Quantitative Analysis of Fat Content in Rice by Near‐Infrared Spectroscopy Technique

Fat content in rice is one of the most important nutritional quality properties. But the chemical analysis of fat content is time‐consuming and costly and could result in poor reproduction between replicates. Near‐infrared spectroscopy (NIRS) can solve those problems by providing a rapid, nondestructive, and quantitative analysis. Based on the NIRS technique and partial least squares (PLS) algorithm, four calibration models were established to quantitatively analyze fat content in brown rice grain and flour and milled rice grain and flour with 248 representative samples. The determination coefficients (R²) of these calibration models were 0.79, 0.84, 0.89, and 0.91, respectively, with the corresponding root mean square errors 0.16, 0.14, 0.09, and 0.08%. The R² were 0.73, 0.81, 0.81, and 0.89 with the corresponding root mean square errors 0.17, 0.15, 0.12, and 0.09%, respectively, in cross validation. The R² were 0.62, 0.80, 0.81, and 0.87, respectively, with the root mean square errors 0.25, 0.31, 0.28, and 0.30% in external validation. These results indicate that the method of NIRS has relatively high accuracy in the prediction of rice fat content. The four calibration models established in the present study should be useful for nutrient quality improvement in rice breeding.     

Quality Prediction of Rice Flour by Multiple Regression Model with Instrumental Texture Parameters of Single Cooked Milled Rice Grains

The purpose of this study was to develop highly accurate regression models with texture parameters of cooked milled rice grains for predicting pasting properties in terms of quality index of rice flour. Two methods were adopted as the texture measurement to acquire predictors for the models. In the calibration set, all the multiple regression models by a single‐grain method exhibited a higher R² than those by a three‐grain method. Each of the former models also showed a lower SEP and a higher RPD in the validation set. The prediction performance was best for consistency (RPD = 2.4). The single‐grain method was more advantageous for the pasting prediction. These results suggest that the models based on grain texture could predict rice flour quality.     

Rice Grain Morphological Characteristics Correlate with Grain Weight and Milling Quality

Using image analysis technology, the morphological measurements of paddy, brown (BR), and milled (MR) grains of 408 rice (Oryza sativa L.) lines representing a wide range of grain morphology were obtained, and the measurements' relationships with grain weight, milling quality, and chalkiness were investigated. The principal component analysis identified two morphological traits with a total variance of 95.7% in MR. Weight of MR was modeled as a function of the principal components using linear regression (R² = 0.95). For milling quality measurements, dominance analyses indicated that single grain weight of the paddy and BR grains were two primary parameters in determination of BR and MR recovery, whereas surface area, perimeter, and diameter were primary parameters that controlled head milled rice (HR) recovery. All measured morphological properties (surface area, perimeter, diameter, length, width, length‐to‐width ratio, roundness, and thickness) and grain weight significantly correlated with percentage of chalky (%C) grain and chalkiness (%C × % area with chalk) for all milled grains. The grain width, roundness, and thickness positively correlated with chalkiness, whereas the length and length‐to‐width ratio negatively correlated with chalkiness. The growing environment significantly affected grain HR recovery, %C grain, and chalkiness but had little effect on grain morphology and weight.     

Development and Validation of Prediction Models for Rice Surface Lipid Content and Color Parameters Using Near‐Infrared Spectroscopy: A Basis for Predicting Rice Degree of Milling

Degree of milling (DOM) of rice plays a key role in determining rice quality and value. Therefore, accurate, nondestructive, quick, and automated surface lipid content (SLC) measurement would be useful in a commercial milling environment. This study was undertaken to provide calibration models for commercial use to provide quick and accurate evaluation of milled rice SLC and Hunterlab color parameters (L,a,b) as indications of rice DOM. In all, 960 samples, including seven cultivars from seven southern United States locations, stored for 0, 1, 2, 3, and 6 months, were milled for four durations to obtain samples of varying DOM. The samples were used to develop calibration models of milled rice SLC and L,a,b values. Another sample set (n = 58) was commercially milled and used to validate the developed models. A DA 7200 diode array analyzer was used to scan milled rice samples in wavelength spectra of 950–1,650 nm. SLC and color parameters were measured using a Soxtec system and a HunterLab colorimeter, respectively. The partial least squares regression (PLS) method using the full near‐infrared spectra was used to develop prediction models for rice SLC and color parameters. Milled rice SLC was well fitted with a correlation of determination of predicted and measured values of (R² = 0.934). Color parameters were also successfully fitted for L (R² = 0.943), a (R² = 0.870), and b (R² = 0.855). Performance of the developed models to predict rice DOM was superior in predicting SLC and L,a,b values with R² predicted and measured values of 0.958, 0.836, 0.924, and 0.661, respectively.     

