Automation for food engineering: Food quality quantization and process control

Automation for Food Engineering: Food Quality Quantization and Process Control

Volumn , Issue , 2001, Pages 1-222

  Huang, Yanbo ^a   Whittaker, A Dale ^a   Lacey, Ronald E ^a  

^a TEXAS A AND M UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATION; DATA ACQUISITION; ELECTRONIC NOSE; ELECTRONICS INDUSTRY; MEDICAL IMAGING; NEURAL NETWORKS;

ELASTOGRAPHY; ELECTRONICS AND COMPUTER TECHNOLOGIES; FOOD ENGINEERING; FOOD INDUSTRIES; MATHEMATICAL ANALYSIS; STATISTICAL MODELING; SYSTEM PROTOTYPE; TECHNICAL INFORMATION;

PROCESS CONTROL;

EID: 85056552118     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (175)

1. Besterfield, Dale H., Quality Control., 3rd ed., Prentice-Hall, Englewood Cliffs, NJ, 1990.
2. Osborn, G. S., Lacey, R. E., and Singleton, J. A., A method to detect peanut off-flavors using an electronic nose, Trans. ASAE, in press.
3. U.S. Department of Agriculture, Official United States Standards for Grades of Carcass Beef., 1997.
4. American Society of Agricultural Engineers, Moisture Measurement-Peanuts, ASAE Standards, 44th ed., St. Joseph, MI., 1980.
5. American Society for Testing and Materials, Standard practice for determination of odor and taste thresholds by a forced-choice ascending concentration series method of limits, in E679-91, ASTM Committee E-18, Philadelphia, PA, 1991, 1.
6. American Society for Testing and Materials, Standard practice for referencing supra-threshold odor intensity, in E544-75 (reapproved 1988), ASTM Committee E-18, Philadelphia, PA, 1988, 20.
7. Amrani, M. E. H., Persaud, K. C., and Payne, P. A., High frequency measurements of conducting polymers: development of a new technique for sensing volatile chemicals. Measurement Science & Technology, 6(10), 1500, 1995.
8. Bartlett, P. N., Elliott, J. M., and Gardner., J. W., Electronic noses and their application in the food industry, Food Technol., 51, 44, 1997.
9. Berglund, B., Berglund, U., and Lindvall, T., Quality assurance in olfactometry, Volatile Emissions from Livestock Farming and Sewage Operations, Edited by V. C. Nielsen et al., Elsevier, New York, 1988.
10. Brailsford, A. D., Yussouff, M., and Logothetis, E. M., Steady state model of electrochemical gas sensors with multiple reactions, Sensors and Actuators B, 35–36, 392, 1996.
11. Brethour, J. R., Relationship of ultrasound speckle to marbling score in cattle, J. Anim. Sci., 68, 2603, 1990.
12. Brynzari, V., Dmitriev, S., and Korotchenkov, G., Theory of the thin film gas sensor design, Int. Conf. Solid-State Sensors and Actuators, IEEE, 1997, 983.
13. Buehler, M. G., Technical support package on gas-sensor test chip, NASA Tech. Brief., 21(8), item 115, 1997.
14. Cespedes, I. and Ophir, J., Elastography: experimental results from phantoms and tissues, Ultrason. Imag., 13, 187, 1991.
15. Cespedes, I., Ophir, J., Ponnekanti, H., and Maklad, N., Elastography: elasticity imaging using ultrasound with application to muscle and breast in vivo, Ultrason. Imag., 15, 73, 1993.
16. Chen, Y., Meat inspection system general description, University of Texas at Houston Medical School, Radiology Dept., 1996.
17. Christensen, D. A., Ultrasonic Bioinstrumentation, John Wiley & Sons, New York, 1988.
18. Collins, G. E. and Rose-Pehrsson, S. L., Chemiluminescent chemical sensors for inorganic and organic vapors, 8th Int. Conf. Solid-State Sensors and Actuators, and Eurosensors, IX, 1995.
