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1.
The use of near-infrared spectroscopy (NIRS) for the prediction of whole-grain triticale moisture and protein content was evaluated. Because triticale is genetically close to wheat, commercially available wheat prediction models for Foss Infratec analyzers were applied in a year-by-year basis to triticale samples harvested in Iowa between 2002 and 2006. Wheat models were not applicable to moisture prediction (SEPavg = 0.37% pt; expected SEP on wheat samples 0.15% pt), but usable for screening for protein (SEPavg = 0.38% pt; expected SEP on wheat samples 0.25% pt). Dedicated triticale calibrations were developed from 2002 to 2005 data. Prediction results for 2006 samples only were compared. Triticale calibrations performed better than wheat calibrations for 2006 samples (moisture SEPtriticale = 0.29% pt, SEPwheat = 0.50% pt; protein SEPtriticale = 0.30% pt, SEPwheat = 0.68% pt).  相似文献   

2.
This article describes a proof-of-concept exercise to examine the ability of near infrared spectroscopy (NIRS)–based methods to predict the major nutrient properties of sugar mill by-products, particularly mill mud, ash, and mixtures of mud and ash. Sixty mill mud, mixed mud/ash, and ash samples were subsampled three times and analyzed using traditional analytical techniques for carbon (C), nitrogen (N), silicon (Si), phosphorus (P), and potassium (K), and the NIR spectra were recorded. Two different partial least squares (PLS) regression models were constructed, one using all samples and the other without the ash samples included in the model development. Three mud, one mixed mud/ash, and two ash samples were retained for predictive purposes and were not included in the model development process. R2 values in the range of 0.77 to 0.98 were obtained for all constituents across both sets of PLS models. The standard errors of prediction (SEP) were similar for both models for N (0.10 and 0.08), P (0.17 and 0.16), and K (0.05 and 0.05). However, the SEP obtained for Si (3.53 and 1.04) and C (1.92 and 1.00) varied between the two models. These preliminary results are very encouraging. Future research will extend to robust NIRS calibrations for these nutrients and develop applications for their use within laboratory or field situations to permit nutrient monitoring in various sugar mill by-products.  相似文献   

3.
Triticale (Triticosecale Wittmack) grown with legume has a better forage quality and greater yield potential than triticale grown alone. The objective of the study was to determine the suitable mixture rate of legume and triticale grown under the rainfed conditions in the northeast of Turkey. Field experiments, designed in a factorial randomized complete block with three replications, were carried out during 1998–1999 and 1999–2000 starting in the first week of November, 1998 and 1999. The highest dry matter yield (10.96 t ha?1) was obtained from the mixture including 50% Hungarian vetch (Vicia pannonica Crantz.) and 50% triticale (Triticosecale Wittmack). Decreasing the seed rate of triticale in mixtures decreased dry matter yield while it increased the crude protein concentration of the hay mixture. The mixtures of 50% grasspea line 38 (Lathyrus sativus L.) and 50% triticale (Triticosecale Wittmack) and 50% hairy vetch and 50% triticale produced the highest seed and crude protein yield. Similarly, 50% Hungarian vetch (Vicia pannonica Crantz.) and 50% triticale (Triticosecale Wittmack) mixture produced the highest crude fiber and ash yield. Pure hairy vetch (Vicia villosa Roth.) and grasspea line 38 (Lathyrus sativus L.) yielded the maximum amount of NO3 ? -N to soil, and the highest plant concentration of crude protein, respectively. The mixtures outyielded the pure sowings with respect to dry matter (RYT=1.58) and grain yield (RYT=1.76).  相似文献   

