scholarly journals Optimizing the tray dryer temperature and time of white corn flour culture

Food Research ◽  
2021 ◽  
Vol 5 (5) ◽  
pp. 95-104
Author(s):  
D. Hunaefi ◽  
Rahmawati R. ◽  
D. Saputra ◽  
R.R. Maulani ◽  
T. Muhandri
Keyword(s):  
Response Surface Methodology ◽  
Aspergillus Niger ◽  
Water Content ◽  
Response Surface ◽  
Water Activity ◽  
Penicillium Citrinum ◽  
Drying Process ◽  
Corn Flour ◽  
Acremonium Strictum ◽  
Candida Famata

This research aimed to optimize the tray dryer temperature and time of white corn flour culture by Response Surface Methodology (RSM). There were two cultures used in this research, namely Amylolytic Culture (AC) and Complete Culture (CC). AC consisted of Penicillium citrinum, Aspergillus niger, Acremonium strictum, and Candida famata, while CC consisted of Penicillium chrysogenum, Penicillium citrinum, Aspergillus niger, Rhizopus stolonifer, Rhizopus oryzae, Fusarium oxysporum, Acremonium strictum, Candida famata, Kodamaea ohmeri and Candida krusei/incospicua. The independent variables in this study were drying temperature and time, where the quality indicators used were total viability of mold and yeast, water content, water activity, and pH. This research used a factor response surface methodology. Data were analyzed by ANOVA with an α level of 95%. The result of this research showed that the optimum drying process for AC starter was 40°C for 10 hrs, with characteristic response viability 8.8×107 CFU/g, water activity 0.43, water content 8.90%, and pH 4.05. CC starter showed an optimum drying process at 49°C for 4.5 hrs, with characteristic response viability 4.9×107 CFU/g, water activity 0.49, water content 7.02%, and pH 3.95. The optimum tray dryer temperatures and times were achieved for AC and CC starters.

scholarly journals Optimasi Suhu Pengeringan dan Ketebalan Irisan pada Proses Pengeringan Jahe Merah (Zingiber officinale var. rubrum) dengan Response Surface Methodology (RSM)

2021 ◽  
Vol 9 (1) ◽  
pp. 66
Author(s):  
Desak Agung Hepi ◽  
Ni Luh Yulianti ◽  
Yohanes Setiyo
Keyword(s):  
Response Surface Methodology ◽  
Water Content ◽  
Response Surface ◽  
Water Activity ◽  
Ash Content ◽  
Heat Energy ◽  
Slice Thickness ◽  
Drying Process ◽  
Drying Temperature ◽  
Evaporation Heat

Suhu pengeringan dan ketebalan irisan merupakan dua hal yang mempengaruhi proses pengeringan jahe merah. Penelitian dirancang dengan tujuan untuk mendapatkan kombinasi suhu pengeringan dan ketebalan irisan optimum melalui Response Surface Methodology (RSM), serta memperoleh model matematika untuk memprediksi kadar air, aktivitas air, kadar abu dan energi panas penguapan. Pengujian dilakukan untuk mengetahui pengaruh suhu dan ketebalan irisan terhadap respon kadar air, aktivitas air, kadar abu dan energi panas penguapan. Pengolahan data menggunakan aplikasi Design Expert ® 12. Hasil penelitian menunjukkan model linier untuk memprediksi respon kadar air dan kadar abu. Model kuadratik untuk memprediksi respon aktivitas air dan energi panas penguapan. Hasil verifikasi model menunjukkan kombinasi suhu pengeringan dan ketebalan irisan optimum terpilih adalah 67,30C dan 3 mm. Proses pengeringan dengan kombinasi suhu pengeringan dan ketebalan irisan optimum menghasilkan nilai aktual aktivitas air 0,393 aw, kadar air 9,877%, kadar abu 3,513% dan energi panas penguapan sebesar 68,354 kJ/Jam. Respon dari kombinasi suhu pengeringan dan ketebalan irisan optimum terpilih dapat memenuhi keinginan sesuai kriteria dengan nilai desirability 81,3%.   Drying temperature and thickness of slices are two things that affect the drying process of red ginger. The research was designed with the aim of obtaining a combination of drying temperature and optimum slice thickness through the Response Surface Methodology (RSM), as well as obtaining mathematical models to predict water content, water activity, ash content and evaporation heat energy. Testing was conducted to determine the effect of the temperature and thickness of the slices on the response of water content, water activity, ash content and evaporation heat energy. Data processing using design expert application ® 12. The results showed linear models to predict the response of water levels and ash levels. Quadratic models to predict the response of water activity and evaporation heat energy. Model verification results show the combination of drying temperature and optimum slice thickness selected is 67.30C and 3 mm. The drying process with a combination of drying temperature and optimum slice thickness resulted in an actual water activity value of 0.393 aw, water content of 9.877%, ash content of 3.513% and evaporation heat energy of 68,354 kJ/h. The response of the combination drying temperature and thickness of selected optimum slices can meet the wishes according to the criteria with a desirability value of 81.3%.