19. Corcoran, P., The effects of signal conditioning and quantization upon gas and odour sensing system performance, Sensors and Actuators B, 18–19, 649, 1994.
20. Davide, F. A. M., Natale, C. D., and D’Amico, A., Self-organizing sensory maps in odour classification mimicking, Biosens. Bioelectron., 10, 203, 1995.
21. Dickens, J. W., Slate, A. B., and Pattee, H. E., Equipment and procedures to measure peanut headspace volatiles, Peanut Sci., 14, 96, 1987.
22. di Natale, C., Brunink, J. A. J., and Bungaro, F., Recognition of fish storage time by a metalloporphyrins-coated QMB sensor array, Meas. Sci. Technol., 7, 1103, 1996.
23. Egashira, M., Functional design of semiconductor gas sensors for measurement of smell and freshness, Int. Conf. Solid-State Sensors and Actuators, IEEE, 1997, 1385.
24. Egashira, M. and Shimizu, Y., Odor sensing by semiconductor metal oxides, Sensors and Actuators B, 13–14, 443, 1993.
25. Eklov, T., Martensson, P., and Lundstrom, I., Enhanced selectivity of MOSFET gas sensors by systematical analysis of transient parameters, Anal. Chim. Acta, 353, 291, 1997.
26. Engen, T., The Perception of Odors, Academic Press, New York, 1982.
27. Ergun, D. L., Peppler, W. W., Dobbins, III, J. T., Zink, F. E., Kruger, D. G., Kelcz, F., de Bruijn, F. J., Nellers, E. B., Wang, Y., Althof, R. J., and Wind, M. G. J., Dual energy computed radiography: improvements in processing. Medical imaging: image processing, M. H. Loew, Ed., Proc. SPIE, 2167, 663, 1994.
28. Gardner, J. W., Craven, M., Dow, C., and Hines, E. L., The prediction of bacterial type and culture growth phase by an electronic nose with a multi-layer perceptron network, Meas. Sci. Technol. 9, 120, 1998.
29. Hanaki, S., Nakamoto, T., and Moriizumi, T., Artificial odor recognition system using neural network for estimating sensory quantities of blended fragrance, Sensors and Actuators A, 57, 65, 1996.
30. Henning, R., The hull-scrape method to determine when to dig peanuts, The Peanut Farmer, Aug., 11, 1983.
31. Huang, Y., Lacey, R. E., and Whittaker, A. D., Neural network prediction modeling based on ultrasonic elastograms for meat quality evaluation, Trans. ASAE, 41(4), 1173, 1998.
32. Huang, Y., Lacey, R. E., Whittaker, A. D., Miller, R. K., Moore, L., and Ophir, J., Wavelet textural features from ultrasonic elastograms for meat quality prediction, Trans. ASAE, 40(6), 1741, 1997.
33. Ishida, H., Nakamoto, T., and Moriizumi, T., Study of odor compass, IEEE/SICE/RSJ Int. Conf. Multisensor Fusion and Integration for Intelligence Systems, IEEE, 1996, 222.
34. Lacey, R. E. and Osborn, G. S., Applications of electronic noses in measuring biological systems, ASAE paper No. 98-6116, St. Joseph, MI, 1998.
35. Lawless, H. T., Olfactory psychphysics, in Tasting and Smelling, 2nd ed., Beauchamp, G. K., and Bartoshuk, L., Eds., Academic Press, San Diego, CA, 1997, 125.
36. Llobet, E., Brezmes, J., Vilanova, X., Fondevila, L., and Correig, X., Quantative vapor analysis using the transient response of non-selective thick-film tin oxide gas sensors, Transducers ‘97, Int. Conf. Solid-State Sensors and Actuators, IEEE, 1997, 971.
37. Lozano, M. S. R., Ultrasonic elastography to evaluate beef and pork quality, Ph.D. Dissertation, Texas A&M University, College Station, TX, 1995.