4.
The chemometric calibration of near‐infrared Fourier‐transform Raman (NIR‐FT/Raman) spectroscopy was investigated for the purpose of providing a rigorous spectroscopic technique to analyze rice flour for protein and apparent amylose content. Ninety rice samples from a 1996 collection of short, medium, and long grain rice grown in four states of the United States, as well as Taiwan, Korea, and Australia were investigated. Milled rice flour samples were scanned in rotating cups with a 1,064 nm (NIR) excitation laser using 500 mW of power. Raman scatter was collected using a liquid N2 cooled Ge detector over the Raman shift range of 175–3,600 cm‐1. The spectral data was preprocessed using baseline correction with and without derivatives or with derivatives alone and normalization. Nearly equivalent results were obtained using all of the preprocessing methods with partial least squares (PLS) models. However, models using baseline correction and normalization of the entire spectrum, without derivatives, showed slightly better performance based on the criteria of highest r2 and the lowest SEP with low bias. Calibration samples (n = 57) and validation samples (n = 33) were chosen to have similar respective distributions for protein and apparent amylose. The best model for protein was obtained using six factors giving r2 = 0.992, SEP = 0.138%, and bias = ‐0.009%. The best model for apparent amylose was obtained using eight factors giving r2 = 0.985, SEP = 1.05%, and bias = ‐0.006%.  相似文献   

5.
Poor zinc (Zn) nutrition of wheat is one of the main causes of poor human health in developing countries. A field experiment with no zinc and foliar zinc application (0.5% ZnSO4.7H2O) on bread wheat (8), durum wheat (3), and triticale (4) cultivars was conducted in a randomized block design with three replications in 2 years. The experimental soil texture was loamy sand with slightly alkalinity. The grain yields of bread wheat, triticale, and durum wheat cultivars increased from 43.6 to 56.4, 46.5 to 51.6, and 49.4 to 53.5 t ha?1, respectively, with foliar application of 0.5% ZnSO4.7H2O. The highest grain yield was recorded by PBW 550 (wheat), TL 2942 (triticale), and PDW 291 (durum), which was 5.22, 4.24, and 4.56% and significantly higher over no zinc. Foliar zinc application increased zinc in bread wheat, triticale, and durum wheat cultivars grains varying from 31.0 to 63.0, 29.3 to 61.8, and 30.2 to 62.4?mg kg?1, respectively. So, agronomic biofortification is the best way which enriching the wheat grains with zinc for human consumption.  相似文献   

6.
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.  相似文献   

7.
High cost and painstaking procedures associated with fatty acid analyses of maize kernel necessitate the use of alternative methods. NIR spectroscopy offers advantages in this respect for a variety of areas such as plant breeding, food and feed industries, and biofuel production, in which different forms of maize kernel (e.g., intact kernel, flour, or oil) are used as material. We investigated the possibility of estimating maize oil quality traits by using different samples (intact kernel, flour, and oil) and conventional regression methods (multiple linear regression [MLR] and partial least squares regression [PLSR]) applied to their NIR spectra. MLR and PLSR calibration models were developed for oleic acid, linoleic acid, oleic/linoleic acid ratios, total monounsaturated fatty acid, total polyunsaturated fatty acid (PUFA), and total saturated fatty acid by analyzing 120 maize samples. Robustness in terms of prediction accuracy of the models developed here was tested with a reserved set of samples (n = 30). The results suggested that fatty acids could be possibly estimated by calibrations developed from flour and oil samples with a high degree of accuracy, whereas intact samples did not offer satisfactory results. PLSR and MLR methods gave better results in flour and oil samples, respectively. PUFA was the trait that was most successfully estimated from both flour (for the PLSR model, standard error of the estimate [SEP] of 1.78%, relative performance to deviation [RPD] of 3.09, R2 = 0.93) and oil (for the MLR model, SEP of 0.85%, RPD of 6.52, R2 = 0.98) samples. We concluded that sample type and chemometric method should be handled as important factors in calibration development, and the effects of these factors may vary depending on the trait being analyzed.  相似文献   

