【目的/意义】 苹果“冰糖心”又称水心病，是一种常见的果实病害，严重的水心病果会随着储藏时间的增加发生霉变，造成食品安全隐患。为实现不同等级水心病苹果快速无损检测，本研究旨在构建有效的分级与可溶性固形物（Soluble Solids Content, SSC）预测模型。 【方法】 本研究选取了230个富士苹果，其中正常、轻度、中度、重度水心苹果数量分别为113、61、47和9个，分别采集了400~1 000 nm范围的反射光谱和X射线计算机断层成像（X-ray Computed Tomography, X-ray CT）数据，并测定了SSC含量。 【结果和讨论】 SSC随水心程度加剧呈上升趋势，重度水心苹果呈现更高的光谱反射率，X-ray CT扫描成像观察到水心区域的组织体积平均密度高于健康组织，基于三维重建算法实现不同等级水心苹果内部水心组织可视化分布。基于偏最小二乘判别分析（Partial Least Squares Discriminant Analysis, PLSDA）构建的不同水心程度苹果果实分级模型建模集和测试集准确率分别为98.7%和95.9%；构建不同水心程度苹果果实SSC回归模型，校正集决定系数（Correlation Coefficient of Calibration, R_{\mathrm{C}}^{\mathrm{2}}）为0.962，均方根误差（Root Mean Squares Error of Calibration, RMSEC）为0.264，测试集决定系数（Correlation Coefficient of Prediction, R_{\mathrm{P}}^{\mathrm{2}}）为0.879，均方根误差（Root Mean Squares Error of Prediction, RMSEP）为0.435。 【结论】 该研究构建的不同水心程度苹果果实分级模型能够实现苹果不同等级水心病的预测，构建的不同水心程度苹果果实SSC回归模型能够较好地预测苹果果实的SSC，为苹果水心病无损检测和品质评估提供了有效方法。

[Objective] Apple "sugar-glazed core" (also known as watercore) is a common physiological disorder in apple fruits. Apples with watercore possess a distinctive flavor and are highly favored by consumers. However, severely affected apples are prone to mold growth during storage, posing potential food safety risks. Currently, the primary method for detecting sugar-glazed core in apple relies on manual destructive inspection, which is inefficient for large-scale applications and fails to meet the demands of modern automated and intelligent industrial production. To achieve rapid and non-destructive detection of apples with varying watercore severity levels, effective grading and soluble solids content (SSC) prediction models were developed in this study. [Methods] The Xinjiang Aksu Red Fuji apples were used as the research subject. A total of 230 apple samples were selected, comprising 113 normal, 61 mild, 47 moderate, and 9 severe watercore apples. The watercore severity was quantified through image processing of the apples' cross-sectional images. X-ray computed tomography (X-ray CT) data were acquired, and SSC values were measured. A hyperspectral imaging system was used to collect reflectance spectra within the 400~1 000 nm range. After performing black-and-white correction and selecting regions of interest (ROI), the Sample Set Partitioning based on Joint X-Y Distances (SPXY) algorithm was applied to divide the dataset into modeling (training) and prediction sets at a 3:1 ratio. Using the iToolbox in MATLAB, discriminant models were constructed based on partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and convolutional neural network (CNN) algorithms with reflectance spectral data as the input. Regression models for predicting SSC across different watercore severity levels were also established. Feature wavelength selection was carried out using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE) methods. [Results and Discussions] The results indicated that as watercore severity increased, the SSC of Red Fuji apples exhibited an upward trend. The average SSC values were 13.4% for normal apples, 14.9% for mild watercore apples, 15.0% for moderate watercore apples, and 16.0% for severe watercore apples. X-ray CT imaging revealed that the average tissue density of watercore-affected regions was higher than that of healthy tissues. Three-dimensional reconstruction algorithms allowed visualization of the internal spatial distribution of watercore tissues at different severity levels. The spatial volume proportions of watercore tissues were 3.92% in mild, 6.11% in moderate, and 10.23% in severe watercore apples. Apples with severe watercore demonstrated higher spectral reflectance. The PLS-DA-based grading model achieved accuracies of 98.7% in the training set and 95.9% in the test set. The model based on feature wavelengths selected by the UVE algorithm also showed high precision, with accuracies of 95.67% in the training set and 86.06% in the test set. For SSC regression modeling, the partial least squares regression (PLSR) model performed best, with a coefficient of determination for calibration (R_{\mathrm{C}}^{\mathrm{2}}) of 0.962, root mean square error of calibration (RMSEC) of 0.264, coefficient of determination for prediction (R_{\mathrm{P}}^{\mathrm{2}}) of 0.879, and root mean square error of prediction (RMSEP) of 0.435. The model based on feature wavelengths selected by the SPA algorithm exhibited further improved prediction performance, yielding R_{\mathrm{C}}^{\mathrm{2}} 0.846, RMSEC 0.532, R_{\mathrm{P}}^{\mathrm{2}} 0.792, RMSEP 0.576, coefficient of determination for cross-validation (R_{\mathrm{C}\mathrm{V}}^{\mathrm{2}}) 0.781, and root mean square error of cross-validation (RMSECV) 0.637. [Conclusions] This study leveraged hyperspectral imaging and X-ray CT technologies to analyze differences in optical reflectance and microstructural characteristics of apple tissues across different watercore severity levels. The developed grading model effectively predicted watercore severity in apples, providing critical technical support for the development of intelligent post-harvest sorting equipment. The SSC regression model accurately predicted SSC values in apples with varying watercore severity, offering an efficient method for non-destructive detection and quality assessment of watercore-affected apples.

本研究不存在研究者以及与公开研究成果有关的利益冲突。