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傅里叶变换红外光谱(FTIR)技术在中红外(MIR)和近红外(NIR)光谱中的应用,用于测定植物物种和粪便样品中的正烷烃和长链醇含量。

Application of Fourier transform infrared spectroscopy (FTIR) techniques in the mid-IR (MIR) and near-IR (NIR) spectroscopy to determine n-alkane and long-chain alcohol contents in plant species and faecal samples.

机构信息

Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Trás-os-Montes and Alto Douro (UTAD-CITAB)/Inov4Agro (Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production), Vila Real, Portugal.

CoLAB Vines&Wines - National Collaborative Laboratory for the Portuguese Wine Sector, Associação para o Desenvolvimento da Viticultura Duriense (ADVID), Régia Douro Park, 5000-033 Vila Real, Portugal.

出版信息

Spectrochim Acta A Mol Biomol Spectrosc. 2022 Nov 5;280:121544. doi: 10.1016/j.saa.2022.121544. Epub 2022 Jun 21.

Abstract

n-Alkanes and long-chain alcohols (LCOH) have been used as faecal markers to assess the feeding behaviour of both wild and domestic herbivore species. However, their chemical analysis is time-consuming and expensive, making it necessary to develop more expeditious methodologies to evaluate concentrations of these markers. This work aimed to evaluate the use of Fourier Transform Infrared Spectroscopy (FTIR) technology in the near infrared (NIR) and mid infrared (MIR) intervals, for the determination of n-alkane and LCOH concentrations of different plant species and faecal samples of domestic herbivores. Spectra of 33 feed samples, namely L. perenne, T. repens, U. gallii, short heathers (mixture of Erica spp. and Calluna vulgaris), improved pasture grasses (mixture of L. perenne and A. capillaris), heath grasses (mixture of P. longifolium and A. curtissii), improved pasture species (mixture of L. perenne, T. repens and A. capillaris) and herbaceous species (mixture of all herbaceous species found in the plot)) and 181 faecal samples (cattle and horses) were recorded. In order to develop calibrations for the prediction of n-alkanes and LCOH concentrations, partial least squares (PLS) regression was used. Regarding the models developed for plant species, the best results were observed for the calibrations using NIR. The best external validation coefficients of determination (Rv) obtained were 0.90 and 0.79 for LCOH and n-alkanes, respectively. For faecal samples, in the NIR interval, results indicate similar external validation predictions (Rv) for both animal species (0.64). On the contrary, in the MIR interval, differences between cattle (0.70) and horses (0.57) faecal samples in Rv were observed. Regarding the models created for both animal species faeces, LCOH (C-OH and C-OH concentrations ranging from 713.3 to 4451.9 mg/kg DM, respectively; Rv values ranging from 0.72 to 0.95) and n-alkanes (C31 and C33 concentrations ranging from 112.8 to 643.2 mg/kg DM, respectively; Rv values ranging from 0.19 to 0.90) present in higher concentrations tended to be those with better estimates. Results obtained suggest that the selection of the technique to be used may depend on the type of matrix, being the homogeneity of the matrices one of the most important factors for its success. In order to improve the accuracy and robustness of the models created for the estimation of the concentrations of these markers using these methodologies, the database (greater variability) used for the calibrations of these models must be increased.

摘要

正癸烷和长链醇(LCOH)已被用作评估野生和家养草食动物摄食行为的粪便标志物。然而,它们的化学分析既耗时又昂贵,因此有必要开发更快捷的方法来评估这些标志物的浓度。本研究旨在评估傅里叶变换近红外(NIR)和中红外(MIR)光谱技术在不同植物物种和草食动物粪便样本中测定正烷烃和 LCOH 浓度的应用。对 33 种饲料样本(白车轴草、匍匐翦股颖、三叶草、矮生石楠(帚石楠属和欧洲石楠混合)、改良草地草(白车轴草和 A. capillaris 混合)、石楠草地草( P. longifolium 和 A. curtissii 混合)、改良草地物种(白车轴草、匍匐翦股颖和 A. capillaris 混合)和草本物种(研究区域内所有草本物种的混合物))和 181 个粪便样本(牛和马)进行了光谱记录。为了建立预测正烷烃和 LCOH 浓度的校准模型,使用了偏最小二乘法(PLS)回归。对于植物物种建立的模型,使用 NIR 得到的最佳结果。获得的最佳外部验证决定系数(Rv)分别为 LCOH 和正烷烃的 0.90 和 0.79。对于粪便样本,在 NIR 范围内,两种动物物种(牛和马)的外部验证预测(Rv)结果相似(分别为 0.64)。相反,在 MIR 范围内,牛(0.70)和马(0.57)粪便样本的 Rv 存在差异。对于两种动物粪便建立的模型,LCOH(C-OH 和 C-OH 浓度分别为 713.3 至 4451.9mg/kg DM;Rv 值分别为 0.72 至 0.95)和正烷烃(C31 和 C33 浓度分别为 112.8 至 643.2mg/kg DM;Rv 值分别为 0.19 至 0.90)的浓度较高,估计值较好。研究结果表明,所选技术的选择可能取决于基质的类型,基质的均一性是其成功的最重要因素之一。为了提高使用这些方法估算这些标志物浓度的模型的准确性和稳健性,必须增加这些模型校准所用数据库(更大的变异性)的大小。

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