8.
A study was conducted to investigate methods of improving a near-infrared transmittance spectroscopy (NITS) amylose calibration that could serve as a rapid, nondestructive alternative to traditional methods for determining amylose content in corn. Calibrations were developed using a set of genotypes possessing endosperm mutations in single- and double-mutant combinations ranging in starch-amylose content (SAC) from -8.5 to 76%, relative to a standard curve. The influence of three factors were examined including comparing calibrations made against SAC versus grain amylose content (GAC), developing calibrations using partial least squares (PLS) analysis versus artificial neural networking (ANN), and using all samples in the calibrations set versus using progressively narrower ranges of SAC or GAC in the calibration set. Grain samples were divided into calibration and validation sets for PLS analysis while samples used in ANN were assigned to a training set, test set, and validation set. Performance statistics of the validation sets that were considered were the coefficient of determination (R), the standard error of prediction (SEP), and the ratio of the standard deviation of amylose values to the SEP (RPD), which were used to compare all NITS models. The study revealed an NITS prediction model for SAC (R = 0.96, SEP = 5.1%, RDP = 3.8) of similar precision to the best GAC model (R = 0.96, SEP = 2.7%, RPD = 3.5). Narrowing the amylose range of the calibration set generally did not improve performance statistics except for PLS models for SAC in which a decrease in SEP values was observed. In one model, the SEP improved while R and RPD remained constant (R = 0.94, SEP = 4.2%, RPD = 2.8) when samples with SAC values <20% were removed from the calibration set. Although the NITS amylose calibrations in this study are of limited precision, they may be useful when a rough screening method is needed for SAC. For example, NITS may be useful to detect severe contamination during transport and storage of specialty grains or to aid breeders when selecting for amylose content from large numbers of grain samples.  相似文献   

9.
《Cereal Chemistry》2017,94(4):677-682
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.  相似文献   

10.
Near-infrared (NIR) spectroscopy calibrations that will allow prediction of the solid fat content (SFC) of milk fat extracted from butter by one measurement during manufacture were developed. SFC is a measure of the amount of the solid fraction of fat crystallized at a temperature expressed as a percentage (w/w). At-line SFC determinations are currently performed by nuclear magnetic resonance (NMR) spectroscopy, which involves a 16 h delay period for tempering of the milk fat at 0 degrees C prior to the SFC measurements, from 0 to 35 degrees C in a series of 5 degrees C increments. The NIR spectra (400-2500 nm) were obtained using a sample holder maintained at 60 degrees C. Accurate predictions for the SFC (%) were developed by principal component analysis (PCA) and partial least-squares (PLS) regression models to relate the NIR spectra to the corresponding NMR values. The independent validation samples (N = 22) had a standard error of prediction (SEP) of 0.385-0.762% for SFC between 0 and 25 degrees C, with SFC reference values ranging between 70.42 and 8.96% with a standard deviation range of 3.36-1.47. The low bias (from -0.351 to -0.025), the slopes (0.935-1.077), and the excellent predictive ability (R2; 0.923-0.978) supported the validity of these calibrations.  相似文献   

11.
This study investigated the potential for visible–near‐infrared (vis–NIR) spectroscopy to predict locally volumetric soil organic carbon (SOC) from spectra recorded from field‐moist soil cores. One hundred cores were collected from a 71‐ha arable field. The vis–NIR spectra were collected every centimetre along the side of the cores to a depth of 0.3 m. Cores were then divided into 0.1‐m increments for laboratory analysis. Reference SOC measurements were used to calibrate three partial least‐squares regression (PLSR) models for bulk density (ρb), gravimetric SOC (SOCg) and volumetric SOC (SOCv). Accurate predictions were obtained from averages of spectra from those 0.1‐m increments for SOCg (ratio of performance to inter‐quartile (RPIQ) = 5.15; root mean square error (RMSE) = 0.38%) and SOCv (RPIQ = 5.25; RMSE = 4.33 kg m?3). The PLSR model for ρb performed least well, but still produced accurate results (RPIQ = 3.76; RMSE = 0.11 Mg m?3). Predictions for ρb and SOCg were combined to compare indirect and direct predictions of SOCv. No statistical difference in accuracy between these approaches was detected, suggesting that the direct prediction of SOCv is possible. The PLSR models calibrated on the 10‐cm depth intervals were also applied to the spectra originally recorded on a 1‐cm depth increment. While a bigger bias was observed for 1‐cm than for 10‐cm predictions (1.13 and 0.19 kg m?3, respectively), the two populations of estimates were not distinguishable statistically. The study showed the potential for using vis–NIR spectroscopy on field‐moist soil cores to predict SOC at high depth resolutions (1 cm) with locally derived calibrations.  相似文献   

