{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:01:21Z","timestamp":1778346081579,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T00:00:00Z","timestamp":1602547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Fund for Colleges and Universities in Jiangsu Province","award":["19KJB210003"],"award-info":[{"award-number":["19KJB210003"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["KJQN202045"],"award-info":[{"award-number":["KJQN202045"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural and Science Foundation of China","award":["31901769"],"award-info":[{"award-number":["31901769"]}]},{"name":"Science and Technology Innovation Project for Overseas Students in Nanjing","award":["013040106"],"award-info":[{"award-number":["013040106"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Blueberries, which are rich in nutrition, are susceptible to fungal infection during postharvest or storage. However, early detection of diseases in blueberry is challenging because of their opaque appearance and the inconspicuousness of spots in the early stage of disease. The goal of this study was to investigate the potential of hyperspectral imaging over the spectral range of 400\u20131000 nm to discriminate early disease in blueberries. Scanning electron microscope observation verified that fungal damage to the cellular structure takes place during the early stages. A total of 400 hyperspectral images, 200 samples each of healthy and early disease groups, were collected to obtain mean spectra of each blueberry samples. Spectral correlation analysis was performed to select an effective spectral range. Partial least square discrimination analysis (PLSDA) models were developed using two types of spectral range (i.e., full wavelength range of 400\u20131000 nm and effective spectral range of 685\u20131000 nm). The results showed that the effective spectral range made it possible to provide better classification results due to the elimination of the influence of irrelevant variables. Moreover, the effective spectral range combined with an autoscale preprocessing method was able to obtain optimal classification accuracies, with recognition rates of 100% and 99% for healthy and early disease blueberries. This study demonstrated that it is feasible to use hyperspectral imaging to measure early disease blueberries.<\/jats:p>","DOI":"10.3390\/s20205783","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T21:24:39Z","timestamp":1602710679000},"page":"5783","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Measurement of Early Disease Blueberries Based on Vis\/NIR Hyperspectral Imaging System"],"prefix":"10.3390","volume":"20","author":[{"given":"Yuping","family":"Huang","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dezhen","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyan","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7731","DOI":"10.1021\/jf020690l","article-title":"Absorption of anthocyanins from blueberries and serum antioxidant status in human subjects","volume":"50","author":"Mazza","year":"2002","journal-title":"J. Agric. Food Chem."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.biosystemseng.2020.01.018","article-title":"Fully convolutional networks for blueberry bruising and calyx segmentation using hyperspectral transmittance imaging","volume":"192","author":"Zhang","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.compag.2013.12.001","article-title":"Measurement of mechanical impacts created by rotary, slapper, and sway blueberry mechanical harvesters","volume":"101","author":"Yu","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.postharvbio.2019.01.011","article-title":"Detection of early decay in peaches by structured-illumination reflectance imaging","volume":"151","author":"Sun","year":"2019","journal-title":"Postharvest Biol. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.jfoodeng.2010.06.026","article-title":"NIR spectral imaging with discriminant analysis for detecting foreign materials among blueberries","volume":"101","author":"Sugiyama","year":"2010","journal-title":"J. Food Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.postharvbio.2017.10.011","article-title":"Blueberry bruise detection by pulsed thermographic imaging","volume":"136","author":"Kuzy","year":"2018","journal-title":"Postharvest Biol. