{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T00:13:20Z","timestamp":1782605600215,"version":"3.54.5"},"reference-count":62,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:00:00Z","timestamp":1674864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Translational Data Analytics Institute Pilot seed grant"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hyperspectral imaging is capable of capturing information beyond conventional RGB cameras; therefore, several applications of this have been found, such as material identification and spectral analysis. However, similar to many camera systems, most of the existing hyperspectral cameras are still passive imaging systems. Such systems require an external light source to illuminate the objects, to capture the spectral intensity. As a result, the collected images highly depend on the environment lighting and the imaging system cannot function in a dark or low-light environment. This work develops a prototype system for active hyperspectral imaging, which actively emits diverse single-wavelength light rays at a specific frequency when imaging. This concept has several advantages: first, using the controlled lighting, the magnitude of the individual bands is more standardized to extract reflectance information; second, the system is capable of focusing on the desired spectral range by adjusting the number and type of LEDs; third, an active system could be mechanically easier to manufacture, since it does not require complex band filters as used in passive systems. Three lab experiments show that such a design is feasible and could yield informative hyperspectral images in low light or dark environments: (1) spectral analysis: this system\u2019s hyperspectral images improve food ripening and stone type discernibility over RGB images; (2) interpretability: this system\u2019s hyperspectral images improve machine learning accuracy. Therefore, it can potentially benefit the academic and industry segments, such as geochemistry, earth science, subsurface energy, and mining.<\/jats:p>","DOI":"10.3390\/s23031437","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T02:01:18Z","timestamp":1675044078000},"page":"1437","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Active and Low-Cost Hyperspectral Imaging for the Spectral Analysis of a Low-Light Environment"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0864-5842","authenticated-orcid":false,"given":"Yang","family":"Tang","sequence":"first","affiliation":[{"name":"Geospatial Data Analytics Laboratory, The Ohio State University, Columbus, OH 43210, USA"},{"name":"Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0037-1499","authenticated-orcid":false,"given":"Shuang","family":"Song","sequence":"additional","affiliation":[{"name":"Geospatial Data Analytics Laboratory, The Ohio State University, Columbus, OH 43210, USA"},{"name":"Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8932-0577","authenticated-orcid":false,"given":"Shengxi","family":"Gui","sequence":"additional","affiliation":[{"name":"Geospatial Data Analytics Laboratory, The Ohio State University, Columbus, OH 43210, USA"},{"name":"Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weilun","family":"Chao","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA"},{"name":"Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chinmin","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA"},{"name":"Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5896-1379","authenticated-orcid":false,"given":"Rongjun","family":"Qin","sequence":"additional","affiliation":[{"name":"Geospatial Data Analytics Laboratory, The Ohio State University, Columbus, OH 43210, USA"},{"name":"Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA"},{"name":"Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA"},{"name":"Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hassaballah, M., and Hosny, K.M. (2019). Recent Advances in Computer Vision: Theories and Applications, Springer International Publishing. Studies in Computational Intelligence.","DOI":"10.1007\/978-3-030-03000-1"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, J., Pei, Y., Zhao, S., Xiao, R., Sang, X., and Zhang, C. (2020). A Review of Remote Sensing for Environmental Monitoring in China. Remote Sens., 12.","DOI":"10.3390\/rs12071130"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Song, W., Song, W., Gu, H., and Li, F. (2020). Progress in the Remote Sensing Monitoring of the Ecological Environment in Mining Areas. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17061846"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"50","DOI":"10.3844\/ajabssp.2010.50.55","article-title":"A Review: The Role of Remote Sensing in Precision Agriculture","volume":"5","author":"Liaghat","year":"2010","journal-title":"Am. J. Agric. Biol. