Aline Umuhoza, Bhattacharya Tanima, Faqeerzada Mohammad Akbar, Kim Moon S, Baek Insuck, Cho Byoung-Kwan
Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea.
Department of Smart Agricultural Systems, Chungnam National University, Daejeon, Republic of Korea.
Front Plant Sci. 2023 Aug 16;14:1240361. doi: 10.3389/fpls.2023.1240361. eCollection 2023.
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.
热带水果和蔬菜的品质以及全球对食用健康食品兴趣的不断增加，促使可靠、快速且经济高效的质量保证方法持续发展。本综述讨论了用于评估主要热带水果和蔬菜品质的无损光谱测量技术的进展。傅里叶变换红外光谱（FTIR）、近红外光谱（NIR）、拉曼光谱和高光谱成像（HSI）被用于监测木瓜、菠萝、牛油果、芒果和香蕉的外部和内部参数。HSI检测光谱和空间维度的能力证明了其在测量外部品质方面的有效性，例如对516根香蕉进行分级，以及分别以98.45%、97.95%和99.9%的准确率检测10个芒果和10个牛油果的缺陷。除了NIR外，所有这些技术都能有效评估内部特性，如总可溶性固形物（TSS）、可溶性固形物含量（SSC）和水分含量（MC），因为发现NIR对包括牛油果、菠萝和香蕉在内的有厚外皮或果皮的水果和蔬菜的穿透深度有限。适当选择NIR光学几何结构和波长范围有助于提高这些作物的预测准确性。光谱测量与机器学习和深度学习技术相结合的进展提高了估计木瓜果实从未成熟到过熟六个成熟阶段的效率，通过对HSI和可见光生成的数据进行特征拼接，F1分数高达0.90。无损光谱测量技术进步的研究结果为热带水果和蔬菜提供了有前景的质量保证。
