Linear partial least square and non-linear support vector machine regression analysis with various preprocessing techniques and their combinations were used to determine the soluble solids content of hardy kiwi fruits by a handheld, portable near-infrared spectroscopy. Fruits of four species, namely Autumn sense (A), Chungsan (C), Daesung (D), and Green ball (Gb) were collected from five different areas of Gwangyang (G), Muju (M), Suwon (S), Wonju (Q), and Yeongwol (Y) in South Korea. The dataset for calibration and prediction was prepared based on each area, species, and in combination. Half of the dataset of each area, species, and combined dataset was used as calibrated data and the rest was used for model validation. The best prediction correlation coefficient ranges between 0.67 and 0.75, 0.61 and 0.77, and 0.68 for the area, species, combined dataset, respectively using partial least square regression (PLSR) method with different preprocessing techniques. On the other hand, the best correlation coefficient of predictions using the support vector machine regression (SVM-R) algorithm was 0.68 and 0.80, 0.62 and 0.79, and 0.74 for the area, species, and combined dataset, respectively. In most cases, the SVM-R algorithm produced better results with Autoscale preprocessing except G area and species Gb, whereas the PLS algorithm shows a significant difference in calibration and prediction models for different preprocessing techniques. Therefore, the SVM-R method was superior to the PLSR method in predicting soluble solids content of hardy kiwi fruits and non-linear models may be a better alternative to monitor soluble solids content of fruits. The finding of this research can be used as a reference for the prediction of hardy kiwi fruits soluble solids content as well as harvesting time with better prediction models.

中文翻译：

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用便携式Vis / NIR光谱仪定量测定强力猕猴桃可溶性固形物含量的各种预处理技术对PLSR和SVM-R的比较研究。

线性偏最小二乘和非线性支持向量机通过各种预处理技术及其组合进行回归分析，通过手持式，便携式近红外光谱法确定强硬猕猴桃的可溶性固形物含量。从光阳（G），木州（M），水原（S），南北五省五个地区采集了秋季感（A），中山（C），大成（D）和绿球（Gb）四种水果。韩国的Wonju（Q）和Yeongwol（Y）。基于每个区域，物种及其组合，准备了用于校准和预测的数据集。每个区域，物种和组合数据集的数据集的一半用作校准数据，其余数据用于模型验证。对于该区域，物种，物种和物种，最佳预测相关系数的范围为0.67至0.75、0.61至0.77和0.68。组合数据集，分别使用偏最小二乘回归（PLSR）方法和不同的预处理技术。另一方面，使用支持向量机回归（SVM-R）算法的预测的最佳相关系数对于面积，种类和组合数据集分别为0.68和0.80、0.62和0.79和0.74。在大多数情况下，除了G面积和Gb物种外，SVM-R算法在Autoscale预处理中产生了更好的结果，而PLS算法在不同预处理技术的校准和预测模型上显示出显着差异。因此，在预测硬质猕猴桃的可溶性固形物含量方面，SVM-R方法优于PLSR方法，非线性模型可能是监测水果可溶性固形物含量的更好选择。