38. Matsuno, G., Yamazaki, D., Ogita, E., Mikuriya, K., and Ueda, T., A quartz crystal microbalance type odor sensor using PVC blended lipid membrane, IEEE Trans. Instrum. Meas. 44(3), 739, 1995.
39. Miasik, J. J., Hooper, A., and Tofield, B. C., Conducting polymer gas sensors, J. Chem. Soc., Faraday Trans. 1, 82, 1117, 1986.
40. Moore, L. L., Ultrasonic elastography to predict beef tenderness, M.S. thesis, Texas A&M University, College Station, TX, 1996.
41. Moriizumi, T., Saitou, A., and Nomura, T., Multi-channel SAW chemical sensor using 90 MHz SAW resonator and partial casting molecular films, IEEE Ultrason. Symp. 499, 1994.
42. Moy, L. and Collins, M., Electronic noses and artificial neural networks, Am. Lab., Vol. 28 (February), 22, 1996.
43. Nakata, S., Akakabe, S., Nakasuji, M., and Yoshikawa, K., Gas sensing based on a nonlinear response: discrimination between hydrocarbons and quantification of individual components in a gas mixture, Anal. Chem., 68(13), 2067, 1996.
44. Nanto, H., Sokooshi, H., and Kawai, T., Aluminum-doped ZnO thin film gas sensor capable of detecting freshness of sea foods, Sensors and Actuators B, 13–14, 715, 1993.
45. Nayak, M. S., Dwivedi, R., and Srivastava, S. K., Transformed cluster analysis: an approach to the identification of gasesodours using an integrated gas-sensor array, Sensors and Actuators B, 12: 103, 1993.
46. Ophir, J., Cespedes, I., Ponnekanti, H., Yazdi, Y., and Li, X., Elastography: a quantitative method for imaging the elasticity of biological tissues, Ultrason. Imag., 13, 111, 1991.
47. Ophir, J., Miller, R. K., Ponnekanti, H., Cespedes, I., and Whittaker, A. D., Elastography of beef muscle, Meat Sci., 36, 239, 1994.
48. Osborn, G. S., Young, J. H., and Singleton, J. A., Measuring the kinetics of acetalde-hyde, ethanol, and ethyl acetate within peanut kernels during high temperature drying, Trans. ASAE, 39(3), 1039, 1995.
49. Osborn, G. S., Lacey, R. E., and Singleton, J. A., A method to detect peanut off-flavors using an electronic nose, Trans. ASAE, in press.
50. Park, B., Non-invasive, objective measurement of intramuscular fat in beef through ultrasonic A-mode and frequency analysis, Ph.D. dissertation, Texas A&M University, College Station, TX, 1991.
51. Park, B. and Whittaker, A. D., Ultrasonic frequency analysis for beef quality grading, ASAE paper No. 91-6573, St. Joseph, MI, 1991.
52. Park, B. and Whittaker, A. D., Determination of beef marbling score using ultrasound A-scan, ASAE paper No. 90-6058, St. Joseph, MI, 1990.
53. Persaud, K. C., DeSimone, J. A., and Heck, G. L., Physiology and psychophysics in taste and smell, in Sensors and Sensory Systems for Advanced Robots, Vol. F43, Dario, P., Ed., Springer-Verlag, Berlin, 1988, 49.
54. Ping, W. and Jun, X., A novel method combined neural network with fuzzy logic for odour recognition, Meas. Sci. Technol. 7, 1707, 1996.
55. Pisanelli, A. M., Qutob, A. A., Travers, P., Szyszko, S., and Persaud, K. C., Applications of multi array polymer sensors to food industry, Life Chem. Rep., 11, 303, 1994.
56. Ponnekanti, H., Ophir, J., and Cespedes, I. Axial stress distributions between coaxial compressors in elastography: an analytical model, Ultrasoun. Med. Biol., 18(8), 667, 1992.
57. Ruan, R., Schmidt, S., and Litchfield, J. B., Nondestructive measurement of transient moisture profiles and the moisture diffusion coefficient in potato during drying and absorption by NMR imaging, J. Food Process. Eng., 14, 297, 1991.