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.tifs.2020.02.024","article-title":"Recent advances in imaging techniques for bruise detection in fruits and vegetables","volume":"99","author":"Du","year":"2020","journal-title":"Trends Food Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.compag.2014.08.009","article-title":"Hyperspectral band selection for detecting different blueberry fruit maturity stages","volume":"109","author":"Yang","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.compag.2016.01.015","article-title":"Classification and characterization of blueberry mechanical damage with time evolution using reflectance, transmittance and interactance imaging spectroscopy","volume":"122","author":"Hu","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jfoodeng.2012.10.001","article-title":"Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imagings","volume":"115","author":"Leivavalenzuela","year":"2013","journal-title":"J. Food Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.eaef.2019.11.006","article-title":"Research on simultaneous detection of SSC and FI of blueberry based on hyperspectral imaging combined MS-SPA","volume":"12","author":"Qiao","year":"2019","journal-title":"Eng. Agric. Environ. Food"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.ifset.2014.02.006","article-title":"Assessment of internal quality of blueberries using hyperspectral transmittance and reflectance images with whole spectra or selected wavelengths","volume":"24","author":"Leivavalenzuela","year":"2014","journal-title":"Innov. Food Sci. Emerg. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.jfoodeng.2017.11.030","article-title":"Prediction of Firmness Parameters of Tomatoes by Portable Visible and Near-Infrared Spectroscopy","volume":"222","author":"Huang","year":"2018","journal-title":"J. Food Eng."},{"key":"ref_14","first-page":"2362","article-title":"Measurement of Tomato Quality Attributes Based on Wavelength Ratio and Near-Infrared Spectroscopy","volume":"38","author":"Huang","year":"2018","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_15","first-page":"2183","article-title":"Tomato Maturity Classification Based on Spatially Resolved Spectra","volume":"38","author":"Huang","year":"2018","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.postharvbio.2007.06.024","article-title":"Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review","volume":"46","author":"Nicolai","year":"2007","journal-title":"Postharvest Biol. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1255\/jnirs.1129","article-title":"Quantitative analysis of ingredients of blueberry fruits by near infrared spectroscopy","volume":"22","author":"Bai","year":"2014","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1016\/j.foodres.2011.02.046","article-title":"Near infrared (NIR) spectroscopy as a tool for monitoring blueberry osmo\u2013air dehydration process","volume":"44","author":"Sinelli","year":"2011","journal-title":"Food Res. Int."},{"key":"ref_19","first-page":"174","article-title":"Non-destructive Assessment of Highbush Blueberry Fruit Maturity Parameters and Anthocyanins by Using a Visible\/Near Infrared (vis\/NIR) Spectroscopy Device: A Preliminary Approach","volume":"16","author":"Noferini","year":"2016","journal-title":"J. Soil Sci. Plant Nutr."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103467","DOI":"10.1016\/j.infrared.2020.103467","article-title":"Application of hyperspectral imaging for detecting and visualizing leaf lard adulteration in minced pork","volume":"110","author":"Jiang","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111320","DOI":"10.1016\/j.postharvbio.2020.111320","article-title":"Authentication of the geographic origin of Yangshan region peaches based on hyperspectral imaging","volume":"171","author":"Sun","year":"2021","journal-title":"Postharvest Biol. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jiang, H., Jiang, X., Ru, Y., Chen, Q., Xu, L., and Zhou, H. (2020). Sweetness detection and grading of peaches and nectarines by combining short- and long-wave fourier-transform near-infrared spectroscopy. Anal. Lett.","DOI":"10.1080\/00032719.2020.1795186"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jfoodeng.2018.05.008","article-title":"Assessment of Tomato Soluble Solids Content and pH by Spatially-Resolved and Conventional Vis\/NIR Spectroscopy","volume":"236","author":"Huang","year":"2018","journal-title":"J. Food Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Huang, Y., Yang, Y., Sun, Y., Zhou, H., and Chen, K. (2020). Identification of Apple Varieties Using a Multichannel Hyperspectral Imaging System. Sensors, 20.","DOI":"10.3390\/s20185120"},{"key":"ref_25","first-page":"3585","article-title":"Assessment of Tomato Color by Spatially Resolved and Conventional Vis\/NIR Spectroscopies","volume":"39","author":"Huang","year":"2019","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111065","DOI":"10.1016\/j.postharvbio.2019.111065","article-title":"Detection of internal defect of apples by a multichannel Vis\/NIR spectroscopic system","volume":"161","author":"Huang","year":"2020","journal-title":"Postharvest Biol. