Single Rice Kernel Research Articles

Establishment of Non-Destructive Methods for the Detection of Amylose and Fat Content in Single Rice Kernels Using Near-Infrared Spectroscopy

For the efficient selection of high-quality rice varieties, the near-infrared spectroscopy (NIRS) technique has been widely applied to detect constituents in single rice kernels. Compared with other constituents, amylose content (AC) and fat content (FC) are the key parameters that can affect the quality of rice. Based on two modified AC and FC trace detection methods, two NIRS methods to detect AC and FC in single rice kernels were developed. Using the proposed methods, the AC and FC in two groups of rice kernel datasets were measured. The datasets were collected on two spectrometers with different sample movement states (static and dynamic) and measurement modes (diffuse reflectance (NIRr) and diffuse transmission (NIRt)). By optimizing the pre-treatment method and spectral range, the determination coefficients of cross-validation (R2cv) and prediction (R2p) of the NIRS models under different measurement conditions were all above 0.6. The results indicated that the proposed methods were applicable to the rapid, non-destructive detection and sorting of individual rice seeds with different AC and FC, and it was shown that these methods can meet the requirements of the rough screening of rice seed varieties.

An authenticity method for determining hybrid rice varieties using fusion of LIBS and NIRS

AbstractThis study proposes a method to rapidly determine the authenticity of hybrid rice seeds using the fusion of laser‐induced breakdown spectroscopy (LIBS) and near‐infrared spectroscopy (NIRS). The male parent, F1 generation, and female parent of two hybrid rice vaieties (“Zhongheyou 1” and “Liangyou 688”) were identified by the proposed method. The LIBS and NIR spectra of rice seeds with different physical state (single rice kernels [SRK] and single brown‐rice kernels [SBRK]) were measured, and the primary fusion and intermediate fusion were used for the construction of the LIBS and NIRS data fusion models. The comparation results under different conditions show that the intermediate SRK and SBRK fusion models achieve the best test accuracy (96.88% and 98.52% for “Zhongheyou 1”, and 98.96% and 95.26% for “Liangyou 688”, respectively). The results indicate that the proposed method is effective for rapid and accurate identification of parents and progenies of hybrid rice.

Identification of SSR Markers for Differentiating Rice (Oryza sativa L.) Varieties Marketed in Turkey

This study was carried out to identify SSR markers for the differentiation and identification of rice cultivars marketed in Turkey between 2016 and 2017. In this study 60 registered or production permitted, some local and foreign rice varieties were used as a material. DNA was isolated from single polished rice kernels for PCR amplification and rice cultivars were genotyped by 50 SSR markers. We found that 45 out of 50 SSR markers produced reproducible and polymorphic alleles. Thirty-six rice varieties had variety specific alleles among 60 rice varieties analysed in the study. This variety specific alleles belong to only one variety and they can be used to identify rice variety among 60 rice varieties studied. Osmancık-97, Cammeo, Ronaldo and Baldo are the most import 4 varieties in Turkish rice production and milled rice market. In this study, it was found that RM152, RM144, RM259 and RM118 SSR markers can be used to differentiate and identify these most important four varieties with different combinations. Collectively, this study provided some variety specific SSR markers that can be used to differentiate and identify rice varieties sold in Turkey.

A cascade network for the classification of rice grain based on single rice kernel

This paper describes the classification of four different varieties of rice grain based on four sets of features, namely morphology, colour, texture and wavelet. The classification is carried out on single rice kernel using image pre-processing steps followed by a cascade network classifier. The performance of the classifiers based on the above feature sets is also compared. It is found that morphological feature is more suitable for the classification of rice kernels, as compared to other features. The number of input features is reduced by a feature selection process using statistical analysis system (SAS) software. The classification accuracy based on selected features is compared with that of original features using different classifiers. It is found that the selected features are able to provide classification accuracy very close to the original features. The performance of the proposed cascade classifier is also tested against standard datasets from the University of California, Irvine (UCI), and the results are compared with other classifiers. The results show that the proposed classifier provides better classification accuracy as compared to other classifiers.