Predicting Wheat Quality Characteristics and Functionality Using Near‐Infrared Spectroscopy

The accuracy of using near‐infrared spectroscopy (NIRS) for predicting 186 grain, milling, flour, dough, and breadmaking quality parameters of 100 hard red winter (HRW) and 98 hard red spring (HRS) wheat and flour samples was evaluated. NIRS shows the potential for predicting protein content, moisture content, and flour color b* values with accuracies suitable for process control (R² > 0.97). Many other parameters were predicted with accuracies suitable for rough screening including test weight, average single kernel diameter and moisture content, SDS sedimentation volume, color a* values, total gluten content, mixograph, farinograph, and alveograph parameters, loaf volume, specific loaf volume, baking water absorption and mix time, gliadin and glutenin content, flour particle size, and the percentage of dark hard and vitreous kernels. Similar results were seen when analyzing data from either HRW or HRS wheat, and when predicting quality using spectra from either grain or flour. However, many attributes were correlated to protein content and this relationship influenced classification accuracies. When the influence of protein content was removed from the analyses, the only factors that could be predicted by NIRS with R² > 0.70 were moisture content, test weight, flour color, free lipids, flour particle size, and the percentage of dark hard and vitreous kernels. Thus, NIRS can be used to predict many grain quality and functionality traits, but mainly because of the high correlations of these traits to protein content.     

锌离子活度对水稻产量及子粒锌含量的影响及基因型差异

试验采用HEDTA螯合缓冲营养液培养法,选用子粒含锌量有明显差异的两个水稻基因型(碧玉早糯和浙农921),采用4种锌离子活度(pZn2+9.7,10.3,11.0,11.4),研究了锌离子活度对水稻产量和子粒锌积累、分配与子粒品质的影响及基因型差异。结果表明,锌离子活度通过对水稻有效穗数、每穗颖花数、结实率和千粒重的影响而显著影响水稻单株产量,其中影响最大的是单株有效穗数,其次是每穗颖花数,而对结实率的影响相对较小,但均存在明显的基因型差异。锌离子活度显著影响水稻子粒锌含量,随锌离子活度下降,水稻子粒含锌量降低,当锌离子活度低于pZn2+10.3时,两基因型水稻的含锌量均显著降低,但基因型间存在明显差异。不论在何种锌离子活度下,碧玉早糯的子粒含锌量均显著高于浙农921。从子粒锌分配看,颖壳、糙米、精米锌含量均随着锌离子活度提高而提高,当pZn2+从10.3升高到9.7时,碧玉早糯的糙米和精米锌含量开始降低,颖壳锌含量则开始超过糙米的锌含量继续升高;而浙农921糙米和精米锌含量的升高幅度较颖壳锌含量小。不同锌离子活度下,糙米和精米锌含量的比值在0.790~.90之间变化,并以pZn2+为9.7时为最小。因此,通过筛选子粒富锌水稻品种来提高稻米锌含量经济可行;通过增加环境锌离子活度,改善水稻的锌营养能显著提高水稻子粒的锌含量。     

