{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T19:34:38Z","timestamp":1780601678292,"version":"3.54.1"},"reference-count":39,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,19]],"date-time":"2018-04-19T00:00:00Z","timestamp":1524096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Support Program of China","award":["2015BAD19B03"],"award-info":[{"award-number":["2015BAD19B03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study aimed to develop an approach for quickly and noninvasively differentiating the roasting degrees of coffee beans using hyperspectral imaging (HSI). The qualitative properties of seven roasting degrees of coffee beans (unroasted, light, moderately light, light medium, medium, moderately dark, and dark) were assayed, including moisture, crude fat, trigonelline, chlorogenic acid, and caffeine contents. These properties were influenced greatly by the respective roasting degree. Their hyperspectral images (874\u20131734 nm) were collected using a hyperspectral reflectance imaging system. The spectra of the regions of interest were manually extracted from the HSI images. Then, principal components analysis was employed to compress the spectral data and select the optimal wavelengths based on loading weight analysis. Meanwhile, the random frog (RF) methodology and the successive projections algorithm were also adopted to pick effective wavelengths from the spectral data. Finally, least squares support vector machine (LS-SVM) was utilized to establish discriminative models using spectral reflectance and corresponding labeled classes for each degree of roast sample. The results showed that the LS-SVM model, established by the RF selecting method, with eight wavelengths performed very well, achieving an overall classification accuracy of 90.30%. In conclusion, HSI was illustrated as a potential technique for noninvasively classifying the roasting degrees of coffee beans and might have an important application for the development of nondestructive, real-time, and portable sensors to monitor the roasting process of coffee beans.<\/jats:p>","DOI":"10.3390\/s18041259","type":"journal-article","created":{"date-parts":[[2018,4,20]],"date-time":"2018-04-20T04:24:21Z","timestamp":1524198261000},"page":"1259","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Development of Noninvasive Classification Methods for Different Roasting Degrees of Coffee Beans Using Hyperspectral Imaging"],"prefix":"10.3390","volume":"18","author":[{"given":"Bingquan","family":"Chu","sequence":"first","affiliation":[{"name":"School of Biological and Chemical Engineering\/School of Light Industry, Zhejiang Provincial Key Laboratory of Chemical and Biological Processing Technology of Farm Products, Zhejiang Provincial Collaborative Innovation Center of Agricultural Biological Resources Biochemical Manufacturing, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keqiang","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanru","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6752-1757","authenticated-orcid":false,"given":"Yong","family":"He","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.jfoodeng.2008.12.012","article-title":"A preliminary evaluation of the effect of processing temperature on coffee roasting degree assessment","volume":"92","author":"Franca","year":"2009","journal-title":"J. Food Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/S0950-3293(00)00042-2","article-title":"Optimization of the roasting of robusta coffee (C. canephora conillon) using acceptability tests and RSM","volume":"12","author":"Mendes","year":"2001","journal-title":"Food Qual. Prefer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5836","DOI":"10.1021\/jf800327j","article-title":"Coffee roasting and aroma formation: Application of different time-temperature conditions","volume":"56","author":"Baggenstoss","year":"2008","journal-title":"J. Agric. Food Chem."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1652","DOI":"10.1021\/jf303067q","article-title":"Non-separative headspace solid phase microextraction-mass spectrometry profile as a marker to monitor coffee roasting degree","volume":"61","author":"Liberto","year":"2013","journal-title":"J. Agric. Food Chem."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1007\/s11947-016-1843-6","article-title":"A non-invasive real-time methodology for the quantification of antioxidant properties in coffee during the roasting process based on near-infrared spectroscopy","volume":"10","author":"Catelani","year":"2017","journal-title":"Food Bioprocess Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.foodchem.2004.02.039","article-title":"Roast effects on coffee amino acid enantiomers","volume":"89","author":"Casal","year":"2005","journal-title":"Food Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.foodchem.2016.03.114","article-title":"In-line monitoring of the coffee roasting process with near infrared spectroscopy: Measurement of sucrose and color","volume":"208","author":"Santos","year":"2016","journal-title":"Food Chem."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.foodchem.2016.09.189","article-title":"Qualitative properties of roasting defect beans and development of its classification methods by hyperspectral imaging technology","volume":"220","author":"Cho","year":"2017","journal-title":"Food Chem."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.aca.2008.07.013","article-title":"Near infrared spectroscopy: An analytical tool to predict coffee roasting degree","volume":"625","author":"Alessandrini","year":"2008","journal-title":"Anal. Chim. Acta"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1016\/j.foodchem.2011.05.059","article-title":"Comparative study of polyphenols and caffeine in different coffee varieties affected by the degree of roasting","volume":"129","author":"Komes","year":"2011","journal-title":"Food Chem."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0260-8774(00)00116-3","article-title":"A preliminary study on the feasibility of using composition of coffee roasting exhaust gas for the determination of the degree of roast","volume":"47","author":"Dutra","year":"2001","journal-title":"J. Food Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2943","DOI":"10.1007\/s11947-012-0928-0","article-title":"Application of time series hyperspectral imaging (TS-HSI) for determining water distribution within beef and spectral kinetic analysis during dehydration","volume":"6","author":"Wu","year":"2013","journal-title":"Food Bioprocess Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Qiu, Z.J., Chen, J., Zhao, Y.Y., Zhu, S.S., He, Y., and Zhang, C. (2018). Variety identification of single rice seed using hyperspectral imaging combined with convolutional neural network. Appl. Sci., 8.","DOI":"10.3390\/app8020212"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11947-016-1817-8","article-title":"Extraction of spectral information from hyperspectral data and application of hyperspectral imaging for food and agricultural products","volume":"10","author":"Ravikanth","year":"2017","journal-title":"Food Bioprocess Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.ifset.2012.06.003","article-title":"Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression","volume":"16","author":"Kamruzzaman","year":"2012","journal-title":"Innov. Food Sci. Emerg."