Prediction of Baking Characteristics of Hard Winter Wheat Flours Using Computer‐Analyzed Mixograph Parameters

The objective of this research was to determine whether computer‐analyzed (objective) mixograph parameters could replace conventional mixograph parameters in the evaluation of flour quality. The 642 hard winter wheat flours, collected from federal regional performance nurseries in 1995 and 1996, were analyzed by a conventional and computerized mixograph. Mixograph bandwidths at 6 min (BW6) showed the most significant linear correlation with subjective mixing tolerance scores (r = 0.81, P < 0.1%, n = 642). Prediction models of conventional and experimental baking parameters were developed by continuum regression using computer‐analyzed mixograph parameters of a calibration set (n = 282). The developed models could estimate conventional mixograph mixing time and tolerance scores, baking water absorption and mixing time, and bread loaf volume, showing R² values of 0.86, 0.74, 0.68, 0.80, and 0.51, respectively, for a validation set (n = 380). These results indicated that computer‐analyzed mixograph parameters could be applied to develop prediction models to be used for flour quality evaluation in wheat breeding programs.     

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.     

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.     

精料补充料中肉骨粉含量的近红外光谱检测

为了保证饲料安全,精料补充料中肉骨粉的检测是十分必要的。该文探讨了精料补充料中肉骨粉含量的近红外光谱分析方法,123个样品作为校正集,采用偏最小二乘法(PLS),分别对光谱进行散射校正和卷积平滑、一阶微分、二阶微分预处理建立校正模型,以最大的决定系数(R²)和最小的标准差(RMSEC)为选择依据,通过比较,以多元散射校正和卷积平滑处理与二阶微分相结合的处理效果最好,其预测值与测量值的决定系数(R²)和标准差(RMSEC)分别为0.9751和0.437。34个样品作为检验集进行外部验证,决定系数(r²)和标准差(RMSEP)分别为0.9749和0.420,平均绝对误差和相对误差分别为0.326和13.89％。结果表明,利用近红外分析技术可以检测精料补充料中肉骨粉的含量。     

New NIRS calibrations for fiber fractions reveal broad genetic variation in Brassica napus seed quality

Near-infrared reflectance spectroscopy (NIRS) calibrations were developed for the estimation of neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) in intact seeds of oilseed rape ( Brassica napus ). A set of 338 diverse winter oilseed rape genotypes showing broad variation for seed color was used as a basis for the new calibrations. Different calibrations were generated for 10 or 1 mL seed volumes, respectively. In both seed volumes good coefficients of determination for external validation (R(2)) of the calibrations were obtained for ADL, the major antinutritional fiber fraction in oilseed rape meal, and adequate calibrations for NDF and ADF. Evaluation of diverse B. napus germplasm with the new calibrations revealed a surprisingly broad variation in contents of ADL in dark-seeded oilseed rape. The ability to use NIRS for efficient selection of low-fiber genotypes, irrespective of seed color, represents an important breakthrough in breeding for improved nutritional quality of seed extraction meals from oilseed rape.     

Use of near‐infrared reflectance spectroscopy to assess nitrogen concentration in different plant tissues of rapeseed

Abstract

An evaluation of the performance of near‐infrared reflectance spectroscopy (NIRS) in the analysis of nitrogen (N) concentration in different rapeseed (Brassica napus L.) tissues was made. A total of 228 samples from an N‐efficiency study corresponding to leaves and stems at flowering, fallen leaves, mature stems, and mature pod walls were oven dried, ground, and then analyzed by NIRS. The N concentration was determined by Dumas combustion. Two different calibration strategies were followed: (i) separate calibration equations were developed for each type of tissue, resulting in r² above 0.95 in crossvalidation for all tissues with the ratio of the standard error of crossvalidation (SECV) to the standard deviation of the population (SD) ranging from 0.10 to 0.22, and (ii) a NIRS calibration equation was developed from a set integrating 149 samples from the five groups of tissues. External . validation with a set containing 79 further samples from all the groups resulted in an r² of 0.99 and a ratio of the standard error of performance (SEP) to the SD of 0.08. External validation for each group separately resulted in r² from 0.91 to 0.99 and SEP/SD from 0.10 to 0.27. It was concluded that a universal NIRS calibration equation integrating samples from all the types of tissues is an adequate approach for the accurate analysis of N concentration in rapeseed. Based on our results, the NIRS technique can reliably replace the Kjeldahl or Dumas methods to determine the N concentration in investigations of the N efficiency in rapeseed.     