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.infrared.2015.12.008","article-title":"High Speed Measurement of Corn Seed Viability Using Hyperspectral Imaging","volume":"75","author":"Ambrose","year":"2016","journal-title":"Infrared Phys. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e1661","DOI":"10.1002\/wnan.1661","article-title":"Dark-Field Hyperspectral Imaging for Label Free Detection of Nano-Bio-Materials","volume":"13","author":"Mehta","year":"2021","journal-title":"WIREs Nanomed. Nanobiotechnol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1080\/15502724.2022.2067866","article-title":"Accuracy of Hyperspectral Imaging Systems for Color and Lighting Research","volume":"19","author":"Raza","year":"2023","journal-title":"LEUKOS"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.aca.2004.12.037","article-title":"Infrared Hyperspectral Imaging for Qualitative Analysis of Pharmaceutical Solid Forms","volume":"535","author":"Roggo","year":"2005","journal-title":"Anal. Chim. Acta"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.1021\/acs.est.7b04618","article-title":"Assessing Soil Contamination Due to Oil and Gas Production Using Vegetation Hyperspectral Reflectance","volume":"52","author":"Lassalle","year":"2018","journal-title":"Environ. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"111252","DOI":"10.1016\/j.compstruct.2019.111252","article-title":"Using Passive and Active Acoustic Methods for Impact Damage Assessment of Composite Structures","volume":"226","author":"Saeedifar","year":"2019","journal-title":"Compos. Struct."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dong, L., Pei, Z., Xie, X., Zhang, Y., and Yan, X. (2022). Early Identification of Abnormal Regions in Rock-Mass Using Traveltime Tomography. Engineering.","DOI":"10.1016\/j.eng.2022.05.016"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2752","DOI":"10.1007\/s11771-021-4806-7","article-title":"Fracture Evolution and Localization Effect of Damage in Rock Based on Wave Velocity Imaging Technology","volume":"28","author":"Zhang","year":"2021","journal-title":"J. Cent. South Univ."},{"key":"ref_13","unstructured":"Lacar, F.M., Lewis, M., and Grierson, I. (2001, January 9\u201313). Use of Hyperspectral Imagery for Mapping Grape Varieties in the Barossa Valley, South Australia. Proceedings of the IGARSS 2001, Scanning the Present and Resolving the Future, IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217), Sydney, Australia."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"013536","DOI":"10.1117\/1.2794018","article-title":"Carnegie Airborne Observatory: In-Flight Fusion of Hyperspectral Imaging and Waveform Light Detection and Ranging for Three-Dimensional Studies of Ecosystems","volume":"1","author":"Asner","year":"2007","journal-title":"J. Appl. Remote Sens."},{"key":"ref_15","unstructured":"Ferwerda, J.G. (2005). Charting the Quality of Forage: Measuring and Mapping the Variation of Chemical Components in Foliage with Hyperspectral Remote Sensing, Wageningen University."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/S0034-4257(02)00132-3","article-title":"Mapping Mine Wastes and Analyzing Areas Affected by Selenium-Rich Water Runoff in Southeast Idaho Using AVIRIS Imagery and Digital Elevation Data","volume":"84","author":"Mars","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, M., He, T., Li, G., Xiao, W., Song, H., Lu, D., and Wu, C. (2021). Continuous Detection of Surface-Mining Footprint in Copper Mine Using Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13214273"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.oregeorev.2018.08.019","article-title":"Monitoring Surface Mining Belts Using Multiple Remote Sensing Datasets: A Global Perspective","volume":"101","author":"Yu","year":"2018","journal-title":"Ore Geol. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1364\/OL.33.001023","article-title":"Multispectral Imaging Using Multiple-Bandpass Filters","volume":"33","author":"Themelis","year":"2008","journal-title":"Opt. Lett."},{"key":"ref_20","unstructured":"Du, H., Tong, X., Cao, X., and Lin, S. (October, January 29). A Prism-Based System for Multispectral Video Acquisition. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1007\/s11947-013-1158-9","article-title":"Development of a Hyperspectral Computer Vision System Based on Two Liquid Crystal Tuneable Filters for Fruit Inspection. Application to Detect Citrus Fruits Decay","volume":"7","author":"Lorente","year":"2014","journal-title":"Food Bioprocess Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"114105","DOI":"10.1117\/1.OE.55.11.114105","article-title":"High Spatial Resolution Hyperspectral Camera Based on a Linear Variable Filter","volume":"55","author":"Renhorn","year":"2016","journal-title":"Opt. Eng."},{"key":"ref_23","unstructured":"(2022, December 18). Functionality of Measuring Systems\u2014LLA Instruments GmbH & Co KG. Available online: https:\/\/www.lla-instruments.de\/en\/how-it-works-en\/functionality-of-measuring-systems.html."