{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T20:57:06Z","timestamp":1771275426928,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,26]],"date-time":"2019-11-26T00:00:00Z","timestamp":1574726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UID\/Multi\/00631\/2019"],"award-info":[{"award-number":["UID\/Multi\/00631\/2019"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["Ci\u00eancia 2008"],"award-info":[{"award-number":["Ci\u00eancia 2008"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["OtiCalFrut (ALG-01-0247-FEDER-033652)"],"award-info":[{"award-number":["OtiCalFrut (ALG-01-0247-FEDER-033652)"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Caixa Geral de Dep\u00f3sitos - CGD","award":["Ideias em Caixa"],"award-info":[{"award-number":["Ideias em Caixa"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper we report a method to determine the soluble solids content (SSC) of \u2018Rocha\u2019 pear (Pyrus communis L. cv. Rocha) based on their short-wave NIR reflectance spectra (500\u20131100 nm) measured in conditions similar to those found in packinghouse fruit sorting facilities. We obtained 3300 reflectance spectra from pears acquired from different lots, producers and with diverse storage times and ripening stages. The macroscopic properties of the pears, such as size, temperature and SSC were measured under controlled laboratory conditions. For the spectral analysis, we implemented a computational pipeline that incorporates multiple pre-processing techniques including a feature selection procedure, various multivariate regression models and three different validation strategies. This benchmark allowed us to find the best model\/preproccesing procedure for SSC prediction from our data. From the several calibration models tested, we have found that Support Vector Machines provides the best predictions metrics with an RMSEP of around 0.82      \u2218    Brix and 1.09      \u2218    Brix for internal and external validation strategies respectively. The latter validation was implemented to assess the prediction accuracy of this calibration method under more \u2018real world-like\u2019 conditions. We also show that incorporating information about the fruit temperature and size to the calibration models improves SSC predictability. Our results indicate that the methodology presented here could be implemented in existing packinghouse facilities for single fruit SSC characterization.<\/jats:p>","DOI":"10.3390\/s19235165","type":"journal-article","created":{"date-parts":[[2019,11,26]],"date-time":"2019-11-26T10:57:27Z","timestamp":1574765847000},"page":"5165","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Non-Destructive Soluble Solids Content Determination for \u2018Rocha\u2019 Pear Based on VIS-SWNIR Spectroscopy under \u2018Real World\u2019 Sorting Facility Conditions"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5345-5119","authenticated-orcid":false,"given":"D\u00e1rio","family":"Passos","sequence":"first","affiliation":[{"name":"CEOT, Universidade do Algarve, Campus de Gambelas, FCT Ed.2, 8005-189 Faro, Portugal"}]},{"given":"Daniela","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"CEOT, Universidade do Algarve, Campus de Gambelas, FCT Ed.2, 8005-189 Faro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2708-5991","authenticated-orcid":false,"given":"Ana","family":"Cavaco","sequence":"additional","affiliation":[{"name":"CEOT, Universidade do Algarve, Campus de Gambelas, FCT Ed.2, 8005-189 Faro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8913-6136","authenticated-orcid":false,"given":"Maria","family":"Antunes","sequence":"additional","affiliation":[{"name":"MeditBio, Universidade do Algarve, Campus de Gambelas, FCT Ed.8, 8005-189 Faro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8642-5792","authenticated-orcid":false,"given":"Rui","family":"Guerra","sequence":"additional","affiliation":[{"name":"CEOT, Universidade do Algarve, Campus de Gambelas, FCT Ed.2, 8005-189 Faro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1098\/rstb.2009.0201","article-title":"Food security: Contributions from science to a new and greener revolution","volume":"365","author":"Beddington","year":"2010","journal-title":"Philos. Trans. R. Soc. B"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zambon, I., Cecchini, M., Egidi, G., Saporito, M.G., and Colantoni, A. (2019). Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs. Processes, 7.","DOI":"10.3390\/pr7010036"},{"key":"ref_3","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":"Beullens","year":"2007","journal-title":"Postharvest Biol. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ozaki, Y., McClure, W.F., and Christy, A.A. (2007). Near Infrared Spectroscopy in Food Science and Technology, Wiley.","DOI":"10.1002\/0470047704"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.postharvbio.2008.08.013","article-title":"Rocha pear firmness predicted by a Vis\/NIR segmented model","volume":"51","author":"Cavaco","year":"2009","journal-title":"Postharvest Biol. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lu, R. (2016). Light Scattering Technology for Food Property, Quality and Safety Assessment, Chapman and Hall\/CRC.","DOI":"10.1201\/b20220"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.postharvbio.2017.05.014","article-title":"A TSS classification study of \u2019Rocha\u2019 pear (Pyrus communis L.) based on non-invasive visible\/near infra-red reflectance spectra","volume":"132","author":"Bexiga","year":"2017","journal-title":"Postharvest Biol. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Franca, A.S., and Nollet, L.M.L. (2018). Spectroscopic Methods in Food Analysis, CRC Press.","DOI":"10.1201\/9781315152769"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.scienta.2015.01.001","article-title":"Analytical methods for determination of sugars and sweetness of horticultural products\u2014A review","volume":"184","author":"Magwaza","year":"2015","journal-title":"Sci. Hortic."},{"key":"ref_10","unstructured":"(2018). Estatisticas Agricolas 2017, INE I.P. Statistics, P.L.P."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.postharvbio.2007.06.001","article-title":"Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear","volume":"47","author":"Verlinden","year":"2008","journal-title":"Postharvest Biol. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1016\/j.lwt.2007.10.017","article-title":"Nondestructive determination of pear internal quality indices by visible and near-infrared spectrometry","volume":"41","author":"Liu","year":"2008","journal-title":"LWT Food Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.postharvbio.2017.04.004","article-title":"Using absorption and reduced scattering coefficients for non-destructive analyses of fruit flesh firmness and soluble solids content in pear (Pyrus communis \u2018Conference\u2019)\u2014An update when using diffusion theory","volume":"130","author":"Adebayo","year":"2017","journal-title":"Postharvest Biol. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.postharvbio.2017.03.012","article-title":"Development of multi-cultivar models for predicting the soluble solid content and firmness of European pear (Pyrus communis L.) using portable VIS-NIR spectroscopy","volume":"129","author":"Wang","year":"2017","journal-title":"Postharvest Biol. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1007\/s12161-018-1326-7","article-title":"Comparison and Optimization of Models for Determination of Sugar Content in Pear by Portable Vis-NIR Spectroscopy Coupled with Wavelength Selection Algorithm","volume":"12","author":"Li","year":"2018","journal-title":"Food Anal. Methods"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lu, M., Li, C.R., Li, L., Wu, Y., and Yang, Y. (2018, January 25\u201327). Rapid Detecting Soluble Solid Content of Pears Based on Near-Infrared Spectroscopy. Proceedings of the 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi\u2019an, China.","DOI":"10.1109\/IMCEC.2018.8469315"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1021\/ac9709920","article-title":"Influence of Temperature on Vibrational Spectra and Consequences for the Predictive Ability of Multivariate Models","volume":"70","author":"Wulfert","year":"1998","journal-title":"Anal. Chem."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S0169-7439(00)00069-1","article-title":"Linear techniques to correct for temperature-induced spectral variation in multivariate calibration","volume":"51","author":"Wulfert","year":"2000","journal-title":"Chemom. Intell. Lab."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/S1474-6670(17)31867-0","article-title":"Modelling Temperature-Induced Spectral Variations in Chemical Process Monitoring","volume":"37","author":"Chen","year":"2004","journal-title":"IFAC Proc. Vol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1255\/jnirs.457","article-title":"Temperature Robust Multivariate Calibration: An Overview of Methods for Dealing with Temperature Influences on near Infrared Spectra","volume":"13","author":"Hageman","year":"2005","journal-title":"J. Near Infrared Spec."},{"key":"ref_21","first-page":"9","article-title":"Influence of temperature on visible and near-infrared spectra and the predictive ability of multivariate models","volume":"Volume 7676","author":"Kim","year":"2010","journal-title":"Sensing for Agriculture and Food Quality and Safety II"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1255\/jnirs.882","article-title":"The Importance of Choosing the Right Validation Strategy in Inverse Modelling","volume":"18","author":"Kemps","year":"2010","journal-title":"J. Near Infrared Spec."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s41664-018-0068-2","article-title":"On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning","volume":"2","author":"Xu","year":"2018","journal-title":"J. Anal. Test."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.infrared.2018.06.019","article-title":"Non-destructive prediction of soluble solids content of pear based on fruit surface feature classification and multivariate regression analysis","volume":"92","author":"Tian","year":"2018","journal-title":"Infrared Phys. Tech."