Effect of Cultural Management Practices on Grain Quality of Two Rice Cultivars

To reduce fuel and labor costs and increase profits, farmers are trying new ways of growing rice (Oryza sativa L.). This includes changing crop rotations, tillage systems, and fertilization levels. There is little information on how these changes affect the cooking quality of rice. We therefore looked at the parameters associated with cooking and processing quality (apparent amylose, gelatinization temperature, lipid and protein contents, and pasting properties) of two U.S. long grains (Cybonnet and Wells) that were grown using two different tillage systems, standard rate and high rates of fertilization, and different crop rotations (continuous rice R‐R, rice after soybeans R‐SB, and rice after corn R‐C). No differences in quality traits were observed among any of the tillage systems. Rice grown in continuous rice rotation had the lowest protein content of brown and milled rice (8.6 and 8.1%, respectively) as compared to the highest levels observed in the rice‐soybean rotation (9.3 and 8.6%, respectively). Rice grown in continuous rice rotation also had higher peak viscosity than other crop rotations. Increasing the fertilization rate increased the protein content of brown rice and decreased peak, trough, and final viscosities. Apparent amylose content, gelatinization temperature, and lipid content were not affected by crop rotation or fertility; however, they were influenced by cultivar. Although the results indicated statistical differences for some quality parameters, the differences were small enough that they are unlikely to have a major impact on processing quality of long grain rice if co‐mingled.     

Rapid Gas Chromatographic Technique for Quantifying 2‐Acetyl‐1‐Pyrroline and Hexanal in Rice (Oryza sativa,L.)

The aroma of rice plays a role in its consumer acceptability. The popcorn‐like smell of aromatic rice stemming primarily from its 2‐ acetyl‐1‐pyrroline (2‐AP) content is considered desirable by many consumers. Conversely, hexanal has been correlated with off odors in rice that develop from lipid oxidation. A rapid method for 2‐AP and hexanal quantification suitable for use in breeding programs, large‐scale research efforts, and quality assurance programs is needed. While developing such a method, sample preparation (degree of milling, particle size), solvent extraction time and temperature, and gas chromatographic parameters were studied. Particle size had no influence on 2‐AP or hexanal recovered. One extraction solubilized ≈80% of the 2‐AP and 56% of the hexanal present in milled rice. The optimum extraction method was assessed to require 0.3 g of ground brown or milled rice in methylene chloride held at 85°C for 2.5 hr. The complete gas chromatographic run requires ≈25 min, and 50 samples can be analyzed per day. The optimized method's linear response (R² = 0.99) and reproducibility was demonstrated. The stability of 2‐AP and hexanal in frozen milled rice and in refrigerated methylene chloride extracts was excellent for at least six months. Milled and unmilled commercial and breeders' aromatic rice samples contained 10–1,104 ng/g of 2‐AP and 148–2,541 ng/g of hexanal. Genotype had the greatest effect on the 2‐AP and hexanal content of two lines grown over four years and in four states.     

Phosphorus and Mineral Concentrations in Whole Grain and Milled Low Phytic Acid (lpa) 1‐1 Rice

Phytic acid (myo‐inositol‐1,2,3,4,5,6‐hexakisphosphate) is the most abundant form of phosphorus (P) in cereal grains and is important to grain nutritional quality. In mature rice (Oryza sativa L.) grains, the bulk of phytic acid P is found in the germ and aleurone layer, deposited primarily as a mixed K/Mg salt. Phosphorus components and minerals were measured in whole grain produced by either the rice (Oryza sativa L.) cv. Kaybonnet (the nonmutant control) or the low phytic acid 1‐1 (lpa1‐1) mutant, and in these grains when milled to different degrees (10, 12, 17, 20, 22, and 25%, w/w). Phytic acid P is reduced by 42–45% in lpa1‐1 whole grain as compared with Kaybonnet, but these whole grains had similar levels of total P, Ca, Fe, K, Mg, Mn, and Zn. In both genotypes, the concentration of phytic acid P, total P, Ca, Fe, K, Mg, and Mn in the milled products was reduced by 60–90%, as compared with whole grain. However, a trend was observed for higher (25–40%) total P, K, and Mg concentrations in lpa1‐1 milled products as compared with Kaybonnet milled products. The reduction in whole grain phytic acid P in rice lpa1‐1 is accompanied by a 5‐ to 10‐fold increase in grain inorganic P, and this increase was observed in both whole grain and milled products. Phytic acid P was also reduced by 45% in bran obtained from lpa1‐1 grain, and this was accompanied by a 10‐fold increase in inorganic P. Milling had no apparent effect on Zn concentration. Therefore, while the block in the accumulation of phytic acid in lpa1‐1 seed has little effect on whole grain total P and mineral concentration, it greatly alters the chemistry of these seed constituents, and to a lesser but detectable extent, alters their distribution between germ, central endosperm, and aleurone. These studies suggest that development of a low phytate rice might improve the nutritional quality of whole grain, milled rice and the bran produced during milling.     

