Comparison of Different Methods for Phenotyping Preharvest Sprouting in White‐Grained Wheat

The objective of this study was to identify a suitable method for phenotyping preharvest sprouting (PHS) resistance in white bread wheat. Forty doubled‐haploid (DH) lines derived from a cross between two white‐grained spring wheats (Triticum aestivum L.) cultivar Argent (nondormant) and wheat breeding line W98616 (dormant) were evaluated for germination frequency, Falling Number (FN), and α‐amylase activity in dry and water‐imbibed seeds and spikes. The α‐amylase activity in dry seeds or spikes did not differ significantly between parent lines or lines of the DH population. Wetting of seeds or spikes for two days caused a five‐ to sevenfold increase in α‐amylase activity but only in Argent and the nondormant subgroup (49–100% germination) of the DH lines. A positive association (r = 0.60***) was detected between germination frequency and α‐amylase activity in imbibed seeds and spikes. Germination frequency could not be correlated to FN or α‐amylase activity in dry‐harvested seeds. FN showed a strong correlation (r = –0.83***) to α‐amylase activity in the dry‐harvested seeds but could not be correlated to α‐amylase activity in the imbibed seeds. The germination test was the most reliable method for measuring PHS resistance because seed dormancy provides potential resistance to PHS, whereas high α‐amylase activity may occur in grains without causing PHS.     

Detection of Wheat Preharvest Sprouting Using a Pregelatinized Starch Substrate and Centrifugation

Wheat sprouting in the field before harvest is a serious negative quality attribute. Even low levels of preharvest sprouting affect the economic value of the grain. Unreleased test lines of wheat should be screened for resistance to preharvest sprouting. However, screening large numbers of test lines is relatively time‐consuming or expensive, depending on the existing method used. A new screening test for preharvest sprouting was developed and compared with the viscograph and α‐amylase activity (AAA) methods. The new method used the activity of sprout‐related elevation in α‐amylase to partially degrade added pregelatinized starch. The hydrolytic products were centrifuged and the weight of the centrifugate was expressed as a percentage of the original weight of the added pregelatinized starch plus the original meal or flour weight. The result reflected the AAA on pregelatinized starch (AAAPGS) as a measure of the degree of preharvest sprouting. The AAAPGS test had less standard error and was more sensitive at low levels of preharvest sprouting than the AAA method. Three grinders to produce wheat meal were compared for their effect on AAAPGS values. Flours produced slightly lower AAAPGS values than meals, but the coefficients of variation of each were comparable and both were less than that of the AAA method. The lowest levels of sensitivity to preharvest sprouting that could be detected by the AAA and AAAPGS methods were identified as areas of uncertainly, below which very low levels of preharvest sprouting could not be differentiated from sound, unsprouted background values. The new AAAPGS method was equally rapid and will be more economical than the AAA method or the viscograph when used for preharvest sprouting susceptibility of large numbers of samples.     

Infrared Drying Characteristics of Long‐Grain Hybrid,Long‐Grain Pureline,and Medium‐Grain Rice Cultivars

The objective for this study was to investigate the effectiveness of scaled‐up infrared (IR) heating followed by tempering steps to dry freshly harvested rough rice. An industrial‐type, pilot‐scale, IR heating system designed to dry rough rice was used in this study. The heating zone of the equipment had catalytic IR emitters that provided heat energy to the sample as it was conveyed on a vibrating belt. The sample comprised freshly harvested rough rice of long‐grain pureline (Cheniere), long‐grain hybrid (6XP 756), and medium‐grain (CL 271) cultivars at initial moisture contents of 23, 23.5, and 24% wb, respectively. Samples at a loading rate of 1.61 kg/m² were heated with IR of radiation intensity 5.55 kW/m² for 30, 50, 90, and 180 s followed by tempering at 60°C for 4 h, at a product‐to‐emitter‐gap size of 450 mm, in one‐ and two‐pass drying operations. Control samples were gently natural air dried in an equilibrium moisture content chamber set at relative humidity of 65% and temperature of 26°C to moisture content of 12.5% wb. The effects of IR treatments followed by tempering on percentage points of moisture removed, head rice yield, energy use, rice color, and pasting characteristics were evaluated. For all cultivars, percentage point moisture removed increased with increase in IR drying duration. For all rice cultivars, one‐pass IR treatments for 180 s resulted in head rice yield significantly lower than that of rice dried with natural air in the controlled‐environment conditions (P < 0.05). Energy required to dry rice increased with increase in drying duration. Viscosity values of all the experimental samples were significantly greater (P value < 0.05) than that of the control samples for all the cultivars, except those treated with IR for 180 s. There was a significant difference (P < 0.05) in the color index (ΔE ) of treated milled samples and the controls. In conclusion, the study provided information crucial to understanding the effects of scaled‐up radiant heating and tempering of rough rice on drying rates and rice quality for long‐grain pureline, long‐grain hybrid, and medium‐grain rice cultivars.     

