College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai'an, Shandong, China.
Nanjing Research Institute For Agricultural Mechanization, Ministry of Agriculture, Nanjing, Jiangsu, China.
PLoS One. 2019 Sep 18;14(9):e0222633. doi: 10.1371/journal.pone.0222633. eCollection 2019.
Determination and classification of the bruise degree for cherry can improve consumer satisfaction with cherry quality and enhance the industry's competiveness and profitability. In this study, visible and near infrared (Vis-NIR) reflection spectroscopy was used for identifying bruise degree of cherry in 350-2500 nm. Sampling spectral data were extracted from normal, slight and severe bruise samples. Principal component analysis (PCA) was implemented to determine the first few principal components (PCs) for cluster analysis among samples. Optimal wavelengths were selected by loadings of PCs from PCA and successive projection algorithm (SPA) method, respectively. Afterwards, these optimal wavelengths were empolyed to establish the classification models as inputs of least square-support vector machine (LS-SVM). Better performance for qualitative discrimination of the bruise degree for cherry was emerged in LS-SVM model based on five optimal wavelengths (603, 633, 679, 1083, and 1803 nm) selected directly by SPA, which showed acceptable results with the classification accuracy of 93.3%. Confusion matrix illustrated misclassification generally occurred in normal and slight bruise samples. Furthermore, the latent relation between spectral property of cherries in varying bruise degree and its firmness and soluble solids content (SSC) was analyzed. The result showed both colour, firmness and SSC were consistent with the Vis-NIR reflectance of cherries. Overall, this study revealed that Vis-NIR reflection spectroscopy integrated with multivariate analysis can be used as a rapid, intact method to determine the bruise degree of cherry, laying a foundation for cherry sorting and postharvest quality control.
确定和分类樱桃的瘀伤程度可以提高消费者对樱桃质量的满意度，增强行业的竞争力和盈利能力。本研究采用可见近红外（Vis-NIR）反射光谱法在 350-2500nm 范围内识别樱桃的瘀伤程度。从正常、轻微和严重瘀伤样本中提取采样光谱数据。实施主成分分析（PCA）以确定样本间聚类分析的前几个主成分（PC）。通过 PCA 的 PC 载荷和连续投影算法（SPA）方法分别选择最佳波长。之后，将这些最佳波长用作最小二乘支持向量机（LS-SVM）分类模型的输入。基于 SPA 直接选择的 5 个最佳波长（603、633、679、1083 和 1803nm）的 LS-SVM 模型对樱桃瘀伤程度的定性判别具有更好的性能，其分类准确率为 93.3%，结果可接受。混淆矩阵表明，正常和轻微瘀伤样本的分类通常存在错误。此外，还分析了不同瘀伤程度下樱桃光谱特性与其硬度和可溶性固形物含量（SSC）之间的潜在关系。结果表明，颜色、硬度和 SSC 与樱桃的 Vis-NIR 反射率一致。总体而言，本研究表明，Vis-NIR 反射光谱结合多元分析可用于快速、完整地确定樱桃的瘀伤程度，为樱桃分拣和采后质量控制奠定基础。