Screening of Wheat for Flour Swelling Volume by Near-Infrared Spectroscopy

Selection for starch quality is an important consideration in the breeding of wheat for Asian noodles, particularly Japanese udon, and the flour swelling volume (FSV) test was developed for this purpose. Near-infrared reflectance spectroscopy (NIRS) analysis has also been a key tool in recent years in wheat quality selection. The development and validation of NIRS calibrations for the prediction of FSV on whole grain involved 22 cultivars and breeding lines grown at four locations in two seasons. Eight calibrations were developed, each based on samples from seven trials, with the eighth trial used for validation. Over the eight calibrations, r² between predicted and actual values was 0.56–0.86 (mean 0.74) and the standard error of prediction (SEP) was 0.77–1.65 (mean 1.14) mL/g of dry meal. Separate calibrations were also developed for hard (n = 461), soft (n = 150), and soft+hard grain (n = 616), with standard errors of cross-validation (SECV) of 1.03, 1.39, and 1.21 mL/g of dry meal, respectively. Corresponding r² between predicted and actual values were 0.76, 0.78, and 0.76, respectively. Thus, NIRS offers good potential for the screening of early-generation lines to identify those with high or low FSV.     

Determining Weight and Moisture Properties of Sound and Fusarium‐Damaged Single Wheat Kernels by Near‐Infrared Spectroscopy

Single kernel moisture content (MC) is important in the measurement of other quality traits in single kernels because many traits are expressed on a dry weight basis. MC also affects viability, storage quality, and price. Also, if near‐infrared (NIR) spectroscopy is used to measure grain traits, the influence of water must be accounted for because water is a strong absorber throughout the NIR region. The feasibility of measurement of MC, fresh weight, dry weight, and water mass of single wheat kernels with or without Fusarium damage was investigated using two wheat cultivars with three visually selected classes of kernels with Fusarium damage and a range of MC. Calibration models were developed either from all kernel classes or from only undamaged kernels of one cultivar that were then validated using all spectra of the other cultivar. A calibration model developed for MC when using all kernels from the wheat cultivar Jagalene had a coefficient of determination (R²) of 0.77 and standard error of cross validation (SECV) of 1.03%. This model predicted the MC of the wheat cultivar 2137 with R² of 0.81 and a standard error of prediction (SEP) of 1.02% and RPD of 2.2. Calibration models developed using all kernels from both cultivars predicted MC, fresh weight, dry weight, or water mass in kernels better than models that used only undamaged kernels from both cultivars. Single kernel water mass was more accurately estimated using the actual fresh weight of kernels and MC predicted by calibrations that used all kernels or undamaged kernels. The necessity for evaluating and expressing constituent levels in single kernels on a mass/kernel basis rather than a percentage basis was elaborated. The need to overcome the effects of kernel size and water mass on single kernel spectra before using in calibration model development was also highlighted.     

Development of a Near‐Infrared Imaging System for Determination of Rice Moisture

The objective of this study was to develop a near‐infrared (NIR) imaging system to determine rice moisture content. The NIR imaging system fitted with 15 band‐pass filters (wavelengths of 870–1,014 nm) was used to capture the spectral image. In this work, calibration methods including multiple linear regression (MLR), partial least squares regression (PLSR), and artificial neural network (ANN) were used in both near‐infrared spectrometry (NIRS) and the NIR imaging system to determine the moisture content of rice. Comprehensive performance comparison among MLR, PLSR, and ANN approaches has been conducted. To reduce repetition and redundancy in the input data and obtain a more accurate network, six significant wavelengths selected by the MLR model, which had high correlation with the moisture content of rice, were used as the input data of the ANN. The performance of the developed system was evaluated through experimental tests for rice moisture content. This study adopted the coefficient of determination (r_val²), the standard error of prediction (SEP), and the relative performance determinant (RPD) as the performance indices of the NIR imaging system with respect to the tests of rice moisture content. Utilizing these three models, the analysis results of r_val², SEP, and RPD for the validation set were within 0.942–0.952, 0.435–0.479%, and 4.2–4.6, respectively. From experimental results, the performance of NIR imaging system was almost the same as that of NIRS. Using the developed NIR imaging system, all of the three different calibration methods (MLR, PLSR, and ANN) provided a high prediction capacity for the determination of moisture in rice samples. These results indicated that the NIR imaging system developed in this study can be used as a device for the measurement of rice moisture content.     

