Abstract The assessment of dry matter content (DMC) as a surrogate for oil content of single olive fruit was considered in terms of spatial variation in DMC, optimisation of spectral pre-treatments, wavelength range and calibration transfer procedure. There was no consistent variation in DMC from apical to distal end of the fruit. Short wave near infrared spectra (to 1100 nm) were acquired of single fruit using an interactance geometry, with the small size of the fruit resulting in baseline variation between samples. The pre-treatment methods of first derivative (D1), second derivative (D2), standard normal variate transformation (SNV), normalized spectral ratio (NSR), variable sorting for normalization (VSN) and their combinations were applied prior to development of partial least squares regression models on DMC. Root mean square error of prediction (RMSEP) values were reduced with use of the SNV-VSN-D2 pre-treatment (by 34 and 35 %) to 0.95 and 0.88 %, for two handheld NIRS instruments, used in prediction of an external set, n = 212. The percentage of the predicted population within the limits of ± 5 % of actual values was 92.5 and 88.7 % for the VSN PLSR models and 83.0 and 77.3 % for the original models, for two instruments, respectively. Variable selection allowed a further improvement in RMSEP values (to 0.93 and 0.87 %) for the two instruments, respectively. For transfer of models between instruments, model updating based on SNV-VSN-D2 pre-treated spectra resulted in a RMSEP of 1.00 %, compared to 1.79 % for D2 pre-treated spectra, outperforming the classical calibration transfer methods of piecewise direct standardization (PDS) and spectral space transformation (SST). The results have significance to the practical implementation of NIRS-DMC estimation of olive fruit in field conditions.

中文翻译：

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考虑到标准化预处理的变量分选的单个橄榄果实干物质含量的 NIRS 预测

摘要 在 DMC 的空间变化、光谱预处理的优化、波长范围和校准转移程序方面考虑了干物质含量 (DMC) 作为单个橄榄果实含油量替代物的评估。从果实的顶端到远端，DMC 没有一致的变化。使用相互作用几何学获得单个水果的短波近红外光谱（至 1100 nm），水果的小尺寸导致样品之间的基线变化。一阶导数 (D1)、二阶导数 (D2)、标准正态变量变换 (SNV)、归一化谱比 (NSR)、归一化变量排序 (VSN) 及其组合的预处理方法在开发偏DMC 上的最小二乘回归模型。使用 SNV-VSN-D2 预处理后，预测均方根误差 (RMSEP) 值降低（分别降低 34% 和 35%）至 0.95% 和 0.88%，对于用于预测外部集合的两个手持式 NIRS 仪器, n = 212。对于两种仪器，VSN PLSR 模型在实际值 ± 5 % 范围内的预测总体百分比分别为 92.5 % 和 88.7 %，原始模型为 83.0 % 和 77.3 %。变量选择允许进一步改进两种仪器的 RMSEP 值（分别为 0.93 和 0.87 %）。对于仪器之间的模型转移，基于 SNV-VSN-D2 预处理光谱的模型更新导致 RMSEP 为 1.00 %，而 D2 预处理光谱为 1.79 %，优于分段直接标准化 (PDS) 和光谱空间变换 (SST) 的经典校准传输方法。该结果对野外条件下橄榄果实的NIRS-DMC估计的实际实施具有重要意义。