Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review.

Autor:	Aline U; Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea., Bhattacharya T; Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea., Faqeerzada MA; Department of Smart Agricultural Systems, Chungnam National University, Daejeon, Republic of Korea., Kim MS; Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States., Baek I; Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States., Cho BK; Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea.; Department of Smart Agricultural Systems, Chungnam National University, Daejeon, Republic of Korea.
Jazyk:	angličtina
Zdroj:	Frontiers in plant science [Front Plant Sci] 2023 Aug 16; Vol. 14, pp. 1240361. Date of Electronic Publication: 2023 Aug 16 (Print Publication: 2023).
DOI:	10.3389/fpls.2023.1240361
Abstrakt:	The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Aline, Bhattacharya, Faqeerzada, Kim, Baek and Cho.)
Databáze:	MEDLINE
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