Authentication of Rice Using Near‐Infrared Reflectance Spectroscopy

The authentication of rice (Korean domestic rice vs. foreign rice) has been attempted using near‐infrared spectroscopy (NIRS). Two sample sets (n₁ = 280 and n₂ = 200) were used to obtain calibration equations and the spectral regions used for this study were 500–600 nm, 700–900nm, and 980–2,498 nm. Modified partial least square (MPLS) regression was used to develop the prediction model. The standard error of cross validation (SECV) and the r² were 0.165 and 0.91 respectively for 1st calibration set and 0.165 and 0.93 for 2nd calibration set respectively. The results of the independent validation (n₃ = 80) showed that all of 80 samples were identified correctly. Even though authentication of rice was performed successfully using NIRS, the calibration statistics in this study showed that further effort is needed for implementation of NIRS for authentication of rice for industry purposes.     

Determination of Rapid Visco Analyser Parameters in Rice by Near‐Infrared Spectroscopy

The objective of these studies was to find alternative Rapid Visco Analyser (RVA) viscoelastic parameters that are predictable by near‐infrared spectroscopy (NIRS). Currently, RVA instruments are widely used in assessing cooking and processing characteristics in rice. The ability to predict RVA parameters by NIRS would be useful in rapidly determining rice pasting qualities, but NIRS does not correlate with the traditional parameters (peak viscosity, final viscosity, breakdown, consistency, and setback). Alternative RVA parameters were sought by collecting RVA and NIRS data for a total of 86 short, medium, and long grain rice cultivars. The amylose contents were 0.41–24.90% (w/w) and protein concentrations were 8.47–11.35% (w/w). Partial least squares (PLS) regression models generated for the entire NIR spectrum against the RVA curve showed viscosity at 212–228 sec (80°C ± 1) varied linearly with NIR spectra (1,100 to ‐2,500 nm). Regression coefficient values were R = 0.961 for 212 sec and R = 0.903 for 228 sec. The PLS correlation coefficient for the prediction of amylose at 212–228 sec decreases along with the NIRS correlation to the same time frame. An opposite trend was observed for the correlation with protein at 212–228 sec. This comparison suggests the importance of amylose and protein in water absorption during this time frame.     

Physical,Chemical, and Sensory Properties of Antioxidant‐Enriched Raw and Cooked Rice by Vacuum‐Drying Impregnation in a Semidry State

Vacuum drying was employed with a vacuum impregnation technique in a semidry state to enrich rice with antioxidants of beetroot juice. The properties of the vacuum‐dried raw and cooked rice grains were characterized. The various raw rice grains (three varieties and two storage time periods) exhibited a significant absorption of beetroot juice, which was evident from the red‐violet beetroot color of the rice, as distinguished from the white color of the control. The color increase (ΔE= 20−40) was linear with the juice content (R² = 0.96−0.99). Their total phenolic (TP) contents and 2,2‐diphenyl‐2‐picrylhydrazyl radical (DPPH) scavenging activities were enhanced (ΔTP = 21−260 mg of gallic acid equivalents/100 g of rice db and ΔDPPH = 22−64 mg of vitamin C equivalents/100 g of rice db). Their grain integrity was reduced (Δforce = −1 to −63), which was potentially associated with the formation of grain surface cracks (linear relationship of %crack and %juice with R² = 0.94−0.98). After cooking, the enriched rice grains were linearly elongated with added juice (R² = 0.88−0.97, up to 1.6‐, 2.0‐, and 2.0‐fold for Sanpatong 1, Khao Dawk Mali 105, and Chainart 1 rice samples, respectively), and the overall volume of the cooked rice was increased (likely not linear, up to 3.2‐, 4.3‐, and 4.8‐fold for Sanpatong 1, Khao Dawk Mali 105, and Chainart 1 rice samples, respectively). Such improvements in cooking qualities were obtained by this simple vacuum‐drying technique, in comparison to existing rice‐aging processes that are more time consuming. The sensorial scores of the resultant rice products were excellent. Vacuum drying is an effective tool to improve the antioxidant value of rice as well as its cooking quality, and the raw quality remains appreciable. It is a simple and rapid process that could be practical for manufacturing healthy rice products.     

