":"10.1002\/jsfa.70397","article-title":"Non-destructive beef adulteration detection using hyperspectral imaging and independent component analysis","volume":"106","author":"Gao","year":"2026","journal-title":"J. Sci. Food Agric."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"4545","DOI":"10.1002\/jsfa.12528","article-title":"Non-destructive discrimination of homochromatic foreign materials in cut tobacco based on VIS-NIR hyperspectral imaging","volume":"103","author":"Liang","year":"2023","journal-title":"J. Sci. Food Agric."},{"key":"ref_201","doi-asserted-by":"crossref","unstructured":"Peng, Y., Sun, J., Cai, Z., Shi, L., Wu, X., Dai, C., and Xie, Y. (2025). Copper Stress Levels Classification in Oilseed Rape Using Deep Residual Networks and Hyperspectral False-Color Images. Horticulturae, 11.","DOI":"10.3390\/horticulturae11070840"},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"e13293","DOI":"10.1111\/jfpe.13293","article-title":"Estimating cadmium content in lettuce leaves based on deep brief network and hyperspectral imaging technology","volume":"42","author":"Sun","year":"2019","journal-title":"J. Food Process Eng."},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"143799","DOI":"10.1016\/j.foodchem.2025.143799","article-title":"Nondestructive detection of cadmium content in oilseed rape leaves under different silicon environments using deep transfer learning and Vis-NIR hyperspectral imaging","volume":"479","author":"Zhou","year":"2025","journal-title":"Food Chem."},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"135251","DOI":"10.1016\/j.foodchem.2022.135251","article-title":"A deep learning method for predicting lead content in oilseed rape leaves using fluorescence hyperspectral imaging","volume":"409","author":"Zhou","year":"2023","journal-title":"Food Chem."},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"126503","DOI":"10.1016\/j.foodchem.2020.126503","article-title":"Hyperspectral technique combined with deep learning algorithm for detection of compound heavy metals in lettuce","volume":"321","author":"Zhou","year":"2020","journal-title":"Food Chem."},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"107994","DOI":"10.1016\/j.jfca.2025.107994","article-title":"Nondestructive detection of heavy metal lead in eggs using hyperspectral imaging combined with deep learning-based feature extraction method","volume":"146","author":"Yao","year":"2025","journal-title":"J. Food Compos. Anal."},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"2690","DOI":"10.1002\/jsfa.12376","article-title":"Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves","volume":"103","author":"Cheng","year":"2023","journal-title":"J. Sci. Food Agric."},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"e13897","DOI":"10.1111\/jfpe.13897","article-title":"Visualization of heavy metal cadmium in lettuce leaves based on wavelet support vector machine regression model and visible-near infrared hyperspectral imaging","volume":"44","author":"Zhou","year":"2021","journal-title":"J. Food Process Eng."},{"key":"ref_209","doi-asserted-by":"crossref","unstructured":"Wang, Y., Mao, H., Zhang, X., Liu, Y., and Du, X. (2021). A Rapid Detection Method for Tomato Gray Mold Spores in Greenhouse Based on Microfluidic Chip Enrichment and Lens-Less Diffraction Image Processing. Foods, 10.","DOI":"10.3390\/foods10123011"},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1016\/j.foodchem.2018.09.058","article-title":"Noise-free microbial colony counting method based on hyperspectral features of agar plates","volume":"274","author":"Shi","year":"2019","journal-title":"Food Chem."},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1002\/jsfa.8613","article-title":"Non-destructive detection for mold colonies in rice based on hyperspectra and GWO-SVR","volume":"98","author":"Sunli","year":"2018","journal-title":"J. Sci. Food Agric."},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"e14198","DOI":"10.1111\/jfpp.14198","article-title":"Fluorescence hyperspectral image technique coupled with HSI method to predict solanine content of potatoes","volume":"43","author":"Lu","year":"2019","journal-title":"J. Food Process. Preserv."