12.
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.  相似文献   

13.
Fusarium head blight (FHB) is a serious disease in wheat that affects grain quality owing to the accumulation of mycotoxins such as deoxynivalenol (DON) in grains. Near‐infrared (NIR) spectroscopy has been used to develop techniques to estimate DON levels in single wheat kernels to facilitate rapid, nondestructive screening of FHB resistance in wheat breeding lines. The effect of moisture content (MC) variation on the accuracy of single‐kernel DON prediction by NIR spectroscopy was investigated. Sample MC considerably affected accuracy of the current NIR DON calibration by underestimating or overestimating DON at higher or lower moisture levels, respectively. DON in single kernels was most accurately estimated at 13–14% MC. Major NIR absorptions related to Fusarium damage were found around 1,198–1,200, 1,418–1,430, 1,698, and 1,896–1,914 nm. Major moisture related absorptions were observed around 1,162, 1,337, 1,405–1,408, 1,892–1,924, and 2,202 nm. Fusarium damage and moisture related absorptions overlapped in the 1,380–1,460 and 1,870–1,970 nm regions. These results show that absorption regions associated with water are often close to absorption regions associated with Fusarium damage. Thus, care must be taken to develop DON calibrations that are independent of grain MC.  相似文献   

14.
Effects of varied irrigation and zinc (Zn) fertilization (0, 7, 14, 21 kg Zn ha‐1 as ZnSO47.H2O) on grain yield and concentration and content of Zn were studied in two bread wheat (Triticum aestivum), two durum wheat (Triticum durum), two barley (Hordeum vulgare), two triticale (xTriticosecale Wittmark), one rye (Secale cereale), and one oat (Avena sativa) cultivars grown in a Zn‐deficient soil (DTPA‐extractable Zn: 0.09 mg kg‐1) under rainfed and irrigated field conditions. Only minor or no yield reduction occurred in rye as a result of Zn deficiency. The highest reduction in plant growth and grain yield due to Zn deficiency was observed in durum wheats, followed by oat, barley, bread wheat and triticale. These decreases in yield due to Zn deficiency became more pronounced under rainfed conditions. Although highly significant differences in grain yield were found between treatments with and without Zn, no significant difference was obtained between the Zn doses applied (7–21 kg ha‐1), indicating that 7 kg Zn ha‐1 would be sufficient to overcome Zn deficiency. Increasing doses of Zn application resulted in significant increases in concentration and content of Zn in shoot and grain. The sensitivity of various cereals to Zn deficiency was different and closely related to Zn content in the shoot but not to Zn amount per unit dry weight. Irrigation was effective in increasing both shoot Zn content and Zn efficiency of cultivars. The results demonstrate the existence of a large genotypic variation in Zn efficiency among and within cereals and suggest that plants become more sensitive to Zn deficiency under rainfed than irrigated conditions.  相似文献   

15.
Analysis of the chemical components of lignocellulosic biomass is essential to understanding its potential for utilization. Mid-infrared spectroscopy and partial least-squares regression were used for rapid measurement of the carbohydrate (total glycans; glucan; xylan; galactan; arabinan; mannan), ash, and extractives content of triticale and wheat straws. Calibration models for total glycans, glucan, and extractives showed good and excellent predictive performance on the basis of slope, r2, RPD, and R/SEP criteria. The xylan model showed good and acceptable predictive performance. However, the ash model was evaluated as providing only approximate quantification and screening. The models for galactan, arabinan, and mannan indicated poor and insufficient prediction for application. Most models could predict both triticale and wheat straw samples with the same degree of accuracy. Mid-infrared spectroscopic techniques coupled with partial least-squares regression can be used for rapid prediction of total glycans, glucan, xylan, and extractives in triticale and wheat straw samples.  相似文献   

16.
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17.
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 (rval2), 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 rval2, 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.  相似文献   