Rapid Prediction of Rice Quality Characteristics by Near‐Infrared Reflectance Spectroscopy for Breeding Programs

Breeding of high‐quality rice requires quick methods to evaluate the quality characteristics such as milling, grain appearance, nutritional, eating, and cooking qualities. Because routine measurements of these quality traits are time consuming and expensive, a rapid predictive method based on near‐infrared spectroscopy (NIRS) can be applied to measure these quality parameters. In this study, calibration models for measurement of grain quality were developed using a total of 570 brown and milled rice samples. The results indicated that the models developed from the spectra of brown rice for all the quality traits had the coefficient of determination for external validation (R²) larger than 0.64 except for gel consistency. The best model was developed for the protein content, with R² of 0.94 for external validation. The model for the total score of physicochemical characteristics (TSPC), a comprehensive index reflecting all other traits, had R² of 0.70 and SD/SEP of 1.70, which indicates that high or low TSPC for a given rice could be discriminated by NIRS. The models developed from brown rice were as accurate as those from milled rice. Results suggest that NIRS‐based predictions for rice quality traits may be used as indicator traits to improve rice quality in breeding programs.     

Rainforest litter quality and chemical controls on leaf decomposition with near‐infrared spectrometry

Plant‐litter chemical quality is an important driver of many ecosystem processes, however, what actually constitutes high‐ or low‐quality litter (chemical potential for fast and slow decomposition, respectively) is often interpreted by the indices available. Here, near‐infrared spectroscopy (NIRS) was used to explore leaf‐litter chemical quality and the controls on decomposition in the tropical rainforest region of north Queensland Australia. Leaf‐litter samples from litterfall collections and litterbag studies were used. NIRS was used to calibrate the chemical compositions of the material (N, P, C, Mg, Ca, acid detergent fiber, acid detergent lignin, α‐cellulose, and total phenolics) from a smaller sample set covering the spectral range in the full set of samples. Calibrations were compared for both separate (local) and combined models, for litterbags, and litterfall. Coefficients of determination (r²) in the local models ranged from 0.88 (litterbag Mg) to 0.99 (litterfall N), with residual prediction deviation ratios > 3 for all constituents except Mg (≈ 2.5). Mass loss in the litterbags was strongly related to the NIR spectra, with model r²'s of 0.75 (in situ leaves) and 0.76 (common control leaf). In situ decomposability was determined from modeling the initial NIR spectra prior to decomposition with litterbag exponential‐decay rates (model r² of 0.81, n = 85 initial samples). A best subset model including litter‐quality, climate, and soil variables predicted decay better than the NIR decomposability model (r² = 0.87). For litter quality alone the NIR model predicted decay rate better than all of the best predictive litter–chemical quality indices. The decomposability model was used to predict in situ decomposability in the litterfall samples. The chemical variables explaining NIR decomposability for litterfall were initial P, C, and phenolics (linear model r² = 0.80, n = 2471). NIRS is a holistic technique that is just as, if not more accurate, than litter–chemical quality indices, when predicting decomposition and decomposability, shown here in a regional field study.     

Modified Dough Preparation for Alveograph Analysis with Limited Flour Sample Size

Dough rheological characteristics obtained by alveograph testing, such as extensibility and resistance to extension, are important traits for determination of wheat and flour quality. A challenging issue that faces wheat breeding programs and some wheat research projects is the relatively large flour sample size of 250 g required for the standard alveograph method (AACCI Approved Method 54‐30.02). A modified dough preparation procedure for a small flour sample size was developed for the alveograph test method. A dough was prepared by mixing 80 g of flour with 60% water absorption (2.5% salt solution) for 4 min in a 100 g pin mixer; it was then sheeted and cut into three patties of defined thickness. Data generated by the modified dough preparation method were significantly correlated with the results from the approved alveograph method. The correlation coefficients (r) for each of six alveograph dough characteristics of 40 different advanced breeding lines and wheat varieties were 0.92 for P (mm H₂O), 0.73 for L (mm), 0.83 for W (10^–4 J), 0.90 for P/L, 0.90 for le (%), and 0.76 for G. The modified dough preparation was easier and more convenient than the approved method, and test time for the modified dough preparation was shorter by 20–25 min. This modified dough preparation procedure for the alveograph may be useful for wheat breeding programs as well as an alternative to the approved alveograph method for milling and baking industries and wheat quality research.     

