PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• Amirvaresi, A., Nikounezhad, N., Amirahmadi, M., Daraei, B., & Parastar, H. (2021). Comparison of near-infrared (NIR) and mid-infrared (MIR) spectroscopy based on chemometrics for saffron authentication and adulteration detection. Food Chemistry, 344, Article 128647. https://doi.org/10.1016/j.foodchem.2020.128647

• Anami, B. S., Malvade, N. N., & Palaiah, S. (2019). Automated recognition and classification of adulteration levels from bulk paddy grain samples. Information Processing in Agriculture, 6(1), 47-60. https://doi.org/10.1016/j.inpa.2018.09.001

• Azimi, S., Kaur, T., & Gandhi, T. K. (2021). A deep learning approach to measure stress level in plants due to Nitrogen deficiency. Measurement, 173, Article 108650. https://doi.org/10.1016/j.measurement.2020.108650

• Bragagnolo, L., Rezende, L. R., da Silva, R. V., & Grzybowski, J. M. V. (2021). Convolutional neural networks applied to semantic segmentation of landslide scars. CATENA, 201, Article 105189. https://doi.org/10.1016/j.catena.2021.105189

• Cancilla, J. C., Izquierdo, M., Semenikhina, A., Flores, E. G., Mejias, M. L., & Torrecilla, J. S. (2020). Exposing adulteration of Muscatel wines and assessing its distribution chain with fluorescence via intelligent and chaotic networks. Food Control, 118, Article 107428. https://doi.org/10.1016/j.foodcont.2020.107428

• Cardoso, V. G. K., & Poppi, R. J. (2021). Cleaner and faster method to detect adulteration in cassava starch using Raman spectroscopy and one-class support vector machine. Food Control, 125, Article 107917. https://doi.org/10.1016/j.foodcont.2021.107917

• Cebi, N., Yilmaz, M. T., & Sagdic, O. (2017). A rapid ATR-FTIR spectroscopic method for detection of sibutramine adulteration in tea and coffee based on hierarchical cluster and principal component analyses. Food Chemistry, 229, 517-526. https://doi.org/10.1016/j.foodchem.2017.02.072

• Combes, M. C., Joet, T., & Lashermes, P. (2018). Development of a rapid and efficient DNA-based method to detect and quantify adulterations in coffee (Arabica versus Robusta). Food Control, 88, 198-206. https://doi.org/10.1016/j.foodcont.2018.01.014

• Daniel, D., Lopes, F. S., Santos, V. B., & Lago, C. L. (2018). Detection of coffee adulteration with soybean and corn by capillary electrophoresis-tandem mass spectrometry. Food Chemistry, 243, 305-310. https://doi.org/10.1016/j.foodchem.2017.09.140

• Eltrass, A. S., Tayel, M. B., & Ammar, A. I. (2021). A new automated CNN deep learning approach for identification of ECG congestive heart failure and arrhythmia using constant-Q non-stationary Gabor transform. Biomedical Signal Processing and Control, 65, Article 102326. https://doi.org/10.1016/j.bspc.2020.102326

• Hendrawan, Y., Damayanti, R., Al-Riza, D. F., & Hermanto, B. (2021). Classification of water stress in cultured Sunagoke moss using deep learning. TELKOMNIKA, 19(5), 1594-1604. http://dx.doi.org/10.12928/telkomnika.v19i5.20063

• Hendrawan, Y., Widyaningtyas, S., & Sucipto. (2019). Computer vision for purity, phenol, and pH detection of Luwak Coffee green bean. TELKOMNIKA, 17(6), 3073-3085. http://dx.doi.org/10.12928/telkomnika.v17i6.12689

• Huang, X., Li, Z., Zou, X., Shi, J., Tahir, H. E., Xu, Y., Zhai, X., & Hu, X. (2019). A low cost smart system to analyze different types of edible Bird’s nest adulteration based on colorimetric sensor array. Journal of Food and Drug Analysis, 27(4), 876-886. https://doi.org/10.1016/j.jfda.2019.06.004