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.postharvbio.2019.04.005","article-title":"Pathogenetic process monitoring and early detection of pear black spot disease caused by Alternaria alternata using hyperspectral imaging","volume":"154","author":"Pan","year":"2019","journal-title":"Postharvest Biol. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.foodcont.2017.10.013","article-title":"Early detection of fungal infection of stored apple fruit with optical sensors\u2014Comparison of biospeckle, hyperspectral imaging and chlorophyll fluorescence","volume":"85","author":"Pieczywek","year":"2018","journal-title":"Food Control."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.compag.2016.07.016","article-title":"Fast detection and visualization of early decay in citrus using Vis-NIR hyperspectral imaging","volume":"127","author":"Li","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","first-page":"1","article-title":"Fault Diagnosis of Rolling-Element Bearing Using Multiscale Pattern Gradient Spectrum Entropy Coupled with Laplacian Score","volume":"2020","author":"Yan","year":"2020","journal-title":"Complexity"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yan, X., Liu, Y., and Jia, M. (2020). A Fault Diagnosis Approach for Rolling Bearing Integrated SGMD, IMSDE and Multiclass Relevance Vector Machine. Sensors, 20.","DOI":"10.3390\/s20154352"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yan, X., Liu, Y., Huang, D., and Jia, M. (2020). A new approach to health condition identification of rolling bearing using hierarchical dispersion entropy and improved Laplacian score. Struct. Health Monit.","DOI":"10.1177\/1475921720948620"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1007\/s11760-018-1382-x","article-title":"An improved method for single image super-resolution based on deep learning","volume":"13","author":"Xie","year":"2018","journal-title":"Signal. Image Video Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"109684","DOI":"10.1016\/j.jfoodeng.2019.109684","article-title":"Honey Botanical Origin Classification using Hyperspectral Imaging and Machine Learning","volume":"265","author":"Noviyanto","year":"2020","journal-title":"J. Food Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105321","DOI":"10.1016\/j.compag.2020.105321","article-title":"Progress of hyperspectral data processing and modelling for cereal crop nitrogen monitoring","volume":"172","author":"Fu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.foodchem.2017.05.064","article-title":"Hyperspectral imaging detection of decayed honey peaches based on their chlorophyll content","volume":"235","author":"Sun","year":"2017","journal-title":"Food Chem."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.compag.2010.12.006","article-title":"Early detection of Fusarium infection in wheat using hyper-spectral imaging","volume":"75","author":"Bauriegel","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"126704","DOI":"10.1016\/j.foodchem.2020.126704","article-title":"Assessment of the optical properties of peaches with fungal infection using spatially-resolved diffuse reflectance technique and their relationships with tissue structural and biochemical properties","volume":"321","author":"Sun","year":"2020","journal-title":"Food Chem."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lu, R. (2016). Light Scattering Technology for Food Property, Quality and Safety Assessment, CRC Press.","DOI":"10.1201\/b20220"},{"key":"ref_40","first-page":"8895875","article-title":"Detection and Classification of Early Decay on Blueberry Based on Improved Deep Residual 3D Convolutional Neural Network in Hyperspectral Images","volume":"2020","author":"Qiao","year":"2020","journal-title":"Sci. Program."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"35679","DOI":"10.1038\/srep35679","article-title":"Nondestructive Detection and Quantification of Blueberry Bruising using Near-infrared (NIR) Hyperspectral Reflectance Imaging","volume":"6","author":"Yu","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.postharvbio.2017.08.012","article-title":"Detection of blueberry internal bruising over time using NIR hyperspectral reflectance imaging with optimum wavelengths","volume":"134","author":"Fan","year":"2017","journal-title":"Postharvest Biol. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/20\/5783\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:20:14Z","timestamp":1760178014000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/20\/5783"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,13]]},"references-count":42,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["s20205783"],"URL":"https:\/\/doi.org\/10.3390\/s20205783","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,13]]}}}