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

机译：使用基于高光谱成像的空间分辨技术进行桃成熟度/质量评估

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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：这项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的吸收率（μa）和降低的散射系数（μs'），以评估其成熟度/质量。一种新开发的光学性质测量仪器用于获取500个“ Redstar”桃子的高光谱反射率图像。使用与逆算法耦合的扩散模型，从空间分辨的反射率曲线中提取515-1,000 nm的μa和μs光谱。桃果实的吸收光谱在花色苷的525 nm，叶绿素-b的620 nm，叶绿素-a的675 nm和水的970 nm处出现几个吸收峰，而在大多数情况下，μs随波长的增加而一致地降低。测试样品。 μa和μs'与桃子的硬度，可溶性固形物含量（SSC）以及皮肤和果肉的颜色参数相关。使用μa和μs'的组合值（即，μa×μs'和μeff）比使用μa或μs'获得更好的偏最小二乘模型预测结果，其中μeff= [3μa（μa+μs'）] 1 / 2是有效衰减系数。使用最小二乘支持向量机模型进一步改善了结果，其紧实度，SSC，皮肤亮度和肤色亮度的最佳相关系数值为0.749（预测的标准误差或SEP = 17.39 N），0.504（SEP = 0.92°Brix） ），0.898（SEP = 3.45）和0.741（SEP = 3.27）。这些结果与声学和冲击硬度测量值分别为0.639和0.631的相关系数相比较。基于高光谱成像的空间分辨技术可用于测量桃果实的光学特性，并且在评估果实成熟度/品质属性方面也具有良好的潜力。

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