Physiological characters of Moringa oleifera Lamk in Madura

Wasonowati, Catur; Sulistyaningsih, Endang; Indradewa, Didik; Kurniasih, Budiastuti

doi:10.1063/1.5115628

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…Previous studies have reported that the total chlorophyll contents in green tea were 170-710 mg/100 g, and the chlorophyll a and b contents were 71-401 mg/100 g and 93-309 mg/100 g, respectively (Lee et al, 2010). The total chlorophyll contents in moringa leaves were reported as 232-532 mg/100 g, while the chlorophyll a and b contents were 170-359 mg/100 g and 61-173 mg/100 g, respectively (Wasonowati et al, 2019). These reports indicated that the chlorophyll content of green tea and moringa leaves was wide and varied, and corroborate the results of the present study.…”

Section: Chlorophyll Content Of the Moringa Leaf And Green Tea Powderssupporting

confidence: 90%

“…During cultivation, the chlorophyll content is influenced by weather conditions, such as temperature and humidity. Furthermore, as the effect of light on photosynthesis varies from region to region, there are regional differences in the chlorophyll content of plants (Lee et al, 2010;Wasonowati et al, 2019). In addition, the chlorophyll content may decrease as chlorophyll may undergo decomposition into pheophytin or pheophorbide owing to its sensitivity to temperature, pH, O 2 , and light during storage; additionally, chlorophyll a is thermally less stable than chlorophyll b (Choe et al, 2001).…”

Section: Chlorophyll Content Of the Moringa Leaf And Green Tea Powdersmentioning

confidence: 99%

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Evaluation of the quality characteristics of protein cubes supplemented with moringa leaf (Moringa oleifera Lam.) and green tea powders

Baek¹,

Lee²

2021

Korean J. Food Preserv.

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The biological activities of moringa leaf and green tea powders were compared, and the quality characteristics of protein cubes supplemented with these powders (5% and 7.5%, respectively) were evaluated. The green tea powder had higher chlorophyll, essential fatty acid, total polyphenol (TPC), and flavonoid (TFC) contents than the moringa leaf powder. The green tea powder had higher levels of 2,2-diphenyl-1-picrylhydrazyl radical scavenging capacity (DPPH RSC), Trolox equivalent antioxidant capacity (TEAC), and ferric reducing antioxidant power (FRAP) than the moringa leaf powder, indicating higher antioxidant activity. The green tea powder inhibited the nitric oxide production to a greater extent than the moringa leaf powder. The protein cubes that were supplemented with either the moringa leaf or green tea powders had smaller volumes and were harder, chewier, and more easily fractured than the control protein cubes. The protein cubes supplemented with green tea powder had higher TPC and TFC, exhibited higher antioxidant activity (p<0.05), and had better sensory properties in terms of aroma, taste, and texture than the protein cubes supplemented with moringa leaf powder; however, the differences were not significant(p>0.05). The results of the present study indicate that green tea is a more suitable health-oriented functional additive than moringa leaf in the development of high-protein and nutrient-rich bakery products with high consumer acceptability as well as antioxidant and anti-inflammatory properties. Key words：antioxidant, green tea, Moringa oleifera, protein cube, quality characteristics

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Section: Chlorophyll Content Of the Moringa Leaf And Green Tea Powderssupporting

confidence: 90%

Section: Chlorophyll Content Of the Moringa Leaf And Green Tea Powdersmentioning

confidence: 99%

Evaluation of the quality characteristics of protein cubes supplemented with moringa leaf (Moringa oleifera Lam.) and green tea powders

Baek¹,

Lee²

2021

Korean J. Food Preserv.

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“…Therefore, stomatal size is unsuitable as an indicator of the number of seeds produced by both varieties. The opening and closing of stomata are related to photosynthetic processes, given that stomata transport CO2, O2, and H2O and absorb K + ions (Wasonowati et al 2019).…”

Section: Stomatal Density and Stomatal Aperture Widthmentioning

confidence: 99%

Morphophysiological and yield traits of soybean varieties tolerant of intercropping with maize

