A common near infrared-based partial least squares regression model for the prediction of wood density of Pinus pinaster and Larix x eurolepis
A common near infrared-based partial least squares regression model for the prediction of wood density of Pinus pinaster and Larix x eurolepis
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DOI：
10.1007/s00226-010-0383-x
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发表时间：
2012-01-01
影响因子：
3.4
通讯作者：
Rodrigues, Jose
中科院分区：
文献类型：
--
作者：
Alves, Ana;Santos, Antonio;Rodrigues, Jose
木材密度定义为质量与体积之比，因此原则上应该可以计算多个物种的独特偏最小二乘回归（PLS-R）模型。基于 X 射线微密度数据的木材密度 PLS-R 模型针对松树和欧洲落叶松的每个物种以及这两个物种一起计算。经过交叉验证和测试集验证后，组合数据集并计算最终模型。通用模型的残差预测偏差 (RPD) 为 3.1，范围误差比 (RER) 为 11.7，SEP/SEC 为 1.06。 Pinus pinaster 和 Larix x eurolepis 的单一模型给出的 RPD 为 3.5 和 3.2，RER 为 13 和 11，SEP/SEC 为 1.2。据作者所知，所有获得的 PLS-R 模型都是第一个满足 AACC 方法 39-00（AACC 中的 AACC 方法，39-00:15，1999）要求的模型，至少用于筛选（RPD 千分之 2.5）。尽管该方法和定义的限值是为分析谷物而开发的，但在木材限值可用之前，它们可以用作粗略的经验法则。与已发表的结果相比，PLS-R 模型的改进可能是由于三个事实：(1) 为单个光谱收集的扫描次数较多，(2) NIR 光谱和 X 射线微密度值更好地代表了样品，(3) NIR 光谱和 X 射线微密度值的测量位置尽可能严格地重合。
Wood density is defined as the ratio of mass to volume and therefore in principle it should be possible to calculate a unique partial least squares regression (PLS-R) model for several species. PLS-R models for wood density based on X-ray microdensity data were calculated for each species Pinus pinaster and Larix x eurolepis and for both species together. After cross-validation and test set validation the data sets were combined and final models were calculated. The common model gave a residual prediction deviation (RPD) of 3.1, a range error ratio (RER) of 11.7, and a SEP/SEC of 1.06. The single models for Pinus pinaster and Larix x eurolepis gave RPD's of 3.5 and 3.2, RER's of 13 and 11, and a SEP/SEC of 1.2. To the best knowledge of the authors all obtained PLS-R models are the first ones that fulfil the requirements according to AACC Method 39-00 (AACC in AACC Method, 39-00:15, 1999) to be used at least for screening (RPD a parts per thousand yen 2.5). Although this method and the defined limits were developed for the analysis of grains they can be used as a rough rule of thumb until limits for wood are available. The improvement of the PLS-R models, compared to published results, might be due to three facts (1) the higher number of scans collected for a single spectrum, (2) that the samples were better represented by the NIR spectra and X-ray microdensity values, and (3) that the sites for the measurement of NIR spectra and X-ray microdensity were coincided as strictly as possibly.