Near-infrared spectroscopy and hyperspectral image for evaluation of texture of parboiled rice and Khao Dawk Mali 105 (KDML 105) rice authentication

เทคนิคเนียร์อินฟราเรดสเปกโทรสโกปีและไฮเปอร์สเปกตรัลอิมเมจสำหรับการประเมินเนื้อสัมผัสของข้าวนึ่งและการพิสูจน์ความแท้ของข้าวขาวดอกมะลิ 105

Name: Jiraporn Onmankhong

ThaSH: Rice, Parboiled

ThaSH: Rice -- Processing

Abstract: xiv, 148 leaves : illustrations, tables This research studied the feasibility of using Near-infrared (NIR) spectroscopic technique to evaluate the texture properties (i.e., hardness and toughness) of cooked parboiled rice and the Near-infrared hyperspectral image (NIR-HSI) for Khao Dawk Mali 105 (KDML105) rice authentication. This thesis has three objectives. The first objective was to conduct a preliminary study of the effect of chemical components (i.e. amylose and fat content) on different parboiled rice process conditions which affect the texture properties of cooked parboiled rice to determine the feasibility of applying NIR spectroscopy in the evaluation of the texture properties. It was found that the amylose content had a significantly negative correlation with the hardness of cooked parboiled rice (Correlation Coefficient (r)=-0.52), and a negative correlation with its toughness (r=-0.38). The amylose content decreased with the increasing hardness of cooked parboiled rice. This might be because the amylose content leached out into the water during the soaking process. The increasing hardness for cooked parboiled rice may be due to changes in starch during gelatinization and recrystallization processes. Therefore, hardness of cooked parboiled rice increases although the amylose decreases. The fat content had a low correlation with the hardness (r=0.20) and the toughness (r=0.12) of texture properties of cooked parboiled rice. The second objective was to develop the best calibration model of texture properties prediction of cooked parboiled rice from spectra of milled parboiled rice using the NIR spectroscopy. The Fourier-transform NIR (FT-NIR) spectrometer (MPA, Bruker Ltd., Ettlingen, Germany) at a wavelength 12,500-4,000 cm-1 (800-2,500 nm) was used in this part. The ISO 11747 |aRice-Determination of Rice Kernel Resistance to Extrusion after Cooking Method was used as a reference test. The Partial Least Squares Regression (PLSR) was the optimal calibration model of hardness with moving average smoothing pre-processing with the coefficient of determination of prediction set (r²), root mean square error of prediction (RMSEP) and ratio of prediction to deviation (RPD) of 0.70, 7.24 N and 1.93, respectively. The Principal Components Regression (PCR) was the best model for toughness using mean normalization preprocessing provided r², RMSEP and RPD of 0.66, 38.00 Nmm and 1.75, respectively. The RPD threshold of both models was fair for prediction application. The third objective was to develop the calibration classification model of KDML105 rice authentication using the NIR-HSI, which was evaluated to classify three rice varieties including KDML105 (Thai Jasmine rice), PTT1 (Pathum Thani1), and PSL2 (Phitsanulok 2) at both brown and milled rice. The push-broom line scanning system (Compovision, Sumitomo Electric Industries, Ltd., Osaka, Japan) was applied to acquire NIR-HSI at the wavelength from 913 to 2519 nm. The optimal model for brown rice was the Support Vector Machine (SVM) model based on the averaged NIR spectra, of which the classification accuracy of the test set was 93.0%. For the optimal model for milled rice was the Convolutional Neural Network (CNN) model based on the NIR-HSI data, of which the classification accuracy of the test set was 95.2%. The results support that the HSI method works as a rapid and non-destructive tool for preventing the adulteration of Thai Jasmine rice compared with traditional methods (1-2 days)

King Mongkut's Institute of Technology Ladkrabang. Central Library

Address: BANGKOK

Email: Lifelong@kmitl.ac.th

Name: Panmanas Sirisomboon

Role: Thesis Advisor

Email : panmanas.si@kmitl.ac.th

Created: 2021

Modified: 2023-01-16

Issued: 2023-01-16

วิทยานิพนธ์/Thesis

application/pdf

eng

DegreeName: Doctor of Engineering

Level: Doctoral Degree

Descipline: Agricultural Engineering

©copyrights King Mongkut's Institute of Technology Ladkrabang

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