58. Sayeed, M. S., Whittaker, A. D., and Kehtarnavaz, N. D., Snack quality evaluation method based on image features and neural network prediction, Trans. ASAE, 38(4), 1239, 1995.
59. Scott, A. O., Biosensors for Food Analysis, Royal Society of Chemistry, Cambridge, UK, 1998.
60. Simon, J. E., Hetzroni, A., Bordelon, B., Miles, G. E., and Charles, D. J., Electronic sensing of aromatic volatiles for quality sorting of blueberries, J. Food Sci. 61(5), 967, 972, 1996.
61. Singleton, J. A. and Pattee, H. E., Effect of chilling injury on windrowed peanuts, Peanut Sci., 16, 51, 1989.
62. Singleton, J. A. and Pattee, H. E., A preconcentration and subsequent gas liquid chromatographic analysis method for trace volatiles, J. Am. Oil Chem. Soc., 57, 405, 1980.
63. Strassburger, K. J., Electronic nose evaluation in the flavor industry; it really works!, Food Test. Anal., 2, 22, 1996.
64. Sundgren, H., Winquist, F., and Lindstrom, I., Artificial neural networks and statistical pattern recognition improve MOSFET gas sensor array calibration, IEEE, 574, 1991.
65. Taylor, R. F., Applications of biosensors in the food processing industry, Proc. Food Process. Automa. Conf., ASAE, St. Joseph, MI, 1990, 156.
66. Thane, B. R., Prediction of intramuscular fat in live and slaughtered beef animals through processing of ultrasonic images, M.S. thesis, Texas A&M University, College Station, TX, 1992.
67. U. S. Department of Agriculture, Official United States Standards for Grades of Carcass Beef, 1997.
68. Vernat-Rossi, V., Garcia, C., Talon, R., Denoyer, C., and Berdague, J. L., Rapid discrimination of meat products and bacterial strains using semiconductor gas sensors, Sensors and Actuators B, 37, 43, 1996.
69. Wagner, G. and Guilbault, G. G., Food Biosensor Analysis, Marcel Dekker, NY, 1994.
70. White, J., Kauer, J. S., Dickinson, T. A., and Walt, D. R., Rapid analyte recognition in a device based on optical sensors and the olfactory system, Anal. Chem. 68(13), 2191, 1996.
71. Whittaker, A. D., Park, B., Thane, B. R., Miller, R. K., and Savell, J. W., Principles of ultrasound and measurement of intramuscular fat, J. Anim. Sci., 70, 942, 1992.
72. Wide, P., Winquist, F., and Driankov, D., An air-quality sensor system with fuzzy classification. Meas. Sci. Technol., 8, 138, 1997.
73. Williams, A. G., Physical calibration of olfactometric measurements, in Odour Prevention and Control of Organic Sludge and Livestock Farming, Edited by V. C. Nielsen et al., Elsevier, New York, 1986.
74. Williams, E. J. and Drexler, J. S., A non-destructive method for determining peanut pod maturity, Peanut Sci., 8, 134, 1981.
75. Winquist, F., Hornsten, E. G., Sundgren, H., and Lundstrom, I., Performance of an electronic nose for quality estimation of ground meat, Meas. Sci. Technol., 4, 1493, 1993.
76. Yea, B., Konihi, R., Osaki, T., and Sugahara, K., The discrimination of many kinds of odor species using fuzzy reasoning and neural networks, Sensors and Actuator A, 45, 159, 1994.
77. Yim, H. S., Kibbey, C., Ma, S. C., Kliza, D. M., Liu, D., Park, S. B., Torre, C. E., and Meyerhoff, M. E. Polymer membrane-based ion-, gas-and bio-selective pro-tensensors, Biosens. Bioelectronics., 8, 1, 1993.
78. Zeng, X., Ruan, R., Fulcher, R., and Chen, P., Evaluation of soybean seedcoat cracking during drying, Drying Technol., 14 (7–8), 1595, 1996.
