基于近红外光谱和化学计量学的李果实成熟度鉴别方法研究

采收成熟度是影响李果实贮藏质量的重要因素,为实现快速无损判别李果实的成熟度,本文根据开花后发育时间的不同,将163个李果实样品分为早期(n=53)、中期(n=55)和晚期(n=55)三个成熟度,利用近红外光谱分析技术,对不同成熟度的李果实进行了分类。通过对马氏距离判别法、簇类独立软模式分类法、最小二乘-支持向量机的分类模型结果进行比较,发现原始光谱前20个主成分得分作为输入时的马氏距离判别法模型结果最优,校正集和预测集判别正确率分别为96.33%和96.30%。对不同成熟度样品的可溶性固形物、可滴定酸及坚实度进行单因素方差分析发现,各指标均存在显著差异,坚实度差异最大。提取品质指标数据的主成分发现,其聚类趋势与光谱主成分聚类趋势相似。结果表明,使用近红外光谱技术结合化学计量学方法对李果实成熟度进行鉴别是有效的、可行的,且其品质指标含量的差异可作为近红外光谱分类结果的理化验证。

Determination of Plum Maturity Levels by Using NIR and Chemometrics

Harvest maturity is an important factor that can affect plum quality during the storage period. In order to achieve rapid non-destructive determination of plum maturity level, in this study, 163 plum samples were classified into three maturity levels-early stage (n = 53), middle stage (n = 55), and late stage (n = 55)-according to different fruit development times after flowering. Near infrared spectroscopy (NIR) was used to classify plum samples into different maturity levels. The accuracies of the classification models established by Mahalanobis distances analysis, soft independent modeling of class analogy, and least squares-support vector machine were compared. Mahalanobis distance model with the first 20 principal components (PCs) extracted from original spectra as inputs yielded the best result, and the correct classification rates for calibration set and prediction set were 96.33% and 96.30%, respectively. Soluble solid content (SSC), titratable acid, and firmness were analyzed using single factor one-way analysis of variance (ANOVA). There were significant differences in each index, and the greatest difference was found in SSC. The PCs that were extracted from the data of the three quality indices had very similar clustering tendencies as those extracted from spectrum data. These results showed that combining NIR with chemometrics to discriminate plums of different maturity levels was feasible and effective, and the difference in quality indices could be a physicochemical validation for the classification results from NIR.