Detection of Scab‐Damaged Hard Red Spring Wheat Kernels by Near‐Infrared Reflectance

Scab (Fusarium head blight) is a fungal disease that has become increasingly prevalent in North American wheat during the past 15 years. It is of concern to growers, processors, and the consumers because of depressed yields, poor flour quality, and the potential for elevated concentrations of the mycotoxin, deoxynivalenol (DON). Both wheat breeder and wheat inspector must currently deal with the assessment of scab in harvested wheat by manual human inspection. The study described herein examined the accuracy of a semi‐automated wheat scab inspection system that is based on near‐infrared (NIR) reflectance (1,000–1,700 nm) of individual kernels. Using statistical classification techniques such as linear discriminant analysis and nonparametric (k‐nearest‐neighbor) classification, upper limits of accuracy for NIR‐based classification schemes of ≈88% (cross‐validation) and 97% (test) were determined. An exhaustive search of the most suitable wavelength pairs for the spectral difference, [log(1/R)_λ1 ‐ log(1/R)_λ2], revealed that the slope of the low‐wavelength side of a broad carbohydrate absorption band (centered at ≈1,200 nm) was very effective at discriminating between healthy and scab‐damaged kernels with test set accuracies of 95%. The achieved accuracy levels demonstrate the potential for the use of NIR spectroscopy in commercial sorting and inspection operations for wheat scab.     

Detection of Sprouted and Midge‐Damaged Wheat Kernels Using Near‐Infrared Hyperspectral Imaging

Sprout damage which results in poor breadmaking quality due to enzymatic activity of α‐amylase is one of the important grading factors of wheat in Canada. Potential of near‐infrared (NIR) hyperspectral imaging was investigated to detect sprouting of wheat kernels. Artificially sprouted, midge‐damaged, and healthy wheat kernels were scanned using NIR hyperspectral imaging system in the range of 1000–1600 nm at 60 evenly distributed wavelengths. Multivariate image analysis (MVI) technique based on principal components analysis (PCA) was applied to reduce the dimensionality of the hyperspectral data. Three wavelengths 1101.7, 1132.2, and 1305.1 nm were identified as significant and used in analysis. Statistical discriminant classifiers (linear, quadratic, and Mahalanobis) were used to classify sprouted, midge‐damaged, and healthy wheat kernels. The discriminant classifiers gave maximum accuracy of 98.3 and 100% for classifying healthy and damaged kernels, respectively.     

Single‐Kernel Near‐Infrared Analysis for Evaluating Wheat Samples for Fusarium Head Blight Resistance

This report describes a method to estimate the bulk deoxynivalenol (DON) content of wheat grain samples with the single‐kernel DON levels estimated by a single‐kernel near‐infrared (SKNIR) system combined with single‐kernel weights. The described method estimated the bulk DON levels in 90% of 160 grain samples to within 6.7 ppm of DON when compared with the DON content determined with the gas chromatography–mass spectrometry method. The single‐kernel DON analysis showed that the DON content among DON‐containing kernels (DCKs) varied considerably. The analysis of the distribution of DON levels among all kernels and among the DCKs of grain samples is helpful for the in‐depth evaluation of the effect of varieties or fungicides on Fusarium head blight (FHB) reactions. The SKNIR DON analysis and estimation of the single‐kernel DON distribution patterns demonstrated in this study may be helpful for wheat breeders to evaluate the FHB resistance of varieties in relation to their resistance to the spread of the disease and resistance to DON accumulation.     

