Effect of Long-Term Storage on Mycobiota of Barley Grain and Malt

Identifying defects and varieties of Malting Barley Kernels

This study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system. The proposed method achieves precise classification into multiple classes, aligning with quality standards for malting material assessment. Throughout the study, various image analysis techniques were assessed, including traditional feature engineering, established transfer learning deep neural network architectures, and our custom-designed convolutional neural network tailored for barley kernel image analysis. Comparative analysis underscores the superior performance of our network model. The study reveals that our proposed deep learning network achieves a 94% accuracy in classifying barley kernel defects and varieties, outperforming well-established transfer learning models to complex architectures that attain 93% accuracy. Additionally, it surpasses the traditional machine learning approach involving feature extraction and support vector machine classifiers, which achieve accuracy below 90% in detecting defective kernels and below 70% in varietal classification. However, we also noted the traditional approach's advantage in morphological feature recognition. This observation guides new research toward integrating morphological feature extraction techniques with modern convolutional networks. This paper presents a deep neural network designed specifically for the analysis of cereal kernel images in two applications: defect and variety classification. It emphasizes the importance of standardizing kernel orientation and merging images from both sides of the kernel, and introduces a device for image acquisition that fulfills this need.

A comparative review on methods of detection and quantification of mycotoxins in solid food and feed: a focus on cereals and nuts

Many emerging factors and circumstances urge the need to develop and optimize the detection and quantification techniques of mycotoxins in solid food and feed. The diversity of mycotoxins, which have different properties and affinities, makes the standardization of the analytical procedures and the adoption of a single protocol that covers the attributes of all mycotoxins a tedious or even an impossible mission. Several modifications and improvements have been undergone in order to optimize the performance of these methods including the extraction solvents, the extraction methods, the clean-up procedures, and the analytical techniques. The techniques range from the rapid screening methods, which lack sensitivity and specificity such as TLC, to a spectrum of more advanced protocols, namely, ELISA, HPLC, and GC-MS and LC-MS/MS. This review aims at assessing the current studies related to these analytical techniques of mycotoxins in solid food and feed. It discusses and evaluates, through a critical approach, various sample treatment techniques, and provides an in-depth examination of different mycotoxin detection methods. Furthermore, it includes a comparison of their actual accuracy and a thorough analysis of the observed benefits and drawbacks.

The Microbiological Quality of Concentrates for Horses-A Retrospective Study on Influencing Factors and Associations with Clinical Symptoms Reported by Owners or Referring Vets

Evidence has already been provided that feed-borne mold spores and endotoxins can trigger chronic, non-infectious respiratory disease if inhaled. Furthermore, deficiencies in feed microbiology are suspected to trigger gastrointestinal and liver disorders in horses, but the connection needs further clarification. Most of the previous studies regarding horse feed hygiene focused on forage, whereas research regarding hygienic quality of concentrates is scarce. In the present study, results of reports on hygienic quality of compound feed and cereals for horses were evaluated secondarily. Results included sensory findings, and counts of aerobic bacteria, molds and yeasts determined by cultivation and lipopolysaccharide (LPS) contents. It was found that microbial counts of compound feed exceeded VDLUFA orientation values significantly more frequently than cereals (38.4 vs. 22.6%). However, average counts of bacteria, molds and yeasts were higher in cereals than in compound feeds (p < 0.0001, respectively). Mold counts in grains were significantly higher if dry matter contents were below 86% (p = 0.0201). No relation could be established between the anamnestically reported gastrointestinal disorders or elevated liver enzyme activities and microbiological deviations. Mold counts of concentrates which were suspected to cause coughing in horses were significantly higher than mold counts of control samples (3.29 vs. 2.40 log10 cfu g−1, p = 0.0313). These results indicate that hygienic status of concentrates is relevant for horse health in the respiratory tract.

Fungal Diversity in Barley Under Different Storage Conditions

The diversity of fungi in barley in simulated storage environments was analyzed. Barley was stored at different temperatures (15, 25, 35°C) and relative humidity (55, 65, 75, 85 RH) for 180 and 360 days. Alpha diversity, beta diversity, species composition, and species differences were analyzed using Illumina HiSeq technology. The fungal communities in all barley samples before and after storage belonged to 3 phyla, 18 classes, 39 orders, 71 families, 103 genera, and 152 species. The relative abundance of the dominant phylum Ascomycota was 77.98–99.19%. The relative abundance of Basidiomycota was 0.77–21.96%. At the genus level, the dominant genera of fungi in barley initially included Fusarium, Aspergillus, Microdochium, Alternaria, and Epicoccum. After 360 days of storage, the dominant genera became Epicoccum, Alternaria, Bipolar, Cladosporium, Fusarium, and Aspergillus. According to Venn diagrams and principal coordinates analysis, the fungal community diversity in barley initially was much higher than in barley stored at different temperatures and humidity. The application of PLS-DA could accurately distinguish between barley stored for 180 and 360 days. Some high-temperature and high-humidity environments accelerated storage. The dominant genera differed in different storage conditions and constantly changed with increasing storage duration. Epicoccum was one of the dominant genera after longer storage periods. This study provides theoretical support for optimizing safe storage conditions in barley.