79. Aldroubi, A. and Unser, M., Wavelets in Medicine and Biology, CRC Press, Boca Raton, FL, 1996.
80. Cheng, S. C. and Tsai, W. H., A neural network implementation of the moment-preserving technique and its applications to thresholding, IEEE Trans. Comput., 42, 501, 1993.
81. Daubechies, I., Orthonormal bases of compactly supported wavelets, Comm. Pure Appl. Math., 41(7), 909, 1988.
82. Gerrard, D. E., Gao, X., and Tan, J., Beef marbling and color score determination by image processing, J. Food Sci., 61(1), 145, 1996.
83. Gonzalez, R. C. and Woods, R. E., Digital Image Processing., Addison-Wesley Reading, MA, 1992.
84. Gonzalez, R. C. and Wintz, P., Digital Image Processing, 2nd ed., Addison-Wesley, Reading, MA, 1987.
85. Haralick, R. M., Shanmugam, K., and Dinstein, I., Textural features for image classification, IEEE Trans. Syst., Man, Cybern., 3(6), 610, 1973.
86. Huang, Y., Snack food frying process input–output modeling and control through artificial neural networks, Ph.D. dissertation, Texas A&M University, College Station, TX, 1995.
87. Huang, Y. and Whittaker, A. D., Input–output modeling of biological product processing through neural networks, ASAE paper No. 93-3507. St. Joseph, MI, 1993.
88. Huang, Y., Lacey, R. E., Moore, L. L., Miller, R. K., Whittaker, A. D., and Ophir, J., Wavelet textural features from ultrasonic elastograms for meat quality prediction, Trans. ASAE, 40(6), 1741, 1997.
89. Katz, Y. H., Pattern recognition of meteorological satellite photography, Proc. Symp. Remote Sens. Environ., 12, 173, 1965.
90. Mallat, S., A theory of multiresolution signal decomposition: the wavelet representation, IEEE Trans. Patt. Anal. Mach. Intell., 11(7), 674, 1989.
91. Milton, J. S. and Arnold, J. C., Probability and Statistics in the Engineering and Computing Sciences, McGraw-Hill, New York, 1986.
92. Moore, L. L., Ultrasonic elastography to predict beef tenderness, M.Sc. thesis, Texas A&M University, College Station, TX, 1996.
93. Osborn, G. S., Lacey, R. E., and Singleton, J. A., A method to detect peanut off-flavors using an electronic nose, Trans. ASAE, in press.
94. Osborn, G. S., Lacey, R. E., and Singleton, J. A., Detecting high temperature curing off-flavors in peanuts using an electronic nose, ASAE Paper no. 986075, 1998.
95. Park, B., Non-invasive, objective measurements of intramuscular fat in beef through ultrasonic A-model and frequency analysis, Ph.D. dissertation, Texas A&M University, College Station, TX, 1991.
96. Perwitt, J. and Mendlesohn, M., The analysis of cell images, Ann. N.Y. Acad. Sci., 135, 1035, 1966.
97. Sayeed, M. S., Whittaker, A. D., and Kehtarnavaz, N. D., Snack quality evaluation method based on image features and neural network prediction, Trans. ASAE, 38(4), 1239, 1995.
98. Thane, B. R., Prediction of intramuscular fat in live and slaughtered beef animals through processing of ultrasonic images, M.Sc. thesis, Texas A&M University, College Station, TX, 1992.
99. Wang, W., Development of elastography as a non-invasive method for hard spots detection of packaged beef rations, M.Sc. thesis, Texas A&M University, College Station, TX, 1998.
100. Whittaker, A. D., Park, B., Thane, B. R., Miller, R. K., and Savell, J. W., Principles of ultrasound and measurement of intramuscular fat, J. Anim. Sci., 70, 942, 1992.
101. Wilson, L. S., Robinson, D. E., and Doust, B. D., Frequency domain processing for ultrasonic attenuation measurement in liver, Ultrason. Imag., 6, 278, 1984.
