Wang Fuxiang, Wang Chunguang, Song Shiyong, Xie Shengshi, Kang Feilong
Inner Mongolia Agriculture University Hohhot China.
Food Sci Nutr. 2021 Jun 22;9(8):4420-4430. doi: 10.1002/fsn3.2415. eCollection 2021 Aug.
Starch is an important quality index in potato, which contributes greatly to the taste and nutritional quality of potato. At present, the determination of starch depends on chemical analysis, which is time consuming and laborious. Thus, rapid and accurate detection of the starch content of potatoes is important. This study combined hyperspectral imaging with chemometrics to predict potato starch content. Two varieties of Kexin No.1 and Holland No.15 potatoes were used as experimental samples. Hyperspectral data were collected from three sampling sites (the top, umbilicus, and middle regions). Standard normal variate (SNV) was used for spectral preprocessing, and three different methods of competitive adaptive reweighted sampling (CARS), iterative variable subset optimization (IVSO), and the variable iterative space shrinkage approach (VISSA) were used for characteristic wavelength selection. Linear partial least-squares regression (PLSR) and nonlinear support vector regression (SVR) models were then established. The results indicated that the sampling site has a considerable impact on the accuracy of the prediction model, and the umbilicus region with CARS-SVR model gave best performance with correlation coefficients in calibration (Rc) of 0.9415, in prediction (Rp) of 0.9346, root mean square errors in calibration (RMSEC) of 15.9 g/kg, in prediction (RMSEP) of 17.4 g/kg, and residual predictive deviation (RPD) of 2.69. The starch content in potatoes was visualized using the best model in combination with pseudo-color technology. Our research provides a method for the rapid and nondestructive determination of starch content in potatoes, providing a good foundation for potato quality monitoring and grading.
淀粉是马铃薯的一项重要品质指标，对马铃薯的口感和营养品质有很大影响。目前，淀粉的测定依赖于化学分析，既耗时又费力。因此，快速准确地检测马铃薯淀粉含量非常重要。本研究将高光谱成像与化学计量学相结合来预测马铃薯淀粉含量。选用克新1号和荷兰15号两个马铃薯品种作为实验样本。从三个采样部位（顶部、脐部和中部）采集高光谱数据。采用标准正态变量变换（SNV）进行光谱预处理，并使用竞争性自适应重加权采样（CARS）、迭代变量子集优化（IVSO）和变量迭代空间收缩法（VISSA）三种不同方法进行特征波长选择。然后建立线性偏最小二乘回归（PLSR）模型和非线性支持向量回归（SVR）模型。结果表明，采样部位对预测模型的准确性有相当大的影响，采用CARS-SVR模型的脐部区域性能最佳，校正相关系数（Rc）为0.9415，预测相关系数（Rp）为0.9346，校正均方根误差（RMSEC）为15.9 g/kg，预测均方根误差（RMSEP）为17.4 g/kg，剩余预测偏差（RPD）为2.69。结合伪彩色技术，利用最佳模型对马铃薯中的淀粉含量进行了可视化。本研究为马铃薯淀粉含量的快速无损检测提供了一种方法，为马铃薯品质监测和分级奠定了良好基础。
