Abstract Development of the online and nondestructive technologies for inspecting and grading the quality of fruit in the postharvest period can improve industry competitiveness and profitability. The effect of fruit temperature, diameter and weight on online evaluation system of soluble solids content (SSC) of ‘Ya’ pears using visible/near infrared (Vis/NIR) spectroscopy was studied. To establish calibration models, partial least square (PLS) regression and least squares-support vector machine (LS-SVM) were employed in 630–900 nm and two fruit orientations (stem-calyx axis vertical with stem upward (T1), stem-calyx axis horizontal with stem towards belt moving direction (T2)), respectively. After pretreatments of Savitzky-Golay smoothing (SGS), multiplicative scattering correction (MSC), standard normal variate (SNV), and competitive adaptive reweighted sampling (CARS) for effective wavelength (EWs) selection, models were optimized and compared to evaluate calibration strategies. 36 EWs using PLS (rp = 0.89, RMSEP = 0.56) with the consideration of diameter (T1) and 34 EWs using LS-SVM (rp = 0.90, RMSEP = 0.57) with the consideration of temperature and diameter (T2) were finally selected, respectively. The fusion information of temperature and diameter showed beneficial effect and the best prediction results based on the designed online Vis/NIR half-transmittance system after MSC and 7-SGS for SSC evaluation of pears using LS-SVM, which would be effective to simplify models and promote computing efficiency and further make this proposed nondestructive detection technique have the practical application.

中文翻译：

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使用 Vis/NIR 在线半透射技术检测梨（Pyrus bretschneideri 'Ya'）可溶性固形物含量的多因素融合模型

摘要 发展果实采后品质在线无损检测分级技术，可以提高行业竞争力和盈利能力。采用可见/近红外（Vis/NIR）光谱研究了果实温度、果实直径和果实重量对'雅'梨可溶性固形物含量（SSC）在线评价系统的影响。为了建立校准模型，偏最小二乘 (PLS) 回归和最小二乘支持向量机 (LS-SVM) 在 630-900 nm 和两个果实方向（茎 - 花萼轴垂直，茎向上（T1），茎 -花萼轴水平与茎朝向带移动方向（T2）），分别。经过 Savitzky-Golay 平滑 (SGS)、乘法散射校正 (MSC)、标准正态变量 (SNV) 预处理后，和用于有效波长 (EW) 选择的竞争性自适应重加权采样 (CARS)，优化模型并进行比较以评估校准策略。最终选择了 36 个使用 PLS (rp = 0.89, RMSEP = 0.56) 并考虑直径 (T1) 的 EW 和 34 个使用 LS-SVM (rp = 0.90, RMSEP = 0.57) 并考虑温度和直径 (T2) 的 EW ， 分别。基于设计的MSC和7-SGS后的在线Vis/NIR半透射系统，温度和直径的融合信息显示出有益的效果和最好的预测结果，用于使用LS-SVM对梨的SSC评价，这将有效地简化模型并提高计算效率并进一步使所提出的无损检测技术具有实际应用。