Effect of Grain Protein Concentration on Falling Number of Ungerminated Soft White Winter Wheat

Ross, Andrew S.; Flowers, Michael Darrell; Zemetra, Robert S.; Kongraksawech, Teepakorn

doi:10.1094/cchem-07-12-0085-rc

Cited by 17 publications

(18 citation statements)

References 8 publications

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“…The current study was designed to address this answer while following the best laboratory practices established in the previous study. Ross et al (2012) observed that protein content affected FN; however in the current study, the CV of FN and protein content, as well as their direct values, were uncorrelated (r < 0.2). The first situation, Washington 2014, represented the case when grain is sampled from the truck's dump stream during unloading.…”

Section: Discussioncontrasting

confidence: 75%

Falling Number Sampling Variation Within Trucks at First Point of Sale

Delwiche

Vinyard

2017

Cereal Chem

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Cereal Chem. 94(3):480-484Falling number (FN) is a test widely performed on raw samples of wheat and barley as a means to indicate the level of enzyme activity (a-amylase) associated with seed germination. In most circumstances of wheat, high activity levels are associated with decreased quality of the end products and, because of this, grain lots with low FN are discounted at points of sale, including the first point of delivery when wheat is brought in from the field. Confidence in the validity of an FN assessed on a grain lot is dependent on knowledge of the precision of the analytical method as well as the sampling protocol. The current study examined the latter component; that is, the variation caused by sampling. Soft red and white commercial wheats from two seasons and two regions (states of Washington and Ohio) were sampled upon arrival at country elevators and a flour mill. By catch-stream sampling or probe sampling, catches and probings were run in quadruplicate according to standard methodology for FN. Among-catches and among-probings variance estimates, inclusive of sampling error and method error, were typically less than 100 s 2 , corresponding to a coefficient of variation of 3% or less. Estimated variances from the sampling alone, as determined by a mixed-effects analysis of variance, were typically less than 50 s 2 . Based on the amongprobings variance estimates and a simple model that assumes normality of FN and linearity in mathematically combining individual probe readings into the equivalent composite reading for a truck, retesting a truck in borderline FN conditions (i.e., FN £ 294 s) at the same facility will not yield a new FN value of 300 s or greater more than 95% of the time.

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Section: Discussioncontrasting

confidence: 75%

Falling Number Sampling Variation Within Trucks at First Point of Sale

Delwiche

Vinyard

2017

Cereal Chem

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“…Furthermore, there are many other conditions that, in addition to rainfall, influence the occurrence of PHS (Mares & Mrva, ). An additional challenge with PHS has been the range of observed responses (Barnard & Smith, ; Clarke, Christensen, & DePauw, ; Gooding, Ellis, Shewry, & Schofield, ; Jiménez et al, ; Lunn, Kettlewell, Major, & Scott, ; Okuyama, Fonseca Junior, Caramori, & Kohli, ; Ross et al, ; Shorter, Munro, & Hodgkinson, ; Smith & Gooding, ; Trethowan, Rajaram, & Ellison, ). Contributing to the diversity of responses will be environments studied (i.e., climatic; field vs. glasshouse design; and with and without intervention), diverse genotypes, and different ways to measure PHS (i.e., from dormancy to germination tests and falling number).…”

Section: Discussionmentioning

confidence: 99%

Using big data to predict the likelihood of low falling numbers in wheat

2019

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Background and objectives Preharvest sprouting in wheat reduces quality and impacts farmer profitability. The international recognized falling number test can be used to measure that damage. Trying to understand the complex interactions that cause a reduction in wheat quality, equating to low falling number levels, is challenging. An alternative research approach to replicated experiments was to use a multiseason dataset of load‐by‐load delivery information to investigate whether correlations between falling number levels and 40 climate measurements could be identified. Findings This study used over 250,000 falling number data points from individual truckloads tested during seven harvests in Western Australia. The analyses identified relative humidity measured at the maximum temperature and daily temperature range as having consistent correlations with falling number levels over multiple seasons. Other climate measurements were also observed to have significant correlations with falling number, but these were less consistent within and between seasons. Conclusions The linkage of humidity and temperature range levels in the period before harvest commences to the occurrence of low falling number levels helps to further understand the complex interactions that change starch quality. Significance and novelty The findings demonstrate that value can be obtained from the use of a large, nonexperimentally designed dataset.

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“…Depending on the elevator's practice, the penalty is either imposed in full (harshest) or in a graduated (unit‐by‐unit) structure within the 25 unit range ( personal communication , Washington Grain Commission). Recent research of Ross and coworkers (2012) suggests that FN values near 300 or lower for U.S. Pacific Northwest–grown wheat may occur in low‐protein‐content wheat that is not sprout damaged, whereby the protein acts to modulate FN in a positive relationship. Thus, the penalty for wheat lots having FNs below 300 may be even more dubious.…”

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confidence: 99%

Repeatability Precision of the Falling Number Procedure Under Standard and Modified Methodologies

Delwiche

Vinyard

Bettge³

2014

Cereal Chem

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The falling number (FN) procedure is used worldwide to assess the integrity of the starch stored within wheat seed. As an indirect measurement of the activity level of α‐amylase, FN relies on a dedicated viscometer that measures the amount of time needed for a metal stirring rod of precise geometry to descend a fixed distance through a column of water–flour or water–meal slurry that undergoes enzyme‐activated starch hydrolysis under controlled mixing and heating conditions. For U.S. wheat, FN values of 300 s and above generally indicate soundness in the condition of the seed starches, whereas values less than 300 s often indicate that some seeds have broken dormancy, which deleteriously affects bread‐, cake‐, and noodle‐making quality of products derived from their flour. Domestic and especially overseas sales contracts will often specify a minimum FN value for consignments, thus making it critical to ensure that the FN procedure be highly precise. The study described herein examined the level of repeatability precision of the FN procedure under strictly controlled laboratory conditions as a means to establish precision levels arising alone from the random nature of the viscous properties of starchy meal undergoing mixing and heating. Six representative samples of Pacific Northwest–grown soft white and club wheat, ranging in FN between 168 and 404 s, were repeatedly measured with the conventional FN procedure and three modifications thereof, with the modifications being increased meal and water amounts (8 g of meal + 30 mL of H2O instead of 7 g + 25 mL) or the addition of a polysorbate surfactant (0.1% Tween 20) to the mixture water. Based on 16 FN runs for each sample and treatment, estimated variances and coefficients of variation (CV) were determined for each treatment–sample combination. The results indicated that CVs between 1 and 4% were achieved for all treatments and samples. The treatment modification of an augmented test sample size improved precision, whereas incorporating a surfactant had a negligible effect. Precision and treatment (conventional versus augmented) findings were corroborated by two external laboratories with two of the six original samples and by the main laboratory on an independent set of 14 wheat meal samples of commercial origin. Another possible improvement in precision is in the sequencing of water and meal addition to be half the volume of water, then the meal, followed by the remaining volume of water, all at the conventional levels of meal and water. Preliminary experimental results indicate an improvement in precision by using the “sandwich” approach; however, further testing is warranted to substantiate this discovery.

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Effect of Grain Protein Concentration on Falling Number of Ungerminated Soft White Winter Wheat

Cited by 17 publications

References 8 publications

Falling Number Sampling Variation Within Trucks at First Point of Sale

Falling Number Sampling Variation Within Trucks at First Point of Sale

Using big data to predict the likelihood of low falling numbers in wheat

Repeatability Precision of the Falling Number Procedure Under Standard and Modified Methodologies

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