A rapid, automated method for measuring α-amylase in pre-harvest sprouted (sprout damaged) wheat

Development of Novel Monoclonal Antibodies to Wheat Alpha-Amylases Associated with Grain Quality Problems That Are Increasing with Climate Change

Accurate, rapid testing platforms are essential for early detection and mitigation of late maturity α-amylase (LMA) and preharvest sprouting (PHS) in wheat. These conditions are characterized by elevated α-amylase levels and negatively impact flour quality, resulting in substantial economic losses. The Hagberg-Perten Falling Number (FN) method is the industry standard for measuring α-amylase activity in wheatmeal. However, FN does not directly detect α-amylase and has major limitations. Developing α-amylase immunoassays would potentially enable early, accurate detection regardless of testing environment. With this goal, we assessed an expression of α-amylase isoforms during seed development. Transcripts of three of the four isoforms were detected in developing and mature grain. These were cloned and used to develop E. coli expression lines expressing single isoforms. After assessing amino acid conservation between isoforms, we identified peptide sequences specific to a single isoform (TaAMY1) or that were conserved in all isoforms, to develop monoclonal antibodies with targeted specificities. Three monoclonal antibodies were developed, anti-TaAMY1-A, anti-TaAMY1-B, and anti-TaAMY1-C. All three detected endogenous α-amylase(s). Anti-TaAMY1-A was specific for TaAMY1, whereas anti-TaAMY1-C detected TaAMY1, 2, and 4. Thus, confirming that they possessed the intended specificities. All three antibodies were shown to be compatible for use with immuno-pulldown and immuno-assay applications.

Enhancing traceability of wheat quality through the supply chain

With the growing global population, the need for food is expected to grow tremendously in the next few decades. One of the key tools to address such growing food demand is minimizing grain losses and optimizing food processing operations. Hence, several research studies are underway to reduce grain losses/degradation at the farm (upon harvest) and later during the milling and baking processes. However, less attention has been paid to changes in grain quality between harvest and milling. This paper aims to address this knowledge gap and discusses possible strategies for preserving grain quality (for Canadian wheat in particular) during unit operations at primary, process, or terminal elevators. To this end, the importance of wheat flour quality metrics is briefly described, followed by a discussion on the effect of grain properties on such quality parameters. This work also explores how drying, storage, blending, and cleaning, as some of the common post-harvest unit operations, could affect grain's end-product quality. Finally, an overview of the available techniques for grain quality monitoring is provided, followed by a discussion on existing gaps and potential solutions for quality traceability throughout the wheat supply chain.

Detection of amylase activity and moisture content in rice by reflectance spectroscopy combined with spectral data transformation

In this study, reflectance spectroscopy was used to achieve rapid and non-destructive detection of amylase activity and moisture content in rice. Since rice husk can interfere with spectral measurements, spectral data transformation was used to remove the husk interference. Reflectance spectra of rice were transformed by direct standardization, convolutional autoencoder network, and kernel regression (KR). Then, random frog and elliptical envelope were adopted to select effective wavelengths, and partial least squares regression (PLSR) and support vector regression were used to establish analysis models. The optimal transformation was from KR, and PLSR and effective wavelengths of the transformed spectra obtained excellent performance with coefficient of determination of test of 0.6987 and 0.8317 and root-mean-square error of test of 0.3359 and 2.2239, respectively. The result was better than that of the rice spectra and was close to that of the husked rice spectra. When the moisture content was integrated into the regression model of amylase activity, a better result was obtained. Thus, the proposed method can detect amylase activity and moisture content in rice accurately.

As the number falls, alternatives to the Hagberg-Perten falling number method: A review

This review examines the application, limitations, and potential alternatives to the Hagberg-Perten falling number (FN) method used in the global wheat industry for detecting the risk of poor end-product quality mainly due to starch degradation by the enzyme α-amylase. By viscometry, the FN test indirectly detects the presence of α-amylase, the primary enzyme that digests starch. Elevated α-amylase results in low FN and damages wheat product quality resulting in cakes that fall, and sticky bread and noodles. Low FN can occur from preharvest sprouting (PHS) and late maturity α-amylase (LMA). Moist or rainy conditions before harvest cause PHS on the mother plant. Continuously cool or fluctuating temperatures during the grain filling stage cause LMA. Due to the expression of additional hydrolytic enzymes, PHS has a stronger negative impact than LMA. Wheat grain with low FN/high α-amylase results in serious losses for farmers, traders, millers, and bakers worldwide. Although blending of low FN grain with sound wheat may be used as a means of moving affected grain through the marketplace, care must be taken to avoid grain lots from falling below contract-specified FN. A large amount of sound wheat can be ruined if mixed with a small amount of sprouted wheat. The FN method is widely employed to detect α-amylase after harvest. However, it has several limitations, including sampling variability, high cost, labor intensiveness, the destructive nature of the test, and an inability to differentiate between LMA and PHS. Faster, cheaper, and more accurate alternatives could improve breeding for resistance to PHS and LMA and could preserve the value of wheat grain by avoiding inadvertent mixing of high- and low-FN grain by enabling testing at more stages of the value stream including at harvest, delivery, transport, storage, and milling. Alternatives to the FN method explored here include the Rapid Visco Analyzer, enzyme assays, immunoassays, near-infrared spectroscopy, and hyperspectral imaging.

Novel substrates for the automated and manual assay of endo-1,4-β-xylanase

endo-1,4-β-Xylanase (EC 3.2.1.8) is employed across a broad range of industries including animal feed, brewing, baking, biofuels, detergents and pulp (paper). Despite its importance, a rapid, reliable, reproducible, automatable assay for this enzyme that is based on the use of a chemically defined substrate has not been described to date. Reported herein is a new enzyme coupled assay procedure, termed the XylX6 assay, that employs a novel substrate, namely 4,6-O-(3-ketobutylidene)-4-nitrophenyl-β-4⁵-O-glucosyl-xylopentaoside. The development of the substrate and associated assay is discussed here and the relationship between the activity values obtained with the XylX6 assay versus traditional reducing sugar assays and its specificity and reproducibility were thoroughly investigated.