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1703","DOI":"10.1039\/c0an00387e","article-title":"Support vector machine regression (SVR\/LS-SVM)\u2014An 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_26","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1039\/C3AY42165A","article-title":"A combination algorithm for variable selection to determine soluble solid content and firmness of pears","volume":"6","author":"Li","year":"2014","journal-title":"Anal. Methods"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.1016\/j.fuel.2010.11.038","article-title":"Neural network (ANN) approach to biodiesel analysis: Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy","volume":"90","author":"Balabin","year":"2011","journal-title":"Fuel"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"439567","DOI":"10.1155\/2012\/439567","article-title":"Using Reflectance Spectroscopy and Artificial Neural Network to Assess Water Infiltration Rate into the Soil Profile","volume":"2012","author":"Goldshleger","year":"2012","journal-title":"Appl. Environ. Soil Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1590\/S0100-06832014000600014","article-title":"Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy","volume":"38","author":"Tekin","year":"2014","journal-title":"Revista Brasileira de Ci\u00eancia do Solo"},{"key":"ref_30","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1016\/j.trac.2009.07.007","article-title":"Review of the most common pre-processing techniques for near-infrared spectra","volume":"28","author":"Rinnan","year":"2009","journal-title":"TRAC Trend. Anal. Chem."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1366\/0003702894202201","article-title":"Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra","volume":"43","author":"Barnes","year":"1989","journal-title":"Appl. Spectrosc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1366\/000370203321535033","article-title":"Short-Wavelength Near-Infrared Spectra of Sucrose, Glucose, and Fructose with Respect to Sugar Concentration and Temperature","volume":"57","author":"Golic","year":"2003","journal-title":"Appl. Spectrosc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.postharvbio.2018.03.013","article-title":"Validation of short wave near infrared calibration models for the quality and ripening of Newhall orange on tree across years and orchards","volume":"141","author":"Cavaco","year":"2018","journal-title":"Postharvest Biol. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0003-2670(86)80028-9","article-title":"Partial least-squares regression: A tutorial","volume":"185","author":"Geladi","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.chemolab.2012.07.010","article-title":"A review of variable selection methods in Partial Least Squares Regression","volume":"118","author":"Mehmood","year":"2012","journal-title":"Chemom. Intell. Lab."},{"key":"ref_38","unstructured":"Pellicia, D. (2018). A Variable Selection Method for PLS in Python, Instruments & Data Tools Pty Ltd."},{"key":"ref_39","unstructured":"Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., and Vapnik, V. (1996, January 3\u20135). Support Vector Regression Machines. Proceedings of the 9th International Conference on Neural Information Processing Systems, NIPS\u201996, Denver, CO, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.asoc.2017.07.017","article-title":"A hyperparameters selection technique for support vector regression models","volume":"61","author":"Tsirikoglou","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.chemolab.2015.01.001","article-title":"Fast optimization of hyperparameters for support vector regression models with highly predictive ability","volume":"142","author":"Kaneko","year":"2015","journal-title":"Chemom. Intell. Lab."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0893-6080(03)00169-2","article-title":"Practical selection of SVM parameters and noise estimation for SVM regression","volume":"17","author":"Cherkassky","year":"2004","journal-title":"Neural Netw."},{"key":"ref_44","unstructured":"Tang, Y., Guo, W., and Gao, J. (April, January 30). Efficient model selection for Support Vector Machine with Gaussian kernel function. Proceedings of the 2009 IEEE Symposium on Computational Intelligence and Data Mining, Nashville, TN, USA."},{"key":"ref_45","unstructured":"Heaton, J. (2008). Introduction to Neural Networks with JAVA, Heaton Research."},{"key":"ref_46","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.aca.2009.06.046","article-title":"Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration","volume":"648","author":"Li","year":"2009","journal-title":"Anal. Chim. Acta"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Malek, S., Melgani, F., and Bazi, Y. (2017). One-dimensional convolutional neural networks for spectroscopic signal regression. J. Chemom., 32.","DOI":"10.1002\/cem.2977"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.chemolab.2018.07.008","article-title":"Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration","volume":"182","author":"Cui","year":"2018","journal-title":"Chemom. Intell. Lab."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.saa.2018.10.028","article-title":"Variable weighted convolutional neural network for the nitrogen content quantization of Masson pine seedling leaves with near-infrared spectroscopy","volume":"209","author":"Ni","year":"2019","journal-title":"Spectrochim. Acta A"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5165\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:37:29Z","timestamp":1760189849000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5165"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,26]]},"references-count":50,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19235165"],"URL":"https:\/\/doi.org\/10.3390\/s19235165","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,26]]}}}