Abstrak. Buah jeruk mudah mengalami penurunan mutu selama penyimpanan. Pengetahuan tentang perubahan mutu jeruk perlu diketahui karena menjadi faktor yang mempengaruhi proses penanganan selanjutnya. Penelitian ini bertujuan untuk mengidentifikasi perubahan karakteristik fisik, mekanik, kimia dan optik buah jeruk selama penyimpanan pada suhu ruang serta mengetahui korelasi kualitas buah jeruk dengan fitur citranya menggunakan analisis regresi. Sebanyak 135 sampel jeruk disimpan pada suhu ruang (25-27 oC) selama 40 hari. Buah jeruk ditangkap citranya menggunakan webcam kemudian diukur karakteristik fisik (bobot dan diameter), mekanik (kekerasan dan koefisien gesek), dan kimia (total padatan terlarut). Fitur citra yang dianalisis adalah area citra biner, warna RGB, warna HSV, warna CIE-Lab, warna abu-abu dan fitur tekstur. Hasil penelitian menunjukkan bahwa area citra biner memiliki korelasi yang kuat terhadap bobot dan diameter buah jeruk dengan koefisien determinasi (R2) masing-masing sebesar 0,8285 dan 0,8282. Kekerasan dan koefisien gesek statis buah jeruk mempunyai korelasi yang rendah terhadap fitur citra abu-abu. Total padatan terlarut jeruk yang disimpan pada suhu ruang mempunyai korelasi yang cukup kuat dengan rata-rata nilai Hue dengan R2= 0,7473 dan rata-rata nilai kroma a* dengan R2= 0,7029. Analisis warna dengan teknik pengolahan citra dapat diaplikasikan untuk menduga beberapa karakteristik mutu buah jeruk.Regression Analysis for Orange Quality Evaluation during Storage Based on Image Color FeaturesAbstract. Orange fruits can easily deteriorate during storage. Information about quality changes of orange fruits is essential to figure out since it can be a key factor that will affect further fruit handlings. This research aimed to identify the changes of physical, mechanical, chemical and optical properties of orange during storage at room temperature as well as to discover the correlation between orange quality and image features by means of regression analysis. A sample size of 135 oranges was stored in a room temperature (25-27 oC) for 40 days. Each orange was captured its image using a webcam and was subsequently measured its physical, mechanical and chemical characteristics, i.e. weight, diameter, hardness, static friction coefficient, and total soluble solids (TSS). Several image features, i.e. binary area, RGB color, HSV color, CIE-Lab color, gray value, and texture features, were then measured. The results showed that there was a high correlation between fruit weight and diameter and binary area with R2 = 0.8285 and R2 = 0.8282, respectively. On the other hand, color values had low correlation with fruit hardness and static friction coefficient. Likewise the physical properties, the chemical properties of orange fruit had a relatively strong correlation with image color. Based on the experiments, the R2 values of the correlation between hue value of HSV color model and a* component of CIE-Lab color and orange TSS were 0.7473 and 0.7029, respectively. Color analysis with image processing technique can be applied to estimate several orange properties.
This research aimed to develop image processing program to extract several features of Siamese orange images and to determine the appropriate key features of the orange images which were highly correlated with physical and chemical characteristics of Siamese orange. A sample size of 210 oranges was stored in a room temperature (25-27 °C) and cold temperature (8-10 °C) for 30 days. All oranges were captured their images using a webcam and were subsequently measured their physical and chemical characteristics, i.e. weight, hardness, and total soluble solids (TSS). The visual parameters of images measured were binary area in pixels, RGB color, HSV color, CIE-Lab color and gray value. The results showed that there was a high correlation between fruit weight and binary area in both room and cold temperature storage with r2= 0.8197 and r2= 0.8291, respectively. On the other hand, color values cannot be used to estimate fruit hardness since the correlation coefficient was too small. The highest correlation coefficient between them was r= 0.114 which was achieved from the correlation between fruit hardness and hue value. The r value of TSS and some color components was nevertheless relatively strong. Based on the experiments, hue value of HSV color model and a* component of CIE-Lab color model have fairly strong correlations with TSS of oranges stored in room temperature which were indicated from the lvalue of 0.7473 and 0.7029,respectively.
This paper describes the application of Metal Oxide Semiconductor (MOS) gas sensors which are intrinsically designed to sense volatile compounds for measuring the vapor of formalin. We utilized 7
Evaluasi mutu buah jeruk secara umum masih dilakukan secara destruktif. Penelitian ini bertujuan untuk memprediksi kandungan kimia buah jeruk siam secara non-destruktif menggunakan Near Infrared Spectrometer portable dengan sensor AS7263 dan aplikasi Neural Network Ensemble (NNE) dengan genetic algorithm (GA) untuk optimasi. Keluaran dari enam channel NIRS portable digunakan sebagai input NNE. NNE yang dikembangkan terdiri atas empat buah Backpropagation Neural Network (BPNN) dengan dua buah lapisan tersembunyi dan kombinasi transfer function yang berbeda-beda. Keluaran dari keempat BPNN ini digabung untuk menghasilkan keluaran NNE yang baru dan dioptimasi menggunakan GA. Karakteristik kimia buah jeruk yang diestimasi adalah total padatan terlarut (TPT) dan vitamin C. Hasil penelitian menunjukkan bahwa akurasi estimasi NNE lebih tinggi dibandingkan akurasi sebuah BPNN tunggal. Estimasi kadar TPT buah jeruk siam menggunakan NNE berbasis GA tergolong sangat akurat dengan nilai Mean Absolut Percentage Error (MAPE) 8,04%. Adapun estimasi kadar vitamin C menggunakan NNE berbasis GA tergolong akurat dengan MAPE sebesar 11,02%. Namun demikian, hasil penelitian ini masih perlu dilanjutkan untuk mengetahui performansi alat yang dikembangkan untuk memprediksi mutu internal jeruk varietas lain yang berbeda karakteristik fisikokimianya.
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