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035			_a.b11933781
099		9	_aAIT Thesis no.PH-03-4
100	1		_aJumanazarovich, Chyngyz Erkinbaev
245	1	0	_aNondestructive quality evaluation of apples using near infrared reflectance spectroscopy
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2003
300			_a74 leaves
490	1		_aThesis ; _vno. PH-03-4
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering
502			_aThesis (M.Eng.) - Asian Institute of Technology, 2003
520			_aMultivariate models for non-destructively predicting internal quality of Red Delicious apples such as, total soluble solids (TSS), total acidity (TA) and fruit firmness (FF) were developed from NIR reflectance measurements using a spectral range from 1100 to 2500 nm. To estimate the quality indices of apple samples, the multiple linear regression (MLR), partial least square regression (PLSR), and artificial neural networks (ANNs) models were developed. The three models were compared, based on the correlation coeffcient R and root mean standard error of prediction (RMSEP). The data were devided into calibration and prediction sets. The calibration data set was used to select the wavelengths best correlated with the constituent and to fit a MLR equation and later calculate the constituent value in the prediction data set. The most significant R-values of 0.939, 0.907, 0.723 and corresponding RMSEP of 0.381, 0.0221 and 0.098 were found for TSS, TA and FF of apples, respectively. The overall statistics in prediction by PLSR method showed R-values of 0.961, 0.908 and 0.517 with RMSEP of 0.233, 0.0159 and 0.098 for TSS, TA and FF, respectively. In addition, three-layer backpropagation neural networks (NNs) were developed with different number of neurons in first input layer. The best network models were selected for predicting TSS, TA and FF with R-values of 0.955, 0.872 and 0.67 and RMSEP of 0.269, 0.0185 and 0.087, respectively. Both TSS and TA could be predicted with very good accuracy by all three methods. However, the fruit firmness was not predicted so well. In general, the best results of were obtained from PLSR analysis when discarding the extreme outliers. The MLR technique could also be used based on several selected wavelengths. The artificial NNs approach can be used with selected input data in wavelength regions with more prediction accuracy and without pretreatments of the NIR spectral data. It was concluded that the NIR spectroscopic method seemed reliable for non-destructive and rapid determination of internal quality of apples in terms of TSS, TA and FF with possible application in online grading systems.
650		0	_aNear infrared spectroscopy
650		0	_aApples
700	1		_aJindal, Vinod Kumar, _eChairperson
700	0		_aAthapol Noomhorm, _eExamination Committee
700	1		_aRakshit, Sudip Kumar, _eExamination Committee
700	0		_aT. Warunee, _eExamination committee
710	2		_aAsian Development Bank, _eScholarship donor
710	2		_aJapan Government, _eScholarship donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. PH-03-4
856			_3Full-Text _u http://203.159.5.9/ait-thesis/detail.php?q=B08771
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