102. Akaike, H., Fitting autoregressive models for prediction, Ann. Inst. Stat. Math., 21, 243, 1969.
103. Akaike, H., Information theory and an extension of the maximum likelihood principle, Proc. 2nd Int. Symp. Inf. Theor., Budapest, 1973, 267.
104. Aleksander, I., Neural Computing Architecture, MIT Press, Cambridge, MA, 1989.
105. Bullock, D., Modeling of a continuous food process with neural networks, M.Sc. thesis. Texas A&M University, College Station, TX, 1995.
106. Cybenko, G., Approximator by superpositions of a sigmoidal function, Math. Contr. Sign. Syst., 2, 303, 1989.
107. Duda, R. O. and Hart, P. E., Pattern Classification and Scene Analysis, John Wiley & Sons, New York, 1973.
108. Efroymson, M. A., Multiple regression analysis, in Mathematical Methods for Digital Computers, Ralston, A., and Wilf, H. S., Eds., John Wiley & Sons, New York, 1962.
109. Finnoff, W., Hergert, F., and Zimmermann, H. G., Improving model selection by nonconvergent methods, Neur. Net., 6, 771, 1993.
110. Grossberg, S., Adaptive pattern classification and universal recording. I. Parallel development and coding of neural feature detectors, Biol. Cybern., 23, 121, 1976.
111. Hagan, M. T. and Menhaj, M. B., Training feedforward networks with the Marquardt algorithm, IEEE Trans. Neur. Net., 596, 989, 1994.
112. Haralick, R. M., Shanmugam, K., and Dinstein, I., Textural features for image classification, IEEE Trans. Syst., Man, Cybern., 3(6), 610, 1973.
113. Hebb, D. O., The Organization of Behavior, John Wiley & Sons, New York, 1949.
114. Hopfield, J. J., Neural networks and physical systems with emergent collective computational abilities, Proc. Nat. Acad. Sci., 79, 2554, 1982.
115. Hornik, K., Stinchcombe, M., and White, H., Multilayer feedforward neural networks are universal approximators, Neur. Net., January, 359, 1989.
116. Huang, Y., Lacey, R. E., and Whittaker, A. D., Neural network prediction modeling based on elastographic textural features for meat quality evaluation, Trans. ASAE, 41(4), 1173, 1998.
117. Huang, Y., Whittaker, A. D., and Lacey, R. E., Neural network prediction modeling for a continuous, snack food frying process, Trans. ASAE, 41(5), 1511, 1998a.
118. Kohonen, T., Self-Organization and Associative Memory, Springer-Verlag, Berlin, 1984.
119. Lacey, R. E. and Osborn, G. S., Applications of electronic noses in measuring biological systems, ASAE paper No. 98-6116, St. Joseph, MI, 1998.
120. Lippman, R. P., An introduction to computing with neural nets, IEEE ASSP Mag., April, 4, 1987.
121. Ljung, L., System Identification: Theory for the User, 2nd ed., Prentice-Hall, Upper Saddle River, NJ, 1999.
122. Lozano, M. S. R., Ultrasonic elastography to evaluate beef and pork quality, Ph.D. dissertation, Texas A&M University, College Station, TX, 1995.
123. McCulloch, W. S. and Pitts, W., A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys., 9, 127, 1943.
124. Milton, J. S. and Arnold, J. C., Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences, 2nd ed., McGraw-Hill, New York, 1990.
125. Minsky, M. L. and Papert, S. A., Perceptrons, MIT Press, Cambridge, MA, 1969.
126. Moore, L. L., Ultrasonic elastography to predict beef tenderness, M.Sc. thesis, Texas A&M University, College Station, TX, 1996.
127. Moreira, R. G., Palau, J., and Sun, X., Simultaneous heat and mass transfer during deep fat frying of tortilla chips, J. Food Proc. Eng., 18, 307, 1995.
128. Nasrabadi, N. M. and Feng, Y., Vector quantization of images based upon the Kohonen self-organizing feature maps, Proc. IEEE Int. Conf. Neur. Net., 1, 101, 1988.