scholarly journals Optimization of temperature and drying time of indigenous cocktail yeast mold culture using response surface methodology (RSM)

Food Research ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 389-395
Author(s):  
Rahmawati Rahmawati ◽  
D. Hunaefi ◽  
I. Basriman ◽  
Dede Saputra ◽  
A.A. Apriliani ◽  
...  
Keyword(s):  
Response Surface ◽  
Drying Process ◽  
Drying Time ◽  
Acremonium Strictum ◽  
Drying Temperature ◽  
Response Surface Methods ◽  
Candida Famata ◽  
Optimum Formula ◽  
Mold And Yeast ◽  
Total Yeast

The study was conducted to obtain an optimal combination of time and temperature of the drying process of indigenous cocktail yeast mold culture using RSM. The cocktail yeast mold culture was dried using an oven. The cocktail cultures contain Penicillium citrinum, Aspergillus niger, Acremonium strictum, and Candida famata, namely AC (Amylolytic Culture). The Response Surface Methods (RSM) with Design-Expert® 7.00 software, namely Mixture design with D-optimal was performed. The drying time was between 24- 48 hrs and the drying temperature was between 40-50oC. The total of 16 formulas of the combination of drying time and temperature for processing the dried cultures were produced by RSM. The response chosen was total viability of mold and yeast, water content, water activity, and pH. The result of optimization and verification was obtained by the model: pH (AC) = -0.058A - 1.56 x 10-003B + 7.13, where A = drying temperature ( oC), B = drying time (hr). The AC optimization was achieved at a combination of drying temperatures and time of 50oC for 48 hrs. Desirability values were 0.729. The optimum formula for AC has viability of total yeast mold of 7.39 x 106 CFU/g, moisture content of 5.62%, aw 0.303, and pH 4.18.

scholarly journals Optimization of Drying Process for Squid-Laver Snack by a Combined Method of Fuzzy Synthetic and Response Surface Methodology

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Chang-Cheng Zhao ◽  
Gui-Hun Jiang ◽  
Jong-Bang Eun
Keyword(s):  
Response Surface Methodology ◽  
Moisture Content ◽  
Response Surface ◽  
Water Activity ◽  
Shear Force ◽  
Process Conditions ◽  
Drying Process ◽  
Drying Temperature ◽  
Overall Acceptability ◽  
Drying Conditions

The aim of this study was to investigate the effects of drying temperature (50–70°C) and drying time (3–5 h) on the physical properties and quality of squid-laver snack (SLS) using response surface methodology combined with a synthetic evaluation method to optimize the drying process conditions. Moisture content, water activity, color (L⁎, a⁎, b⁎), shear force, and overall acceptability were evaluated as responses. Increased drying times and higher temperatures significantly reduced the moisture content and water activity of SLS from 9.07% to 4.76% and 0.136 to 0.056, respectively (p<0.05). There was no significant difference in the L⁎ and a⁎ values under different drying conditions. The quadratic effect of temperature and time was observed for the b⁎ value and overall acceptability of SLS. For shear force, a quadratic and interaction term for drying temperature and time on shear force was observed. In conclusion, the recommended optimal hot air-drying conditions for SLS are temperature and time of 70°C and 3 h, respectively.

scholarly journals Corn Wingko Processing Optimization Using Response Surface Methodology

2020 ◽  
Vol 1 (2) ◽  
pp. 28-33
Author(s):  
Jariyah Jariyah ◽  
Wicaksono L A ◽  
Septi N D
Keyword(s):  
Response Surface Methodology ◽  
Moisture Content ◽  
Water Content ◽  
Response Surface ◽  
Starch Content ◽  
Rice Flour ◽  
Crude Fiber ◽  
Corn Flour ◽  
Glutinous Rice ◽  
Tapioca Flour