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1179\/sic.2006.51.Supplement-1.3","article-title":"Multispectral and Hyperspectral Imaging Technologies in Conservation: Current Research and Potential Applications","volume":"51","author":"Fischer","year":"2006","journal-title":"Stud. Conserv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1255\/jnirs.1003","article-title":"Hyperspectral Imaging: A Review of Best Practice, Performance and Pitfalls for in-Line and on-Line Applications","volume":"20","author":"Boldrini","year":"2012","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"034206","DOI":"10.1088\/1674-1056\/28\/3\/034206","article-title":"Active Hyperspectral Imaging with a Supercontinuum Laser Source in the Dark","volume":"28","author":"Guo","year":"2019","journal-title":"Chin. Phys. B"},{"key":"ref_27","unstructured":"(2022, December 18). Multispectral Imaging Systems. Available online: https:\/\/spectraldevices.com\/collections\/multispectral-imaging-system."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107536","DOI":"10.1016\/j.optlastec.2021.107536","article-title":"Design of Active Hyperspectral Light Source Based on Compact Light Pipe with LED Deflection Layout","volume":"145","author":"Song","year":"2022","journal-title":"Opt. Laser Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Park, J.I., Lee, M.H., Grossberg, M.D., and Nayar, S.K. (2007, January 14\u201321). Multispectral Imaging Using Multiplexed Illumination. Proceedings of the 2007 IEEE 11th International Conference on Computer Vision, Rio De Janeiro, Brazil.","DOI":"10.1109\/ICCV.2007.4409090"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, H.N., Feng, J., Yang, W.P., Wang, L., Xu, H.B., Cao, P.F., and Duan, J.J. (2012, January 16\u201318). Multi-Spectral Imaging Using LED Illuminations. Proceedings of the 2012 5th International Congress on Image and Signal Processing, Chongqing, China.","DOI":"10.1109\/CISP.2012.6469964"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"026107","DOI":"10.1063\/1.5048795","article-title":"An Active Hyperspectral Imaging System Based on a Multi-LED Light Source","volume":"90","author":"Wang","year":"2019","journal-title":"Rev. Sci. Instrum."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Goel, M., Whitmire, E., Mariakakis, A., Saponas, T.S., Joshi, N., Morris, D., Guenter, B., Gavriliu, M., Borriello, G., and Patel, S.N. (2015, January 7\u201311). HyperCam: Hyperspectral Imaging for Ubiquitous Computing Applications. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp\u201915), Osaka, Japan.","DOI":"10.1145\/2750858.2804282"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107359","DOI":"10.1016\/j.compag.2022.107359","article-title":"A Low-Cost Multispectral Imaging System for the Characterisation of Soil and Small Vegetation Properties Using Visible and near-Infrared Reflectance","volume":"202","author":"Orlando","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.optlaseng.2019.04.014","article-title":"Hyperspectral Image Reconstruction Using Multi-colour and Time-multiplexed LED Illumination","volume":"121","author":"Tschannerl","year":"2019","journal-title":"Opt. Lasers Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7489","DOI":"10.3390\/s140407489","article-title":"Non-Destructive Quality Evaluation of Pepper (Capsicum annuum L.) Seeds Using LED-induced Hyperspectral Reflectance Imaging","volume":"14","author":"Mo","year":"2014","journal-title":"Sensors"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"JSSS (2022, December 18). Near-infrared LED System to Recognize Road Surface Conditions for Autonomous Vehicles. Available online: https:\/\/jsss.copernicus.org\/articles\/11\/187\/2022\/.","DOI":"10.5194\/jsss-11-187-2022"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.optlaseng.2015.08.002","article-title":"Road Condition Analysis Using NIR Illumination and Compensating for Surrounding Light","volume":"77","author":"Casselgren","year":"2016","journal-title":"Opt. Lasers Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Th\u00f6rnberg, B. (2022, January 1\u20133). The Material Imaging Analyzer MIA. Proceedings of the 2022 IEEE Sensors Applications Symposium (SAS), Sundsvall, Sweden.","DOI":"10.1109\/SAS54819.2022.9881374"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.ijrefrig.2016.10.014","article-title":"Identification of Freezer Burn on Frozen Salmon Surface Using Hyperspectral Imaging and Computer Vision Combined with Machine Learning Algorithm","volume":"74","author":"Xu","year":"2017","journal-title":"Int. J. Refrig."