Thermal properties of maize seed components

Hernández, Gemima Lara; Aguilar, Claudia Hernández; Pacheco, Arturo Domínguez; Sibaja, Albino Martínez; Orea A., Alfredo Cruz; de Jesus Agustin Flores Cuautle, Jose

doi:10.1080/23311932.2023.2231681

Cited by 4 publications

(5 citation statements)

References 39 publications

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“…With the increase in moisture level from 8.0% to 32.5% (db), the seeds' T cd increased logarithmically from 0.168 to 0.345 W m À1 K À1 . This behavior might be due to the higher T cd of moisture in the seed compared with the samples' dry material associated with air-filled pores (Hernández et al, 2023;Singh & Meghwal, 2019). The differences between the mean values of the seed T cd were statistically significant ( p ≤ 0.05) at all moisture levels as analyzed using DMRT except at 22.6% and 32.5% is shown in Table 2.…”

Section: T CD Of Sweetsop Seedsmentioning

confidence: 91%

“…Therefore, compared to dry seed, an oilseed with a higher mc requires more energy to heat or cool (Hashemifesharaki, 2021). Researchers have extensively studied the thermal and physical properties of various seeds and kernels, such as soursop seed and kernel (Oloyede et al, 2015(Oloyede et al, , 2017, Australian chia seed (Timilsena et al, 2017), white mustard seed (Ropelewska et al, 2018), camelina seed (Ropelewska & Jankowski, 2019), African star apple (Onwe et al, 2020), paddy and wheat seed (Jadhav et al, 2020), arugula seed (Mirzable et al, 2021), maize seed (Hernández et al, 2023), and wild banana seed powder (Meghwal et al, 2024). However, information specifically on the thermal and physical behavior of sweetsop seeds has not been reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Moisture‐modulated thermo‐physical analysis of sweetsop seed (Annona squamosa L.): A potential biofuel feedstock plant

Oloyede,

Jekayinfa,

Adebonojo

et al. 2024

J Food Process Engineering

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Sweetsop seed holds significant economic value as an oil seed, with ca. 25% oil content that finds applications as a feedstock for energy generation. The moisture‐modulated thermophysical properties of the seed were determined at varying moisture contents (8.0%–32.5%). Physical properties (length [L], width [W], thickness [T], arithmetic [Amd], and geometric mean diameters [Gmd], sphericity [Sty], surface area [SA], and bulk density [ρd]) were determined using standard methods while the thermal properties (specific heat capacity [SHC], thermal conductivity [Tcd], and thermal diffusivity [Tdf]) were analyzed using a TEMPOS thermal analyzer. The results showed that the seed L, W, T, Amd, and Gmd, Sty, SA, and ρd ranged from 13.22–14.95 mm, 7.32–7.95 mm, 5.25–5.35 mm, 8.60–8.75 mm, and 7.96–8.11 mm, 0.61–0.60, 196.72–205.28 mm2, and 210.00–270.00 kg m−3, respectively. The SHC, Tcd, and Tdf ranged from 0.14–0.52 J kg−1 K−1, 0.17–0.35 W m−1 K−1, and 0.10–0.20 m2 s−1, respectively. The ANOVA results indicated that the thermo‐physical properties studied were significantly (p ≤ 0.05) affected by moisture content. By utilizing the determined properties, engineers can develop efficient machines to harness the economic potential of sweetsop seed oil in various industries, including biofuel generation.Practical applicationsThe thermal and physical properties of sweetsop seed are needed by agricultural and mechanical engineers and food scientists to explore the potential application of the seed and the seed product, like oil for industrial and commercial purposes. Data obtained on the specific heat capacity of the seed would be valuable in designing of heating compartment of an oil expeller, thermal conductivity and diffusivity would be needed to design drying systems that balance efficiency and quality preservation, facilitate efficient removal of moisture from the seed while minimizing the risk of over‐drying or under‐drying. Thus, affects the design of the seed storage systems. Data on the seed axial dimensions, sphericity, mean diameters, surface area, and bulk density would be useful in the design and fabrication of agricultural equipment like sheller, grinder, packaging machines, discharge chute, aperture, and planter, to ensure proper seed placement, flow and to determine machine throughput capacity.

