Identifikasi Kualitas Beras Putih (Oryza sativa L.) Berdasarkan Kandungan Amilosa dan Amilopektin di Pasar Tradisional dan “Selepan” Kota Salatiga

Sari, Andriyani Rosita; Martono, Yohanes; Rondonuwu, Ferdy S.

doi:10.30599/jti.v12i1.599

Cited by 16 publications

(16 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…If grain is stored for a long period of time it will be easy to mold and result in low rice yields (Andika et al, 2021). Milling time affects the amount of head of rice, when the milling time increases, the percentage of head of rice decreases and the percentage of the amount of bran increases (Sari et al, 2020). Grain grain mass is used to determine the effective diameter that can be used in the theory of seed volume estimation (David & Kartinaty, 2019).…”

Section: Discussionmentioning

confidence: 99%

Pragilling Cooling Technique on Quality Characteristics of Milled Rice based on Cooling Temperature and Duration

Putra

Cahyono

2022

agaricus

View full text Add to dashboard Cite

This study aims to determine the effect of temperature, duration of cooling, and physical quality of rice that is influenced by pragiling refrigeration technique. The resultsshowed a decrease of moisture content in the process of drying of impari varieties (22.4% -13.4%) and cisadane varieties (25% -14.3%). Dry shrinkage occurs due to the amount of water that evaporates during the drying process on the 16 kg varieties of impari and cisadane varieties of 21 kg. In addition to the varieties of grain milled rice varieties also affected by the characteristics of grain quality, the varieties of impari obtained average (52.40 ± 3.23%) while in cisadane varieties obtained average (54.54 ± 1.2%). Visual appearance, color degree measurement (Lab *) of milled rice with pragiling and non cooling cooling technique has no significant effect on the given treatment. In the analysis of quality characteristics of rice with pragiling cooling technique, the quality of rice produced is better than non-coolant treatment this is seen from the percentage of head grains, broken grains and groats that produced reached 87.68%. This is in accordance with the analysis data of variance where temperature has significant effect (0.00 <0.05) on the four temperatures used. The cooling time had a significant effect on the physical quality of rice produced with significance value (0.00 <0.05) at three time levels used.

show abstract

Section: Discussionmentioning

confidence: 99%

Pragilling Cooling Technique on Quality Characteristics of Milled Rice based on Cooling Temperature and Duration

Putra

Cahyono

2022

agaricus

View full text Add to dashboard Cite

show abstract

“…Beras merupakan sumber energi yang memiliki kandungan karbohidrat tinggi. Di Indonesia, terdapat berbagai macam beras, antara lain beras putih (Oryza sativa L.), beras hitam (O. sativa L. indica), dan beras merah (O. rufipogon G.) (Sari et al, 2020). Pada tahun 2020 jumlah kebutuhan konsumsi beras penduduk Indonesia sekitar 25.348.135 ton/tahun (Hariyanto et al, 2021) dengan total produksi beras pada tahun 2021 adalah 55,27 juta ton (BPS, 2021).…”

Section: Pendahuluanunclassified

PERTUMBUHAN POPULASI Rhyzopertha dominica (F.) DAN Oryzaephilus surinamensis (L.) DALAM ASOSIASI PADA BERAS MERAH, PUTIH, DAN HITAM

