Mass and surface modelling of green plantain banana fruit based on physical characteristics

2021 ◽  
Vol 186 ◽  
pp. 106194
Author(s):  
Meenatai G. Kamble ◽  
Anurag Singh ◽  
Vijendra Mishra ◽  
Murlidhar Meghwal ◽  
Pramod K. Prabhakar
1971 ◽  
Vol 26 (02) ◽  
pp. 211-223 ◽  
Author(s):  
Ch R. Muirhead ◽  
D. C Triantaphyllopoulos

SummaryChromatographed thrombin in the presence of both 50 Kallikrein inhibitor units of Trasylol per ml and 0.1 M E-ACA solubilized fibrin and the products of lysis possessed anticoagulant properties. The peak of the antithrombic activity coincided with the time of complete lysis of the fibrin clot, plasmin lysed fibrin exhibited the peak of its antithrombic activity much earlier. The effect of thrombin lysed fibrin on the prothrombin consumption of shed blood was found to be inhibitory.The products of the digestion of fibrin by thrombin and by plasmin, isolated at an advanced stage of proteolysis were compared by gel filtration, disc electrophoresis and DEAE cellulose chromatography. Differences in physical characteristics of these fibrin breakdown products offer evidence that they were produced by two different enzymes.


2018 ◽  
Vol 5 (2) ◽  
pp. 60-67 ◽  
Author(s):  
Dwi Yulianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah

ABSTRACT   Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%.   Keywords: banana maturity, classification, image processing     ABSTRAK   Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu  buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes  sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%.   Kata Kunci : kematangan pisang,  klasifikasi, pengolahan citra


Sign in / Sign up

Export Citation Format

Share Document