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Peach maturity/quality assessment using hyperspectral imaging-based spatially resolved technique

机译:桃成熟/质量评估使用超光线成像的空间解决技术

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The objective of this research was to measure the absorption (μa) and reduced scattering coefficients (μs') of peaches, using a hyperspectral imaging-based spatially-resolved method, for their maturity/quality assessment. A newly developed optical property measuring instrument was used for acquiring hyperspectral reflectance images of 500 'Redstar' peaches. μa and μs' spectra for 515-1,000 nm were extracted from the spatially-resolved reflectance profiles using a diffusion model coupled with an inverse algorithm. The absorption spectra of peach fruit presented several absorption peaks around 525 nm for anthocyanin, 620 nm for chlorophyll-b, 675 nm for chlorophyll-a, and 970 nm for water, while μs' decreased consistently with the increase of wavelength for most of the tested samples. Both μa and μs' were correlated with peach firmness, soluble solids content (SSC), and skin and flesh color parameters. Better prediction results for partial least squares models were obtained using the combined values of μa and μs' (i.e., μa × μs' and μeff) than using μa or μs', where μeff = [3 μa (μa + μs')]1/2 is the effective attenuation coefficient. The results were further improved using least squares support vector machine models with values of the best correlation coefficient for firmness, SSC, skin lightness and flesh lightness being 0.749 (standard error of prediction or SEP = 17.39 N), 0.504 (SEP = 0.92 °Brix), 0.898 (SEP = 3.45), and 0.741 (SEP = 3.27), respectively. These results compared favorably to acoustic and impact firmness measurements with the correlation coefficient of 0.639 and 0.631, respectively. Hyperspectral imaging-based spatially-resolved technique is useful for measuring the optical properties of peach fruit, and it also has good potential for assessing fruit maturity/quality attributes.
机译:本研究的目的是使用基于高光谱成像的空间分辨方法测量桃子的吸收(μA)和减少的散射系数(μs'),以获得其成熟/质量评估。新开发的光学性能测量仪用于获取500'Redstar'桃子的高光谱反射图像。使用与逆算法耦合的扩散模型,从空间分辨的反射率分布中提取μA和μs光谱515-1,000nm。桃子果实的吸收光谱呈现出几个约525nm的吸收峰,对于花青素,620nm,用于叶绿素-a,675nm,用于水的970nm,μs'随着大部分波长的增加而持续减少测试样品。 μA和μs'与桃固件,可溶性固体含量(SSC)和皮肤和肉颜色参数相关。使用比使用μA或μs'的μA和μs'(即,μa×μs'和μfff)的组合值获得偏最小二乘模型的更好的预测结果。其中μaf= [3μA(μa+μs')] 1 / 2是有效的衰减系数。使用最小二乘支持向量机模型进一步改善了结果,该载体模型具有最佳相关系数的硬度,SSC,皮肤亮度和肉点为0.749(预测标准误差或SEP = 17.39 n),0.504(SEP = 0.92°Brix ),0.898(SEP = 3.45)和0.741(SEP = 3.27)。这些结果有利地与声学和冲击力测量相比,分别具有0.639和0.631的相关系数。基于高光谱成像的空间分辨技术可用于测量桃果的光学性质,并且还具有评估果实成熟度/质量属性的良好潜力。

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