Zinc Blotting Assay for Detection of Zinc‐Binding Prolamin in Barley (Hordeum vulgare) Grain

In plants, zinc is commonly found bound to proteins. In barley (Hordeum vulgare), major storage proteins are alcohol‐soluble prolamins known as hordeins, and some of them have the potential to bind or store zinc. ⁶⁵Zn overlay and blotting techniques have been widely used for detecting zinc‐binding protein. However, to our knowledge so far this zinc blotting assay has never been applied to detect a prolamin fraction in barley grains. A radioactive zinc (⁶⁵ZnCl₂) blotting technique was optimized to detect zinc‐binding prolamins, followed by development of an easy‐to‐follow nonradioactive colorimetric zinc blotting method with a zinc‐sensing dye, dithizone. Hordeins were extracted from mature barley grain, separated by SDS‐PAGE, blotted on a membrane, renatured, overlaid, and probed with zinc; subsequently, zinc‐binding specificity of certain proteins was detected either by autoradiography or color formation. The dithizone staining method gave similar reproducibility to the radioactive blotting. The detected zinc‐binding protein was identified as B‐hordein by Western blotting.     

Prediction of Triticale Grain Quality Properties,Based on Both Chemical and Indirectly Measured Reference Methods,Using Near‐Infrared Spectroscopy

The increasing demand for triticale as food, feed, and fuel has resulted in the availability of cultivars with different grain quality characteristics. Analyses of triticale composition can ensure that the most appropriate cultivars are obtained and used for the most suitable applications. Near‐infrared (NIR) spectroscopy is often used for rapid measurements during quality control and has consequently been investigated as a method for the measurement of protein, moisture, and ash contents, as well as kernel hardness (particle size index [PSI]) and sodium dodecyl sulfate (SDS) sedimentation from both whole grain and ground triticale samples. NIR spectroscopy prediction models calculated using ground samples were generally superior to whole grain models. Protein content was the most effectively modeled quality property; the best ground grain calibration had a ratio of the standard error of test set validation to the standard deviation of the reference data of the test set (RPD_test) of 4.81, standard error of prediction (SEP) of 0.52% (w/w), and r² of 0.95. Whole grain protein calibrations were less accurate, with optimum RPD_test of 3.54, SEP of 0.67% (w/w), and r² of 0.92. NIR spectroscopy calibrations based on direct chemical reference measurements (protein and moisture contents) were better than those based on indirect measurements (PSI, ash content, and SDS sedimentation). Calibrations based on indirect measurements would, however, still be useful to identify extreme samples.     

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.     

An Automated Near‐Infrared System for Selecting Individual Kernels Based on Specific Quality Characteristics

An automated sorting system was developed that nondestructively measured quality characteristics of individual kernels using near‐infrared (NIR) spectra. This single‐kernel NIR system was applied to sorting wheat (Triticum aestivum L.) kernels by protein content and hardness, and proso millet (Panicum miliaceum L.) into amylose‐bearing and amylose‐free fractions. Single wheat kernels with high protein content could be sorted from pure lines so that the high‐protein content portion was 3.1 percentage points higher than the portion with the low‐protein kernels. Likewise, single wheat kernels with specific hardness indices could be removed from pure lines such that the hardness index in the sorted samples was 29.4 hardness units higher than the soft kernels. The system was able to increase the waxy, or amylose‐free, millet kernels in segregating samples from 94% in the unsorted samples to 98% in the sorted samples. The portion of waxy millet kernels in segregating samples was increased from 32% in the unsorted samples to 55% after sorting. Thus, this technology can be used to enrich the desirable class within segregating populations in breeding programs, to increase the purity of heterogeneous advanced or released lines, or to measure the distribution of quality within samples during the marketing process.     

X‐ray Image Analysis to Detect Infestations Caused by Insects in Grain

Insect infestations in stored wheat affect the chemical characteristics and baking qualities of wheat flour, and insect‐infested flours are unacceptable in the baking industry. The efficiency of the soft X‐ray method to detect infestations caused by Cryptolestes ferrugineus (Stephens), Tribolium castaneum (Herbst), Plodia interpunctella (Hübner), Sitophilus oryzae (L.), and Rhyzopertha dominica (F.) in wheat kernels was determined in this study. Wheat kernels infested by different insects were prepared by artificial implantation of insect eggs or by introducing adult insects in wheat samples. Kernels infested by different stages of the insects were X‐rayed until the adults emerged from the kernels. A total of 57 features using histogram groups, histogram and shape moments, and textural features were extracted from the X‐ray images and a linear‐function parametric classifier was used to identify the insect‐infested kernels. The parametric classifier identified more than 84% of infestations due to C. ferrugineus and T. castaneum larvae. The infestations by C. ferrugineus pupae‐adults and P. interpunctella larvae were identified with >96% accuracy. Kernels infested by different stages of S. oryzae and R. dominica larvae were identified with >98% accuracy. Using the Berlese funnel method, 67, 51, and 81% of first, second, and third instars of C. ferrugineus, respectively, were extracted in 6 hr. The same infested kernels were all categorized as infested by the parametric classifier. When kernels infested by different insects were pooled together, the parametric classifier correctly identified 74% of uninfested and 94% of infested kernels by the internal and external grain feeders. The 26% false positives identified from the independent test was caused by one sample infested by T. castaneum. When that sample was removed from the training set, the false positives were reduced to 16%, and 92.7% of infested kernels by different insects were correctly identified.     