Genotypic variation in grain cadmium concentration of lowland rice

Cadmium (Cd) contamination of paddy rice soils is commonly observed in the Yangtse River Delta, China. Large Cd uptake by rice plants and its translocation into the grains can entail human‐health risks. Genotypic variations in Cd uptake and a differential Cd partitioning into grains will be the basis for developing a rice screening or breeding tool for low grain Cd. A field experiment, conducted at the experimental farm of Jiaxing, Zhejiang province from 2002 to 2004, compared 38 rice genotypes of different types (indica vs. japonica) collected from the Yangtse River Delta. The results showed large differences in Cd concentrations in straw, brown rice, and grain chaff among the rice genotypes grown on Cd‐contaminated soil. Concentrations in brown rice ranged from 0.06 to 0.99 mg Cd kg^–1. The total Cd uptake in brown rice varied between 0.96 and 28.58 μg plant ^{– 1}. In general, indica‐type cultivars accumulated significantly more Cd than the japonica‐type cultivars. The Cd concentration in straw was highly correlated with that in brown rice. While significant differences in the Cd‐partitioning ratio (% share of total Cd uptake found in brown rice) among rice genotypes were observed, these were not correlated with Cd concentration of brown rice. This indicates that the Cd accumulation in rice grains appears to be governed mainly by the Cd uptake by the plant and probably not by differential Cd partitioning. The large genotypic variation suggests the possibility to lower the Cd content of rice by genotype selection. The development of such breeding tools should focus on low Cd uptake rather than Cd partitioning between straw and grain.     

Analysis of dry matter and protein contents in fresh yam bean tubers by near‐infrared reflectance spectroscopy

Abstract

Fast screening methods are needed for plant breeding. The objective of this research was to evaluate the potential of near‐infrared reflectance spectroscopy (NIRS) for the simultaneous analysis of dry matter and protein contents in intact discs of fresh yam bean (Pachyrhizus spp.) tubers. Discs from 210 tubers were extracted with a punch few hours after harvesting and scanned by NIRS using a specially designed adapter. External validation revealed a close relationship between NIRS and reference methods for dry matter content (r²=0.94; standard error of performance, SEP=1.2%) and protein content (r²=0.87; SEP=1.94%). The calibration for protein content was compared with another one developed using dried‐ground tuber samples (r²=0.97; SEP=0.97%). These results suggested that NIRS can be used to determine dry matter and protein contents in fresh tuber samples of yam beans with acceptable accuracy. Further research will have to determine if additional traits can be incorporated into this scheme.     

Glassy State Transition and Rice Drying: Development of a Brown Rice State Diagram

The effect of moisture content (MC) on the glass transition temperature (T_g) of individual brown rice kernels of Bengal, a medium‐grain cultivar, and Cypress, a long‐grain cultivar, was studied. Three methods were investigated for measuring T_g: differential scanning calorimetry (DSC), thermomechanical analysis (TMA), and dynamic mechanical analysis (DMA). Among these methods, TMA was chosen, because it can also measure changes in the thermal volumetric coefficient (β) of the kernel during glass transition. TMA‐measured T_g at similar MC levels for both cultivars were not significantly different and were combined to generate a brown rice state diagram. Individual kernel T_g for both cultivars increased from 22 to 58°C as MC decreased from 27 to 3% wb. Linear and sigmoid models were derived to relate T_g to MC. The linear model was sufficient to describe the property changes in the MC range encountered during rice drying. Mean β values across both cultivars in the rubbery state was 4.62 × 10^‐4/°C and was higher than the mean β value of 0.87 × 10^‐4/°C in the glassy state. A hypothetical rice drying process was mapped onto the combined state diagram generated for Bengal and Cypress.