Predicting Protein Composition,Biochemical Properties,and Dough-Handling Properties of Hard Red Winter Wheat Flour by Near-Infrared Reflectance

Breadmaking quality in wheat is one of several considerations that plant breeders face when developing new cultivars. In routine breeding programs, quality is assessed by small-scale dough-handling and bake tests, and to some extent, by biochemical analysis of gluten proteins. An alternative, not yet fully examined, method for wheat flour quality assessment is near-infrared reflectance (NIR) spectrophotometry. The present study was performed on 30 genotypes of hard red winter wheat grown during two crop years at eight to nine locations in the Great Plains area of the United States. Biochemical testing consisted of measuring protein fractions from size-exclusion HPLC (M _r > 100k, M _r 25–100k, and M _r < 25k designated as glutenin, gliadins, and albumin and globulins, respectively), pentosan content, and SDS sedimentation volume. Dough-handling properties were measured on a mixograph and recorded as the time to peak dough development, the peak resistance, the width of the mixing curve, and the width of the curve at 2 min past peak. Partial least squares analyses on diffuse NIR spectra (1,100–2,498 nm) were developed for each constituent or property. When applied to a separate validation set, NIR models for glutenin content, gliadin content, SDS sedimentation volume, and mixograph peak resistance demonstrated reference vs. predicted correlations ranging from r = 0.87 to r = 0.94. Such models were considered sufficiently accurate for screening purposes in breeding programs. NIR spectra were responsive to each constituent or property at a level higher than expected from a correlation between the constituent or property and protein content (recognizing that protein content is modeled by NIR with high accuracy).     

Solvent Retention Capacities of Indian Wheats and Their Relationship with Cookie‐Making Quality

Ninety‐two wheat genotypes including 50 cultivars released in India and 42 germplasm lines were subjected to solvent retention capacity (SRC) tests using 1 g of flour and 1 g of whole meal to see the relationship with cookie‐making quality and the utility in breeding programs. Very high negative correlations (P < 0.001) were observed between cookie diameter and spread factor and alkaline water retention capacity (AWRC), and solvent retention capacities of both flour and whole meal samples. Multiple regression analysis showed that AWRC explained 43.8%, sodium carbonate SRC 27.3%, lactic acid SRC 15.1%, and protein content 13.8% of the total variability (multiple r = 0.87) in cookie diameter. Total variability (multiple r = 0.85) in spread factor was explained 40.3% by AWRC, 27.4% by SODSRC, 14.5% by LASRC, and 17.8% by protein content. When the technique was further used to reduce the number of parameters contributing to cookie diameter, AWRC explained 67.2% of the total variability (multiple r = 0.85) and the rest by lactic acid SRC and protein content. Surprisingly, multiple regression analysis of whole meal samples exhibited that lactic acid SRC and sodium carbonate SRC explained 88 and 12%, respectively, of the total variability (multiple r = 0.76) in cookie diameter and 78 and 22%, respectively, of the total variability (multiple r = 0.71) in spread factor. Among the soft wheat flour samples selected based on W > 7.70 cm, pentosan content as revealed by sucrose SRC explained 87.7% of the total variability (multiple r = 0.54) of cookie diameter and 83.8% of total variability (multiple r = 0.52) in spread factor. Clustering of genotypes based on SRC profiles using both flour and whole meal produced clusters with similar average cookie diameter and spread factor. The data clearly demonstrate that whole meal tests can be used in screening the recombinant lines as well as in selecting desirable genotypes for making crosses to enhance cookie‐making quality.     

Relationships of Free Lipids with Quality Factors in Hard Winter Wheat Flours

Hard winter wheat (Triticum aestivum L.) flours (n = 72) were analyzed for free lipids (FL) and their relationships with quality parameters. The two main glycolipid (GL) classes showed contrary simple linear correlations (r) with quality parameters. Specifically, kernel hardness parameters, flour yields, and water absorptions had significant negative correlations with monogalactosyldiglycerides (MGDG) but positive correlations with digalactosyldiglycerides (DGDG). MGDG showed negative correlations with gluten content but positive correlations with gluten index. The percentages of DGDG in FL had significant positive correlations among cultivars (n = 12) with mixograph and bake mix times (r = 0.71, P < 0.01 and r = 0.67, P < 0.05, respectively), mixing tolerance (r = 0.67, P < 0.05), and bread crumb grain score (r = 0.71, P < 0.01). These results suggest that increasing DGDG in FL could contribute to enhancing wheat quality attributes including milling, dough mixing, and breadmaking quality characteristics. FL content and composition (ratio of MGDG or DGDG to GL) supplement flour protein content to develop prediction equations of mixograph mix time (R² = 0.89), bake mix time (R² = 0.76), and loaf volume (R² = 0.72).     