Practicability investigation of using near-infrared hyperspectral imaging to detect rice kernels infected with rice false smut in different conditions

Rice false smut (RFS) is a devastating seed-brone rice disease in many rice-growing countries, endangering the health of rice germplasm resources and reducing the yield and quality of rice. This study aimed to propose an effective method for RFS detection in actual production based on near-infrared hyperspectral imaging (NIR-HSI) paired with pathological analysis. The true infection status of rice kernels collected in different conditions was labeled by PCR. The separability between healthy and infected rice kernels was explored using principal component analysis (PCA). Multivariate quantitative analysis models were constructed based on full wavelengths of laboratory-inoculated kernels. Characteristic wavelengths extracted to improve detection performance contained fingerprint information related to RFS infection. The best classification accuracies for healthy and infected mixed kernels with different infection degrees achieved 99.33 % on calibration set and 99.20 % on prediction set, respectively, using RF-ELM model. The practicality of detection model was further verified through obtaining detection accuracies of 91.07 % and 89.38 % for two varieties of field-infected rice kernels and visualizing the category attribute of single rice kernel in hyperspectral images. The overall results indicated the excellent potential of NIR-HSI for on-line large-scale seeds detection in modern seed industry.

A calibration transfer optimized single kernel near-infrared spectroscopic method.

Single kernel near-infrared spectroscopy (SKNIRS) could aid in the quality screening of early-generation seeds, to improve the efficiency of seed breeding. However, the application of SKNIRS is limited due to the irregular physical characteristics, the heterogeneous constituent distributions of individual seeds, and the insufficient detection accuracy of the reference method. The reported near-infrared detection results of single seeds are often less accurate than those of dehusked seeds and seed flour. In this paper, a calibration transfer-optimized single kernel near-infrared spectroscopic method is proposed. This method aims to accurately detect the chemical composition of single seeds by using the calibration model of the corresponding dehusked seeds or seed flour. The proposed method was applied to the analysis of the protein content of a single rice kernel. The near-infrared transmission spectra of three forms of rice (single rice kernel (SRK), single brown rice kernel (SBK) and rice flour (RF)) of 201 individual rice seeds and the corresponding protein content values were obtained. By comparing different pretreatment methods and spectral ranges, the spectral range 950-1250 nm, the standard normal variate transformation (SNV) pretreatment, and 9 PLS factors were selected to construct the optimal partial least squares (PLS) regression models. Then, the protein content of single rice kernels were determined through two different methods: (i) the direct method, in which single rice kernels were analyzed using the single rice kernel model directly; and (ii) the proposed method, in which the spectra of single rice kernels were transferred into the spectra of single brown rice kernels and rice flours with a calibration transfer algorithm, spectral space transformation (SST), and were analyzed by the respective calibration models. The external validation coefficient correlation (R) value of the direct method was 0.971, and the R values of the proposed method were 0.962 (SBK) and 0.975 (RF). The root mean square error of prediction (RMSEP) value of the direct method was 0.423, and the RMSEP of the proposed method were 0.480 (SBK) and 0.401 (RF). In addition, the transfer results among the spectra of three forms of rice were compared. By comparison, the results of the proposed method are fairly close to the results of the direct method. The results indicate that the spectra generated from one individual rice seed can be transferred freely among the three forms by means of calibration transfer. The proposed method is a promising way to overcome the challenges associated with analyzing individual seeds and to improve SKNIRS.

Development and Optimization of an Automatic System to Determine the Moisture Content in Single-Kernel Rough Rice Using Visible/NIR Spectroscopy