• Huitron, V. G., Borges, J. A. L., Mata, A E. R., Sosa, L. E. A., Pereda, B. R., & Rodriguez, H. (2021). Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4. Computers and Electronics in Agriculture, 181, Article 105951. https://doi.org/10.1016/j.compag.2020.105951

• Iymen, G., Tanriver, G., Hayirlioglu, Y. Z., & Ergen, O. (2020). Artificial intelligence-based identification of butter variations as a model study for detecting food adulteration. Innovative Food Science & Emerging Technologies, 66, Article 102527. https://doi.org/10.1016/j.ifset.2020.102527

• Izquierdo, M., Mejias, M. L., Flores, E. G., Cancilla, J. C., Perez, M., & Torrecilla, J. S. (2020a). Convolutional decoding of thermographic images to locate and quantify honey adulterations. Talanta, 209, Article 120500. https://doi.org/10.1016/j.talanta.2019.120500

• Izquierdo, M., Mejias, M. L., Flores, E. G., Cancilla, J. C., Santos, R. A., & Torrecilla, J. S. (2020b). Deep thermal imaging to compute the adulteration state of extra virgin olive oil. Computers and Electronics in Agriculture, 171, Article 105290. https://doi.org/10.1016/j.compag.2020.105290

• Jiang, B., He, J., Yang, S., Fu, H., Li, T., Song, H., & He, D. (2019). Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues. Artificial Intelligence in Agriculture, 1, 1-8. https://doi.org/10.1016/j.aiia.2019.02.001

• Jumhawan, U., Putri, S. P., Yusianto, Bamba, T., & Fukusaki, E. (2015). Application of gas chromatography/flame ionization detector-based metabolite fingerprinting for authentication of Asian palm civet coffee (Kopi Luwak). Journal of Bioscience and Bioengineering, 120(5), 555-561. https://doi.org/10.1016/j.jbiosc.2015.03.005

• Jumhawan, U., Putri, S. P., Yusianto, Bamba, T., & Fukusaki, E. (2016). Quantification of coffee blends for authentication of Asian palm civet coffee (Kopi Luwak) via metabolomics: A proof of concept. Journal of Bioscience and bioengineering, 122(1), 79-84. https://doi.org/10.1016/j.jbiosc.2015.12.008

• Jumhawan, U., Putri, S. P., Yusianto, Marwani, E., Bamba, T., & Fukusaki, E. (2013). Selection of discriminant markers for aunthetication of Asian palm civet coffee (Kopi Luwak): A metabolomics approach. Journal of Agricultural and Food Chemistry, 61(3), 7994-8001. https://doi.org/10.1021/jf401819s

• Kiani, S., Minaei, S., & Varnamkhasti, M. G. (2017). Integration of computer vision and electronic nose as non-destructive systems for saffron adulteration detection. Computers and Electronics in Agriculture, 141, 46-53. https://doi.org/10.1016/j.compag.2017.06.018

• Li, Q., Zeng, J., Lin, J., Zhang, J., Yao, L., Wang, S., Du, J., & Wu, Z. (2021). Mid-infrared spectra feature extraction and visualization by convolutional neural network for sugar adulteration identification of honey and real-world application. LWT, 140, Article 110856. https://doi.org/10.1016/j.lwt.2021.110856

• Lim, D. K., Long, N. P., Mo, C., Dong, Z., Cui, L., Kim, G., & Kwon, S. W. (2017). Combination of mass spectrometry-based targeted lipidomics and supervised machine learning algorithms in detecting adulterated admixtures of white rice. Food Research International, 100(1), 814-821. https://doi.org/10.1016/j.foodres.2017.08.006

• Lin, G., & Shen, W. (2018). Research on convolutional neural network based on improved Relu piecewise activation function. Procedia Computer Science, 131, 977-984. https://doi.org/10.1016/j.procs.2018.04.239

• Lin, L., Xu, M., Ma, L., Zeng, J., Zhang, F., Qiao, Y., & Wu, Z. (2020). A rapid analysis method of safflower (Carthamus tinctorius L.) using combination of computer vision and near-infrared. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 236, Article 118360. https://doi.org/10.1016/j.saa.2020.118360

