Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends

References

• Arboleda, E. R. (2019). Comparing performances of data mining algorithms for classification of green coffee beans. International Journal of Engineering and Advanced Technology, 8(5), 1563–1567. https://www.researchgate.net/profile/Edwin-Arboleda/publication/335104787_Comparing_Performances_of_Data_Mining_Algorithms_for_Classification_of_Green_Coffee_Beans/links/5d4fa54a299bf1995b759db0/Comparing-Performances-of-Data-Mining-Algorithms-for-Classification-of-Green-Coffee-Beans.pdf
   Google Scholar
• Bai, F., Fan, M., Yang, H., & Dong, L. (2021). Image segmentation method for coal particle size distribution analysis. Particuology, 56, 163–170. https://doi.org/10.1016/j.partic.2020.10.002
   Web of Science ®Google Scholar
• Bajwa, I. S., Naweed, M., Asif, M. N., & Hyder, S. (2009). Feature based image classification by using principal component analysis. ICGST International Journal on Graphics, Vision and Image Processing (GVIP), 9(II), 11–17.
   Google Scholar
• Bargshady, G., Zhou, X., Deo, R. C., Soar, J., Whittaker, F., & Wang, H. (2020). Enhanced deep learning algorithm development to detect pain intensity from facial expression images. Expert Systems with Applications, 149, 113305. https://doi.org/10.1016/j.eswa.2020.113305
   Web of Science ®Google Scholar
• Bossard, L., Guillaumin, M., & Van Gool, L. (2014). Food-101–mining discriminative components with random forests. In European conference on computer vision, Zurich, Switzerland.
   Google Scholar
• Brosnan, T., & Sun, D.-W. (2002). Inspection and grading of agricultural and food products by computer vision systems—a review. Computers and Electronics in Agriculture, 36(2), 193–213. https://doi.org/10.1016/S0168-1699(02)00101-1
   Web of Science ®Google Scholar
• Charrad, M., Ghazzali, N., Boiteux, V., & Niknafs, A. (2014). NbClust an R package for determining the relevant number of clusters in a data set. Journal of Statistical Software, 61(6). https://doi.org/10.18637/jss.v061.i06
   Web of Science ®Google Scholar
• Chávez, D. W. H., Ascheri, J. L. R., Carvalho, C. W. P., Godoy, R. L. O., & Pacheco, S. (2017). Sorghum and roasted coffee blends as a novel extruded product: Bioactive compounds and antioxidant capacity. Journal of Functional Foods, 29, 93–103. https://doi.org/10.1016/j.jff.2016.12.012
   Web of Science ®Google Scholar
• Chen, J., Zhu, B., Chong-Wah, N., Chua, T. S., & Jiang, Y. G. (2021). A study of multi-task and region-wise deep learning for food ingredient recognition. IEEE Transactions on Image Processing, 30, 1514–1526. https://doi.org/10.1109/TIP.2020.3045639
   PubMed Web of Science ®Google Scholar
• dos Santos, T. B., da Silva Freire Neto, R., Collantes, N. F., Chávez, D. W. H., Queiroz, V. A. V., & de Carvalho, C. W. P. (2022). Exploring starches from varied sorghum genotypes compared to commercial maize starch. Journal of Food Process Engineering, e14251. https://doi.org/10.1111/jfpe.14251
   Web of Science ®Google Scholar
• Ferreira, F. S., Sampaio, G. R., Keller, L. M., Sawaya, A. C. H. F., Chávez, D. W. H., Torres, E. A. F. S., & Saldanha, T. (2017). Impact of air frying on cholesterol and fatty acids oxidation in sardines: Protective effects of aromatic herbs. Journal of Food Science, 82(12), 2823–2831. https://doi.org/10.1111/1750-3841.13967
   PubMed Web of Science ®Google Scholar
• Han, W., Li, J., Wang, S., Zhang, X., Dong, Y., Fan, R., Zhang, X., & Wang, L. (2022). Geological remote sensing interpretation using deep learning feature and an adaptive multisource data fusion network. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14. https://doi.org/10.1109/TGRS.2022.3183080
   Web of Science ®Google Scholar
• Igathinathane, C., & Ulusoy, U. (2016). Machine vision methods based particle size distribution of ball- and gyro-milled lignite and hard coal. Powder Technology, 297, 71–80. https://doi.org/10.1016/j.powtec.2016.03.032
   Web of Science ®Google Scholar
• Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., & Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 100033. https://doi.org/10.1016/j.jafr.2020.100033
   Google Scholar
• León-Roque, N., Abderrahim, M., Nuñez-Alejos, L., Arribas, S. M., & Condezo-Hoyos, L. (2016). Prediction of fermentation index of cocoa beans (Theobroma cacao L.) based on color measurement and artificial neural networks. Talanta, 161, 31–39. https://doi.org/10.1016/j.talanta.2016.08.022
   PubMed Web of Science ®Google Scholar
• León-Roque, N., Aguilar-Tuesta, S., Quispe-Neyra, J., Mamani-Navarro, W., Alfaro-Cruz, S., & Condezo-Hoyos, L. (2019). A green analytical assay for the quantitation of the total saponins in quinoa (Chenopodium quinoa Willd.) based on macro lens-coupled smartphone. Talanta, 204, 576–585. https://doi.org/10.1016/j.talanta.2019.06.014
   PubMed Web of Science ®Google Scholar
• Mingoti, S. A. (2005). Análise de dados através de métodos de estatística multivariada: uma abordagem aplicada. Editora UFMG.
   Google Scholar
• Ontoum, S., Khemanantakul, T., Sroison, P., Triyason, T., & Watanapa, B. J. (2022). Coffee roast intelligence.
   Google Scholar
• Reddy, V. H., Kumari, S., Muralidharan, V., Gigoo, K., & Thakare, B. S. (2019, May 17-18). Food recognition and Calorie measurement using image processing and convolutional neural network. In 2019 4th international conference on recent trends on electronics. Bangalore, India: Information, Communication & Technology (RTEICT).
   Google Scholar
• Rencher, A. C., & Schimek, M. (2002). Methods of multivariate analysis. Computational Statistics, 12(4), 422–422.
   Google Scholar
• Tharwat, A. (2016). Principal component analysis-a tutorial. International Journal of Applied Pattern Recognition, 3(3), 197–240. https://doi.org/10.1504/IJAPR.2016.079733
   Web of Science ®Google Scholar
• Unal, Y., Taspinar, Y. S., Cinar, I., Kursun, R., & Koklu, M. (2022). Application of pre-trained deep convolutional neural networks for coffee beans species detection. Food Analytical Methods, 15(12), 3232–3243. https://doi.org/10.1007/s12161-022-02362-8
   Web of Science ®Google Scholar
• Zhang, J., Li, C., Rahaman, M. M., Yao, Y., Ma, P., Zhang, J., Zhao, X., Jiang, T., & Grzegorzek, M. (2022). A comprehensive review of image analysis methods for microorganism counting: From classical image processing to deep learning approaches. Artificial Intelligence Review, 55(4), 2875–2944. https://doi.org/10.1007/s10462-021-10082-4
   PubMed Web of Science ®Google Scholar
• Zhu, L., Spachos, P., Pensini, E., & Plataniotis, K. N. (2021). Deep learning and machine vision for food processing: A survey. Current Research in Food Science, 4, 233–249. https://doi.org/10.1016/j.crfs.2021.03.009
   PubMedGoogle Scholar