Abstract. The moisture content of rough rice directly affects the rice quality and market value. It is important to automatically measure the moisture content in single rice kernels to determine the product quality during processing and storage. This study developed an automatic measurement system to determine the moisture content in single-kernel rough rice using a spectrometer and optimized the performance of the system using a response surface method (RSM) experiment. The system consisted of a vibrator, a rotating plate, a spectrometer, and a PC. A Box-Behnken design was used for the RSM experiment to determine three performance indices: successful ratio, successful scan, and error ratio. The vibrator was tested at 352, 423, and 495 Hz. The rotation speed of the plate was 4, 8, and 16 s per cycle, and three scan tests were performed using one, two, or three replications per kernel. The five tested moisture contents were 33.3%, 24.4%, 19.5%, 13.5%, and 12.2% wet basis (w.b.). The RSM results showed that of the 15 response surfaces, eleven surfaces were saddles, three were hills, and one was a valley. The corresponding maxima and minima were determined. The parameter set of 495 Hz, 16 s per cycle, and one scan resulted in a capacity of 60 kernels per minute and an error ratio of 0.0001.HighlightsAn automatic system was developed to determine the moisture content in single-kernel rough rice.The optimal operation condition was found by response surface method.With the optimal operation condition, the system processed 60 kernels per minute and had an error ratio of 0.0001. Keywords: Automation, Moisture content, NIR spectroscopy, Rough rice, Single kernel.

Moisture Adsorption Rate and Effective Diffusivity in Chinese Milled Rice

Four varieties of Chinese milled rice samples with 11.90–15.56% initial moisture content (IMC, wet basis) were used to determine the rate of moisture adsorption using the gravimetric method at 10, 20, 25, 30, and 35°C under 65%, 86%, and 100% relative humidity (RH), respectively. A moisture diffusion equation was modified to fit the relationship between the moisture ratio (MR) of samples and exposure time. In the range of 65% to 100% RH, the lower IMC of milled rice samples corresponded to higher moisture adsorption rate at temperatures of 10 to 35°C. The moisture adsorption rate of samples increased with an increasing temperature. The moisture sorption rate of samples with the same IMC increased with increasing RH at certain temperatures. A single milled rice kernel was geometrically considered a finite homogeneous cylinder shape, and the analytical solution of the partial differential equation of moisture diffusion was given. The effective moisture diffusivity was calculated using a slope method by plotting the experimental data in terms of ln (MR) versus rewetting time. In the range of 10–35°C, the effective moisture adsorption diffusivity of milled rice kernels with normal moisture was 4.321×10-9–1.117×10-7 m2h-1. For the same IMC, the effective moisture diffusivity of milled kernels tended to increase with increases in surrounding temperature at certain relative humidity levels, but decreased with an increase in RH at certain temperatures. It was concluded that the milled rice samples from different types had similar effective moisture diffusivity when similar IMC was given.

The Stress-Relaxation Behavior of Rice as a Function of Time, Moisture and Temperature

Abstract: Stress-relaxation behavior of single rice kernel was studied using a dynamic mechanical analyzer (DMA) in compression mode. The relaxation modulus was measured in a moisture content range of 12–30 % on dry basis (d.b.) and a temperature range of 25–80°C. A constant stain value of 1 % (within the linear viscoelastic range) was selected during the stress-relaxation tests. The relaxation modulus was found to decrease as the temperature and moisture increased. A master curve of relaxation modulus as a function of temperature and moisture content was generated using the time–moisture–temperature superposition principle. Results showed that the generalized Maxwell model satisfactorily fitted the experimental data of the stress-relaxation behavior and the master curve of relaxation modulus (R 2> 0.997). By shifting the temperature curves horizontally, the activation energy of the stress relaxation was obtained which significantly decreased with increase in the moisture content.

Coupling CFD and Diffusion Models for Analyzing the Convective Drying Behavior of a Single Rice Kernel

The drying behavior of a single rice kernel subjected to convective drying was analyzed numerically by solving heat and moisture transfer equations using a coupled computational fluid dynamics (CFD) and diffusion model. The transfer coefficients were computed simultaneously with the external flow field and the internal diffusive field of the grain. The model was validated using results of a thin-layer drying experiments from the literature. The effects of velocity and temperature of the drying air on the rice kernel were analyzed. It was found that the air temperature was the major variable that affected the drying rate of the rice kernel. The initial drying rates (in first 20 min) were 7, 12, and 19% per hour at inlet air temperatures of 30, 45, and 60 ∘ C, respectively. Important temperature gradients within the grain existed only in the first few minutes of the drying process. The moisture content gradients reached a maximum value of 11.7% (db) mm −1 at approximately 45 min along the short axis in the thickness direction. The variation in the inlet air velocity showed a minor effect on the drying rate of the rice kernel. The heat and mass transfer coefficients varied from 16.57 to 203.46 W·m −2·K −1 and from 0.0160 to 0.1959 m·s −1, respectively. The importance of the computation of the transfer coefficients with the heat and mass transfer model is demonstrated.

