03986nas|a2200421 i 4500 20260818170125.0 040715s2003 th uzm rtt 00| a1eng d .b11933781 AIT Thesis no.PH-03-4 Jumanazarovich, Chyngyz Erkinbaev Nondestructive quality evaluation of apples using near infrared reflectance spectroscopy Pathum Thani, Thailand : Asian Institute of Technology, 2003 74 leaves Thesis ; no. PH-03-4 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering Thesis (M.Eng.) - Asian Institute of Technology, 2003 Multivariate 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. Near infrared spectroscopy Apples Jindal, Vinod Kumar, Chairperson Athapol Noomhorm, Examination Committee Rakshit, Sudip Kumar, Examination Committee T. Warunee, Examination committee Asian Development Bank, Scholarship donor Japan Government, Scholarship donor Asian Institute of Technology. Thesis ; no. PH-03-4 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B08771 .b11933781 mnait u 240418 0 040715 m a u 0 mnait mnarc 22 40 Barcode : 30050120651103 CREATED : 2004-07-15 RECORD # : i12406971 LPATRON : 1026142 LCHKIN : 2016-08-27 # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 6 TOT RENEW : 9 Barcode : 30050160005608 CREATED : 2016-03-24 RECORD # : i12907650 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 81447 81447 0 0 0 0 MAIN MAIN mnait 2026-08-18 50.00 0 AIT Thesis no.PH-03-4 30050120651103 2026-08-18 17:01:25 3 2026-08-18 22 0 0 0 0 MAIN MAIN mnarc 2026-08-18 0 AIT Thesis no.PH-03-4 30050160005608 2026-08-18 17:01:25 2026-08-18 40