We screened wild yeasts from the Okhotsk area of Hokkaido, and isolated several strains from materials such as fruit, wood, and flowers. This study examined the fermentation properties of the non-Saccharomyces yeast, Lachancea thermotolerans, which was isolated from plums. Although this yeast produces lactic acid and is used in some food industries, relatively little is known about this yeast. The fermentation of beer using L. thermotolerans has occasionally been reported to be interrupted for unknown reasons. In addition to producing lactic acid, we found that the isolated strain, OK300, preferably ferments sucrose, compared to other sugars tested: glucose, fructose, and maltose. Maltose was not a preferred substrate for alcohol production. Using a rich malt, high fermentation temperature, and excess starter, we found that the strain does not completely ferment wort, resulting in the production of beer with a low alcohol content. The beer produced using strain OK300 contained residual maltotriose, but no maltose remained. By understanding the above profile, sour beer could be produced without using lactic acid bacteria. The resulting beer had a slightly low alcohol content and was acid rich and fruity, indicating that strain OK300 is potentially useful for beer production.
Coffee is consumed as a luxury item around the world. Since 2000, specialty and high-quality coffees have become highly sought after. The production of high-quality coffee is dependent on minimizing the occurrence of defective beans. In coffee production, mechanical sorting is employed to remove defective beans. However, since machine sorting only removes foreign matter and beans of abnormal color, beans with defects must be removed by hand. However, hand-picking and sorting large quantities of green coffee beans is labor intensive. Therefore, the aim of this study was to create a convolutional neural network (CNN) based artificial intelligence for assessing the quality of green coffee beans, obviating the need for hand-picking. However, deep learning is computationally intensive and requires computation time. In this study, we describe the application of a lightweight CNN using structured pruning and quantization for sorting green coffee beans. We compare the discrimination accuracy and execution time of trained and lightweight CNNs. The execution time of the CNNs was compared by implementing the system on general-purpose devices. The experimental results confirmed that the lightweight CNNs performed the sorting task in less time without any deterioration of discrimination accuracy. Furthermore, the trained and lightweight models were comparable in terms of feature extraction results.
γ-Aminobutyric acid (GABA) is produced through the decarboxylation of glutamate by glutamate decarboxylase (GAD). Many fresh vegetables contain endogenous GAD, and residual enzyme activity can influence the measured GABA levels. In this study, we investigated the effects of various pretreatment methods on the quantitative analysis of GABA using commercially produced vegetables. The results showed that using MilliQ water as the extraction solvent yielded higher GABA levels than the actual results, whereas more accurate results could be obtained by using 70% (v/v) ethanol.
The Na, K, Ca, Mg, P, Fe, Zn, Cu, Mn, Mo, Sr, and Ba contents in the edible portion of Brassicaceae crops were measured. Crops including leaf radish, komatsuna, pak choi, kale, rutabaga, and cabbage, all with various genetic backgrounds, were grown in the same field and season. In total, 30 cultivars of six crops were used. Although the number of cultivars tested for each crop was limited, the analysis revealed differences in nearly all of the elements among cultivars and lines (p < 0.05 for each crop). Some cultivars had distinctive inorganic element contents. Significant differences in the contents of Zn (p < 0.05) and Na, K, Ca, Mg, Fe, Cu, Mn, Mo, Sr, and Ba (p < 0.001) were observed. In addition, certain trends were observed in the inorganic element composition of each crop. Using Brussels sprouts and vegetable rape grown in a separate field as a reference, komatsuna, pak choi, kale, leaf radish, Brussels sprouts, and vegetable rape, which are classified as green and yellow vegetables, had high inorganic element contents. The results show that green and yellow vegetables are important sources of inorganic elements.