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利用 365nm 紫外光激发的荧光图像无损估计鳄梨(Persea americana Mill.)中的果肉油含量。

Non-destructive estimation of flesh oil content in avocado (Persea americana Mill.) using fluorescence images from 365-nm UV light excitation.

机构信息

Graduate School of Agriculture, Kyoto University, Kyoto, Japan.

Institute of Science and Technology, Niigata University, Niigata, Japan.

出版信息

Photochem Photobiol Sci. 2024 Oct;23(10):1871-1882. doi: 10.1007/s43630-024-00636-0. Epub 2024 Sep 17.

Abstract

The flesh oil content (OC) is a crucial commercial indicator of avocado maturity and directly correlates with its nutritional quality. To meet export standards and optimize edible characteristics, avocados must be harvested at the appropriate stage of physiological maturity. The significant variability in OC during maturation, without any external morphological indicators, poses a longstanding challenge. Currently, harvesting maturity is optimized through time-consuming, destructive laboratory methods like freeze-drying and chemical extraction, which use representative samples to estimate the maturity of entire orchards. In this study, for the first time, we employed fluorescence imaging of avocado skin using 365-nm UV polarized light excitation to estimate the OC in the 'Bacon' avocado cultivar. We developed a surface fluorescence index that strongly correlates with OC, achieving correlation coefficients up to - 0.91. Our non-destructive and rapid approach achieved a cross-validation accuracy with an R value of 0.81, enabling the classification of avocados with low and high OC. This pioneering method shows considerable potential for further improvement and refinement. This study lays the groundwork for developing a portable, cost-effective, and real-time method for non-destructive in situ monitoring of avocado OC in the field and its integration into large-scale post-harvest grading systems.

摘要

果肉油含量(OC)是鳄梨成熟度的一个重要商业指标,与营养品质直接相关。为了满足出口标准并优化可食用特性,鳄梨必须在生理成熟的适当阶段进行收获。OC 在成熟过程中的显著变化,没有任何外部形态指标,这是一个长期存在的挑战。目前,通过耗时的破坏性实验室方法,如冷冻干燥和化学提取,来优化收获成熟度,这些方法使用代表性样本来估算整个果园的成熟度。在这项研究中,我们首次使用 365nmUV 偏振光激发的鳄梨果皮荧光成像来估算'Bacon'鳄梨品种的 OC。我们开发了一个与 OC 强烈相关的表面荧光指数,相关系数高达-0.91。我们的非破坏性和快速方法实现了交叉验证准确性,R 值为 0.81,能够对 OC 低和高的鳄梨进行分类。这种开创性的方法显示出进一步改进和完善的巨大潜力。本研究为开发一种便携式、经济实惠且实时的非破坏性现场鳄梨 OC 原位监测方法奠定了基础,并将其集成到大规模的采后分级系统中。

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