Liu Yande, Lv Siwei, Jiang Xiaogang, Lu Yeqing, Hu Bo
Institute of Intelligent Mechanical and Electrical Equipment Innovation, East China Jiaotong University, Nanchang 330013, China.
Anhui VSEE Optoelectronic Technology Co., Ltd., Hefei 230093, China.
Foods. 2025 Apr 18;14(8):1412. doi: 10.3390/foods14081412.
This study aims to comprehensively evaluate the internal quality changes in apples during storage via near-infrared spectroscopy. Specifically, we focus on the performance differences in different apple varieties under diverse storage conditions and construct predictive models to determine the optimal storage period. By using near-infrared spectroscopy technology, 384 samples of four apple varieties (Xinjiang Akesu, Wafangdian Huangyuanshuai, Shandong Fuji, and Luochuan Fuji) were analyzed to monitor the changes in their soluble solid content (SSC) and fruit firmness within 7 weeks. The results indicated that, under cold storage conditions, SSC and firmness gradually decreased after peaking between the third and fifth weeks, while the opposite trend was observed at room temperature. To enhance the predictive accuracy of the model, several pretreatment methods were employed, including standardization, multiplicative scatter correction (MSC), and standard normal variate transformation (SNV). Additionally, competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) were utilized for band selection. These pretreatment and selection processes significantly reduced noise and improved model reliability. The best results were achieved with the Normalization-CARS-PLS model for the sugar content at 1 °C, which demonstrated an optimal predictive correlation coefficient (Rp) of 0.904 and a root mean square error of prediction (RMSEP) of 0.67. For firmness at room temperature, the Normalization-CARS-PLS model also showed an excellent performance, with an Rp of 0.823 and an RMSEP of 0.809. The study of the quality of four varieties of apples under three storage conditions in this paper was able to analyze the changes in the internal quality of apples and predict the optimal storage period of different varieties of apples, which is important for guiding the optimal storage period of apples before ripening.
本研究旨在通过近红外光谱法全面评估苹果在储存期间的内部品质变化。具体而言，我们关注不同苹果品种在不同储存条件下的性能差异，并构建预测模型以确定最佳储存期。利用近红外光谱技术，对四个苹果品种（新疆阿克苏、瓦房店黄元帅、山东富士和洛川富士）的384个样本进行了分析，以监测其在7周内可溶性固形物含量（SSC）和果实硬度的变化。结果表明，在冷藏条件下，SSC和硬度在第三至第五周达到峰值后逐渐下降，而在室温下则观察到相反的趋势。为提高模型的预测准确性，采用了几种预处理方法，包括标准化、多元散射校正（MSC）和标准正态变量变换（SNV）。此外，还利用竞争性自适应重加权采样（CARS）和无信息变量消除（UVE）进行波段选择。这些预处理和选择过程显著降低了噪声，提高了模型的可靠性。对于1℃下的糖分含量，归一化-CARS-PLS模型取得了最佳结果，其最佳预测相关系数（Rp）为0.904，预测均方根误差（RMSEP）为0.67。对于室温下的硬度，归一化-CARS-PLS模型也表现出色，Rp为0.823，RMSEP为0.809。本文对三种储存条件下四个苹果品种品质的研究能够分析苹果内部品质的变化，并预测不同品种苹果的最佳储存期，这对于指导苹果成熟前的最佳储存期具有重要意义。
