Abstract The online system can achieve high efficiency towards fruit quality determination in postharvest period. Thus, developing online and nondestructive technology for inspecting and grading fruit is meaningful and profitable in the existing robotic sorting systems. In this study, the effect of fruit diameter differences on online prediction of soluble solids content (SSC) of ‘Fuji’ apples based on visible and near-infrared (Vis/NIR) spectroscopy was studied. Partial least square (PLS) regression was employed to establish calibration models based on three wavelength regions (675–1025, 710–980, 750–1025 ​nm) and two fruit orientations (stem-calyx axis vertical with stem upward (T1) and stem-calyx axis horizontal with stem towards light source (T2)), respectively. A novel diameter correction method was proposed to reduce the effect of fruit diameter differences on original spectra. Combined with pretreatment and effective wavelength (EWs) selection methods, models were optimized and compared to determine the best calibration strategy. Diffuse transmission spectra in 710–980 ​nm and diameter correction method with calculated attenuation coefficient were testified much better than other corresponding regions and correction methods, respectively. Baseline offset correction (BOC) and 7-point Savitzky-Golay smoothing (SGS) of pretreatments and competitive adaptive reweighted sampling (CARS) of EWs selection methods were proved to be outstanding among other methods. 59 and 63 ​EWs achieved the best detection accuracies with correlation coefficient of prediction (rp) and root mean square error of prediction (RMSEP) of 0.92 and 0.50 °Brix, 0.89 and 0.56 °Brix for T1 and T2, respectively. The overall results indicated that online Vis/NIR transmission spectra after BOC and 7-SGS with proposed diameter correction method can make the variation of fruit diameters a small interference for SSC determination, and CARS-PLS would be effective to simplify models and promote computing efficiency to make this nondestructive detection technique promisingly applied.

中文翻译：

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在线Vis/NIR透射结合直径修正法预测苹果可溶性固形物含量的模型优化与比较

摘要 该在线系统可以实现对采后果实品质的高效测定。因此，在现有的机器人分拣系统中，开发用于检测和分级水果的在线无损技术是有意义且有利可图的。在这项研究中，基于可见光和近红外（Vis/NIR）光谱研究了果实直径差异对'富士'苹果可溶性固形物含量（SSC）在线预测的影响。采用偏最小二乘 (PLS) 回归建立基于三个波长区域（675-1025、710-980、750-1025 nm）和两个果实方向（茎-花萼轴垂直，茎向上（T1）和茎 - 花萼轴水平，茎朝向光源（T2）），分别。提出了一种新的直径校正方法，以减少果实直径差异对原始光谱的影响。结合预处理和有效波长 (EW) 选择方法，优化和比较模型以确定最佳校准策略。710-980 nm的漫透射光谱和计算衰减系数的直径校正方法分别比其他相应区域和校正方法好得多。预处理的基线偏移校正 (BOC) 和 7 点 Savitzky-Golay 平滑 (SGS) 以及 EW 选择方法的竞争性自适应重新加权采样 (CARS) 被证明是其他方法中的佼佼者。59 和 63 个 EW 实现了最佳检测精度，预测相关系数 (rp) 和预测均方根误差 (RMSEP) 分别为 0.92 和 0.50°Brix，T1 和 T2 分别为 0.89 和 0.56°Brix。总体结果表明，BOC 和 7-SGS 后的在线 Vis/NIR 透射光谱和建议的直径校正方法可以使果实直径的变化对 SSC 测定的干扰很小，而 CARS-PLS 将有效简化模型并提高计算效率使这种无损检测技术得到很好的应用。