{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T22:19:35Z","timestamp":1775254775299,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,15]],"date-time":"2018-05-15T00:00:00Z","timestamp":1526342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Bitter pit is one of the most important disorders in apples. Some of the fresh market apple varieties are susceptible to bitter pit disorder. In this study, visible\u2013near-infrared spectrometry-based reflectance spectral data (350\u20132500 nm) were acquired from 2014, 2015 and 2016 harvest produce after 63 days of storage at 5 \u00b0C. Selected spectral features from 2014 season were used to classify the healthy and bitter pit samples from three years. In addition, these spectral features were also validated using hyperspectral imagery data collected on 2016 harvest produce after storage in a commercial storage facility for 5 months. The hyperspectral images were captured from either sides of apples in the range of 550\u20131700 nm. These images were analyzed to extract additional set of spectral features that were effective in bitter pit detection. Based on these features, an automated spatial data analysis algorithm was developed to detect bitter pit points. The pit area was extracted, and logistic regression was used to define the categorizing threshold. This method was able to classify the healthy and bitter pit apples with an accuracy of 85%. Finally, hyperspectral imagery derived spectral features were re-evaluated on the visible\u2013near-infrared reflectance data acquired with spectrometer. The pertinent partial least square regression classification accuracies were in the range of 90\u2013100%. Overall, the study identified salient spectral features based on both hyperspectral spectrometry and imaging techniques that can be used to develop a sensing solution to sort the fruit on the packaging lines.<\/jats:p>","DOI":"10.3390\/s18051561","type":"journal-article","created":{"date-parts":[[2018,5,15]],"date-time":"2018-05-15T03:29:34Z","timestamp":1526354974000},"page":"1561","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Hyperspectral Imaging and Spectrometry-Derived Spectral Features for Bitter Pit Detection in Storage Apples"],"prefix":"10.3390","volume":"18","author":[{"given":"Sanaz","family":"Jarolmasjed","sequence":"first","affiliation":[{"name":"Department of Biological Systems Engineering, Washington State University, PO Box 646120, Pullman, WA 99164, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lav","family":"Khot","sequence":"additional","affiliation":[{"name":"Department of Biological Systems Engineering, Washington State University, PO Box 646120, Pullman, WA 99164, USA"},{"name":"Center for Precision and Automated Agricultural Systems, Washington State University, 24106 North Bunn Road, Prosser, WA 99350, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sindhuja","family":"Sankaran","sequence":"additional","affiliation":[{"name":"Department of Biological Systems Engineering, Washington State University, PO Box 646120, Pullman, WA 99164, USA"},{"name":"Center for Precision and Automated Agricultural Systems, Washington State University, 24106 North Bunn Road, Prosser, WA 99350, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,15]]},"reference":[{"key":"ref_1","first-page":"107","article-title":"Factors involved in fruit calcium deficiency disorders","volume":"40","author":"Mitcham","year":"2012","journal-title":"Hortic. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/0304-4238(83)90078-X","article-title":"Cation distribution and balance in apple fruit in relation to calcium treatments for bitter pit","volume":"19","author":"Ferguson","year":"1983","journal-title":"Sci. Hortic."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.scienta.2013.11.029","article-title":"Relationship between xylem functionality, calcium content and the incidence of bitter pit in apple fruit","volume":"165","author":"Miqueloto","year":"2014","journal-title":"Sci. Hortic."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1590\/S0100-204X2011000700003","article-title":"Physiological, physicochemical and mineral attributes associated with the occurrence of bitter pit in apples","volume":"46","author":"Miqueloto","year":"2011","journal-title":"Pesqui. Agropecu\u00e1ria Bras."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.scienta.2013.03.021","article-title":"Fruit sampling methods to quantify calcium and magnesium contents to predict bitter pit development in \u2018Fuji\u2019 apple: A multivariate approach","volume":"157","author":"Miqueloto","year":"2013","journal-title":"Sci. Hortic."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.postharvbio.2010.02.006","article-title":"Cellular approach to understand bitter pit development in apple fruit","volume":"57","author":"Labavitch","year":"2010","journal-title":"Postharvest Biol. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.compag.2005.10.002","article-title":"Integrating multispectral reflectance and fluorescence imaging for defect detection on apples","volume":"50","author":"Ariana","year":"2006","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.postharvbio.2006.02.004","article-title":"Fluorescence imaging as a non-destructive method for pre-harvest detection of bitter pit in apple fruit (Malus domestica Borkh.)","volume":"40","author":"Huybrechts","year":"2006","journal-title":"Postharvest Biol. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/j.foodchem.2014.10.041","article-title":"Evaluation of the overall quality of olive oil using fluorescence spectroscopy","volume":"173","author":"Baeten","year":"2015","journal-title":"Food Chem."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.foodchem.2016.03.037","article-title":"Fluorescence excitation-emission matrix spectroscopy as a tool for determining quality of sparkling wines","volume":"206","author":"Elcoroaristizabal","year":"2016","journal-title":"Food Chem."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.postharvbio.2016.03.014","article-title":"Postharvest bitter pit detection and progression evaluation in \u2018Honeycrisp\u2019 apples using computed tomography images","volume":"118","author":"Jarolmasjed","year":"2016","journal-title":"Postharvest Biol. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.biosystemseng.2009.01.016","article-title":"Assessment of soft X-ray imaging for detection of fungal infection in wheat","volume":"103","author":"Narvankar","year":"2009","journal-title":"Biosyst. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.jfoodeng.2011.03.007","article-title":"Automated fish bone detection using X-ray imaging","volume":"105","author":"Mery","year":"2011","journal-title":"J. Food Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1007\/s11694-017-9473-x","article-title":"Near infrared spectroscopy to predict bitter pit development in different varieties of apples","volume":"11","author":"Jarolmasjed","year":"2017","journal-title":"J. Food Meas. Charact."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.postharvbio.2016.06.013","article-title":"Robustness of near infrared spectroscopy based spectral features for non-destructive bitter pit detection in honeycrisp apples","volume":"120","author":"Kafle","year":"2016","journal-title":"Postharvest Biol. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1255\/nirn.1637","article-title":"Application of near Infrared Spectroscopy to the Quality Control of Citrus Fruits and Mango","volume":"27","author":"Blasco","year":"2016","journal-title":"NIR News"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.foodcont.2013.07.010","article-title":"Comparison and application of near-infrared (NIR) and mid-infrared (MIR) spectroscopy for determination of quality parameters in soybean samples","volume":"35","author":"Ferreira","year":"2014","journal-title":"Food Control"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.snb.2007.07.048","article-title":"Identification of green tea grade using different feature of response signal from E-nose sensors","volume":"128","author":"Yu","year":"2008","journal-title":"Sens. Actuators B Chem."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.foodres.2009.09.018","article-title":"Application of the electronic nose to the identification of different milk flavorings","volume":"43","author":"Wang","year":"2010","journal-title":"Food Res. Int."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.aca.2009.05.008","article-title":"Instrumental measurement of beer taste attributes using an electronic tongue","volume":"646","author":"Rudnitskaya","year":"2009","journal-title":"Anal. Chim. Acta"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"899","DOI":"10.3390\/s150100899","article-title":"Electronic-Nose Applications for Fruit Identification, Ripeness and Quality Grading","volume":"15","author":"Baietto","year":"2015","journal-title":"Sensors"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.jspr.2007.10.006","article-title":"Thermal imaging to detect infestation by Cryptolestes ferrugineus inside wheat kernels","volume":"44","author":"Manickavasagan","year":"2008","journal-title":"J. Stored Prod. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/S1466-8564(03)00021-3","article-title":"Non-contact bruise detection in apples by thermal imaging","volume":"4","author":"Varith","year":"2003","journal-title":"Innov. Food Sci. Emerg. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.lwt.2007.02.022","article-title":"Early detection of apple bruises on different background colors using hyperspectral imaging","volume":"41","author":"ElMasry","year":"2008","journal-title":"LWT Food Sci. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6978","DOI":"10.1007\/s13197-015-1838-8","article-title":"Non-invasive hyperspectral imaging approach for fruit quality control application and classification: Case study of apple, chikoo, guava fruits","volume":"52","author":"Vetrekar","year":"2015","journal-title":"J. Food Sci. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.lwt.2016.11.063","article-title":"Non-destructive assessment of the internal quality of intact persimmon using colour and VIS\/NIR hyperspectral imaging","volume":"77","author":"Munera","year":"2017","journal-title":"LWT"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.postharvbio.2013.07.005","article-title":"Supervised classification of bruised apples with respect to the time after bruising on the basis of hyperspectral imaging data","volume":"86","author":"Baranowski","year":"2013","journal-title":"Postharvest Biol. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.postharvbio.2017.04.005","article-title":"Glare based apple sorting and iterative algorithm for bruise region detection using shortwave infrared hyperspectral imaging","volume":"130","author":"Keresztes","year":"2017","journal-title":"Postharvest Biol. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.compag.2011.05.010","article-title":"Detection of common defects on oranges using hyperspectral reflectance imaging","volume":"78","author":"Li","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.biosystemseng.2015.11.009","article-title":"Image analysis operations applied to hyperspectral images for non-invasive sensing of food quality\u2014A comprehensive review","volume":"142","author":"ElMasry","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.postharvbio.2005.12.006","article-title":"Non-destructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging","volume":"40","author":"Peirs","year":"2006","journal-title":"Postharvest Biol. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.tifs.2007.06.001","article-title":"Hyperspectral imaging\u2014An emerging process analytical tool for food quality and safety control","volume":"18","author":"Gowen","year":"2007","journal-title":"Trends Food Sci. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Li, C., and Takeda, F. (2016). Nondestructive Detection and Quantification of Blueberry Bruising using Near-infrared (NIR) Hyperspectral Reflectance Imaging. Sci. Rep., 6.","DOI":"10.1038\/srep35679"},{"key":"ref_34","unstructured":"Tobias, R.D. (1995). An introduction to partial least squares regression. Proceedings of the Twentieth Annual SAS Users Group International Conference, SAS Institute Inc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1021\/ac60316a008","article-title":"k-Nearest Neighbor Classification Rule (pattern recognition) applied to nuclear magnetic resonance spectral interpretation","volume":"44","author":"Kowalski","year":"1972","journal-title":"Anal. Chem."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1111\/j.1349-7006.2011.01849.x","article-title":"Cancer detection using infrared hyperspectral imaging","volume":"102","author":"Akbari","year":"2011","journal-title":"Cancer Sci."},{"key":"ref_37","unstructured":"Davis, A.J., Hahlweg, C.F., and Mulley, J.R. (2016). An Automated Imaging BRDF Polarimeter for Fruit Quality Inspection, International Society for Optics and Photonics."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.foodcont.2016.02.007","article-title":"Real-time pixel based early apple bruise detection using short wave infrared hyperspectral imaging in combination with calibration and glare correction techniques","volume":"66","author":"Keresztes","year":"2016","journal-title":"Food Control"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.jfoodeng.2005.03.060","article-title":"Performance of hyperspectral imaging system for poultry surface fecal contaminant detection","volume":"75","author":"Park","year":"2006","journal-title":"J. Food Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/S0925-5214(99)00071-X","article-title":"Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment","volume":"18","author":"Lammertyn","year":"2000","journal-title":"Postharvest Biol. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"056004","DOI":"10.1117\/1.JBO.19.5.056004","article-title":"Deep optical imaging of tissue using the second and third near-infrared spectral windows","volume":"19","author":"Sordillo","year":"2014","journal-title":"J. Biomed. Opt."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1016\/j.foodchem.2008.08.066","article-title":"Rapid and non-destructive analysis of apricot fruit quality using FT-near-infrared spectroscopy","volume":"113","author":"Bureau","year":"2009","journal-title":"Food Chem."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.postharvbio.2014.01.022","article-title":"Development of multispectral imaging algorithm for detection of frass on mature red tomatoes","volume":"93","author":"Yang","year":"2014","journal-title":"Postharvest Biol. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.ifset.2012.11.001","article-title":"Non-destructive assessment of microbial contamination in porcine meat using NIR hyperspectral imaging","volume":"17","author":"Barbin","year":"2013","journal-title":"Innov. Food Sci. Emerg. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1016\/j.foodchem.2012.11.040","article-title":"Near-infrared hyperspectral imaging and partial least squares regression for rapid and reagentless determination of Enterobacteriaceae on chicken fillets","volume":"138","author":"Feng","year":"2013","journal-title":"Food Chem."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.talanta.2012.11.042","article-title":"Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms","volume":"105","author":"Feng","year":"2013","journal-title":"Talanta"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1561\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:04:15Z","timestamp":1760195055000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1561"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,15]]},"references-count":47,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["s18051561"],"URL":"https:\/\/doi.org\/10.3390\/s18051561","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,15]]}}}