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.     

Solvent Retention Capacity Values in Relation to Hard Winter Wheat and Flour Properties and Straight‐Dough Breadmaking Quality

Solvent retention capacity (SRC) was investigated in assessing the end use quality of hard winter wheat (HWW). The four SRC values of 116 HWW flours were determined using 5% lactic acid, 50% sucrose, 5% sodium carbonate, and distilled water. The SRC values were greatly affected by wheat and flour protein contents, and showed significant linear correlations with 1,000‐kernel weight and single kernel weight, size, and hardness. The 5% lactic acid SRC value showed the highest correlation (r = 0.83, P < 0.0001) with straight‐dough bread volume, followed by 50% sucrose, and least by distilled water. We found that the 5% lactic acid SRC value differentiated the quality of protein relating to loaf volume. When we selected a set of flours that had a narrow range of protein content of 12–13% (n = 37) from the 116 flours, flour protein content was not significantly correlated with loaf volume. The 5% lactic acid SRC value, however, showed a significant correlation (r = 0.84, P < 0.0001) with loaf volume. The 5% lactic acid SRC value was significantly correlated with SDS‐sedimentation volume (r = 0.83, P < 0.0001). The SDS‐sedimentation test showed a similar capability to 5% lactic acid SRC, correlating significantly with loaf volume for flours with similar protein content (r = 0.72, P < 0.0001). Prediction models for loaf volume were derived from a series of wheat and flour quality parameters. The inclusion of 5% lactic acid SRC values in the prediction model improved R² = 0.778 and root mean square error (RMSE) of 57.2 from R² = 0.609 and RMSE = 75.6, respectively, from the prediction model developed with the single kernel characterization system (SKCS) and near‐infrared reflectance (NIR) spectroscopy data. The prediction models were tested with three validation sets with different protein ranges and confirmed that the 5% lactic acid SRC test is valuable in predicting the loaf volume of bread from a HWW flour, especially for flours with similar protein contents.     

Physicochemical Properties Related to Quality of Rice Noodles

Eleven rice genotypes with diverse Rapid Visco Analyzer (RVA) pasting characteristics were evaluated for their physicochemical and gel textural characteristics relative to their suitability for making rice noodles. Apparent amylose content (AC) was highly correlated with swelling power (r = -0.65, P < 0.05), flour swelling volume (FSV) (r = -0.67, P < 0.05), noodle hardness (r = 0.74, P < 0.01), gumminess (r = 0.82, P < 0.01), chewiness (r = 0.74, P < 0.01), and tensile strength (r = 0.72, P < 0.05). Solubility showed an inverse relationship with the pasting parameters and noodle rehydration, and a positive relationship with cooking loss, noodle hardness, and gumminess. FSV and most of the pasting parameters were negatively correlated with noodle hardness. RVA parameters and textural parameters of gels formed in the RVA canister were well correlated with actual noodle texture and may, therefore, be used for predicting rice noodle quality during early screening of genotypes in breeding programs.     

Milling Quality and White Salt Noodle Color of Chinese Winter Wheat Cultivars

Improvement of milling quality is an important aspect in wheat breeding programs. However, the milling quality of Chinese wheats remains largely unexplored. Fifty‐seven Chinese winter wheat cultivars from four regions were used to investigate the variation of milling quality parameters and to determine the associations between milling quality traits and color of noodle sheet. Substantial variation was presented for all measured parameters in this germplasm pool. Complete soft, hard, and medium‐hard types were observed. Soft wheat and hard wheat show significant differences in flour ash content, flour bran area, and flour color grade. No simple trait can be used to select for flour milling quality. High flour ash content and bran speck area contributed negatively to brightness of dry flour. Correlation coefficients (r) between L* value of dry flour and flour ash content and bran speck area were ‐0.47 and ‐0.65 for hard cultivars, and ‐0.51 and ‐0.72 for soft cultivars, respectively. Flour color grade (FCG) was significantly and positively associated with bran speck area; r = 0.56 and 0.73 for hard and soft wheats, respectively. There was a high correlation between FCG and L* value of flour water slurry (r = ‐0.95). Strong associations were also established between milling quality index (MQI) and FCG, L* value of dry flour, flour‐water slurry, and white salted noodle sheet for both hard and soft wheats. In conclusion, substantial progress could be achieved in improvement of milling quality in Chinese winter wheats through genetic selection, and FCG and MQI could be two important parameters for evaluation of milling quality in breeding programs.     

