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Feasibility study on the potential of visible and near infrared reflectance spectroscopy to measure alpaca fibre characteristics

机译:可见光和近红外反射光谱法测量羊驼毛纤维特性的可行性研究

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Visible (Vis) and near infrared (NIR) reflectance spectroscopy is a rapid and non-destructive technique that has found many applications in assessing the quality of agricultural commodities, including wool. In this study, Vis and NIR spectroscopy combined with multivariate data analysis was investigated regarding its feasibility in predicting a range of fibre characteristics in raw alpaca wool samples. Mid-side samples (n=149) were taken from alpacas from a range of colours and ages at shearing time over 4 years (2000 to 2004) and subsequently analysed for fibre characteristics such as mean fibre diameter (MFD) and standard deviation (and coefficient of variation), spin fineness, curvature degree (and standard deviation), comfort factor, medullation percentage (by weight and number in white samples only) using traditional reference laboratory testing methods. Samples were scanned in a large cuvette using a FOSS NIRSystems 6500 monochromator instrument in reflectance mode in the Vis and NIR regions (400 to 2500 nm). Partial least squares (PLS) regression was used to develop a number of calibration models between the spectral and reference data. Mathematical pre-treatment of the spectra (second derivative) as well as various combinations of wavelength range were used in model development. The best calibration model was found when using the NIR region (1100 to 2500 nm) for the prediction of MFD, which had a coefficient of determination in cross-validation (R2) of 0.88 with a root mean square standard error of cross validation (RMSECV) of 2.62 micro m. The results show the NIR technique to have promise as a semiquantitative method for screening purposes. The lack of grease in alpaca wool samples suggests that the technique might find ready application as a rapid measurement technique for preliminary classing of shorn fleeces or, if used directly on the animal, the technology might offer an objective tool to assist in the selection of animals in breeding programmes or shows..
机译:可见光(Vis)和近红外(NIR)反射光谱是一种快速且无损的技术,已在评估包括羊毛在内的农产品的质量中得到了许多应用。在这项研究中,Vis和NIR光谱结合多元数据分析研究了其在预测羊驼毛原纤维样品中纤维特性范围方面的可行性。在4年(2000年至2004年)的剪切时间,从不同颜色和年龄的羊驼中提取中侧样品(n = 149),然后分析其纤维特性,例如平均纤维直径(MFD)和标准偏差(和变异系数),旋转细度,曲率度(和标准偏差),舒适度系数,髓质百分比(仅按重量和白色样品中的数量)使用传统的参考实验室测试方法。使用FOSS NIRSystems 6500单色仪在大比色皿中在Vis和NIR区域(400至2500 nm)中以反射模式扫描样品。偏最小二乘(PLS)回归用于建立光谱数据和参考数据之间的许多校准模型。在模型开发中使用了光谱的数学预处理(二阶导数)以及波长范围的各种组合。当使用NIR区域(1100至2500 nm)预测MFD时,发现了最佳的校准模型,该模型的交叉验证确定系数(R2)为0.88,交叉验证的均方根标准误差(RMSECV) )2.62微米结果表明,近红外技术有望作为一种半定量筛选方法。羊驼毛样品中缺少油脂,表明该技术可以作为一种快速测量技术用于ready毛的初步分类,或者直接用于动物时,该技术可能会提供客观的工具来协助动物的选择在繁殖计划或表演中

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