College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China.
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China.
Food Chem. 2022 Feb 15;370:131013. doi: 10.1016/j.foodchem.2021.131013. Epub 2021 Sep 2.
Malus micromalus Makino has great commercial and nutritional value. The regression and classification models were investigated by using near-infrared hyperspectral imaging (NIR-HSI) combined with chemometrics to improve the efficiency of non-destructive detection. The successive projections algorithm (SPA), interval random frog, and competitive adaptive reweighted sampling were employed to extract effective wavelengths sensitive to changes of soluble solid content (SSC) and firmness index (FI) information. Two types of assessment models based on full spectrum and effective wavelengths, namely partial least squares regression and extreme learning machine, were established to predict SSC and FI. In addition, the classification models based on the support vector machine improved by the grey wolf optimizer (GWO-SVM) and partial least squares discrimination analysis were constructed to differentiate maturity stage. The SPA-ELM and SPA-GWO-SVM models achieved satisfactory performance. The results illustrate that NIR-HSI is feasible for evaluation of the quality of Malus micromalus Makino.
金冠苹果具有巨大的商业和营养价值。本研究采用近红外高光谱成像（NIR-HSI）结合化学计量学方法构建了偏最小二乘回归和极限学习机模型，旨在提高无损检测效率。连续投影算法（SPA）、区间随机青蛙算法和竞争自适应重加权采样被用于提取与可溶性固形物（SSC）和硬度指数（FI）变化敏感的有效波长。基于全谱和有效波长分别建立了基于偏最小二乘回归和极限学习机的两类评价模型，以预测 SSC 和 FI。此外，还构建了基于支持向量机的分类模型，通过灰狼优化算法（GWO-SVM）进行改进，以区分成熟度阶段。SPA-ELM 和 SPA-GWO-SVM 模型的预测结果令人满意。结果表明，NIR-HSI 可用于评估金冠苹果的品质。
