RESULTS: A higher sensitivity was observed in chromosomal DNA (MPRE-PCR assay), where this assay allows the detection of gelatin DNA at amounts as as low as 1 pg, whereas mitochondrial DNA (CBH-PCR assay) can only detect at levels down to 10 pg of gelatin DNA. When an analysis with commercial gelatin and gelatin capsule samples was conducted, the same result was observed, with a significantly more sensitive detection being provided by the repetitive element of chromosomal DNA.

RESULTS: Here, we investigated the microbial dynamics by next-generation sequencing, and outlined a differential non-targeted metabolite profiling in the process of serofluid dish fermentation using the method of hydrophilic interaction liquid chromatography column with ultra-high-performance liquid chromatography-quadruple time-of-flight mass spectrometry. Lactobacillus was the leading genus of bacteria, while Pichia and Issatchenkia were the dominant fungi. They all accumulated during fermentation. In total, 218 differential metabolites were identified, of which organic acids, amino acids, sugar and sugar alcohols, fatty acids, and esters comprised the majority. The constructed metabolic network showed that tricarboxylic acid cycle, urea cycle, sugar metabolism, amino acids metabolism, choline metabolism, and flavonoid metabolism were regulated by the fermentation. Furthermore, correlation analysis revealed that the leading fungi, Pichia and Issatchenkia, were linked to organic acids, amino acid and sugar metabolism, flavonoids, and several other flavor and functional components. Antibacterial tests indicated the antibacterial effect of serofluid soup against Salmonella and Staphylococcus.

RESULTS: Marker-assisted foreground selection was performed using RM6836 and RM8225 to identify plants possessing blast resistance genes. Seventy microsatellite markers were used to estimate recurrent parent genome (RPG) recovery. Our analysis led to the development of 13 improved blast resistant lines with Piz, Pi2 and Pi9 broad-spectrum blast resistance genes and an MR219 genetic background. The RPG recovery of the selected improved lines was up to 97.70% with an average value of 95.98%. Selected improved lines showed a resistance response against the most virulent blast pathogen pathotype, P7.2. The selected improved lines did not express any negative effect on agronomic traits in comparison with MR219.

RESULTS: The results showed that suitable area for turmeric cultivation is declining in future scenario and this decline can be primarily attributed to fluctuations in temperature and an anticipated increase in rainfall in the year 2050. Notable changes in the spatial distribution of suitable areas over time were observed through the application of geographic information system (GIS) techniques. Importantly, as per the suitability criteria provided by ICAR-National Bureau of Soil Survey and Land Use Planning (ICAR-NBSS & LUP), all the districts in Kerala exhibited moderately suitable conditions for turmeric cultivation. With the GIS tools, the study identified highly suitable, moderately suitable, marginally suitable and not suitable areas of turmeric cultivation in Kerala. Presently 28% of area falls under highly suitable, 41% of area falls under moderately suitable and 11% falls under not suitable for turmeric cultivation. However, considering the projected scenarios for 2050 under the SSP framework, there will be a significant decrease in highly suitable area by 19% under SSP 5-8.5. This reduction in area will have an impact on the productivity of the crop as a result of changes in temperature and rainfall patterns.

RESULTS: In this research, chili pest and disease features extracted using the traditional approach were compared with features extracted using a deep-learning-based approach. A total of 974 chili leaf images were collected, which consisted of five types of diseases, two types of pest infestations, and a healthy type. Six traditional feature-based approaches and six deep-learning feature-based approaches were used to extract significant pests and disease features from the chili leaf images. The extracted features were fed into three machine learning classifiers, namely a support vector machine (SVM), a random forest (RF), and an artificial neural network (ANN) for the identification task. The results showed that deep learning feature-based approaches performed better than the traditional feature-based approaches. The best accuracy of 92.10% was obtained with the SVM classifier.

RESULTS: The concentration of total reducing sugars was reduced by up to 64.61, 77.22 and 82.52% with increased roasting temperature at 150, 200 and 250°C for 50 min, respectively. The hydrophobic amino acids were reduced up to 29.21, 36.41 and 48.87% with increased roasting temperature at 150, 200 and 250°C for 50 min, respectively. A number of pyrazines, esters, aldehydes, alcohols, ketones, carboxyl acids and hydrocarbons were detected in all the samples at different concentration range. Formation of the most flavour active compounds, pyrazines, were the highest concentration (2.96 mg kg-1 ) at 200°C for 10 min.

RESULTS: As pods developed, flavonol accumulated while nitrogen content degraded. Mature pods produced beans with a higher flavonol, catechin, and total phenolic content (TPC). As fermentation progressed, the beans' fat, TPC, antioxidant activity, and catechin content increased, regardless of pod maturity at harvest. Free fatty acid (FFA) levels were highest in 5 day fermented beans. The 3 day fermented beans contained significantly higher epicatechin, with lower FFA content. Chocolate made from mature beans with 3 day fermentation was more pleasant as it scored the highest in flavor intensity and complexity and the lowest in acidity and astringency.