Abstract
Pineapple is one of the healthful and popular tropical fruits in the world. The quality determination of pineapples was mostly evaluated by human inspection which is inconsistent and subjective. The increasing demand for pineapples creates more opportunities for the advancement of rapid and non-destructive approaches to seek quality evaluation of the fruit. This review gives an overview of the non-destructive approaches on the quality determination of pineapples including computer vision, imaging-based approaches, spectroscopy-based approaches, acoustic impulse, and electronic nose. The advance of non-destructive techniques to evaluate the quality of pineapple can produce better yield and improve postharvest handling. This paper also highlighted the recent works on the quality determination of pineapple fruit using non-destructive approaches along with the abundant information that can be explored for real-time purposes. This information is expected to be useful not only for pineapples growers/industries but also for other agro-food commodities.
Subject
General Medicine,General Chemistry
Reference39 articles.
1. Abu Bakar, B. H., Ishak, A. J., Shamsuddin, R., & Wan Hassan, W. Z. (2013). Ripeness level classification for pineapple using RGB and HSI colour maps. Journal of Theoretical and Applied Information Technology, 57(3), 587–593.
2. Ali, M. M., Bachik, N. A., Muhadi, N. ‘Atirah, Tuan Yusof, T. N., & Gomes, C. (2019). Non-destructive techniques of detecting plant diseases: A review. Physiological and Molecular Plant Pathology, 108, 1–12. https://doi.org/10.1016/j.pmpp.2019.101426
3. Amuah, C. L. Y., Teye, E., Lamptey, F. P., Nyandey, K., Opoku-Ansah, J., & Adueming, P. O. W. (2019). Feasibility Study of the Use of Handheld NIR Spectrometer for Simultaneous Authentication and Quantification of Quality Parameters in Intact Pineapple Fruits. Journal of Spectroscopy, 2019, 1–10. https://doi.org/10.1155/2019/5975461
4. Angel, L., Lizcano, S., & Viola, J. (2015). Assessing the state of maturation of the pineapple in its perolera variety using computer vision techniques. 20th Symposium on Signal Processing, Images and Computer Vision, 1–6. https://doi.org/10.1109/STSIVA.2015.7330446
5. Barral, B., Chillet, M., Léchaudel, M., Lartaud, M., Verdeil, J. L., Conéjéro, G., & Schorr-Galindo, S. (2019). An Imaging Approach to Identify Mechanisms of Resistance to Pineapple Fruitlet Core Rot. Frontiers in Plant Science, 10, 1–12. https://doi.org/10.3389/fpls.2019.01065
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