Abstract Short wave near infrared spectroscopy has found use in non-invasive assessment of dry matter content (DMC, % fresh weight) of mango fruit, both as a guide to harvest maturity and ensure eating quality of ripened fruit. In this study, this application is optimised in terms of pre-processing of spectra, the source of variations important to model performance documented, and the performance of cultivar or physiological stage specific partial least squares regression (PLSR) models, global PLSR and an artificial neural network (ANN) model are compared. The data set consisted of 4675 samples acquired across four seasons, ten cultivars and two growing regions, with harvest populations used as cross validation groups. The data of the fourth season was reserved as an independent test set. Spectra pre-treatment of mean centred Savitzy-Golay second derivative (second order polynomial using a 17 point interval) and use of the wavelength range 684−990 nm gave the lowest RMSECV for PLSR models, although other ranges had similar statistics. The fruit physiological stage had the greatest impact on PLSR model performance, compared to cultivar, year or growing region, as estimated using a ‘variable importance metric’ devised and implemented using a random forest regression. The use of specific (cultivar or physiological stage) PLSR models improved prediction results of the independent validation set (RMSEP on DMC decreased from 1.01 to 0.88 %), and was similar to the result of a global ANN model (0.89 %). The use of an ANN model is recommended in terms of ease of use of a single model across all cultivars.

中文翻译：

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针对完整芒果果实干物质含量的 NIRS 模型实现跨季节、地点和品种的稳健性

摘要 短波近红外光谱已用于芒果果实干物质含量（DMC，鲜重百分比）的非侵入性评估，既可作为收获成熟度的指南，又可确保成熟果实的食用质量。在这项研究中，该应用程序在光谱的预处理、对模型性能记录重要的变异源以及栽培品种或生理阶段特定偏最小二乘回归 (PLSR) 模型、全局 PLSR 和人工神经网络（ANN）模型进行了比较。该数据集包括在四个季节、十个品种和两个生长区域中采集的 4675 个样本，收获种群用作交叉验证组。第四季数据保留为独立测试集。平均中心的 Savitzy-Golay 二阶导数（使用 17 点间隔的二阶多项式）的光谱预处理和 684-990 nm 波长范围的使用为 PLSR 模型提供了最低的 RMSECV，尽管其他范围有类似的统计数据。与栽培品种、年份或生长区域相比，水果生理阶段对 PLSR 模型性能的影响最大，这是使用使用随机森林回归设计和实施的“可变重要性度量”估计的。特定（栽培品种或生理阶段）PLSR 模型的使用改进了独立验证集的预测结果（DMC 上的 RMSEP 从 1.01 降低到 0.88%），并且类似于全局 ANN 模型的结果（0.89%）。建议使用 ANN 模型，因为它在所有品种中都易于使用单个模型。