Magnetic resonance imaging and analyses of tempering processes in rice kernels

This research involved a magnetic resonance imaging (MRI) technique that was applied to examine water distribution and migration in single rice kernels during the tempering process. The imaging experiments were performed in a Bruker 9.4T MRI system. Three-dimensional spin-echo (SE) imaging sequences were optimized by adjusting the scanning parameters of echo time (TE) and repetition time (TR) to obtain images with maximum contrast. The MR images showed that the moisture distribution in the rice kernel is non-uniform and compartmental. The embryo region exhibited much higher MR signal intensity than the starchy endosperm portion. The tempering process was analyzed with spatial-temporal signal intensities of the endosperm following the drying process of the rice kernel. The transient change of the signal intensities in the endosperm was well fitted with a double exponential function suggesting that both convection and diffusion contributed to the reduction of the moisture gradient within the rice kernel during tempering. This hypothesis was further supported by the experimental data of the insulated rice kernel whose convective mass transfer was excluded. The experimental results revealed that MR imaging of rice kernels could be used as an efficient tool to examine the mechanisms of moisture migration within cereal grains.

Nondestructive Measurement of Carotenoids in Plant Tissues by Fluorescence Quenching

Carotenoids, compounds valuable for their antioxidant properties in humans and animals, are sometimes present in the bran layers of rice (Oryza sativa L.). We developed a nondestructive technique to screen individual rice kernels for the presence of carotenoids in their bran for genetic selection of the carotenoid‐containing lines. Most plant tissues are highly autofluorescent. Carotenoids have a high absorptivity for visible light, which allows them to absorb or quench this natural fluorescence. Synchronous fluorescence spectroscopy provides the specificity and sensitivity that allows detection of the changes in fluorescence in a single rice kernel that occur when carotenoids are present. The fluorescence quenching technique identified three rice lines as having carotenoids present in the bran, and three other lines as being carotenoid‐deficient. Fluorescence quenching was also used to monitor changes in carotenoid levels in living plants and enabled the measurement of carotenoids within the different colors of a variegated leaf.

Magnetic resonance imaging of single rice kernels during cooking

The RARE imaging method was used to monitor the cooking of single rice kernels in real time and with high spatial resolution in three dimensions. The imaging sequence is optimized for rapid acquisition of signals with short relaxation times using centered out RARE. Short scan time and high spatial resolution are critical factors in the investigation of the cooking behavior of rice kernels since time and spatial averaging may lead to erroneous results. The results are confirming the general pattern of moisture ingress that has been suspected from previous (more limited) studies. Water uptake as determined by analysis of the MRI time series recorded during cooking compares well with gravimetric studies. This allows using these real-time MRI data for developing and validating models that describe the effect of kernel microstructure on its cooking behavior.

A Three-dimensional Numerical Simulation of Transient Heat and Mass Transfer inside a Single Rice Kernel during the Drying Process

A three-dimensional (3-D) theoretical model describing the coupled heat and mass transfer inside a single rice kernel during drying was established. The 3-D shape of a single kernel was characterised by the body-fitted coordinate system. The finite volume method was applied to solve the governing equations. A Fortran-90 based computer code was developed to simulate the transient moisture content distributions inside a rice kernel. The simulated average kernel moisture contents were compared favourably with the thin-layer drying data collected in this study. Moisture contents at some selected nodes and moisture content gradients inside the rice kernel were predicted under three different drying conditions. It was found that the moisture content gradient along the short axis of a rice kernel in the thickness direction almost doubled that along the short axis in the direction of width for the rice variety and drying conditions used in this study. The maximum moisture content gradient along the short axis in the direction of width was comparable to that predicted by a two-dimensional simulation reported in the literature.

