Abstract Visible–near infrared (Vis–NIR) spectroscopy is a rapid and nondestructive method used to characterize organic compounds in postharvest fruit and vegetable assessment. However, developing robust calibration models is a challenge as conventional spectrometers collect only the cumulative effects of light absorption and scattering. In this study, a multifiber-based Vis–NIR spatially resolved spectra measurement system was designed for simultaneous evaluation of soluble solid content (SSC) and firmness in apple. Thirty silica fibers separated into five groups at 1, 2, 3, 4, and 5 mm away from the light illumination point and connected to a cost-effective Vis–NIR hyperspectral imaging camera were used to acquire spectral data with an improved signal-to-noise ratio (S/N) by a two-step signal averaging process (i.e., 30 camera pixels per fiber and six optical fibers per group). Reflectance ratio spectra were then calculated by dividing the diffusely reflected light intensity detected at distance d +△ by that detected at distance d to realize a light reference-free approach. Finally, the useful explanatory variables were selected by competitive adaptive reweighted sampling (CARS) to construct individual calibration models for various regions. The coefficients of determination ( R c a l 2 ) and the root mean square errors (RMSEcal) of the best-performing calibration models were approximately 0.97 and 0.20 % for SSC and 0.96 and 0.37 N for firmness, respectively. Furthermore, the predicted results were 0.92 and 0.35 % for SSC and 0.87 and 0.71 N for firmness. Our method offers low-cost and portable detection of SSC and firmness for postharvest fruit evaluation.

中文翻译：

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使用 Vis-NIR 空间分辨光谱快速无损评估苹果的可溶性固形物含量 (SSC) 和硬度

摘要 可见-近红外 (Vis-NIR) 光谱是一种快速、无损的方法，用于表征采后水果和蔬菜评估中的有机化合物。然而，开发稳健的校准模型是一项挑战，因为传统光谱仪仅收集光吸收和散射的累积效应。在这项研究中，设计了一种基于多纤维的 Vis-NIR 空间分辨光谱测量系统，用于同时评估苹果的可溶性固形物含量 (SSC) 和硬度。将 30 根石英光纤在距离光照射点 1、2、3、4 和 5 毫米处分成五组，并连接到具有成本效益的 Vis-NIR 高光谱成像相机，用于获取光谱数据，并提高信号至- 噪声比 (S/N) 通过两步信号平均过程（即，每条光纤 30 个摄像头像素，每组 6 个光纤）。然后通过将在距离 d +△ 处检测到的漫反射光强度除以在距离 d 处检测到的漫反射光强度来计算反射率光谱，以实现无光参考方法。最后，通过竞争性自适应重加权抽样（CARS）选择有用的解释变量来构建各个区域的个体校准模型。性能最佳的校准模型的决定系数 (R cal 2 ) 和均方根误差 (RMSEcal) 对于 SSC 分别约为 0.97 和 0.20 %，对于硬度分别约为 0.96 和 0.37 N。此外，SSC 的预测结果为 0.92 和 0.35 %，硬度为 0.87 和 0.71 N。我们的方法为采后水果评估提供了低成本和便携式的 SSC 和硬度检测。