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Section: T CD Of Sweetsop Seedsmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Moisture‐modulated thermo‐physical analysis of sweetsop seed (Annona squamosa L.): A potential biofuel feedstock plant

Oloyede,

Jekayinfa,

Adebonojo

et al. 2024

J Food Process Engineering

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“…Maize (Zea mays L.) is a globally important crop and serves as food, fodder, and industrial raw material [1][2][3]. Due to its broad adaptation, maize grows in different agroecological zones around the world, contributing approximately 21% of the global food production [4].…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we therefore performed meta-QTL analysis for drought tolerance in maize. In particular, our objectives were as follows: (1) to identify consensus genomic regions linked to drought tolerance through meta-analysis, (2) to support the identified meta-QTL by comparison with results from genome-wide association studies (GWAS), (3) to identify candidate genes within the MQTL regions by searching for rice homologous genes and by exploring the important MQTL regions, and (4) to further characterize the candidate genes of MQTL.…”

Section: Introductionmentioning

confidence: 99%

Meta-Quantitative Trait Loci Analysis and Candidate Gene Mining for Drought Tolerance-Associated Traits in Maize (Zea mays L.)

Li,

Wang,

et al. 2024

IJMS

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Drought is one of the major abiotic stresses with a severe negative impact on maize production globally. Understanding the genetic architecture of drought tolerance in maize is a crucial step towards the breeding of drought-tolerant varieties and a targeted exploitation of genetic resources. In this study, 511 quantitative trait loci (QTL) related to grain yield components, flowering time, and plant morphology under drought conditions, as well as drought tolerance index were collected from 27 published studies and then projected on the IBM2 2008 Neighbors reference map for meta-analysis. In total, 83 meta-QTL (MQTL) associated with drought tolerance in maize were identified, of which 20 were determined as core MQTL. The average confidence interval of MQTL was strongly reduced compared to that of the previously published QTL. Nearly half of the MQTL were confirmed by co-localized marker-trait associations from genome-wide association studies. Based on the alignment of rice proteins related to drought tolerance, 63 orthologous genes were identified near the maize MQTL. Furthermore, 583 candidate genes were identified within the 20 core MQTL regions and maize–rice homologous genes. Based on KEGG analysis of candidate genes, plant hormone signaling pathways were found to be significantly enriched. The signaling pathways can have direct or indirect effects on drought tolerance and also interact with other pathways. In conclusion, this study provides novel insights into the genetic and molecular mechanisms of drought tolerance in maize towards a more targeted improvement of this important trait in breeding.

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“…Maize is also an essential food ingredient in countries such as Asia, Africa, and Latin America. Due to its high content of starch and various types of glucose, maize is used extensively as a raw material for food and industrial manufacturing, presenting significant potential in the market [2]. Previous studies have shown that soft maize can generate a greater starch content through wet milling.…”

Section: Introductionmentioning

confidence: 99%

Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test

Lin,

Song,

Dai

et al. 2024

Agriculture

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Hardness is a critical mechanical property of grains. Accurate predictions of grain hardness play a crucial role in improving grain milling efficiency, reducing grain breakage during transportation, and selecting high-quality crops. In this study, we developed machine learning models (MLMs) to predict the hardness of Jinsui No.4 maize seeds. The input variables of the MLM were loading speed, loading depth, and different types of indenters, and the output variable was the slope of the linear segment. Using the Latin square design, 100 datasets were generated. Four different types of MLMs, a genetic algorithm (GA), support vector machine (SVM), random forest (RF), and long short-term memory network (LSTM), were used for our data analysis, respectively. The result indicated that the GA model had a high accuracy in predicting hardness values, the R2 of the GA model training set and testing set reached 0.98402 and 0.92761, respectively, while the RMSEs were 1.4308 and 2.8441, respectively. The difference between the predicted values and the actual values obtained by the model is relatively small. Furthermore, in order to investigate the relationship between hardness and morphology after compression, scanning electron microscopy was used to observe the morphology of the maize grains. The result showed that the more complex the shape of the indenter, the more obvious the destruction to the internal polysaccharides and starch in the grain, and the number of surface cracks also significantly increases. The results of this study emphasize the potential of MLMs in determining the hardness of agricultural cereal grains, leading to improved industrial processing efficiency and cost savings. Additionally, combining grain hardness prediction models with the operating mechanisms of industry machinery would provide valuable references and a basis for the parameterization of seed grain processing machinery.

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Thermal properties of maize seed components

Cited by 4 publications

References 39 publications

Moisture‐modulated thermo‐physical analysis of sweetsop seed (Annona squamosa L.): A potential biofuel feedstock plant

Moisture‐modulated thermo‐physical analysis of sweetsop seed (Annona squamosa L.): A potential biofuel feedstock plant

Meta-Quantitative Trait Loci Analysis and Candidate Gene Mining for Drought Tolerance-Associated Traits in Maize (Zea mays L.)

Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test

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