129. Park, B., Non-invasive, objective measurements of intramuscular fat in beef through ultrasonic A-model and frequency analysis, Ph.D. dissertation, Texas A&M University, College Station, TX, 1991.
130. Rosenblatt, F., The perceptron: a probabilistic model for information storage and organization in the brain, Psychol. Rev., 65, 386, 1958.
131. Rumelhart, D. E. and McClelland, J. L., Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Vols. I & II, MIT Press, Cambridge, Massachusetts, 1986.
132. Rumelhart, D. E., Hinton, G. E., and Williams, R. J., Learning representations by back-propagating errors, Nature, 323, 533, 1986a.
133. Rumelhart, D. E., Hinton, G. E., and Williams, R. J., Learning internal representation by error propagation, in Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Vol. 1, MIT Press, Cambridge, MA, 1986b.
134. Sarle, W. S., Neural Nets FAQ, SAS Institute Inc., Cary, NC, 1999.
135. Scales, L. E., Introduction to Nonlinear Optimization, Springer-Verlag, New York, 1985.
136. Shibata, R., Various model selection techniques in time series analysis, in Handbook of Statistics, Vol. 5, 179, North-Holland, Amsterdam, 1985.
137. Werbos, P. J., Beyond regression: new tools for prediction and analysis in the behavior sciences, Ph.D. dissertation, Harvard University, MA, 1974.
138. Whittaker, A. D., Park, B., McCauley, J. D., and Huang, Y., Ultrasonic signal classification for beef quality grading through neural networks, Proc. 1991 Symp. Automat. Agric. 21st century, St. Joseph, MI, 1991.
139. Whittaker, A. D., Park, B., Thane, B. R., Miller, R. K., and Savell, J. W., Principles of ultrasound and measurement of intramuscular fat, J. Anim. Sci., 70, 942, 1992.
140. Bullock, D., Modeling of a continuous food process with neural networks, M.Sc. thesis, Texas A&M University, College Station, TX, 1995.
141. Gardner, J. W. and Bartlett, P. N., Sensors and Sensory Systems for an Electronic Nose. Kluwer, Dordrecht, The Netherlands, 1992.
142. Hotelling, H., Analysis of a complex of statistical variables into principal components, J. Educ. Psychol., 24, 498, 1933.
143. Huang, Y., Whittaker, A. D., and Lacey, R. E., Neural network prediction modeling for a continuous, snack food frying process, Trans. ASAE, 41(5), 1511, 1998a.
144. Pearlmutter, A. B., Dynamic recurrent neural networks. in Tech. Rep. CMU-CS-90-196, Carnegie Mellon University, Pittsburgh, PA, 1990.
145. Pineda, F. J., Recurrent backpropagation and the dynamical approach to adaptive neural computation, Neur. Comp., 1, 167, 1989.
146. Rosenblatt, R., Principles of Neurodynamics, Spartan Books, New York, 1959.
147. Rumelhart, D. E., Hinton, G. E., and Williams, R. J., Learning internal representation by error propagation, in Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Vol. 1, MIT Press, Cambridge, MA, 1986, chap. 8, 318. Sayeed, 1995.
148. Su, H., McAvoy, T. J., and Werbos, P. J., Long-term predictions of chemical processes using recurrent neural networks: a parallel training approach, Ind. Eng. Chem. Res., 31(5), 1338, 1992.
149. Werbos, P. J., Beyond regression: new tools for prediction and analysis in the behavior sciences, Ph.D. dissertation, Harvard University, Boston, MA, 1974.
150. Whittaker, A. D., Park, B., McCauley, J. D., and Huang, Y., Ultrasonic signal classification for beef quality grading through neural networks, Proc. 1991 Symp. Autom. Agric. 21st Century, ASAE, St. Joseph, MI, 1991.
151. Williams, R. J. and Zipser, D. A., A learning algorithm for continually running fully recurrent neural networks, Neur. Comput., 1, 270, 1989.
