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Minarni Shiddiq
Department of Physics, Universitas Riau, Pekanbaru 28293, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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Wavelength dependence of optical electronic nose for ripeness detection of oil palm fresh fruits Ikhsan Rahman Husein; Minarni Shiddiq; Dewi Laila Sari; Annisa Putri
Science, Technology, and Communication Journal Vol. 2 No. 3 (2022): SINTECHCOM Journal (June 2022)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v2i3.212

Abstract

Electronic noses have been developed as an artificial sense to imitate the human nose based on volatile gases. They have been used in agriculture to monitor and predict fruit qualities such as ripeness and chemical contents. Electronic noses with semiconductor gas sensors have a limitation of volatile gases detected. Therefore, optical electronic noses using an output coupler become an alternative due to the wavelength dependency of the gas types. The ripeness of oil palm fresh fruit bunches (FFBs) is one of the main factors in determining the quality of crude palm oil. Electronic detection is preferable to substitute the manual methods for ripeness detection. This study built an optical electronic nose and analyzed the wavelength dependence on the detection performance. The electronic nose consisted of an infrared LED and a photodiode enclosed in a chamber, a microcontroller, and a sample chamber. We tested four infrared LEDs with 760, 780, 840, and 910 nm wavelengths. The samples were fruitlets taken from oil palm FFBs, previously categorized as unripe, ripe, and overripe. The fruits were grounded, inserted into the sample chamber, and preheated to increase the volatile gas concentration.  Trapezoid areas represented the time-varying output voltages for each LED. The results showed that overripe fruits had slightly higher trapezoid areas. LED of 840 nm wavelength obtained higher trapezoid areas. LED of 780 nm was the best candidate for the electronic nose due to linearity in increasing trapezoid areas. The results showed the potential of the optical electronic nose for oil palm fruits.
Fluorescence spectrum analysis on leaf and fruit using the ImageJ software application Defrianto Defrianto; Minarni Shiddiq; Usman Malik; Vepy Asyana
Science, Technology, and Communication Journal Vol. 3 No. 1 (2022): SINTECHCOM Journal (October 2022)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v3i1.218

Abstract

In this study, ImageJ has been used to process fluorescence spectroscopic images of spinach leaf treated with three variations of sunlight. In addition, apples and tomatoes are also used in imaging by treatment immersed in hot water, pierced, and pressed. Leaf and fruits are illuminated by laser diodes and LEDs of different wavelengths. ImageJ is used to calculate the RGB and gray values of the image with two segmentations, namely the intact image and the threshold. The results show that the thresholding method gives the best results because it automatically reduces the image background. In addition, the threshold background can also be easily set in this imaging. For the spinach leaf experiment, LED with a wavelength of 680 nm showed significant differences in each treatment of sunlight intensity. Meanwhile, in the apple and tomato experiment, the diode laser with a wavelength of 405 nm showed significant results. Both types of fruit with this puncture treatment turned out to provide higher intensity than pressed fruit.
Physical properties of oil palm fresh fruit bunch varieties Minarni Shiddiq; Yanuar Hamzah; Zulfa Nasir; Farid Amanullah; Mohammad Fisal Rabin; Vicky Vernando Dasta
Science, Technology, and Communication Journal Vol. 6 No. 1 (2025): SINTECHCOM Journal (October 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i1.336

Abstract

Identification of oil palm fresh fruit bunches (FFB) based on variety is a crucial step in sorting and grading FFBs to produce good-quality crude palm oil (CPO). Most palm oil mills receive two varieties of FFBs at the reception stations, Tenera and Dura, and only a certain percentage of the Dura variety is allowed in a transporting truck. The conventional identification is destructive, cutting several fruits off an FFB bunch and checking for fruit Mesocarp and shell thickness. The method suffers a high increase in free fatty acid (FFA) content. This study is a preliminary study using computer vision and image processing to differentiate the two varieties based on their physical properties. The samples consisted of 20 Dura and 20 Tenera FFBs, 10 unripe and 10 ripe FFBs. The FFB images were acquired for both front and back sides using a color CMOS camera. ImageJ software was used to obtain the number of outer fruits and bunch surface area, used to calculate fruitlet density. Both varieties are also compared based on mass and by red, green, and blue (RGB) intensities. The results were compared to the results measured manually. The results showed that the Tenera variety exhibited higher fruit density, fruitlet count, RGB intensity compared to the Dura variety. Both varieties have higher correlations between fruit density and their masses. These results show the potential of computer vision and image processing methods to differentiate Tenera and Dura varieties, used for sorting and grading oil palm FFBs.
Identification of tenera and dura variety of oil palm fresh fruit bunches based on RGB color and fruit firmness Melisa Zuliana; Minarni Shiddiq; Herman Syahdan; Farid Amanullah; Tiya Novita Sari; Mita Virdina; Ola Noviza; Vicky Vernando Dasta
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.381

Abstract

Sorting and grading oil palm fresh fruit bunches (FFBs) by variety in oil palm mills is destructive and inefficient, necessitating a more accurate, rapid, non-destructive approach. This preliminary study aims to develop a computer vision system to identify oil palm FFBs varieties (dura and tenera) using RGB intensities and fruit firmness levels. The study used 20 dura FFBs and 20 tenera FFBs, each with 10 ripe and 10 unripe FFBs, while fruit firmness was measured with a GY-3 needle-type penetrometer. Analysis of RGB intensities showed that ripe tenera had the highest values, while unripe dura had the lowest. In addition to RGB intensity analysis, this study also used principal component analysis (PCA) to visualize the separation patterns of varieties and ripeness levels based on RGB values. The PCA results showed that RGB intensity values clearly distinguished the dura and tenera groups in both ripe and unripe conditions. In terms of firmness, unripe fruits of both varieties had significantly higher firmness values than ripe fruits, with unripe dura showing the highest value of 12.5 kg/cm2 and ripe tenera showing the lowest value of 7.23 kg/cm2, indicating an inverse relationship between ripeness and fruit firmness. This study demonstrates that RGB intensities and fruit firmness levels can serve as potential parameters for distinguishing between the dura and tenera varieties in a computer-vision-based oil palm FFBs sorting system.
Co-Authors Annisa Putri Defrianto Defrianto Dewi Laila Sari Farid Amanullah Herman Syahdan Ikhsan Rahman Husein Melisa Zuliana Mita Virdina Mohammad Fisal Rabin Ola Noviza Tiya Novita Sari Usman Malik Vepy Asyana Vicky Vernando Dasta Yanuar Hamzah Zulfa Nasir