Abstract Visible/Near-infrared (Vis/NIR) spectroscopy at a range of 450–1000 nm was used to predict the values of three qualitative variables (starch, reducing sugar, and moisture content) on 200 potato tubers from 2 potato genotypes (‘Agria’ and ‘Clone 397009–8’) stored in both traditional and cold storages. After spectroscopy measurements, these variables were measured using reference methods. Then, Partial Least Square (PLS) models were developed. To evaluate developed models, Root Mean Square Error of calibration and cross validation (RMSEC and RMSECV), as well as coefficient of determination for calibration and cross validation (R 2 C and R 2 CV), and Residual Predictive Deviation (RPD) were used. The best prediction belonged to reducing sugar with statistical values of R 2 C = 0.99, R 2 CV = 0.98, RMSEC = 0.029, RMSECV = 0.037, and RPD = 7.57 in ‘Clone’ genotype stored under cold storage. The weakest prediction was related to moisture content with statistical values of R 2 C = 0.93, R 2 CV = 0.92, RMSEC = 0.268, RMSECV = 0.279, and RPD = 6.45 in stored ‘Clone’ genotypes under cold storage. Results of the study showed that, Vis/NIR spectroscopy as a non-destructive, fast, and reliable technique can be used for prediction of inner compositions of stored potatoes.

中文翻译：

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使用可见/近红外 (Vis/NIR) 光谱预测储存期间马铃薯的内部成分变化

摘要 450-1000 nm 范围内的可见/近红外 (Vis/NIR) 光谱用于预测来自 2 个马铃薯基因型的 200 个马铃薯块茎的三个定性变量（淀粉、还原糖和水分含量）的值（' Agria' 和 'Clone 397009–8') 存储在传统和冷库中。在光谱测量之后，使用参考方法测量这些变量。然后，开发了偏最小二乘 (PLS) 模型。为了评估开发的模型，使用了校准和交叉验证的均方根误差（RMSEC 和 RMSECV），以及校准和交叉验证的决定系数（R 2 C 和 R 2 CV）和残余预测偏差 (RPD) . 最佳预测属于还原糖，其统计值为 R 2 C = 0.99、R 2 CV = 0.98、RMSEC = 0.029、RMSECV = 0.037 和 RPD = 7。57 在冷藏下储存的“克隆”基因型。最弱的预测与水分含量有关，在冷藏条件下储存的“克隆”基因型中，R 2 C = 0.93、R 2 CV = 0.92、RMSEC = 0.268、RMSECV = 0.279 和 RPD = 6.45 的统计值。研究结果表明，可见/近红外光谱作为一种无损、快速、可靠的技术，可用于预测储存马铃薯的内部成分。