000			03517nas|a2200409 i 4500
005			20260817161441.0
008			021226s2001 th uzm rtt 00| a1eng d
035			_a.b11872020
099		9	_aAIT Thesis no.PH-01-3
100	0		_aKunchalee Luechapattanaporn
245	1	0	_aAcoustic testing for evaluating the crispness of breakfast cereals
260			_aBangkok : _bAsian Institute of Technology, _c2001
300			_a81 leaves
490	1		_aThesis ; _vno. PH-01-3
500			_aA thesis submitted in the partial fulfillment of the requirements for the degree of Master of Science, School of Environment, Resources and Development
502			_aThesis (M.Sc.) - Asian Institute of Technology, 2001
520			_aCrispness is an important attribute of breakfast cereals product quality. Crispness of breakfast cereals is dependent on the moisture content of the products. Three brands of breakfast cereals, i.e. Kellogg's com flake, Honey Stars and Koko Krunch were evaluated their crispness using acoustic testing and mechanical testing. The sound was produced by crnshing the samples with the spring loaded pliers. The original amplitude-time curves were converted to the power spectrum of frequencies by using Fast Fourier Transform (FFT). The different moisture content samples gave the different sound signals in both time domain and frequency domain. The greatest moisture content gave the smallest amplitude, the least number of peak sound signal and the shortest signal duration. Mechanical testing was done in bulk compression. The breaking force is increased when the crispness decreased or the moisture content increased. Backpropagation neural networks was used to predict moisture content using sound signal amplitude at fixed frequencies as inputs. The high coefficient of determination was performed for Honey Stars and Koko Krunch. Both backpropagation neural networks and probabilistic neural networks were applied to classify three crispness grades. The performance of probabilistic neural networks was better. The overall accuracy were 81, 93 and 100 percent for Kellogg's corn flake, Honey Stars and Koko Krunch respectively. The results of this study showed that acoustic wave analysis can be used as an alternative to evaluate the crispness of breakfast cereal products.
650		0	_aCereals, Prepared _xEvaluation
700	1		_aJindal, Vinod Kumar, _eChairperson
700	0		_aAthapol Noomhorm, _eExamination Committee
700	1		_aRakshit, Sudip Kumar, _eExamination Committee
710	2		_aH.M. Queen's scholarship, _eScholarship donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. PH-01-3
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B09175
907			_a.b11872020 _bmnait _cu
902			_a240403
998			_b0 _c021226 _dm _ea _fu _g0
945			_lmnait
945			_lmnait
945			_lmnarc
942			_c20
942			_c40
909			_aBarcode : 30050120651350 _bCREATED : 2002-12-26 _cRECORD # : i12310694 _dLPATRON : 1019168 _eLCHKIN : 2007-08-20 _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 14 _jTOT RENEW : 7
909			_aBarcode : 30050120651376 _bCREATED : 2002-12-26 _cRECORD # : i12310700 _dLPATRON : 1017416 _eLCHKIN : 2006-11-10 _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 6 _jTOT RENEW : 3
909			_aBarcode : 30050160005699 _bCREATED : 2016-03-24 _cRECORD # : i1290739x _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
999			_c1349 _d1349