18.
Grains of triticale are one of the feedstocks suitable for bioethanol production because they are characterised by high starch and low protein contents. In the present study, spring and winter triticale were comparatively studied to evaluate the influence of N fertilisation intensity on the productivity and bioethanol yield, as well as to assess the relationship between the meteorological factors and ethanol yield. Six treatments of N – 0, 60, 90, 120, 150, and 180?kg?ha?1 were compared in spring triticale and in winter triticale crops. The analysis of variance showed that nitrogen level (factor A), year (factor B) and their interaction (A × B) significantly (P?≤?.01) influenced grain yield, starch yield and bioethanol yield of both spring and winter triticale. Fertilisation was the main factor explaining 47.6% and 41.0% of the total variability of bioethanol yield of spring and winter triticale, respectively. Nitrogen fertiliser rates 120–180?kg?ha?1 resulted in maximum bioethanol yield of spring triticale (2417–2480?l?ha?1) and winter triticale (4311–4420?l?ha?1). Bioethanol conversion efficiency of nitrogen-fertilised spring and winter triticale was similar 492?l?t?1 and 508?l??1, respectively. Meteorological factors had a greater impact on grain productivity and bioethanol yield for winter triticale than for spring triticale. Both seasonal types of triticale could be good feedstocks for bioethanol production in the areas with congenial weather conditions for their cultivation.  相似文献   

19.
Near‐infrared reflectance (NIR) spectroscopy can be used for fast and reliable prediction of organic compounds in complex biological samples. We used a recently developed NIR spectroscopy instrument to predict starch, protein, oil, and weight of individual maize (Zea mays) seeds. The starch, protein, and oil calibrations have reliability equal or better to bulk grain NIR analyzers. We also show that the instrument can differentiate quantitative and qualitative seed composition mutants from normal siblings without a specific calibration for the constituent affected. The analyzer does not require a specific kernel orientation to predict composition or to differentiate mutants. The instrument collects a seed weight and a spectrum in 4–6 sec and can collect NIR data alone at a 20‐fold faster rate. The spectra are acquired while the kernel falls through a glass tube illuminated with broad spectrum light. These results show significant improvements over prior single‐kernel NIR systems, making this instrument a practical tool to collect quantitative seed phenotypes at high throughput. This technology has multiple applications for studying the genetic and physiological influences on seed traits.  相似文献   

20.
Grain hardness (kernel texture) is of central importance in the quality and utilization of wheat (Triticum aestivum L.) grain. Two major classes, soft and hard, are delineated in commerce and in the Official U.S. Standards for Grain. However, measures of grain hardness are empirical and require reference materials for instrument standardization. For AACC Approved Methods employing near‐infrared reflectance (NIR) and the Single Kernel Characterization System (39‐70A and 55‐31, respectively), such reference materials were prepared by the U.S. Dept. of Agriculture Federal Grain Inspection Service. The material was comprised of genetically pure commercial grain lots of five soft and five hard wheat cultivars and was made available through the National Institute of Standards and Technology (SRM 8441, Wheat Hardness). However, since their establishment, the molecular‐genetic basis of wheat grain hardness has been shown to result from puroindoline a and b. Consequently, we sought to define the puroindoline genotype of these 10 wheat cultivars and more fully characterize their kernel texture through Particle Size Index (PSI, Method 55‐30) and Quadrumat flour milling. NIR, SKCS, and Quadrumat break flour yield grouped the hard and soft cultivars into discrete texture classes; PSI did not separate completely the two classes. Although all four of these methods of texture measurement were highly intercorrelated, each was variably influenced by some minor, secondary factors. Among the hard wheats, the two hard red spring wheat cultivars that possess the Pina‐D1b (a‐null) hardness allele were harder than the hard red winter wheat cultivars that possess the Pinb‐D1b allele based on NIR, PSI, and break flour yield. Among the soft wheat samples, SKCS grouped the Eastern soft red winter cultivars separate from the Western soft white. A more complete understanding of texture‐related properties of these and future wheat samples is vital to the use and calibration of kernel texture‐measuring instruments.  相似文献   

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