},{"key":"ref_213","first-page":"185","article-title":"Fast detection of the early decay in oranges using visible-LED structured-illumination imaging combined with spiral phase transform and feature-based classification model","volume":"17","author":"Cai","year":"2024","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"108420","DOI":"10.1016\/j.foodcont.2021.108420","article-title":"Rapid and nondestructive quantification of deoxynivalenol in individual wheat kernels using near-infrared hyperspectral imaging and chemometrics","volume":"131","author":"Shen","year":"2022","journal-title":"Food Control"},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s12161-023-02548-8","article-title":"Identification of Peanut Kernels Infected with Multiple Aspergillus flavus Fungi Using Line-Scan Raman Hyperspectral Imaging","volume":"17","author":"Yang","year":"2024","journal-title":"Food Anal. Methods"},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"e14238","DOI":"10.1111\/jfpp.14238","article-title":"Detection of viability of soybean seed based on fluorescence hyperspectra and CARS-SVM-AdaBoost model","volume":"43","author":"Li","year":"2019","journal-title":"J. Food Process. Preserv."},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"4403","DOI":"10.1111\/1750-3841.17151","article-title":"Rapid and nondestructive watermelon (Citrullus lanatus) seed viability detection based on visible near-infrared hyperspectral imaging technology and machine learning algorithms","volume":"89","author":"Sun","year":"2024","journal-title":"J. Food Sci."},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"e12509","DOI":"10.1111\/jfpe.12509","article-title":"Discrimination of pesticide residues in lettuce based on chemical molecular structure coupled with wavelet transform and near infrared hyperspectra","volume":"40","author":"Sun","year":"2017","journal-title":"J. Food Process Eng."},{"key":"ref_219","first-page":"231","article-title":"Identification of pesticide residue level in lettuce based on hyperspectra and chlorophyll fluorescence spectra","volume":"9","author":"Sun","year":"2016","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"e12510","DOI":"10.1111\/jfpe.12510","article-title":"Visualizing distribution of pesticide residues in mulberry leaves using NIR hyperspectral imaging","volume":"40","author":"Jiang","year":"2017","journal-title":"J. Food Process Eng."},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.foodchem.2017.02.075","article-title":"A rapid and nondestructive method to determine the distribution map of protein, carbohydrate and sialic acid on Edible bird\u2019s nest by hyper-spectral imaging and chemometrics","volume":"229","author":"Shi","year":"2017","journal-title":"Food Chem."},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"141511","DOI":"10.1016\/j.foodchem.2024.141511","article-title":"Distributional uniformity quantification in heterogeneous prepared dishes combined the hyperspectral imaging technology with Moran\u2019s I: A case study of pizza","volume":"466","author":"Gao","year":"2025","journal-title":"Food Chem."},{"key":"ref_223","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1109\/TETC.2026.3703954","article-title":"Hyperspectral Imaging-Based Grain Quality Assessment with Limited Labelled Data","volume":"14","author":"Karmakar","year":"2026","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_224","doi-asserted-by":"crossref","first-page":"112034","DOI":"10.1016\/j.foodcont.2026.112034","article-title":"Multimodal fusion-based classification method for small-sample imperfect wheat kernels using hyperspectral imaging","volume":"185","author":"Bai","year":"2026","journal-title":"Food Control"},{"key":"ref_225","doi-asserted-by":"crossref","unstructured":"Chu, X., Huang, Y., Yun, Y.-H., and Bian, X. (2022). Nonlinear Calibration Methods. Chemometric Methods in Analytical Spectroscopy Technology, Springer Nature.","DOI":"10.1007\/978-981-19-1625-0"},{"key":"ref_226","doi-asserted-by":"crossref","unstructured":"Feng, R., Wu, C., Zhou, M., Wang, D., Jia, D., and Bai, Z. (2026). DPMSCH-Net: A Dual Prior-Guidance Multi-Scale SSM-CNN Hybrid Network with Manifold Constraints for Hyperspectral Image Classification Under Small-Sample Conditions. Remote Sens., 18.","DOI":"10.3390\/rs18152452"},{"key":"ref_227","doi-asserted-by":"crossref","first-page":"108860","DOI":"10.1016\/j.jfca.2026.108860","article-title":"Review of advances in hyper-spectral imaging-based non-destructive testing methods in food application","volume":"150","author":"Wu","year":"2026","journal-title":"J. Food Compos. Anal."},{"key":"ref_228","doi-asserted-by":"crossref","first-page":"145712","DOI":"10.1016\/j.foodchem.2025.145712","article-title":"Deep learning-based multimodal fusion for quality prediction of chili paste using hyperspectral imaging and near-infrared spectroscopy","volume":"493","author":"Li","year":"2025","journal-title":"Food Chem."},{"key":"ref_229","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.tifs.2015.05.006","article-title":"Selection of feature wavelengths for developing multispectral imaging systems for quality, safety and authenticity of muscle foods-a review","volume":"45","author":"Pu","year":"2015","journal-title":"Trends Food Sci. 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