Color Calibration of Scanners for Scanner‐Independent Grain Grading

Scanner technology is emerging as a cost‐effective and robust imaging alternative to camera‐based systems in many applications. However, scanner technology is changing so fast that image quality can vary from model to model. It is critical that images scanned with different scanners be brought to a common basis for processing and measurement through a calibration process that eliminates scanner‐to‐scanner variability. The focus of this research was to investigate scanner‐to‐scanner variability and develop color correction or mapping functions to allow for machineindependent grain inspection. Various makes and models of scanners were compared for optical and color characteristics. Three different color correction methods wereevaluated: grayscale (GS) transformation, redgreen‐blue (RGB) transformation, and histogram matching. All three models of color correction worked within satisfactory tolerance for a multicolor Q60 chart. However, for grain samples of a limited color range, the histogram matching approach performed better than GS and RGB transformations for scanner calibration. The color‐corrected test images matched the reference images within 3 grey values. Differences between the three models of color correction are discussed.     

Single Kernel Near‐Infrared Analysis of Tetraploid (Durum) Wheat for Classification of the Waxy Condition

Plant breeding programs are active worldwide in the development of waxy hexaploid (Triticum aestivum L.) and tetraploid (T. turgidum L. var. durum) wheats. Conventional breeding practices will produce waxy cultivars adapted to their intended geographical region that confer unique end use characteristics. Essential to waxy wheat development, a means to rapidly and, ideally, nondestructively identify the waxy condition is needed for point‐of‐sale use. The study described herein evaluated the effectiveness of near‐infrared (NIR) reflectance single‐kernel spectroscopy for classification of durum wheat into its four possible waxy alleles: wild type, waxy, and the two intermediate states in which a null allele occurs at either of the two homologous genes (Wx‐1A and Wx‐1B) that encodes for the production of the enzyme granule bound starch synthase (GBSS) that controls amylose synthesis. Two years of breeders' samples (2003 and 2004), corresponding to 47 unique lines subdivided about equally into the four GBSS genotypes, were scanned in reflectance (1,000–1,700 nm) on an individual kernel basis. Linear discriminant analysis models were developed using the best set of four wavelengths, best four wavelength differences, and best four principal components. Each model consistently demonstrated the high ability (typically >95% of the time) to classify the fully waxy genotype. However, correct classification among the three other genotypes (wild type, wx‐A1 null, and wx‐B1 null) was generally not possible.     

Distribution of β‐Glucan in the Grain of Hull‐less Barley

Nine hull‐less barley (HB) containing waxy (0–7% amylose), normal (≈25% amylose), or high amylose (≈42% amylose) starch with normal or fractured granule make‐up and 4–9% (1→3)(1→4)‐β‐d ‐glucans (β‐glucan) were pearled to remove 70% of the original grain weight in 10% intervals. The pearled fractions were analyzed for β‐glucan distribution within HB grain. Protein content of the pearled fractions indicated that the three outermost fractions contained pericarp and testa, aleurone, and subaleurone tissues, respectively. For all HB, β‐glucan and acid‐extract viscosity were very low in the outermost 20% of the kernel. For low β‐glucan HB, β‐glucan content was the greatest in the subaleurone region and declined slightly toward inner layers. For high β‐glucan HB, however, more than 80% of grain β‐glucan was distributed more evenly throughout the endosperm. Acid extract viscosity was significantly (P < 0.01) correlated with total (r = 0.75) and soluble (r = 0.87) β‐glucan content throughout the kernel of all HB. Growing conditions, location and year, had significant effects on the concentration of protein, starch and β‐glucan. However, protein, starch, and β‐glucan distribution patterns were not affected by growing conditions. The difference in β‐glucan distribution between low and high β‐glucan HB may explain the difference in milling performance of HB with low or high β‐glucan.     