152. Wold, H., Nonlinear estimation by iterative least squares procedures, in Research Papers in Statistics, David, F., Ed., John Wiley & Sons, New York, 1966.
153. Yan, D., Huang, Y., and Lacey, R. E., Assessment of principal component neural networks for extraction of elastographic textural features in meat attribute prediction, ASAE paper No. 98-3018, St. Joseph, MI, 1998.
154. Choi, Y. S., Whittaker, A. D., and Bullock, D. C., Predictive neuro-fuzzy controller for multivariate process control, Trans. ASAE, 39(4), 1535, 1996.
155. Clarke, D. W., Mohtadi, C., and Tuffs, P. S., Generalized predictive control Part I. The basic algorithm, Automatica, 23(2), 137, 1987.
156. Cutler, C. R. and Ramaker, B. L., Dynamic matrix control—a computer control algorithm, AIChE 86th National Meeting, 1979.
157. Economou, C. G., Morari, M., and Palsson, B. O., Internal model control. 5. Extension to nonlinear systems, Ind. Engin. Chem. Process Design Devel., 25, 403, 1986.
158. Garcia, C. E. and Morari, M., Internal model control. 1. A unifying review and some results, Ind. Engin. Chem. Process Design Devel., 21, 308, 1982.
159. Horikawa, S., Furuhashi, T., and Uchikawa, Y., On fuzzy modeling using fuzzy neural networks with the backpropagation algorithm, IEEE Trans. Neural Net., 3(5), 801, 1992.
160. Huang, Y., Snack food frying process input–output modeling and control through artificial neural networks, Ph.D. dissertation, Texas A&M University, College Station, TX, 1995.
161. Huang, Y., Whittaker, A. D., and Lacey, R. E., Neural network prediction modeling for a continuous, snack food frying process, Trans. ASAE, 41(5), 1511, 1998.
162. Huang, Y., Whittaker, A. D., and Lacey, R. E., Internal model control for a continuous, snack food frying process using neural networks, Trans. ASAE, 41(5), 1519, 1998a.
163. Hunt, K. J., Sbarbaro, D., Zbikowski, R., and Gawthrop, P. J., Neural networks for control systems—a survey, Automatica, 28(6), 1083, 1992.
164. Jang, J. R., Self-learning fuzzy controllers based on temporal back propagation, IEEE Trans. Neural Net., 3(5), 714, 1992.
165. Keerthi, S. S. and Gilbert, E. G., Moving-horizon approximation for a general class of optimal nonlinear infinite-horizon discrete-time systems, in Proceedings of the 20th Annual Conference on Information Science and Systems, Princeton University, 1986, 301.
166. Mayne, D. Q. and Michalska, H., Receding horizon control of nonlinear systems, IEEE Trans. Autom. Contr., 35, 814, 1990.
167. Prett, D. M. and Garcia, C. E., Fundamental Process Control, Butterworths, Boston, 1988.
168. Procyk, T. J. and Mamdani, E. H., A linguistic self-organizing process controller, Automatica, 15(1), 15, 1979.
169. Scales, L. E., Introduction to Nonlinear Optimization, Springer-Verlag, New York, 1985.
170. Takagi, T. and Sugeno, M., Derivation of fuzzy control rules from human operator’s control actions, in Proc. IFAC Symp. Fuzzy Information, Knowledge Representation and Decision Analysis, Marseille, 1983, 55.
171. Zadeh, L. A., Fuzzy sets, Inform. Contr., 8(3), 338, 1965.
172. Bourne, S. R., The UNIX System, Addison-Wesley, Reading, MA, 1982.
173. Huang, Y., Whittaker, A. D., Lacey, R. E., and Castell, M. E., Database information management through the World Wide Web for food science and engineering, ASAE/CSAE-SGCR Annual International Meeting, Toronto, Canada, 1999.
174. Ritchie, D. and Kernighan, B., The C Programming Language, Prentice-Hall, Englewood Cliffs, NJ, 1978.
175. Stroustrup, B., The C++ Programming Language, Addison-Wesley, Reading, MA, 1985.