INTRA–KERNEL MOISTURE RESPONSES OF RICE TO DRYING AND TEMPERING TREATMENTS BY FINITE ELEMENT SIMULATION

Moisture content patterns inside rice kernels are important in understanding rice fissure formation, especiallywhen glass transition effects are considered. Unsteady and nonlinear partial differential equations were employed todescribe twodimensional temperature and moisture distributions within a single rice kernel during drying and temperingprocesses. Moisture content gradients (MCGs) inside the kernel were examined. Results showed that the maximum MCG(MMCG) appeared in the direction of the short axis. During the tempering process, moisture content on the kernel surfacehad a much faster and greater change than that at the kernel center. The intrakernel MCG decreased considerably duringthe first 40 min of tempering, after which it decreased slowly to approach zero. A duration of about 40 min of tempering at60C helped eliminate about 90% of the MCGs created inside the rice kernel during drying, and this simulation resultcorrelated favorably with the published tempering data in the literature. The findings from this study provided usefulinformation for determining optimal drying and tempering conditions of rice to enhance its milling quality.

DEVELOPMENT OF COMPUTER SIMULATION SOFTWARE FOR SINGLE GRAIN KERNEL DRYING, TEMPERING, AND STRESS ANALYSIS

The quality traits of a single grain kernel can produce an index of the overall quality of the bulk grain, as individualkernels comprise a grain bulk. Therefore, singlekernel drying behavior is important in understanding the overall quality ofgrain. A Matlab based software package with C++ compiler was developed in this study for describing singlekernel drying,tempering, and internal stress analysis. Nonlinear and transient partial differential heat and mass transfer equationsdescribing the single kernel drying and tempering processes were solved using the finite element method. Temperature andmoisturedependent viscoelastic properties were applied to the stress analysis of a single rice kernel. A graphical userinterface and 2D/3D graphics for temperature, moisture content, and stress changes were developed. This software packageis a useful tool for engineers, operators, and educators to predict temperature, moisture content, and internal stressdistributions inside a single kernel.

Identification of Rice Variety Using near Infrared Spectroscopy

Current techniques for the discrimination of varieties of rice is known not to be objective because of having to depend on the naked eye of a well trained inspector. DNA fingerprint methods, based on the genetic character of rice, has been shown to be inappropriate for on-site application because the method needs a lot of labour and skilled expertise. In order to solve these problems, many researchers have tried to develop a non-destructive method which possesses speed and functionality for the inspection centre of the government. Rice consumers want tot confirm cultivation origin and its variety because they believe price or eating score has a high correlation according to them. Sometimes illegal utilisation of rice has been reported and the variety name has been mismatched with the actual rice variety due to the non-establishment of objective techniques for discrimination of rice origin and variety. On the basis of this situation, we investigated the possibility of developing a non-destructive discriminant method for the domestic rice variety in this research. At the 8th IDRC held at Chambersburg in 1996 we reported that an image processing technique using a CCD camera could discriminate the variety of rice with high accuracy in the case of quite different shaped rice but the accuracy reached 90% for similarly shaped rice. At the prsent conference, near infrared spectroscopy, which can offer internal characters of a single rice kernel, giving useful information for identifying rice variety, was introduced. We have developed several tools for measuing the NIR spectra of whole, polished rice. The spectra using a single grain cell with grinding facilities was more effective than the others in accuracy of identification after principal component analysis. In conclusion, near infrared spectroscopy can be used to identify rice varieties even though even though accuracy of identification needs to be improved.

Modified method for determination of amylose content using a single rice kernel

Modified method for determination of amylose content using a single rice kernel

TEXTURE MEASUREMENT OF COOKED RICE KERNELS USING THE MULTIPLE‐POINT MENSURATION METHOD 1

A new modification of Texture Profile Analysis, a two‐point mensuration method, was shown to give better differentiation between different types of cooked rice. The method involves compressing single rice kernels 50% and 90% at 240 mm/mm. Ratios of values obtained at the two compression depths were better reflectors of the texture of rice as affected by the amount of water used in cooking and by rice variety (glutinous versus non‐glutinous). Data are also presented showing how hardness, cohesiveness, adhesiveness and springiness of rice vary with compression or biting depths and with the position of the kernel in the cooker. The ratio of adhesiveness to hardness at 50% compression and the ratio of adhesiveness to hardness between the two compressions were the most useful indices.; in final form February 18, 1981