A Study on Soluble Solids Content Assessment Using Electronic Nose: Persimmon Fruit Picked on Different Dates

REFERENCES

• Gomez, A.H.; Wang, J.; Hu, G.X.; Pereira, A.G. Electronic nose technique potential monitoring mandarin maturity. Sensor and Actuators B-Chemical 2006, 113, 347–353.
   Web of Science ®Google Scholar
• Gomez, A.H.; Wang, J.; Hu, G.X.; Pereira, A.G. Monitoring storage shelf life of tomato using electronic nose technique. Journal of Food Engineering 2008, 85, 625–631.
   Web of Science ®Google Scholar
• Li, C.Y.; Heinemann, P.; Sherry, R. Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection. Sensor and Actuators B-Chemical 2007, 125, 301–310.
   Web of Science ®Google Scholar
• Pathange, L.P.; Mallikarjunan, P.; Marini, R.P.; O’Keefe, S.; Vaughan, D. Non-destructive evaluation of apple maturity using an electronic nose system. Journal of Food Engineering 2006, 77, 1018–1023.
   Web of Science ®Google Scholar
• Hui, G.H.; Wu, Y.L.; Ye, D.D.; Ding, W.W.; Zhu, L.S.; Wang, L.Y. Study of peach freshness predictive method based on electronic nose. Food Control 2012, 28, 25–32.
   Web of Science ®Google Scholar
• Zhang, H.M.; Chang, M.X.; Wang, J.; Ye, S. Evaluation of peach quality indices using an electronic nose by MLR, QPST, and BP network. Sensor and Actuators B-Chemical 2008, 134, 332–338.
   Web of Science ®Google Scholar
• Ciosek, P.; Brudzewski, K.; Wroblewski, W. Milk classification by means of an electronic tongue and Support Vector Machine neural network. Measurement Science & Technology 2006, 17, 1379–1384.
   Web of Science ®Google Scholar
• Ren, R.E.; Wang, H.W. Multivariate Statistical Data Analysis—Theory, Method, Example. National Defense Industry Press: Beijing, China, 1997.
   Google Scholar
• Ravi, R.; Prakash, M.; Bhat, K.K. Characterization of aroma active compounds of cumin (Cuminum cyminum L.) by GC-MS, E-nose, and sensory techniques. International Journal of Food Properties 2013, 16, 1048–1058,
   Web of Science ®Google Scholar
• Liu, M.; Han, X.M.; Tu, K.; Pan, L.Q.; Tu, J.; Tang, L.; … Xiong, Z.H. Application of electronic nose in Chinese spirits quality control and flavour assessment. Food Control 2012, 26, 564–570.
   Web of Science ®Google Scholar
• Gomez, A.H.; Hu, G.X.; Wang, J.; Pereira, A.G. Evaluation of tomato maturity by electronic nose. Computers and Electronics in Agriculture 2006, 54, 44–52.
   Web of Science ®Google Scholar
• Ciosek, P.; Brzozka, Z.; Wroblewski, W. Classification of beverages using a reduced sensor array. Sensor and Actuators B-Chemical 2004, 103, 76–83.
   Web of Science ®Google Scholar
• Pardo, M.; Sberveglieri, G. Classification of electronic nose data with support vector machines. Sensor and Actuators B-Chemical 2005, 107, 730–737.
   Web of Science ®Google Scholar
• Vapnik, V.N. The Nature of Statistical Learning Theory. Springer: New York, NY, 1995.
   Google Scholar
• Chang, K.W.; Hsieh, C.J.; Lin, C.J. Coordinate descent method for large-scale L2-loss linear support vector machines. Journal of Machine Learning Research 2008, 9, 1369–1398.
   Web of Science ®Google Scholar
• Roussel, S.; Forsberg, G.; Steinmetz, V.; Grenier, P.; Bellon-Maurel, V. Optimisation of electronic nose measurements. Part I: Methodology of output feature selection. Journal of Food Engineering 1998, 37, 207–222.
   Web of Science ®Google Scholar
• Burges, C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998, 2, 121–167.
   Web of Science ®Google Scholar
• Trebar, M.; Steele, N. Application of distributed SVM architectures in classifying forest data cover types. Computers and Electronics in Agriculture 2008, 63, 119–130.
   Web of Science ®Google Scholar
• Li, Q.; Meng, Q.L.; Cai, J.J.; Yoshino, H.; Mochida, A. Applying support vector machine to predict hourly cooling load in the building. Applied Energy 2009, 86, 2249–2256.
   Web of Science ®Google Scholar
• Liu, F.; Yusuf, B.L.; Zhong, J.L.; Feng, L.; He, Y.; Wang, L. Variety identification of rice vinegars using visible and near infrared spectroscopy and multivariate calibrations. International Journal of Food Properties 2011, 14, 1264–1276.
   Web of Science ®Google Scholar
• Wang, X.D.; Ye, M.Y. Hysteresis and non-linearity compensation of relative humidity sensor using support vector machines. Sensor and Actuators B-Chemical 2008, 129, 274–284.
   Web of Science ®Google Scholar