Sohn Miryeong, Barton Franklin E, Morrison Wiley H, Archibald Douglas D
USDA-Agricultural Research Service, Richard B. Russell Agricultural Research Center, PO Box 5677, Athens, Georgia 30605, USA.
Appl Spectrosc. 2003 May;57(5):551-6. doi: 10.1366/000370203321666588.
Shive, the nonfiberous core portion of the stem, in flax fiber after retting is related to fiber quality. The objective of this study is to develop a standard calibration model for determining shive content in retted flax by using near-infrared reflectance spectroscopy. Calibration samples were prepared by manually mixing pure, ground shive and pure, ground fiber from flax retted by three different methods (water, dew, and enzyme retting) to provide a wide range of shive content from 0 to 100%. Partial least-squares (PLS) regression was used to generate a calibration model, and spectral data were processed using various pretreatments such as a multiplicative scatter correction (MSC), normalization, derivatives, and Martens' Uncertainty option to improve the calibration model. The calibration model developed with a single sample set resulted in a standard error of 1.8% with one factor. The best algorithm was produced from first-derivative processing of the spectral data. MSC was not effective processing for this model. However, a big bias was observed when independent sample sets were applied to this calibration model to predict shive content in flax fiber. The calibration model developed using a combination sample set showed a slightly higher standard error and number of factors compared to the model for a single sample set, but this model was sufficiently accurate to apply to each sample set. The best algorithm for the combination sample set was generated from second derivatives followed by MSC processing of spectral data and from Martens' Uncertainty option; it resulted in a standard error of 2.3% with 2 factors. The value of the digital second derivative centered at 1674 nm for these spectral data was highly correlated to shive content of flax and could form the basis for a simple, low-cost sensor for the shive or fiber content in retted flax.
亚麻纤维脱胶后，茎的非纤维状核心部分即麻屑与纤维质量相关。本研究的目的是建立一个标准校准模型，用于通过近红外反射光谱法测定脱胶亚麻中的麻屑含量。校准样品通过手动混合三种不同脱胶方法（水浸、雨露和酶脱胶）处理后的亚麻纯麻屑粉和纯纤维粉制备而成，麻屑含量范围为0%至100%。采用偏最小二乘法（PLS）回归生成校准模型，并使用多种预处理方法（如多元散射校正（MSC）、归一化、导数和马滕斯不确定性选项）对光谱数据进行处理，以改进校准模型。用单个样品集建立的校准模型，单因素情况下标准误差为1.8%。光谱数据的一阶导数处理得到了最佳算法。MSC对该模型的处理效果不佳。然而，当将独立样品集应用于该校准模型以预测亚麻纤维中的麻屑含量时，出现了较大偏差。与单个样品集模型相比，使用组合样品集建立的校准模型标准误差和因子数略高，但该模型足够准确，可应用于每个样品集。组合样品集的最佳算法是对光谱数据先进行二阶导数处理，然后进行MSC处理，并采用马滕斯不确定性选项；得到的标准误差为2.3%，有2个因子。这些光谱数据在1674nm处的数字二阶导数值与亚麻的麻屑含量高度相关，可为一种简单、低成本的脱胶亚麻麻屑或纤维含量传感器奠定基础。
