Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification
暂无分享,去创建一个
Sylvio Barbon Junior | Rafael Gomes Mantovani | Douglas Fernandes Barbin | Sylvio Barbon | Ana Paula Ayub da Costa Barbon | R. G. Mantovani | R. Mantovani | D. Barbin | S. Barbon | Ana Paula Ayub da Costa Barbon
[1] Bjarne K. Ersbøll,et al. Supervised feature selection for linear and non-linear regression of L⁎a⁎b⁎ color from multispectral images of meat , 2014, Eng. Appl. Artif. Intell..
[2] S. Amirhassan Monadjemi,et al. A New Decision Tree for Recognition of Persian Handwritten Characters , 2012 .
[3] Tom Fearn,et al. Practical Nir Spectroscopy With Applications in Food and Beverage Analysis , 1993 .
[4] Reinhold Carle,et al. On-line application of near infrared (NIR) spectroscopy in food production , 2015 .
[5] George-John E. Nychas,et al. A comparison of Raman and FT-IR spectroscopy for the prediction of meat spoilage , 2013 .
[6] Piotr Boniecki,et al. Marbling classification of lambs carcasses with the artificial neural image analysis , 2015, Digital Image Processing.
[7] Patrick De Causmaecker,et al. Real-world production scheduling for the food industry: An integrated approach , 2012, Eng. Appl. Artif. Intell..
[8] Sylvio Barbon Junior,et al. Storage time prediction of pork by Computational Intelligence , 2016, Comput. Electron. Agric..
[9] Piotr Zapotoczny,et al. Evaluation of the quality of cold meats by computer-assisted image analysis , 2016 .
[10] Vassilis S. Kodogiannis,et al. An adaptive neuro-fuzzy identification model for the detection of meat spoilage , 2014, Appl. Soft Comput..
[11] Girish H. Subramanian,et al. A comparison of the decision table and tree , 1992, CACM.
[12] Nour El Islem Karabadji,et al. Improved decision tree construction based on attribute selection and data sampling for fault diagnosis in rotating machines , 2014, Eng. Appl. Artif. Intell..
[13] F. Deatherage,et al. CHANGES IN HYDRATION, SOLUBILITY AND CHARGES OF MUSCLE PROTEINS DURING HEATING OF MEAT , 1960 .
[14] M Novič,et al. Classification of dry-cured hams according to the maturation time using near infrared spectra and artificial neural networks. , 2014, Meat science.
[15] Sylvio Barbon Junior,et al. Digital image analyses as an alternative tool for chicken quality assessment , 2016 .
[16] S. Sathiya Keerthi,et al. Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.
[17] Robyn D. Warner,et al. Influence of high pre-rigor temperature and fast pH fall on muscle proteins and meat quality: a review , 2014 .
[18] Pavel Turčínek,et al. Usage of data mining techniques on marketing research data , 2012 .
[19] Wang Haibin,et al. Study on hyperspectral image technology based on manifold fuzzy clustering for pork quality classification. , 2015 .
[20] George-John E. Nychas,et al. Rapid qualitative and quantitative detection of beef fillets spoilage based on Fourier transform infrared spectroscopy data and artificial neural networks , 2010 .
[21] Sylvio Barbon Junior,et al. Comparison of SVM and REPTRee for Classification of Poultry Quality , 2016 .
[22] Stephen Marshall,et al. Quantitative Prediction of Beef Quality Using Visible and NIR Spectroscopy with Large Data Samples Under Industry Conditions , 2015 .
[23] Xiao Li,et al. Dependence maximization based label space dimension reduction for multi-label classification , 2015, Eng. Appl. Artif. Intell..
[24] Xichang Wang,et al. Prediction of total viable counts on chilled pork using an electronic nose combined with support vector machine. , 2012, Meat science.
[25] Pei-Chann Chang,et al. A CBR-based fuzzy decision tree approach for database classification , 2010, Expert Syst. Appl..
[26] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[27] Tom J. Moir,et al. Comparison of multiclass SVM classification techniques in an audio surveillance application under mismatched conditions , 2014, 2014 19th International Conference on Digital Signal Processing.
[28] Albert Gan,et al. Prediction of Lane Clearance Time of Freeway Incidents Using the M5P Tree Algorithm , 2011, IEEE Transactions on Intelligent Transportation Systems.
[29] K. Honikel,et al. Reference methods for the assessment of physical characteristics of meat. , 1998, Meat science.
[30] Douglas Fernandes Barbin,et al. Prediction of chicken quality attributes by near infrared spectroscopy. , 2015, Food chemistry.
[31] Lixiang Shen,et al. Fault diagnosis based on Rough Set Theory , 2003 .
[32] Huan Liu,et al. Efficient Feature Selection via Analysis of Relevance and Redundancy , 2004, J. Mach. Learn. Res..
[33] อนิรุธ สืบสิงห์,et al. Data Mining Practical Machine Learning Tools and Techniques , 2014 .
[34] Haibiao Ding,et al. Identification of broiler chicken meat using a visible/near-infrared spectroscopic technique , 1999 .
[35] Y. Zhao,et al. Comparison of decision tree methods for finding active objects , 2007, 0708.4274.
[36] S. Oshita,et al. Non-invasive analytical technology for the detection of contamination, adulteration, and authenticity of meat, poultry, and fish: a review. , 2015, Analytica chimica acta.
[37] George-John E. Nychas,et al. Sensory and microbiological quality assessment of beef fillets using a portable electronic nose in tandem with support vector machine analysis , 2013 .
[38] S. Prasher,et al. Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique. , 2007, Meat science.
[39] Jun Wang,et al. Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar , 2013 .
[40] Noel D.G. White,et al. Classification of contaminants from wheat using near-infrared hyperspectral imaging , 2015 .