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.foodres.2017.12.031","article-title":"Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging","volume":"106","author":"Caporaso","year":"2018","journal-title":"Food Res. Int."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s11947-016-1809-8","article-title":"Application of near-infrared hyperspectral imaging with variable selection methods to determine and visualize caffeine content of coffee beans","volume":"10","author":"Zhang","year":"2017","journal-title":"Food Bioprocess Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jfoodeng.2016.06.010","article-title":"Using hyperspectral imaging to characterize consistency of coffee brands and their respective roasting classes","volume":"190","author":"Nansen","year":"2016","journal-title":"J. Food Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/00401706.1969.10490666","article-title":"Computer aided design of experiments","volume":"11","author":"Kennard","year":"1969","journal-title":"Technometrics"},{"key":"ref_20","first-page":"128","article-title":"Simultaneous determination of 10 polyphenolic and alkaloidal components in coffee and coffee-based products by HPLC-double wavelength UV detection","volume":"37","author":"Shao","year":"2016","journal-title":"Food Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"13","DOI":"10.13031\/trans.59.10536","article-title":"Mapping of chlorophyll and SPAD distribution in pepper leaves during leaf senescence using visible and near-infrared hyperspectral imaging","volume":"59","author":"Yu","year":"2016","journal-title":"Trans. ASABE"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.talanta.2012.10.020","article-title":"Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis","volume":"103","author":"Kamruzzaman","year":"2013","journal-title":"Talanta"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2931","DOI":"10.1007\/s11947-012-0825-6","article-title":"Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen-thawed fish fillets","volume":"6","author":"Zhu","year":"2013","journal-title":"Food Bioprocess Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/LGRS.2005.856701","article-title":"A fast iterative algorithm for implementation of pixel purity index","volume":"3","author":"Chang","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.1002\/aic.690440712","article-title":"Multiscale PCA with application to multivariate statistical process monitoring","volume":"44","author":"Bakshi","year":"1998","journal-title":"AIChE J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.aca.2012.06.031","article-title":"Random frog: An efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification","volume":"740","author":"Li","year":"2012","journal-title":"Anal. Chim. Acta"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0169-7439(01)00119-8","article-title":"The successive projections algorithm for variable selection in spectroscopic multicomponent analysis","volume":"57","author":"Araujo","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.chemolab.2004.01.003","article-title":"Application of LS-SVM to non-linear phenomena in NIR spectroscopy: Development of a robust and portable sensor for acidity prediction in grapes","volume":"71","author":"Chauchard","year":"2004","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1703","DOI":"10.1039\/c0an00387e","article-title":"Support vector machine regression (SVR\/LS-SVM)-an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data","volume":"136","author":"Balabin","year":"2011","journal-title":"Analyst"},{"key":"ref_30","unstructured":"Yu, J. (2014). Sensory and Evaluation of Coffee in Different Roasting Degrees and Analysis of the Main Effecting Chemicals. [Ph.D. Thesis, Jiangnan University]. (In Chinese)."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/S0308-8146(01)00204-7","article-title":"Caffeine, trigonelline, chlorogenic acids and sucrose diversity in wild Coffea arabica L. and C. canephora P. accessions","volume":"75","author":"Ky","year":"2001","journal-title":"Food Chem."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"E30","DOI":"10.1017\/S0029665109992187","article-title":"The effect of green-coffee-bean extract rich in chlorogenic acid on antioxidant status of healthy human volunteers","volume":"69","author":"Almoosawi","year":"2009","journal-title":"Proc. Nutr. Soc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.foodchem.2007.05.091","article-title":"Chlorogenic acid and caffeine contents in various commercial brewed coffees","volume":"106","author":"Fujioka","year":"2008","journal-title":"Food Chem."},{"key":"ref_34","first-page":"125","article-title":"Correlation analysis between chemical components and sensory quality of coffee","volume":"34","author":"Hu","year":"2013","journal-title":"Sci. Technol. Food Ind."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.jfoodeng.2018.01.009","article-title":"Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging","volume":"227","author":"Caporaso","year":"2018","journal-title":"J. Food Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.aca.2012.10.007","article-title":"Characterisation of hydrogen bond perturbations in aqueous systems using aquaphotomics and multivariate curve resolution-alternating least squares","volume":"759","author":"Gowen","year":"2013","journal-title":"Anal. Chim. Acta"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.jfoodeng.2011.03.031","article-title":"Prediction of optimal cooking time for boiled potatoes by hyperspectral imaging","volume":"105","author":"Trong","year":"2011","journal-title":"J. Food Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.jfoodeng.2010.12.011","article-title":"Classification of longan fruit bruising using visible spectroscopy","volume":"104","author":"Pholpho","year":"2011","journal-title":"J. Food Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s11947-013-1193-6","article-title":"Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry","volume":"7","author":"Liu","year":"2014","journal-title":"Food Bioprocess Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1259\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:01:19Z","timestamp":1760194879000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1259"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,19]]},"references-count":39,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["s18041259"],"URL":"https:\/\/doi.org\/10.3390\/s18041259","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,19]]}}}