Ethanol‐Production Performance of Ozone‐Treated Tannin Grain Sorghum Flour

Ozone has been reported as being able to degrade macromolecules such as cellulose, starch, lignins, and tannins in the textile, pulping, and water‐treatment industries. Thus, we hypothesized that ozone treatment may also inactivate tannin activity and increase fermentation efficiency of tannin sorghum lines. The objective of this research was to study the physicochemical properties of ozone‐treated whole tannin grain sorghum flour and its fermentation performance in ethanol production. Results showed that the ethanol yields from ozone‐treated tannin grain sorghums were significantly higher than yields from the untreated flour. The fermentation efficiency of ozone‐treated tannin grain sorghum was approximately 90%, which was 8–14% higher than that of untreated samples at the 36th hr of fermentation. At the end of 72 hr of fermentation, the efficiencies of ozone‐treated sorghum flour were 2–5% higher than those of untreated samples. Measured tannin levels of ozone‐treated samples decreased significantly from 3.8 to 2.7%. Gel‐permeation chromatographic results indicated that both degradation and polymerization processes might have happened to starch molecules during ozone treatment. Rapid Visco Analyzer data showed that the setback of viscosity of ozone‐treated flour was lower than that of untreated flours. Distillers dried grains with solubles made from ozone‐treated sorghum were low in residual starch (<1%) and high in crude protein (≈35%). Therefore, ozonation could be a novel and useful method to improve ethanol yield and fermentation efficiency of tannin grain sorghum.     

Accurate Technique for Measuring Color Values of Grain and Grain Products Using a Visible‐NIR Instrument

Measuring color is important when assessing grain and grain products as this has a major influence on the end‐product quality. To objectively measure color, grain processors and plant breeding programs use colorimeters to measure L*a*b* values as defined by the Commission Internationale de L'Eclairage (CIE). In addition to color, most laboratories undertake other tests, often utilizing NIR (near infrared reflectance) technology. It is possible to improve laboratory efficiencies and remove double handling of samples by using one instrument, a visible‐NIR spectrophotometer (400–2,500 nm), to measure color and other quality traits such as protein. In this study, we compared two techniques for measuring color of flour, barley, and lentils with a visible‐NIR instrument. The first technique involved calibrating the visible‐NIR instrument with colorimeter values using calibration models. However, calibrations are product‐specific and require the development and maintenance of specific equations for each product analyzed. For the second technique, we calculated the color values from the visible reflectance spectra using the standard practice (E308) based on the CIE system. Our study showed the most accurate, efficient method for measuring the color of products with a visible‐NIR instrument is to apply calculations using the standard practice based on the CIE system.     

Wavelength Selection for Classifying Paddy Rice by Near‐Infrared Spectroscopy

Using five paddy rice cultivars grown in Central, Eastern, and Southern Taiwan and harvested in the summers of 1997, 1998, and 1999, eight calibrated models were established by discriminant analysis and back‐propagation neural network with four wavelength selection methods. Randomly adding 80 samples of the 2000 year crop in the three‐crop‐year calibrated models for annual recalibration, eight models were used to classify paddy rice harvested in the summer of 2000. With 351 wavelengths of models 1 and 2, the average classification rates by discriminant analysis and backpropagation neural network were 98.1 and 92.5%, respectively. With 69 wavelengths selected by stepwise discrimination of models 3 and 4, the average classification rates by discriminant analysis and backpropagation neural network were 98.5 and 85.5%, respectively. With 69 wavelengths selected by correlation matrix of models 5 and 6, the average classification rates by discriminant analysis and neural network were 72.0 and 72.2%, respectively. With 69 wavelengths from loading values in the first and second principal components of models 7 and 8, the average classification rates by discriminant analysis and neural network were 69.1 and 60.6%, respectively. Model 3 would be recommended for classifying paddy rice to set trading prices because of its highest classification rate (98.5%).     

基于适宜粒度的曹妃甸新区土地利用景观格局分析

选取适宜粒度是保证准确分析土地利用景观格局的关键。本文以曹妃甸新区为研究区,应用RS、GIS等手段并结合景观指数法,探讨景观类型比例、景观类型指数以及综合面积损失随分析粒度变化效应,确定适宜分析粒度,从景观组成形态以及景观构型两个方面分析曹妃甸新区景观格局。结果表明:部分景观指数与粒度可拟合为数学函数关系(R20.905 4),并确定适宜分析粒度域(30,50)和适宜粒度50 m;曹妃甸新区土地利用景观类型较为丰富,但无明显优势类型,要素分布较为密集,景观的破碎化程度较高,各景观类型分布极不均匀,不利于景观空间格局的维持。