Potential methodologies and strategies for the rapid assessment of feed-grain quality

C. W. Wrigley

doi:10.1071/ar98167

Potential methodologies and strategies for the rapid assessment of feed-grain quality

Australian Journal of Agricultural Research ◽

10.1071/ar98167 ◽

1999 ◽

Vol 50 (5) ◽

pp. 789 ◽

Cited By ~ 11

Author(s):

C. W. Wrigley

Keyword(s):

Grain Quality ◽

Near Infrared ◽

Animal Feed ◽

Rapid Assessment ◽

Cost Effective ◽

Beta Glucan ◽

Site Analysis ◽

Direct Indication ◽

Systematic Strategy

The efficient use of grains for animal feed requires the use of analytical methods that can provide rapid indications of the suitability of the grain for animal nutrition. Ideally, these methods need to be applied at the site of grain receival within the tight time and cost confines of grain delivery. In addition, methods are needed in plant breeding to efficiently screen for target aspects of feed-grain quality to facilitate the development of genotypes with improved nutritional quality. This review describes a range of techniques that can fulfil these analytical requirements. These include visual examination of grain samples for species identification and for recognition of defects and contaminants. This long-standing approach is rapid, but it is subjective and dependent on the expertise of the operator. The newer technology of image analysis offers the prospect of providing similar information automatically and quantitatively, without the risk of operator bias. Near-infrared (NIR) analysis is already in general use for grain analysis at many receival depots in wheat-growing countries, mainly for the determination of moisture and protein content. There are promising indications that NIR can be extended to the determination of many other aspects of grain composition, both the positive aspects that contribute to feed quality, as well as components such as beta-glucan content that have negative contributions for non-ruminants. Furthermore, NIR is being developed to provide a direct indication of metabolisable energy for a range of grain types. Whereas NIR is primarily suited to the determination of quantitatively major components of the grain, without the requirement of significant sample preparation, immunological analyses are appropriate for the determination of specific and minor components, such as mycotoxins, lectins, alkaloids, and pesticide residues. These and other methods, suited for on-site analysis, need to be combined with effective sampling to ensure that the results of testing are representative of the whole of the grain consignment, and also integrated into a systematic strategy to ensure cost-effective testing.

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Pitfalls in the Immunochemical Determination of β‑Lactam Antibiotics in Water

Antibiotics ◽

10.3390/antibiotics10030298 ◽

2021 ◽

Vol 10 (3) ◽

pp. 298

Author(s):

Alexander Ecke ◽

Rudolf J. Schneider

Keyword(s):

Limit Of Detection ◽

Cost Effective ◽

Enzyme Linked Immunosorbent Assay ◽

Degree Of Hydrolysis ◽

Site Analysis ◽

Antibody Recognition ◽

Immunochemical Determination ◽

Key Factor ◽

Lactam Ring

Contamination of waters with pharmaceuticals is an alarming problem as it may support the evolution of antimicrobial resistance. Therefore, fast and cost-effective analytical methods for potential on-site analysis are desired in order to control the water quality and assure the safety of its use as a source of drinking water. Antibody-based methods, such as the enzyme-linked immunosorbent assay (ELISA), can be helpful in this regard but can also have certain pitfalls in store, depending on the analyte. As shown here for the class of β-lactam antibiotics, hydrolysis of the β‑lactam ring is a key factor in the immunochemical analysis as it influences antibody recognition. With the antibody used in this study, the limit of detection (LOD) in the immunoassay could be significantly reduced by hydrolysis for the five tested penicillins, with the lowest LOD for carbenicillin (0.2 nmol/L) and the greatest impact on penicillins G and V (reduction by 85%). In addition to enhanced quantification, our strategy also provides access to information about the degree of hydrolysis in water samples as shown for the most abundant penicillin amoxicillin.

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A Novel Genetic Algorithm-Based Optimization Framework for the Improvement of Near-Infrared Quantitative Calibration Models

Computational Intelligence and Neuroscience ◽

10.1155/2020/7686724 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Quanxi Feng ◽

Huazhou Chen ◽

Hai Xie ◽

Ken Cai ◽

Bin Lin ◽

...

Keyword(s):

Genetic Algorithm ◽

Near Infrared ◽

Animal Feed ◽

Calibration Model ◽

Moving Window ◽

Prediction Ability ◽

Efficient Strategy ◽

Optimization Framework ◽

Calibration Models

The global fishmeal production is used for animal feed, and protein is the main component that provides nutrition to animals. In order to monitor and control the nutrition supply to animal husbandry, near-infrared (NIR) technology was utilized for rapid detection of protein contents in fishmeal samples. The aim of the NIR quantitative calibration is to enhance the model prediction ability, where the study of chemometric algorithms is inevitably on demand. In this work, a novel optimization framework of GSMW-LPC-GA was constructed for NIR calibration. In the framework, some informative NIR wavebands were selected by grid search moving window (GSMW) strategy, and then the variables/wavelengths in the waveband were transformed to latent principal components (LPCs) as the inputs for genetic algorithm (GA) optimization. GA operates in iterations as implementation for the secondary optimization of NIR wavebands. In steps of the variable’s population evolution, the parametric scaling mode was investigated for the optimal determination of the crossover probability and the mutation operator. With the GSMW-LPC-GA framework, the NIR prediction effect on fishmeal protein was experimentally better than the effect by simply adopting the moving window calibration model. The results demonstrate that the proposed framework is suitable for NIR quantitative determination of fishmeal protein. GA was eventually regarded as an implementable method providing an efficient strategy for improving the performance of NIR calibration models. The framework is expected to provide an efficient strategy for analyzing some unknown changes and influence of various fertilizers.

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