RESULTS: Increases in homogenization pressure and emulsifier concentration were observed to have significant (P  0.05) differences between the prepared and commercial LNDCs in terms of their color, appearance, and overall acceptability.

RESULTS: Cluster analysis of the ERIC-PCR profiles based on band sizes revealed six main clusters from the sixteen isolates analysed. The largest cluster (cluster 3) grouped isolates from vegetables and manure at a similarity coefficient of 0.72.

RESULTS: Defatted kenaf seed powder yielded four DF fractions: alkali-soluble hemicellulose (146.4 g kg-1 ), calcium-bound pectin (10.3 g kg-1 ) and acid-soluble pectin (25.4 g kg-1 ) made up the soluble fibre fraction, whereas cellulose (202.2 g kg-1 ) comprised the insoluble fraction. All fractions were evaluated for their physicochemical properties. The DF fractions contained glucose, mannose, xylose and arabinose, and a small amount of uronic acid (1.2-2.7 g kg-1 ). The isolated pectin fractions had a low degree of esterification (14-30%). All the isolated DF fractions had high average molecular weights ranging from 0.3 to 4.3 × 106 g mol-1 . X-ray diffractogram analysis revealed that the fractions consisted mainly of an amorphous structure with a relative crystallinity ranging from 31.6% to 44.1%. The Fourier-transform infrared spectroscopy spectrum of kenaf seed and its DF fractions showed typical absorption of polysaccharides, with the presence of hydroxyl, carboxyl, acetyl and methyl groups. Scanning electron microscopy analysis demonstrated that the raw material with the rigid structure resulted in soluble and insoluble DF fractions with more fragile and fibrous appearances, respectively. The soluble DF demonstrated greater flowability and compressibility than the insoluble fractions.

RESULTS: This study was done to assess the hereditary assorted variety of selected genotypes of Capsicum annuum based on their morphophysiological and yield traits in two planting seasons. The biochemical properties, capsaicinoid content (capsaicin and dihydrocapsaicin), total phenolics content and antioxidant action determination of unripe and ripe chili pepper fruits were carried out in dry fruits. AVPP9813 and Kulai 907 were observed to have high fruit yields, with 541.39 and 502.64 g per plant, respectively. The most increased genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) were shown by the fruit number per plant (49.71% and 66.04%, respectively). High heritability was observed in yield characters viz-à-viz fruit weight, length and girth and indicated high genetic advance. Eight groups were obtained from the cluster analysis. For the biochemical analysis, the capsaicinoid content and total phenolic content were high in Chili Bangi 3 at unripe and ripe fruit stages, while for antioxidant activity SDP203 was the highest in ripe dry fruit.

RESULTS: Thirty-two non-repeat Y. enterocolitica strains of three bioserotypes (3 variant/O:3, n = 27; 1B/O:8, n = 3; 1A/O:5, n = 2) were analysed. Approximately 90% of strains were multidrug-resistant with a multiple antibiotic resistance index < 0.2 and the majority of the strains were resistant to nalidixic acid, clindamycin, ampicillin, ticarcillin, tetracycline and amoxicillin. Yersinia enterocolitica could be distinguished distinctly into three clusters by pulsed-field gel electrophoresis, with each belonging to a particular bioserotype. Strains of 3 variant/O:3 were more heterogeneous than others. Eleven of the 15 virulence genes tested (hreP, virF, rfbC, myfA, sat, inv, ail, ymoA, ystA, tccC, yadA) and pYV virulence plasmid were present in all the bioserotpe 3 variant/03 strains.

RESULTS: Yield per plant showed positive and highly significant (P ≤ 0.01) correlations with most of the characters studied at both the phenotypic and genotypic levels. By contrast, disease incidence and days to flowering showed a significant negative association with yield. Fruit weight and number of fruits exerted positive direct effect on yield and also had a positive and significant (P ≤ 0.01) correlation with yield per plant. However, fruit length showed a low negative direct effect with a strong and positive indirect effect through fruit weight on yield and had a positive and significant association with yield.

RESULTS: Sodium dodecyl sulfate-polyacrylamide gel electrophoresis fractionated raw snail extract to approximately 24 protein bands, between 9 and 245 kDa. The prominent band at 33 kDa was detected in all raw and processed snail extracts. Immunoblotting tests of the raw extract demonstrated 19 immunoglobulin E (IgE)-binding proteins, and four of them, at 30, 35, 42 and 49 kDa, were revealed as the major IgE-binding proteins of P. polita. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry identified the 49 and 42 kDa major allergens as actin, whereas the 30 and 35 kDa major allergens were identified as tropomyosin. Immunoblotting revealed that the raw snail had more allergenic proteins than the processed snail. The degree of allergenicity in decreasing order was raw > brine pickled> boiled > roasted > fried > vinegar pickled. The presence of cross-reactivity between P. polita and the shellfish tested was exhibited with either no, complete, or partial inhibitions.

RESULTS: A total of 31 constituents comprising primary and secondary metabolites belonging to the chemical classes of fatty acids, amino acids, sugars, terpenoids and phenolic compounds were identified. Shade-dried leaves were identified to possess the highest concentrations of bioactive secondary metabolites such as chlorogenic acid, caffeic acid, luteolin, orthosiphol and apigenin, followed by microwave-dried samples. Freeze-dried leaves had higher concentrations of choline, amino acids leucine, alanine and glutamine and sugars such as fructose and α-glucose, but contained the lowest levels of secondary metabolites.

RESULTS: Biochar+compost application resulted in 37% and 300% higher soil total organic carbon and available phosphorus content, respectively, during the first 3 years of experimentation compared to control. Similarly, trunk diameter and shoot number of apple trees increased 23-26% by the end of the first year. Nevertheless, there were no significant changes in fruitfulness, fruit weight or starch pattern index as productivity indices.

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.

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: 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.