{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T09:12:47Z","timestamp":1770455567060,"version":"3.49.0"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004901","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Minas Gerais","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004901","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioData Mining"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1186\/s13040-019-0200-5","type":"journal-article","created":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T13:03:25Z","timestamp":1562591005000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["On the utilization of deep and ensemble learning to detect milk adulteration"],"prefix":"10.1186","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0349-1645","authenticated-orcid":false,"given":"Habib Asseiss","family":"Neto","sequence":"first","affiliation":[]},{"given":"Wanessa L.F.","family":"Tavares","sequence":"additional","affiliation":[]},{"given":"Daniela C.S.Z.","family":"Ribeiro","sequence":"additional","affiliation":[]},{"given":"Ronnie C.O.","family":"Alves","sequence":"additional","affiliation":[]},{"given":"Leorges M.","family":"Fonseca","sequence":"additional","affiliation":[]},{"given":"S\u00e9rgio V.A.","family":"Campos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,7,8]]},"reference":[{"key":"200_CR1","volume-title":"Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation. OSDI\u201916","author":"M Abadi","year":"2016","unstructured":"Abadi M, et al.TensorFlow: A System for Large-scale Machine Learning. In: Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation. OSDI\u201916. Savannah: USENIX Association: 2016. p. 265\u201383."},{"issue":"6","key":"200_CR2","doi-asserted-by":"publisher","first-page":"3559","DOI":"10.3168\/jds.2014-8548","volume":"98","author":"R Alves da Rocha","year":"2015","unstructured":"Alves da Rocha R, Paiva IM, Anjos V, Furtado MAM, Valenzuela MJ. Quantification of whey in fluid milk using confocal raman microscopy and artificial neural network. J Dairy Sci. 2015; 98(6):3559\u201367. \n                    https:\/\/doi.org\/10.3168\/jds.2014-8548\n                    \n                  .","journal-title":"J Dairy Sci"},{"key":"200_CR3","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1214\/09-SS054","volume":"4","author":"S Arlot","year":"2010","unstructured":"Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Stat Surv. 2010; 4:40\u201379. \n                    https:\/\/doi.org\/10.1214\/09-SS054\n                    \n                  .","journal-title":"Stat Surv"},{"key":"200_CR4","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.foodchem.2015.02.077","volume":"181","author":"BG Botelho","year":"2015","unstructured":"Botelho BG, Reis N, Oliveira LS, Sena MM. Development and analytical validation of a screening method for simultaneous detection of five adulterants in raw milk using mid-infrared spectroscopy and PLS-DA. Food Chem. 2015; 181:31\u20137. \n                    https:\/\/doi.org\/10.1016\/j.foodchem.2015.02.077\n                    \n                  .","journal-title":"Food Chem"},{"key":"200_CR5","unstructured":"Chollet F, et al.Keras.Microtome Publishing; 2015. Available at \n                    https:\/\/keras.io\n                    \n                  . Accessed 16 Aug 2018."},{"issue":"4","key":"200_CR6","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1111\/j.1471-0307.2011.00722.x","volume":"64","author":"AG Cruz","year":"2011","unstructured":"Cruz AG, Cadena RS, Faria JAF, Oliveira CAF, Cavalcanti RN, Bona E, Bolini HMA, Da Silva MAAP. Consumer acceptability and purchase intent of probiotic yoghurt with added glucose oxidase using sensometrics, artificial neural networks and logistic regression. Int J Dairy Technol. 