Ramadhan,

Astuti

2023

Jurnal HPT

View full text Add to dashboard Cite

Penelitian ini bertujuan untuk mengkaji pertumbuhan dan laju pertumbuhan Rhyzopertha dominica (Coleoptera: Bostrichidae) dan Oryzaephilus surinamensis (Coleoptera: Silvanidae) pada beras merah, putih, dan hitam, serta penurunan berat pakan setelah adanya infestasi hama tersebut. Penelitian dilakukan pada bulan Maret hingga September 2022 di Laboratorium Hama Tumbuhan, Departemen Hama dan Penyakit Tumbuhan, Fakultas Pertanian, Universitas Brawijaya. Penelitian ini terdiri dari dua faktor yaitu (1) spesies serangga hama (R. dominica, O. surinamensis, dan asosiasi) dan (2) jenis pakan (beras merah, putih, dan hitam). Sehingga, penelitian ini memiliki sembilan kombinasi dan diulang sebanyak tiga kali menggunakan Rancangan Acak Lengkap (RAL). Populasi imago baru R. dominica dan O. surinamensis diamati pada infestasi tunggal maupun asosiasi, serta penurunan berat pakan pada beras merah, putih, dan hitam. Data dianalisis menggunakan perangkat lunak R statistic versi 4.2.0. Hasil penelitian menunjukkan bahwa tidak terdapat interaksi antara spesies hama dan jenis pakan terhadap pertumbuhan populasi dan laju pertumbuhan populasi R. dominica dan O. surinamensis, serta penurunan berat pakan. Pertumbuhan populasi dan laju pertumbuhan populasi R. dominica dengan infestasi tunggal pada beras merah lebih tinggi dibandingkan dengan beras putih dan hitam. Pertumbuhan populasi dan laju pertumbuhan populasi O. surinamensis pada beras hitam lebih tinggi dibandingkan dengan beras merah dan putih. Penurunan berat pakan pada beras merah lebih tinggi dibandingkan beras putih dan hitam pada infestasi tunggal maupun asosiasi.

show abstract

“…There were varieties that were not significantly different from other varieties, but there were also varieties that were significantly different in both the average color intensity and amylose content. When classified based on the amylose content, the Inpari IR Nutri Zinc varieties are in the low category with a very fluffier texture, Way Apo Buru and Cibogo are in the medium category with a fluffier texture, while Mapan 05, IR-64, and Inpari 33 are included in the high category with a thick texture (Syamsiah & Masliah, 2019;Sari et al, 2020). The results of the correlation analysis in Table 4 show the correlation values of 0.63, 0.63, and 0.67 for the intensity of the red, green, and blue colors on the amylose content of rice.…”

Section: Rice Amylose Contentmentioning

confidence: 99%

Non-Destructive Measurement of Rice Amylose Content Based on Image Processing and Artificial Neural Networks (ANN) Model

Saputra¹,

Wijayanto²,

Ristiyana³

et al. 2022

JTEP-L

View full text Add to dashboard Cite

The purpose of this study was to develop a method of measuring the amylose content of rice using image processing techniques and an Artificial Neural Network (ANN) model. The rice samples came from six varieties, namely Way Apo Buru, Mapan P05, IR-64, Cibogo, Inpari IR Nutri Zinc, and Inpari 33. The amylose content was measured by laboratory tests and the color intensity was measured based on the RGB (Red, Green, Blue). The ANN model will correlate the RGB color intensity as input with the amylose content as the output. The ANN model used is backpropagation type with 3 input layer nodes and 2 hidden layers with 3-5-5-1 architecture. Variations in the training model used are 27 variations of the activation function. The amount of data used for model training of 30 data while for validation of 12 data. The best ANN model is determined from the high value of accuracy (100%-MAPE) and the value of coefficient of determination (R2). The results showed the best network architecture on the activation function purelin-logsig-tansig. The R2 value on the best training and validation results of 0.98 and 0.66 while the accuracy values for the best training and validation results of 98.15 and 66.82. The validation results show that the developed non-destructive method can be used to quickly and accurately measure the amylose value of rice based on RGB color value. The test results show that the non-destructive method developed cannot be used to measure the amylose content of rice quickly and accurately based on the RGB color intensity, so it needs further development. Keywords: Amylose, Artificial neural networks, Image processing, Rice

show abstract

Identifikasi Kualitas Beras Putih (Oryza sativa L.) Berdasarkan Kandungan Amilosa dan Amilopektin di Pasar Tradisional dan “Selepan” Kota Salatiga

Cited by 16 publications

References 3 publications

Pragilling Cooling Technique on Quality Characteristics of Milled Rice based on Cooling Temperature and Duration

Pragilling Cooling Technique on Quality Characteristics of Milled Rice based on Cooling Temperature and Duration

PERTUMBUHAN POPULASI Rhyzopertha dominica (F.) DAN Oryzaephilus surinamensis (L.) DALAM ASOSIASI PADA BERAS MERAH, PUTIH, DAN HITAM

Non-Destructive Measurement of Rice Amylose Content Based on Image Processing and Artificial Neural Networks (ANN) Model

Contact Info

Product

Resources

About