PERMANASARI,

SULISTYANINGSIH,

KURNIASIH

et al. 2023

Biodiversitas

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Abstract. Permanasari I, Sulistyaningsih E, Kurniasih B, Indradewa D. 2023. Morphophysiological and yield traits of soybean varieties tolerant of intercropping with maize. Biodiversitas 24: 3872-3880. Not all soybean varieties grow optimally when intercropped with maize. Previously, we identified seven varieties (i.e., "Demas 1", Dena 1", "Dena 2", "Derap 1", "Devon 1", "Devon 2", and "Wilis") and two (i.e., "Dega 1" and "Mahameru") as tolerant and intolerant of intercropping based on selection indices and yield. This research aims to determine the morphophysiological and yield traits of tolerant and intolerant soybean groups intercropped with maize. The study used a randomized complete block design with three blocks as replications, and conducted from October 2019 to February 2020. Results showed that light intensity in the intercropping system had decreased by 19.91% relative to that in the monoculture system at 7 weeks after planting. The tolerant group displayed significant gains in seed weight per plant (143.66%), total biomass (50.04%), harvest index (53.33%), total number of pods and seeds per plant (119.39% and 128.86%), leaf nitrogen uptake and content (20.83% and 6.44%), chlorophyll-a:b ratio (7.20%), and stem diameter (7.65%) than the intolerant group. Correlation analysis revealed that leaf nitrogen content showed the highest correlation and significantly contributed to seed weight per plant in the tolerant and intolerant groups. Seed weight in the tolerant group was mainly affected by morphological (total biomass and harvest index), physiological (leaf nitrogen content), and yield components (number of pods), whereas that in the intolerant group was influenced only by physiological parameters (nitrogen content). Nitrogen content was considered suitable as a selection indicator for determining the seed yield of intercropped soybean varieties. Furthermore, using soybean tolerant groups for intercropping may be a solution to increase soybean production in Indonesia.

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“…Remote-sensing satellite imagery data were collected and The poverty of East Java is concentrated in the regencies on Madura Island, the northeast part of the East Java province. Madura Island is lacking in fertile areas and is in the form of a plateau without volcanoes and dry agricultural land [49]. In 2020, the poverty of Sampang Regency was the highest in East Java, reaching 22.78%, followed by Bangkalan (20.56%), and Sumenep (20.18%) [48].…”

Section: Data Used In This Studymentioning

confidence: 99%

Developing Relative Spatial Poverty Index Using Integrated Remote Sensing and Geospatial Big Data Approach: A Case Study of East Java, Indonesia

Putri

Wijayanto

Sakti

2022

IJGI

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Poverty data are usually collected through on-the-ground household-based socioeconomic surveys. Unfortunately, data collection with such conventional methods is expensive, laborious, and time-consuming. Additional information that can describe poverty with better granularity in scope and at lower cost, taking less time to update, is needed to address the limitations of the currently existing official poverty data. Numerous studies have suggested that the poverty proxy indicators are related to economic spatial concentration, infrastructure distribution, land cover, air pollution, and accessibility. However, the existing studies that integrate these potentials by utilizing multi-source remote sensing and geospatial big data are still limited, especially for identifying granular poverty in East Java, Indonesia. Through analysis, we found that the variables that represent the poverty of East Java in 2020 are night-time light intensity (NTL), built-up index (BUI), sulfur dioxide (SO2), point-of-interest (POI) density, and POI distance. In this study, we built a relative spatial poverty index (RSPI) to indicate the spatial poverty distribution at 1.5 km × 1.5 km grids by overlaying those variables, using a multi-scenario weighted sum model. It was found that the use of multi-source remote sensing and big data overlays has good potential to identify poverty using the geographic approach. The obtained RSPI is strongly correlated (Pearson correlation coefficient = 0.71 (p-value = 5.97×10−7) and Spearman rank correlation coefficient = 0.77 (p-value = 1.58×10−8) to the official poverty data, with the best root mean square error (RMSE) of 3.18%. The evaluation of RSPI shows that areas with high RSPI scores are geographically deprived and tend to be sparsely populated with more inadequate accessibility, and vice versa. The advantage of RSPI is that it is better at identifying poverty from a geographical perspective; hence, it can be used to overcome spatial poverty traps.

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Physiological characters of Moringa oleifera Lamk in Madura

Cited by 6 publications

References 7 publications

Evaluation of the quality characteristics of protein cubes supplemented with moringa leaf (Moringa oleifera Lam.) and green tea powders

Evaluation of the quality characteristics of protein cubes supplemented with moringa leaf (Moringa oleifera Lam.) and green tea powders

Morphophysiological and yield traits of soybean varieties tolerant of intercropping with maize

Developing Relative Spatial Poverty Index Using Integrated Remote Sensing and Geospatial Big Data Approach: A Case Study of East Java, Indonesia

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