2011; 64(4):549\u201356. \n                    https:\/\/doi.org\/10.1111\/j.1471-0307.2011.00722.x\n                    \n                  .","journal-title":"Int J Dairy Technol"},{"key":"200_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.foodchem.2014.11.003","volume":"174","author":"BMA de Carvalho","year":"2015","unstructured":"de Carvalho BMA, de Carvalho LM, dos Reis Coimbra JS, Minim LA, de Souza Barcellos E, da Silva J\u00fanior WF, Detmann E, de Carvalho GGP. Rapid detection of whey in milk powder samples by spectrophotometric and multivariate calibration. Food Chem. 2015; 174:1\u20137. \n                    https:\/\/doi.org\/10.1016\/j.foodchem.2014.11.003\n                    \n                  .","journal-title":"Food Chem"},{"issue":"5","key":"200_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10661-015-4489-3","volume":"187","author":"V Eisavi","year":"2015","unstructured":"Eisavi V, Homayouni S, Yazdi AM, Alimohammadi A. Land cover mapping based on random forest classification of multitemporal spectral and thermal images. Environ Monit Assess. 2015; 187(5):1\u201314. \n                    https:\/\/doi.org\/10.1007\/s10661-015-4489-3\n                    \n                  .","journal-title":"Environ Monit Assess"},{"key":"200_CR9","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.foodchem.2017.03.022","volume":"230","author":"CdS Gondim","year":"2017","unstructured":"Gondim CdS, Junqueira RG, de Souza SVC, Ruis\u00e1nchez I, Callao MP. Detection of several common adulterants in raw milk by MID-infrared spectroscopy and one-class and multi-class multivariate strategies. Food Chem. 2017; 230:68\u201375. \n                    https:\/\/doi.org\/10.1016\/j.foodchem.2017.03.022\n                    \n                  .","journal-title":"Food Chem"},{"key":"200_CR10","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"T Hastie","year":"2017","unstructured":"Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd. New York: Springer; 2017, p. 745."},{"key":"200_CR11","volume-title":"Proceedings of the 32nd International Conference on Machine Learning","author":"S Ioffe","year":"2015","unstructured":"Ioffe S, Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In: Proceedings of the 32nd International Conference on Machine Learning. Lille: PMLR: 2015. p. 448\u201356."},{"issue":"2","key":"200_CR12","first-page":"629","volume":"3","author":"M Kartheek","year":"2011","unstructured":"Kartheek M, Anton Smith A, Kottai Muthu A, Manavalan R. Determination of Adulterants in Food: A Review. J Chem Pharm Res. 2011; 3(2):629\u201336.","journal-title":"J Chem Pharm Res"},{"key":"200_CR13","unstructured":"Kingma DP, Ba J. Adam: A method for stochastic optimization. 2014. CoRR http:\/\/arxiv.org\/abs\/1412.6980. Accessed 2 July 2018."},{"key":"200_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-6849-3","volume-title":"Applied Predictive Modeling","author":"M Kuhn","year":"2013","unstructured":"Kuhn M, Johnson K. Applied Predictive Modeling, 1st. New York: Springer; 2013, p. 600. \n                    https:\/\/doi.org\/10.1007\/978-1-4614-6849-3\n                    \n                  ."},{"key":"200_CR15","doi-asserted-by":"publisher","unstructured":"Kumar A, Khadkevich M, Fugen C. Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes. 2018 IEEE Int Conf Acoust, Speech and Sig Process (ICASSP). 2018:326\u201330. \n                    https:\/\/doi.org\/10.1109\/icassp.2017.7952132\n                    \n                  .","DOI":"10.1109\/icassp.2017.7952132"},{"key":"200_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/healthcom.2017.8210784","volume-title":"2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom)","author":"D Li","year":"2017","unstructured":"Li D, Zhang J, Zhang Q, Wei X. Classification of ECG signals based on 1D convolution neural network. In: 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom). Dalian: IEEE: 2017. p. 1\u20136. \n                    https:\/\/doi.org\/10.1109\/healthcom.2017.8210784\n                    \n                  ."},{"issue":"21","key":"200_CR17","doi-asserted-by":"publisher","first-page":"4067","DOI":"10.1039\/C7AN01371J","volume":"142","author":"J Liu","year":"2017","unstructured":"Liu J, Osadchy M, Ashton L, Foster M, Solomon CJ, Gibson SJ. Deep convolutional neural networks for Raman spectrum recognition: A unified solution. Analyst. 