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All Journal Journal of Tropical Life Science : International Journal of Theoretical, Experimental, and Applied Life Sciences Pelita Perkebunan
Misnawi Jati
Indonesian Coffee and Cocoa Research Institute
Agriculture, Biological Sciences & Forestry Environmental Science

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Cocoa extract has activity on selectively killing of breast cancer cells line tunjung-sari, ariza budi; Mahriani, Mahriani; Tiningrum, Gusti Agung Perias; Wahyudi, Teguh; Jati, Misnawi
Journal of Tropical Life Science Vol 5, No 3 (2015)
Publisher : Journal of Tropical Life Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/jtls.05.03.04

Abstract

Effect of the cocoa crude extract on mortality of breast cancer cell lines i.e. MCF-7, T47D and normal cell (Vero), was observed. Crude cocoa extract prepared from a freshly dried cocoa bean that was containing 14% catechin and 0.6% caffeine. Catechin and caffeine content were modulated to 2-folds (28% catechin or 1.2% caffeine) and 3-folds (42% catechin or 1.8% caffeine) by adding pure compounds. Extracts were dissolved in dimethylsulfoxide (DMSO) at concentrations ranging from 200 to 1600 μg/ml. The positive control was doxorubicin (0.5-16 μg/ml in DMSO). Cell lines (MCF-7, T47D, and Vero) were incubated in test sample for 24h at 37°, prior to 3-(4,4-dimetylthiazole-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The absorbance of each well was measured at 550 nm, and lethal concentration (LC50) was calculated. The cocoa extract induced mortality of breast cancer cell lines but not in Vero cells. The effect on MCF-7 was greater than on T47D, given the LC50 was 1236 μg/ml (MCF-7) and 1893 μg/ml (T47D). Cytotoxic potential of cocoa extract was much lower than doxorubicin whose LC50 was0,777 μg/ml (MCF-7) and 0,082 μg/ml (T47D). Increasing catechin content to 2-folds did not significantly affect LC50 value, but 3-folds catechin content reduced LC50 to 1021 μg/ml. Meanwhile increasing caffeine content to 2-folds significantly reduced LC50 to 750 μg/ml, however, 3-fold content resulted in slightly higher LC50 at 780 μg/ml. This indicates that cocoa extract have anti-cancer potential, and purification may improve this property .
Color-based Classification of Dried Cocoa Beans from Various Origins of Indonesia by Image Analysis Using AlexNet and ResNet Architecture-Convolutional Neural Networks Kristianingsih, Wahyu; Dwi Argo, Bambang; Jati, Misnawi; Ariefandie Febrianto, Noor; Hendrawan, Yusuf; Bagus Hermanto, Mochamad; Rahmatullah, Bagus
Pelita Perkebunan (a Coffee and Cocoa Research Journal) Vol. 40 No. 3 (2024)
Publisher : Indonesian Coffee and Cocoa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/iccri.jur.pelitaperkebunan.v40i3.638

Abstract

Cocoa plant is widely cultivated in Indonesia and spread across various regions. Diversity in geographical conditions has been known to significantly affect the quality of cocoa beans. Practically, cocoa beans are often mixed without considering the variation in the quality and its origin. This resulted in reduced global quality and product inconsistency. Improved recognition and classification methods are needed to solve those problems. Non-destructive classification methods can be used to provide a more efficient classification process. The use of artificial intelligence with computer-based deep learning methods was used in this study. Beans samples of various origins (Aceh, Bali, Banten, Yogyakarta, East Kalimantan, West Sulawesi, and West Sumatera) were evaluated. From thecollected samples, 9100 images were then taken for data processing. Data preprocessing included denoising of the background image, cropping, resizing andchanging the storage extension through the training-validation stage and the testing process. AlexNet and ResNet architectures on a Convolutional NeuralNetwork were used for classification. The results showed that the average accuracy of cocoa image classification based on color identification by computer machines using Alexnet and ResNet was high (99.91% and 99.99%, respectively). This method can be applied to provide more efficient color-based cocoa bean classification for industrial purposes.
Co-Authors Ariefandie Febrianto, Noor ariza budi tunjung-sari, ariza budi Bambang Dwi Argo Gusti Agung Perias Tiningrum, Gusti Agung Perias Kristianingsih, Wahyu Mahriani Mahriani Mochamad Bagus Hermanto Rahmatullah, Bagus Teguh Wahyudi Yusuf Hendrawan