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.jfoodeng.2016.10.021","article-title":"Comparison of Hyperspectral Imaging and Computer Vision for Automatic Differentiation of Organically and Conventionally Farmed Salmon","volume":"196","author":"Xu","year":"2017","journal-title":"J. Food Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3172371","article-title":"SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers","volume":"60","author":"Hong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","article-title":"Graph Convolutional Networks for Hyperspectral Image Classification","volume":"59","author":"Hong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chang, J.R., and Chen, Y.S. (2018, January 18\u201322). Pyramid Stereo Matching Network. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00567"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lohumi, S., Lee, H., Kim, M.S., Qin, J., Kandpal, L.M., Bae, H., Rahman, A., and Cho, B.K. (2018). Calibration and Testing of a Raman Hyperspectral Imaging System to Reveal Powdered Food Adulteration. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0195253"},{"key":"ref_45","first-page":"1","article-title":"Caenorhabditis Elegans as a Model to Study the Impact of Exposure to Light Emitting Diode (LED) Domestic Lighting","volume":"52","author":"Okeremgbo","year":"2017","journal-title":"J. Environ. Sci. Health Part A Toxic\/Hazardous Subst. Environ. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Li, S.X. (2018). Filter Selection for Optimizing the Spectral Sensitivity of Broadband Multispectral Cameras Based on Maximum Linear Independence. Sensors, 18.","DOI":"10.3390\/s18051455"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1109\/JSTARS.2020.2983224","article-title":"A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial\u2013Spectral Fusion","volume":"13","author":"Yu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5408","DOI":"10.1109\/TGRS.2018.2815613","article-title":"Hyperspectral Image Classification With Deep Learning Models","volume":"56","author":"Yang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3453","DOI":"10.1109\/TGRS.2012.2184122","article-title":"A Bayesian Restoration Approach for Hyperspectral Images","volume":"50","author":"Zhang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Oh, S.W., Brown, M.S., Pollefeys, M., and Kim, S.J. (2016, January 27\u201330). Do It Yourself Hyperspectral Imaging with Everyday Digital Cameras. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.270"},{"key":"ref_51","unstructured":"Khan, M.N. (2020). Understanding Led Illumination, Taylor & Francis."},{"key":"ref_52","unstructured":"(2022, December 18). Deep Red LED, 3535 Led Chip, Hyper Red Led. Available online: https:\/\/www.moon-leds.com\/product-3535-deep-red-660nm-smd-led.html."},{"key":"ref_53","unstructured":"(2022, December 18). Royal Blue 3535 SMD LED, 3535 LED, Blue Led. Available online: https:\/\/www.moon-leds.com\/product-royal-blue-450nm-3535-smd-led.html."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Huang, Y., Cohen, T.A., and Luscombe, C.K. (2021). Naturally Derived Organic Dyes for LED Lightings of High Color Rendering and Fidelity Index. ChemRxiv.","DOI":"10.26434\/chemrxiv.14607963"},{"key":"ref_55","unstructured":"(2022, December 18). LED Correlated Color Temperature and 5050 LEDs. Available online: https:\/\/www.boogeylights.com\/understanding-led-color-temperature\/."},{"key":"ref_56","unstructured":"(2022, December 18). PANTONE\u00ae USA|Pantone Color Match Card (PCNCT). Available online: https:\/\/www.pantone.com\/pantone-color-match-card."},{"key":"ref_57","first-page":"243","article-title":"Fruits and Vegetables Quality Evaluation Using Computer Vision: A Review","volume":"33","author":"Bhargava","year":"2021","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"133","DOI":"10.2525\/ecb.44.133","article-title":"Bruise Detection Using NIR Hyperspectral Imaging for Strawberry (Fragaria \u00d7 ananassa Duch.)","volume":"44","author":"Nagata","year":"2006","journal-title":"Environ. Control Biol."},{"key":"ref_59","unstructured":"Sun, D.W. (2016). Computer Vision Technology for Food Quality Evaluation, Academic Press. [2nd ed.]."},{"key":"ref_60","first-page":"77","article-title":"From Wavelength to R G B Filter","volume":"69","author":"Mihai","year":"2007","journal-title":"UPB Sci. Bull."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Krauz, L., P\u00e1ta, P., and Kaiser, J. (2022). Assessing the Spectral Characteristics of Dye- and Pigment-Based Inkjet Prints by VNIR Hyperspectral Imaging. Sensors, 22.","DOI":"10.3390\/s22020603"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1104\/pp.19.00094","article-title":"The Spatial Distribution of Chlorophyll in Leaves","volume":"180","author":"Borsuk","year":"2019","journal-title":"Plant Physiol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1437\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:17:41Z","timestamp":1760120261000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1437"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,28]]},"references-count":62,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031437"],"URL":"https:\/\/doi.org\/10.3390\/s23031437","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,28]]}}}