Study on evaluation of gamma oryzanol of germinated brown rice by near infrared spectroscopy

Germinated brown rice(GBR)is rich in gamma oryzanol which increase its consumption popularity,particularly in the health food market.The objective of this research was to apply the near infraredspectroscopy(NIRS)for evaluation of gamma oryzanol of the germinated brown rice.The germinated brown rice samples were prepared from germinated rough rice(soaked for 24 and 48 h,incubated for 0,6,12,18,24,30 and 36 h)and purchased from local supermar kets.The germinated brown rice sampleswere subjected to NIR scanning before the evaluation of gamma oryzanol by using partial extractionmet hodology.The prediction model was established by partial least square regression(PLSR)andvalidated by full cross validation method.The NIRS model established from various varieties of germinated brown rice bought from diferent markets by first derivatives+vector normalizationpretreated spectra showed the optimal prediction with the correlation of determination(R?),root mean squared error of cross validation(RMSECV),and bias of 0.934,8.84×10^(-5) mg/100 g drymatter and 1.06×10^(-5) mg/100 g dry matter,respectively.This is the first report on the application of NIRS in the evaluation of gamma oryzanol of the germinated brown rice.This information is veryuseful to the germinated brown rice production factory and consumers.

Eating quality of cooked rice determination using Fourier transform near infrared spectroscopy

The goal of this research was to study the relationship between the eating quality of cooked rice and near infrared spectra measured by a Fourier Transform near infrared(FT-NIR)Spee-trometer.Samples of milled:parboiled rioe,white rioe,new Jasmine rice(harvested in 2012)and aged Jasmine rice(harvested in 2006 or during the period 2007-2011)were used in this study.The eating quality of the cooked rioe,ie,adhesiveness,hardness,dryness,whiteness and aroma,were evaluated by trained sensory panelists.FT-NIR spectroscopy models for predicting the eating quality of cooked rioe were established using the partial least squares regression.Among the eating quality,the stickiness model indicated its highest prediction ability(ie,R2a=0.71;.RMSEP=0.65;Bias=0.00;RPD=1.87)and SEP/SD of 2.In addition,it was clear that the water content did not affect the eating quality of cooked rice,rather the main chemical com-ponent implicated was starch.

Evaluation of the moisture content of tapioca starch using near-infrared spectroscopy

The purpose of this study was to develop a calibration model to evaluate the moisture content of tapioca starch using the near infrared(NIR)spectral data in conjunction with partial least square(PIS)regression.The prediction ability was asessed using a separate prediction data set.Three groups of tapioca starch samples were used in this study:tapioca starch cake,dried tapioca starch and combined tapioca starch.The opt imum model obtained from the baseline ofset spectra of dried tapioca starch samples at the outlet of the factory drying process provided a cofficient of determination(R^(2)),standard error of prediction(SEP),bias and residual prediction deviation(RPD)of 0.974,0.16%,-0.092%and 7.4,respectively.The NIR spectroscopy protocol developed in this study could be a rapid method for evaluation of the moisture content of the tapioca starch in factory laboratories.It indicated the possibility of real-tine online monitoring and control of the tapioca starch cake feeder in the drying process.In addition,it was determined that there was.a stronger influence of the NIR absorption of both water and starch on the prediction of moisture content of the model.

Improving the non-destructive maturity classification model for durian fruit using near-infrared spectroscopy

The maturity state of durian fruit is a key indicator of quality before trading.This research aims to improve the near-infrared(NIR)model for classifying the maturity stage of durian fruit using a completely non-destructive measurement.Both NIR spectrometers were investigated:the short wavelength NIR(SWNIR)ranging from 450 to 1000 nm and long wavelength NIR(LWNIR)ranging from 860 to 1750 nm.The samples collected for experimentation consisted of four stages:immaturity,prematurity,maturity,and ripe.Each fruit was scanned at the rind position on the main fertile lobe(header,middle,and tail)and stem.The classification models were developed using three supervised machine learning algorithms:linear discriminant analysis(LDA),support vector machine(SVM),and K-Nearest neighbours(KNN).The analysis results revealed that the use of durian rind spectra only obtained between 83.15%and 88.04%accuracy for the LWNIR spectrometer,while the SWNIR spectrometer provided 64.73 to 93.77%accuracy.The performance of model increases when developing with combination between rind and stem spectra.The LDA model developed using a combination of rind and stem spectra provided the greatest efficiency,exhibiting 97.28%and 100%accuracy for LWNIR and SWNIR spectrometers,respectively.The LDAmodelis therefore recommended for obtaining spectra from smoothingmoving average(MA)+baseline of rind position and when used in combination with the MA+standard normal variance(SNV)of stem spectra.The NIR spectroscopy indicated high potential for non-destructive estimation of the durian maturity stage.This process could be used for quality control in the durian export industry to solve the problem of unripe durian being mixed with ripe fruit.