• Liu, Y., Yao, L., Xia, Z., Gao, Y., & Gong, Z. (2021). Geographical discrimination and adulteration analysis for edible oils using two-dimensional correlation spectroscopy and convolutional neural networks (CNNs). Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 246, Article 118973. https://doi.org/10.1016/j.saa.2020.118973

• Lopetcharat, K., Kulapichitr, F., Suppavorasatit, I., Chodjarusawad, T., Phatthara-aneksin, A., Pratontep, S., & Borompichaichartkul, C. (2016). Relationship between overall difference decision and electronic tongue: Discrimination of civet coffee. Journal of Food Engineering, 180, 60-68. https://doi.org/10.1016/j.jfoodeng.2016.02.011

• Lopez, S. P., Calabuig, A. M. P., Cancilla, J. C., Lozano, M. A., Rodrigo, C., Mena, M. L., & Torrecilla, J. S. (2021). Deep transfer learning to verify quality and safety of ground coffee. Food Control, 122, Article 107801. https://doi.org/10.1016/j.foodcont.2020.107801

• Manninen, H., Ramlal, C. J., Singh, A., Rocke, S., Kilter, J., & Landsberg, M. (2021). Toward automatic condition assessment of high-voltage transmission infrastructure using deep learning techniques. International Journal of Electrical Power & Energy Systems, 128, Article 106726. https://doi.org/10.1016/j.ijepes.2020.106726

• Marcone, M. F. (2004). Composition and properties of Indonesian palm civet coffee (Kopi Luwak) and Ethiopian civet coffee. Food Research International, 37(9), 901-912. https://doi.org/10.1016/j.foodres.2004.05.008

• Medus, L. D., Saban, M., Villora, J. V. F., mompean, M. B., & Munoz, A. R. (2021). Hyperspectral image classification using CNN: Application to industrial food packaging. Food Control, 125, Article 107962. https://doi.org/10.1016/j.foodcont.2021.107962

• Mkonyi, L., Rubanga, D., Richard, M., Zekeya, N., Sawahiko, S., Maiseli, B., & Machuve, D. (2020). Early identification of Tuta absoluta in tomato plants using deep learning. Scientific African, 10, Article e00590. https://doi.org/10.1016/j.sciaf.2020.e00590

• Muzaifa, M., Hasni, D., & Syarifudin. (2019). What is Kopi Luwak? A literature review on production, quality and problems. IOP Conf. Series: Earth and Environmental Science, 365, Article 012041. doi:10.1088/1755-1315/365/1/012041

• Nayak, S. R., Nayak, D. R., Sinha, U., Arora, V., & Pachori, R. B. (2021). Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study. Biomedical Signal Processing and Control, 64, Article 102365. https://doi.org/10.1016/j.bspc.2020.102365

• Nunez, N., Saurina, J., & Nunez, O. (2021). Non-targeted HPLC-FLD fingerprinting for the detection and quantitation of adulterated coffee samples by chemometrics. Food Control, 124, Article 107912. https://doi.org/10.1016/j.foodcont.2021.107912

• Ongo, E., Falasconi, M., Sberveglieri, G., Antonelli, A., Montevecchi, G., Scerveglieri, V., Concina, I., & Sevilla, F. (2012). Chemometric discrimination of Philippine civet coffee using electronic nose and gas chromatography mass spectrometry. Procedia Engineering, 47, 977-980. https://doi.org/10.1016/j.proeng.2012.09.310

• Pauli, E. D., Barbieri, F., Garcia, P. S., Madeira, T. B., Junior, V. R. A., Scarminio, I. S., Camara, C. A. P., & Nixdorf, S. L. (2014). Detection of ground roasted coffee adulteration with roasted soybean and wheat. Food Research International. 61, 112-119. https://doi.org/10.1016/j.foodres.2014.02.032

• Raikar, M. M., Meena, S. M., Kuchanur, C., Girraddi, S., & Benagi, P. (2020). Classification and grading of okra-ladies finger using deep learning. Procedia Computer Science, 171, 2380-2389. https://doi.org/10.1016/j.procs.2020.04.258

