Multi-stage intermittent drying of rough rice in terms of tempering and stress cracking indices and moisture gradients interpretation

Ghasemi, Ali; Sadeghi, Morteza; Mireei, Seyed Ahmad

doi:10.1080/07373937.2017.1303777

Cited by 41 publications

(16 citation statements)

References 25 publications

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“…Moreover, the effective moisture diffusivity ( D eff ) and convection mass transfer coefficient ( k m ) were increased with increasing the dehydration air temperature. The reason for this phenomenon is the higher thermal energy for the evaporation of moisture from the samples (Ghasemi, Sadeghi, & Mireei, ). These results were similar to previous study for infrared‐ convective tomato slice dehydration (Sadin et al, ).…”

Section: Resultsmentioning

confidence: 99%

Numerical simulation of vitamin C degradation during dehydration process of fresh tomatoes

Ghasemi

Chayjan

2019

J Food Process Engineering

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In this study, the kinetics of vitamin C degradation is simulated by the development of a model based on the numerical approach of heat and mass transfer processes and the degradation rate of vitamin C during the dehydration of tomato slices at 45, 60, and 75°C. The results achieved by experimental data were consistent with those obtained by the numerical model (R2 > 0.88 and RMSE < 0.27). So, it is concluded that the rate of vitamin C degradation was increased at the beginning of the dehydration process when the temperature of the inner sample layers was raised and then its value decreased with the reduced moisture content at the stable temperature conditions. During the dehydration process, the highest concentrations of vitamin C were obtained at the surface layer of the tomato slice with the least moisture content and the lowest of its value was obtained at the sample center with the highest moisture content. Practical Applications Some of the essential nutrients are degraded during the dehydration process. Understanding factors effective on nutrient degradation is important to optimize the dehydration conditions to produce healthy food. Since vitamin C is an indicator for determining the health food, modeling of its degradation kinetics is an efficient method for understanding the effective factors of retaining nutritional components during the dehydration operation. In this research, a novel method developed to simulate the vitamin C degradation in different layers of a tomato slice during a thermal process. The result of this method is used to interpret the factors affecting on degradation of food component. After the dehydration process, the concentration of vitamin C at the surface node was approximately 47.9, 22.1, and 9.9% higher than its value at the center node at 45, 60, and 75°C, respectively. The effective moisture diffusivity was the most sensitive parameter in the modeling of vitamin C degradation kinetics.

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Section: Resultsmentioning

confidence: 99%

Numerical simulation of vitamin C degradation during dehydration process of fresh tomatoes

Ghasemi

Chayjan

2019

J Food Process Engineering

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“…Rice ( Oryza sativa L.) is the most widely consumed staple food for a large part of the world's human population, especially in Asia 1 . About one‐third of the world's population depends on rice as one staple food, 2 thus rice quality and safety are of great importance. However, during storage and in the market chain, rice grains can be contaminated easily by several toxigenic fungi such as Aspergillus , Fusarium , and Penicillium spp.. Fungal contamination in rice might cause health hazards due to the production of mycotoxins, which is hazardous to humans and constitutes a factor in economic losses in food products 3 .…”

Section: Introductionmentioning

confidence: 99%

Early identification of Aspergillus spp. contamination in milled rice by E‐nose combined with chemometrics

Wang

Chen

et al. 2021

J Sci Food Agric

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BACKGROUND Rice grains can be contaminated easily by certain fungi during storage and in the market chain, thus generating a risk for humans. Most classical methods for identifying and rectifying this problem are complex and time‐consuming for manufacturers and consumers. However, E‐nose technology provides analytical information in a non‐destructive and environmentally friendly manner. Two‐feature fusion data combined with chemometrics were employed for the determination of Aspergillus spp. contamination in milled rice. RESULTS Linear discriminant analysis (LDA) indicated that the efficiency of fusion signals (‘80th s values’ and ‘area values’) outperformed that of independent E‐nose signals. Linear discriminant analysis showed clear discrimination of fungal species in stored milled rice for four groups on day 2, and the discrimination accuracy reached 92.86% by using an extreme learning machine (ELM). Gas chromatography–mass spectrometry (GC–MS) analysis showed that the volatile compounds had close relationships with fungal species in rice. The quantification results of colony counts in milled rice showed that the monitoring models based on ELM and the genetic algorithm optimized support vector machine (GA‐SVM) (R2 = 0.924–0.983) achieved better performances than those based on partial least squares regression (PLSR) (R2 = 0.877–0.913). The ability of the E‐nose to monitor fungal infection at an early stage would help to prevent contaminated rice grains from entering the food chains. CONCLUSIONS The results indicated that an E‐nose coupled with ELM or GA‐SVM algorithm could be a useful tool for the rapid detection of fungal infection in milled rice, to prevent contaminated rice from entering the food chain. © 2021 Society of Chemical Industry

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“…Recently, intermittent drying has been widely applied in the paddy drying industry. Intermittent drying has been recommended for paddy drying by different researchers [11,12].…”

Section: Introductionmentioning

confidence: 99%

Parboiled Paddy Drying with Different Dryers: Thermodynamic and Quality Properties, Mathematical Modeling Using ANNs Assessment

Taghinezhad

Szumny

Kaveh

et al. 2020

Foods

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The effect of hybrid infrared-convective (IRC), microwave (MIC) and infrared-convective-microwave (IRCM) drying methods on thermodynamic (drying kinetics, effective moisture diffusivity coefficient (Deff), specific energy consumption (SEC)) and quality (head rice yield (HRY), color value and lightness) characteristics of parboiled rice samples were investigated in this study. Experimental data were fitted into empirical drying models to explain moisture ratio (MR) variations during drying. The Artificial Neural Network (ANN) method was applied to predict MR. The IRCM method provided shorter drying time (reduce percentage = 71%) than IRC (41%) and microwave (69%) methods. The Deff of MIC drying (6.85 × 10−11–4.32 × 10−10 m2/s) was found to be more than the observed in IRC (1.32 × 10−10–1.87 × 10−10 m2/s) and IRCM methods (1.58 × 10−11–2.31 × 10−11 m2/s). SEC decreased during drying. Microwave drying had the lowest SEC (0.457 MJ/kg) compared to other drying methods (with mean 28 MJ/kg). Aghbashlo’s model was found to be the best for MR prediction. According to the ANN results, the highest determination coefficient (R2) values for MR prediction in IRC, IRCM and MIC drying methods were 0.9993, 0.9995 and 0.9990, respectively. The HRY (from 60.2 to 74.07%) and the color value (from 18.08 to 19.63) increased with the drying process severity, thereby decreasing the lightness (from 57.74 to 62.17). The results of this research can be recommended for the selection of the best dryer for parboiled paddy. Best drying conditions in the study is related to the lowest dryer SEC and sample color value and the highest HRY and sample lightness.

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Multi-stage intermittent drying of rough rice in terms of tempering and stress cracking indices and moisture gradients interpretation

Cited by 41 publications

References 25 publications

Numerical simulation of vitamin C degradation during dehydration process of fresh tomatoes

Numerical simulation of vitamin C degradation during dehydration process of fresh tomatoes

Early identification of Aspergillus spp. contamination in milled rice by E‐nose combined with chemometrics

Parboiled Paddy Drying with Different Dryers: Thermodynamic and Quality Properties, Mathematical Modeling Using ANNs Assessment

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