Adaptation of AACC Method 56‐11, Solvent Retention Capacity,for Use as an Early Generation Selection Tool for Cultivar Development

The solvent retention capacity (SRC) profile is useful for studying flour components contributing to end‐use functionality. The method tests four different solvents with 5 g of flour each. Because of the amount of grain (30–40 g) typically needed to produce 20 g of flour for the SRC test, the method is not well‐suited for assessing end‐use quality of early generation breeding material, where grain quantities are limited. The method was therefore modified to require only 0.2 g of ground wheat instead of 5 g of flour per SRC solvent. The small‐scale SRC results using whole meal had correlations of r = 0.86 for lactic acid, r = 0.85 for sodium carbonate, r = 0.78 for sucrose, r = 0.74 for sodium bicarbonate (the alkaline water retention capacity method) and r = 0.69 for water when compared with SRC values from full‐scale tests using 5 g of flour. Overall, cultivars with SRC values at the extremes of the distribution were in the same ranked order for the small‐ and large‐scale SRC test results. However, variation in ranked order of cultivars between test methods was detected among samples that were not at the extremes of the distribution. Traditionally, successful wheat breeding strategies involve eliminating or advancing lines from the extremes of the distribution to increase the proportion of desirable genotypes within breeding programs. Results indicated that advancing promising germplasm or eliminating germplasm with inferior end‐use quality potential is possible using the small‐scale SRC technique to evaluate early generation wheat breeding material, as a sort of breeding triage.     

A Rapid Microassay for Determination of Peroxidase in Wheat and Flour

A rapid sensitive microplate assay for the determination of peroxidase activity in wheat, flour, or individual wheat kernels has been developed using a commercially prepared 2,2′‐azinobis(3‐ethylbenzothiazoline‐6‐sulfonic acid) diammonium salt (ABTS) solution. The assay derives a six‐point calibration curve based on commercially available horseradish peroxidase (r² > 0.990), while simultaneously analyzing eight extracts in triplicate. The coefficients of variation (CV) of triplicate assays of a single extract from any of the three sources investigated were generally <2.0%. Multiple extracts (n = 5) of either whole meal or flour yielded assays with an average CV < 5.0%. Preparation of the calibration standards with commercially available peroxidase stabilizing buffer allowed the standards to be used for five days without any deterioration in the assay's reproducibility. This assay is ideally suited for high‐throughput operations such as millstream analysis or plant breeder screening evaluations.     

Estimation of the Deoxynivalenol and Moisture Contents of Bulk Wheat Grain Samples by FT‐NIR Spectroscopy

Deoxynivalenol (DON) levels in harvested grain samples are used to evaluate the Fusarium head blight (FHB) resistance of wheat cultivars and breeding lines. Fourier transform near‐infrared (FT‐NIR) calibrations were developed to estimate the DON level and moisture content (MC) of bulk wheat grain samples harvested from FHB screening trials. Grains in a rotating glass petri dish were scanned in the 10,000–4,000 cm⁻¹ (1,000–2,500 nm) spectral range using a Perkin Elmer Spectrum 400 FT‐IR/FT‐NIR spectrometer. The DON calibration predicted the DON levels in test samples with R ² = 0.62 and root mean square error of prediction (RMSEP) = 8.01 ppm. When 5–25 ppm of DON was used as the cut‐off to classify samples into low‐ and high‐DON groups, 60.8–82.3% of the low‐DON samples were correctly classified, whereas the classification accuracy of the high‐DON group was 82.3–94.0%. The MC calibration predicted the MC in grain samples with R ² = 0.98 and RMSEP = 0.19%. Therefore, these FT‐NIR calibrations can be used to rapidly prescreen wheat lines to identify low‐DON lines for further evaluation using standard laboratory methods, thereby reducing the time and costs of analyzing samples from FHB screening trials.     

Determining the Fat Acidity of Rough Rice by Near-Infrared Reflectance Spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used to develop calibration curves for determining the fat acidity of whole-kernel and ground rough rice with 13% moisture content at 25°C. Partial-leastsquares regression (PLSR) uses the optimal calibration curve for wholekernel rough rice to measure the coefficient of determination (r²) of validation and standard error of prediction (SEP) of 0.87 and 0.83 mg of KOH/100 g of dry matter, respectively. However, the optimal calibration curve for ground rough rice has a higher r² of validation and lower SEP of 0.94 and 0.73 mg of KOH/100 g of dry matter, respectively. From 10 to 40°C, the temperature effect causes an increase of 0.24 mg of KOH/100 g of dry matter/°C in the predicted fat acidity of whole-kernel rough rice.