• Reile, C. G., Rodriguez, M. S., Fernandes, D. D. S., Gomes, A. A., Diniz, P. H. G. D., & Anibal, C. V. D. (2020). Qualitative and quantitative analysis based on digital images to determine the adulteration of ketchup samples with Sudan I dye. Food Chemistry, 328, Article 127101. https://doi.org/10.1016/j.foodchem.2020.127101

• Ruuska, S., Hamalainen, W., Kajava, S., Mughal, M., Matilainen, P., & Mononen, J. (2018). Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle. Behavioral Processes, 148, 56-62. https://doi.org/10.1016/j.beproc.2018.01.004

• Sezer, B., Apaydin, H., Bilge, G., & Boyaci, I. H. (2018). Coffee arabica adulteration: Detection of wheat, corn and chickpea. Food Chemistry, 264, 142-148. https://doi.org/10.1016/j.foodchem.2018.05.037

• Silva, A. F. S., & Rocha, F. R. P. (2020). A novel approach to detect milk adulteration based on the determination of protein content by smartphone-based digital image colorimetry. Food Control, 115, Article 107299. https://doi.org/10.1016/j.foodcont.2020.107299

• Simon, P., & Uma, V. (2020). Deep learning based feature extraction for texture classification. Procedia Computer Science, 171, 1680-1687. https://doi.org/10.1016/j.procs.2020.04.180

• Skowron, M. J., Franskowski, R., & Grzeskowiak, A.Z. (2020). Comparison of methylxantines, trigonelline, nicotinic acid and nicotinamide contents in brews of green and processed Arabica and Robusta coffee beans - Influence of steaming, decaffeination and roasting processes on coffee beans. LWT, 125, Article 109344. https://doi.org/10.1016/j.lwt.2020.109344

• Song, W., Yun, Y. H., Wang, H., Hou, Z., & Wang, Z. (2021). Smartphone detection of minced beef adulteration. Microchemical Journal, 164, Article 106088. https://doi.org/10.1016/j.microc.2021.106088

• Suhandy, D., & Yulia, M. (2017). The use of partial least square regression and spectral data in UV-visible region for quantification of adulteration in Indonesian palm civet coffee. International Journal of Food Science, 2017, Article 6274178. https://doi.org/10.1155/2017/6274178

• Takase, T. (2021). Dynamic batch size tuning based on stopping criterion for neural network training. Neurocomputing, 429, 1-11. https://doi.org/10.1016/j.neucom.2020.11.054

• Thenmozhi, K., & Redy, U. S. (2019). Crop pest classification based on deep convolutional neural network and transfer learning. Computers and Electronics in Agriculture, 164, Article 104906. https://doi.org/10.1016/j.compag.2019.104906

• Tian, C., Xu, Y., & Zuo, W. (2020). Image denoising using deep CNN with batch renormalization. Neural Networks, 121, 461-473. https://doi.org/10.1016/j.neunet.2019.08.022

• Ucar, F., & Korkmaz, D. (2020). COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical Hypotheses, 140, Article 109761. https://doi.org/10.1016/j.mehy.2020.109761

• Wojcik, S., & Jakubowska, M. (2021). Deep neural networks in profiling of apple juice adulteration based on voltammetric signal of the iridium quadruple-disk electrode. Chemometrics and Intelligent Laboratory Systems, 209, Article 104246. https://doi.org/10.1016/j.chemolab.2021.104246

• Yu, H., Yang, L. T., Zhang, Q., Armstrong, D., & Deen, M. J. (2021). Convolutional neural networks for medical image analysis: State-of-the-art, comparisons, improvement and perspectives. Neurocomputing, 444, 92-110. https://doi.org/10.1016/j.neucom.2020.04.157

• Yulia, M., & Suhandy, D. (2017). Indonesian palm civet coffee discrimination using UV-visible spectroscopy and several chemometrics methods. Journal of Physics: Conference Series, 835, Article 012010. https://doi.org/10.1088/1742-6596/835/1/012010