Department of Food Science, Faculty of Science and Technology, Aarhus University, Kirstinebjergvej 10, DK-5792, Aarslev, Denmark.
J Sci Food Agric. 2014 Mar 30;94(5):955-62. doi: 10.1002/jsfa.6343. Epub 2013 Sep 13.
Fruit dry matter (DM) and soluble solids content (SSC) are primarily composed of carbohydrate and are standard parameters for assessing quality. Near infrared spectroscopy provides potential for non-destructive fruit quality analysis but the collinearity between DM and SSC is an issue for prediction. Shorter wavelength spectra have been used for the prediction of fruit DM and SSC, but radiation between 1000 and 2500 nm may be suitable for distinguishing between the two forms of carbohydrate.
Spectra and DM and SSC samples were taken for a total of 450 'Elshof' apples 30, 58 and 93 days after harvest. Regression models were built using the interval partial least squares method. Prediction models for DM and SSC for each day yielded R² values between 0.63 and 0.86 and residual predictive deviations (RPDs) between 1.7 and 2.7 for DM, and R² = 0.76-0.85 and RPDs = 2.2-2.6 for SSC.
Model RPD values were not high enough for general quantitative predictions, although they compare well to previous work. Certain factors affected model success, including changes in fruit physiology over time and the range of reference data. The complexity of absorbance spectra for DM and SSC plus their strong correlation suggests that prediction models cannot easily distinguish between soluble and non-soluble forms of carbohydrate.
水果干物质（DM）和可溶性固形物含量（SSC）主要由碳水化合物组成，是评估水果品质的标准参数。近红外光谱技术为水果非破坏性品质分析提供了可能，但 DM 和 SSC 之间的共线性是预测的一个问题。较短波长的光谱已被用于预测水果的 DM 和 SSC，但 1000 到 2500nm 之间的辐射可能更适合区分这两种碳水化合物形式。
共采集了 450 个“Elshof”苹果的光谱和 DM 和 SSC 样本，收获后 30、58 和 93 天分别进行采集。使用区间偏最小二乘法建立回归模型。对每一天的 DM 和 SSC 进行预测的模型，其 R² 值在 0.63 到 0.86 之间，DM 的 RPD 值在 1.7 到 2.7 之间，SSC 的 R²值在 0.76 到 0.85 之间，RPD 值在 2.2 到 2.6 之间。
模型的 RPD 值不足以进行一般的定量预测，尽管与之前的工作相比，这些值已经相当不错。某些因素影响了模型的成功，包括随着时间的推移水果生理变化和参考数据的范围。DM 和 SSC 的吸收光谱复杂，且它们之间相关性很强，这表明预测模型很难区分可溶性和不可溶性碳水化合物的形式。