2017; 142(21):4067\u201374. \n                    https:\/\/doi.org\/10.1039\/c7an01371j\n                    \n                  .","journal-title":"Analyst"},{"key":"200_CR18","volume-title":"Proceedings of the 30th International Conference on Machine Learning","author":"AL Maas","year":"2013","unstructured":"Maas AL, Hannun AY, Ng AY. Rectifier Nonlinearities Improve Neural Network Acoustic Models. In: Proceedings of the 30th International Conference on Machine Learning. Atlanta: Microtome Publishing: 2013."},{"issue":"5","key":"200_CR19","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1093\/bib\/bbw068","volume":"18","author":"S Min","year":"2017","unstructured":"Min S, Lee B, Yoon S. Deep learning in bioinformatics. Brief Bioinform. 2017; 18(5):851\u201369. \n                    https:\/\/doi.org\/10.1093\/bib\/bbw068\n                    \n                  .","journal-title":"Brief Bioinform"},{"key":"200_CR20","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011; 12:2825\u201330.","journal-title":"J Mach Learn Res"},{"issue":"3","key":"200_CR21","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","volume":"6","author":"R Polikar","year":"2006","unstructured":"Polikar R. Ensemble based systems in decision making. IEEE Circ Syst Mag. 2006; 6(3):21\u201345. \n                    https:\/\/doi.org\/10.1109\/MCAS.2006.1688199\n                    \n                  .","journal-title":"IEEE Circ Syst Mag"},{"issue":"1","key":"200_CR22","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.foodchem.2012.10.024","volume":"138","author":"PM Santos","year":"2013","unstructured":"Santos PM, Pereira-Filho ER, Rodriguez-Saona LE. Rapid detection and quantification of milk adulteration using infrared microspectroscopy and chemometrics analysis. Food Chem. 2013; 138(1):19\u201324. \n                    https:\/\/doi.org\/10.1016\/j.foodchem.2012.10.024\n                    \n                  .","journal-title":"Food Chem"},{"key":"200_CR23","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J. Deep learning in neural networks: An overview. Neural Netw. 2015; 61:85\u2013117. \n                    https:\/\/doi.org\/10.1016\/j.neunet.2014.09.003\n                    \n                  .","journal-title":"Neural Netw"},{"issue":"2","key":"200_CR24","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1016\/j.foodchem.2010.06.074","volume":"124","author":"SS Souza","year":"2011","unstructured":"Souza SS, Cruz AG, Walter EHM, Faria JAF, Celeghini RMS, Ferreira MMC, Granato D, de S. Sant\u2019Ana A. Monitoring the authenticity of Brazilian UHT milk: A chemometric approach. Food Chem. 2011; 124(2):692\u20135. \n                    https:\/\/doi.org\/10.1016\/j.foodchem.2010.06.074\n                    \n                  .","journal-title":"Food Chem"},{"key":"200_CR25","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. J Mach Learn Res. 2014; 15:1929\u201358.","journal-title":"J Mach Learn Res"},{"key":"200_CR26","volume-title":"Computer Vision \u2013 ECCV 2014. Lecture Notes in Computer Science","author":"MD Zeiler","year":"2014","unstructured":"Zeiler MD, Fergus R. Visualizing and understanding convolutional networks In: Fleet D, Pajdla T, Schiele B, Tuytelaars T, editors. Computer Vision \u2013 ECCV 2014. Lecture Notes in Computer Science. Cham: Springer: 2014. p. 818\u201333."}],"container-title":["BioData Mining"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-019-0200-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13040-019-0200-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-019-0200-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T23:16:59Z","timestamp":1594077419000},"score":1,"resource":{"primary":{"URL":"https:\/\/biodatamining.biomedcentral.com\/articles\/10.1186\/s13040-019-0200-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,8]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["200"],"URL":"https:\/\/doi.org\/10.1186\/s13040-019-0200-5","relation":{},"ISSN":["1756-0381"],"issn-type":[{"value":"1756-0381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,8]]},"assertion":[{"value":"14 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"13"}}