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Determination of soluble solids content in oranges using visible and near infrared full transmittance hyperspectral imaging with comparative analysis of models
Postharvest Biology and Technology ( IF 7 ) Pub Date : 2020-05-01 , DOI: 10.1016/j.postharvbio.2020.111148
Hailiang Zhang , Baishao Zhan , Fan Pan , Wei Luo

Abstract The feasibility of using visible and near infrared full transmittance hyperspectral imaging for predicting soluble solids content (SSC) in oranges has been assessed. A combination of competitive adaptive reweighted sampling and successive projections algorithm (CARS-SPA) was used to select the effective wavelengths. Size of fruit was used as a compensation factor to establish a calibration model coupled with spectral information. Full transmittance spectra and physiochemical parameters (SSC and size) of samples were extracted. The potential outliers in samples were eliminated by Monte-Carlo outlier detection method. Effective wavelengths were selected by CARS algorithm and the newly proposed CARS-SPA combination method. Three types of models including partial least squares (PLS), multiple linear regression (MLR) and least squares-support vector machine (LS-SVM) were established for SSC analysis of fruit based on different inputs. Results indicated that all models can realize the satisfactory prediction of SSC in oranges. Ranges of coefficient of determination ( R p r e 2 ) and root mean square error of prediction (RMSEP) were 0.88-0.89 and 0.48-0.48 % for PLS models, 0.83-0.85 and 0.49-0.55 % for MLR models, 0.86-0.90 and 0.40-0.48 % for LS-SVM. Compared among all SSC analysis models, CARS-SPA was a powerful effective wavelength selection combination and CARS-SPA-LS-SVM model with size had the optimal prediction accuracy ( R p r e 2 = 0.90, RMSEP = 0.40, RPD = 3.18). Overall, the results revealed that full transmittance hyperspectral imaging can be used to non-invasively to rapidly measure the SSC of oranges. A robust and accurate model could be established based on CARS-SPA-LS-SVM method with size compensation. These results may provide a useful reference for assessment of other internal quality attributes, such as acidity, of the thick-skinned fruit.

中文翻译:

使用可见光和近红外全透射高光谱成像与模型比较分析测定橙子中的可溶性固形物含量

摘要 已经评估了使用可见光和近红外全透射高光谱成像预测橙子中可溶性固体含量 (SSC) 的可行性。竞争性自适应重新加权采样和连续投影算法(CARS-SPA)的组合用于选择有效波长。以果实大小作为补偿因子,建立与光谱信息相结合的校准模型。提取样品的全透射光谱和理化参数(SSC 和尺寸)。采用蒙特卡罗异常值检测方法消除样本中潜在的异常值。通过CARS算法和新提出的CARS-SPA组合方法选择有效波长。包括偏最小二乘法(PLS)在内的三种模型,建立多元线性回归(MLR)和最小二乘支持向量机(LS-SVM),用于基于不同输入的水果SSC分析。结果表明,所有模型都能实现对橙子SSC的满意预测。决定系数 (R pre 2 ) 和预测均方根误差 (RMSEP) 的范围对于 PLS 模型为 0.88-0.89 和 0.48-0.48 %,对于 MLR 模型为 0.83-0.85 和 0.49-0.55 %,0.86-0.90 和 0.40 % -0.48% 用于 LS-SVM。与所有SSC分析模型相比,CARS-SPA是一种强大的有效波长选择组合,大小的CARS-SPA-LS-SVM模型具有最佳预测精度(R pre 2 = 0.90,RMSEP = 0.40,RPD = 3.18)。总体而言,结果表明,全透射高光谱成像可用于非侵入性地快速测量橙子的 SSC。基于具有尺寸补偿的CARS-SPA-LS-SVM方法,可以建立稳健且准确的模型。这些结果可为评估厚皮水果的酸度等其他内部质量属性提供有用的参考。
更新日期:2020-05-01
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