Wingko is semi-wet food from Indonesia made from grated coconut, glutinous rice flour, sugar and other additives that form a distinctive taste and aroma. Utilization of corn in the form of flour aims to reduce the use of glutinous rice flour as the main composition of wingko because in addition to being an economic commodity, corn contains fiber which is useful for digestion. This study aims to determine the effect of the proportion of corn flour, sticky rice flour and tapioca on the physical and chemical properties of wingko. This study uses the Response Surface Methodology (RSM) method with the experimental design of the Central Composite Design (CCD) using 3 factors of 5 levels, namely the proportion of corn flour (43.18; 50; 60; 70; 76.82) (w / w), flour sticky rice (23,18; 30; 40; 50; 56,82) (w / w), tapioca flour (6,59; 10; 15; 20; 23,41) (w / w) are then processed using Design Expert software 7.1.5. The parameters observed were corn flour including water content, ash, starch, amylose, crude fiber and yield, in glutinous rice flour and tapioca including starch and amylose content, while in wingko products included water content, aw, starch, crude fiber and texture. The results showed that corn flour had a moisture content of 7.12%, ash 0.34%, starch 84.72%, amylose 21.22% of the total ingredients, crude fiber 1.15% and yield of 79.8%. Glutinous rice flour has a starch content of 81.98% and amylose 1.02% of the total ingredients while tapioca flour has a starch content of 78.71% and amylose 20.63% of the total ingredients. The optimum conditions of wingko products were obtained in the proportion of corn flour: sticky rice: tapioca 50:38:20 (w / w) with 23.46% moisture content, aw 0.881, starch content 50.87%, crude fiber 2.78% and texture 0.01194 mm / gs had desirability 0.831.

Demulsification of a Water-in-crude Oil Emulsion with a Corn Oil BasedDemulsifier using the Response Surface Methodology: Modelling and Optimization

2021 ◽  
Vol 14 ◽  
Author(s):  
Abed Saad ◽  
Nour Abdurahman ◽  
Rosli Mohd Yunus
Keyword(s):  
Response Surface Methodology ◽  
Crude Oil ◽  
Water Content ◽  
Response Surface ◽  
Separation Efficiency ◽  
Corn Oil ◽  
Oil Emulsion ◽  
Optimal Values ◽  
Input Variables ◽  
Oil Emulsions

: In this study, the Sany-glass test was used to evaluate the performance of a new surfactant prepared from corn oil as a demulsifier for crude oil emulsions. Central composite design (CCD), based on the response surface methodology (RSM), was used to investigate the effect of four variables, including demulsifier dosage, water content, temperature, and pH, on the efficiency of water removal from the emulsion. As well, analysis of variance was applied to examine the precision of the CCD mathematical model. The results indicate that demulsifier dose and emulsion pH are two significant parameters determining demulsification. The maximum separation efficiency of 96% was attained at an alkaline pH and with 3500 ppm demulsifier. According to the RSM analysis, the optimal values for the input variables are 40% water content, 3500 ppm demulsifier, 60 °C, and pH 8.

scholarly journals Modeling and Optimization for Konjac Vacuum Drying Based on Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1430
Author(s):  
Zhiheng Zeng ◽  
Ming Chen ◽  
Xiaoming Wang ◽  
Weibin Wu ◽  
Zefeng Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Vacuum Drying ◽  
Ann Model ◽  
Drying Process ◽  
Drying Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

To reveal quality change rules and establish the predicting model of konjac vacuum drying, a response surface methodology was adopted to optimize and analyze the vacuum drying process, while an artificial neural network (ANN) was applied to model the drying process and compare with the response surface methodology (RSM) model. The different material thickness (MT) of konjac samples (2, 4 and 6mm) were dehydrated at temperatures (DT) of 50, 60 and 70 °C with vacuum degrees (DV) of 0.04, 0.05 and 0.06 MPa, followed by Box–Behnken design. Dehydrated samples were analyzed for drying time (t), konjac glucomannan content (KGM) and whiteness index (WI). The results showed that the DT and MT should be, respectively, under 60 °C and 4 mm for quality and efficiency purposes. Optimal conditions were found to be: DT of 60.34 °C; DV of 0.06 MPa and MT of 2 mm, and the corresponding responses t, KGM and WI were 5 h, 61.96% and 82, respectively. Moreover, a 3-10-3 ANN model was established to compare with three second order polynomial models established by the RSM, the result showed that the RSM models were superior in predicting capacity (R2 > 0.928; MSE < 1.46; MAE < 1.04; RMSE < 1.21) than the ANN model. The main results may provide some theoretical and technical basis for the konjac vacuum drying and the designing of related equipment.

Optimization of Aflatoxin Production of Aspergillus flavus on Peanuts

2014 ◽  
Vol 887-888 ◽  
pp. 605-609
Author(s):  
Chu Shu Zhang ◽  
Jie Sun ◽  
Li Na Yu ◽  
Jie Bi ◽  
Jian Xiong Feng ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Relative Humidity ◽  
Linear Relationship ◽  
Water Content ◽  
Response Surface ◽  
Aspergillus Flavus ◽  
Previous Experiment ◽  
Aflatoxin Production ◽  
Influence Of Water ◽  
Production Conditions

To optimize aflatoxin production of Aspergillus flavus on peanut, the response surface methodology were applied based on previous experiment. The results showed that the influence of water content of peanuts,time and relative humidity were obvious and were not linear relationship. The optimal aflatoxin production conditions were that water content of peanuts was12%, the time was 7 days, relative humidity was 80%.

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