Abstract In this work, non-destructive autofluorescence of plums was employed to study the chlorophylls’ concentration evolution along the maturation process. For that, excitation-emission matrices (EEMs), containing full fluorescence information, were collected with a fibre-optic, assembled to a spectrofluorometer. Data analysis was performed with several second-order multi-way algorithms, such as parallel factor analysis (PARAFAC), multi-way partial least-squares (N-PLS), unfolded partial least-squares (U-PLS), and multivariate curve resolution-alternating least-squares (MCR-ALS). Firstly, the EEMs of each plum, collected each week along the maturation process, were processed with PARAFAC. Two components were used to model the data and the excitation and emission loadings were obtained. Score values for the first PARAFAC component showed a clear evolution with time, increasing during the first five weeks, and decreasing for the last weeks. Also, the chlorophyll concentrations obtained by HPLC analysis, in the skin and the whole fruit, were compared with those obtained with different algorithms mentioned before. Best results were obtained in the case of skin for all algorithms. Similar correlation coefficients (r) were obtained in all cases (0.899 (PARAFAC); 0.940 (U-PLS); 0.936 (N-PLS) and 0.958 (MCR-ALS)). When the elliptical joint confidence region (EJCR), for the slope and intercept, were calculated, the theoretically expected values of 1 and 0, for the slope and intercept, respectively, were included in all ellipses. However, it was observed that for the skin data and U-PLS and N-PLS algorithms, the EJCR confidence region was smaller than in the other cases.

中文翻译：

---

二阶校准结合荧光光纤数据建模作为监测李子成熟阶段的新方法

摘要 本研究利用李子的无损自发荧光技术研究了成熟过程中叶绿素浓度的演变。为此，包含完整荧光信息的激发-发射矩阵 (EEM) 用光纤收集，组装到分光荧光计上。数据分析采用多种二阶多路算法，如平行因子分析（PARAFAC）、多路偏最小二乘法（N-PLS）、未折叠偏最小二乘法（U-PLS）和多元曲线分辨率交替最小二乘法 (MCR-ALS)。首先，在成熟过程中每周收集每个李子的 EEM，用 PARAFAC 进行处理。使用两个组件对数据进行建模，并获得激发和发射载荷。第一个 PARAFAC 组件的得分值随着时间的推移呈现出明显的变化，在前五周增加，在最后几周减少。此外，将通过 HPLC 分析获得的果皮和整个水果中的叶绿素浓度与使用前面提到的不同算法获得的浓度进行比较。对于所有算法，在皮肤的情况下获得了最佳结果。在所有情况下都获得了类似的相关系数 (r)（0.899 (PARAFAC)；0.940 (U-PLS)；0.936 (N-PLS) 和 0.958 (MCR-ALS)）。当计算斜率和截距的椭圆联合置信区 (EJCR) 时，斜率和截距的理论预期值分别为 1 和 0，包括在所有椭圆中。然而，观察到对于皮肤数据和 U-PLS 和 N-PLS 算法，