Comparison Between Portable and Bench-Top Near-Infrared Spectroscopy for Corn Silage Characterization Using Partial Least Square and Support Vector Regression Methods

IF 2.1 4区 化学 Q1 SOCIAL WORK
Jefferson Tales Oliva, Vinicius Herique Kieling, Felipe Augusto Bueno Rossi, Erick Oliveira Rodrigues, Giovanni Alfredo Guarneri, Larissa Macedo dos Santos Tonial
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Abstract

In this work, bench-top and portable near-infrared (NIR) spectroscopy equipment are compared in the context of generating predictive models for the quantification of phosphorus (P), potassium (K), and nitrogen (N) components from corn silage samples. For this, 200 spectral samples resulting from bench-top and portable NIR are preprocessed by the following sequence of approaches: mean centering application for removing the spectral bias/offset, Savitzky–Golay filter for highlighting signal energy absorption in relation to spectral noise, interval partial least square (iPLS) for selection of spectral region, and Monte Carlo method for outlier detection and removal. Then, from the preprocessed spectra, predictive models were built using the partial least squares (PLS) and support vector regression (SVR) methods for each chemical component and NIR equipment. In this sense, six models are generated, three for each NIR spectroscopy (or two for each element). As a result, considering all components and machine learning (ML) methods, bench-top models achieved R2 values between 0.66 (quantification of P using PLS or SVR) and 0.81 (prediction of K and N using SVR regressors) during the validation, whereas portable ones achieved values between 0.50 (prediction of K using SVR) and 0.67 (quantification of N using PLS). Our results can be considered competitive, as robust and accurate predictors are also generated.

Abstract Image

基于偏最小二乘法和支持向量回归的便携式和台式近红外光谱玉米青贮特征分析比较
在这项工作中,比较了台式和便携式近红外(NIR)光谱设备在玉米青贮样品中磷(P)、钾(K)和氮(N)成分定量预测模型的生成情况。为此,我们对200个来自台式和便携式近红外的光谱样本进行了预处理,采用了以下一系列方法:均值居中用于去除光谱偏置/偏置,Savitzky-Golay滤波用于突出与光谱噪声相关的信号能量吸收,区间偏最小二乘法(iPLS)用于选择光谱区域,蒙特卡罗方法用于异常值检测和去除。然后,根据预处理后的光谱,利用偏最小二乘(PLS)和支持向量回归(SVR)方法对各化学成分和近红外设备建立预测模型。在这个意义上,产生了六个模型,每个近红外光谱三个(或每个元素两个)。因此,考虑到所有组件和机器学习(ML)方法,在验证期间,台式模型的R2值在0.66(使用PLS或SVR量化P)和0.81(使用SVR回归量预测K和N)之间,而便携式模型的R2值在0.50(使用SVR预测K)和0.67(使用PLS量化N)之间。我们的结果可以被认为是有竞争力的,因为也产生了稳健和准确的预测因子。
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来源期刊
Journal of Chemometrics
Journal of Chemometrics 化学-分析化学
CiteScore
5.20
自引率
8.30%
发文量
78
审稿时长
2 months
期刊介绍: The Journal of Chemometrics is devoted to the rapid publication of original scientific papers, reviews and short communications on fundamental and applied aspects of chemometrics. It also provides a forum for the exchange of information on meetings and other news relevant to the growing community of scientists who are interested in chemometrics and its applications. Short, critical review papers are a particularly important feature of the journal, in view of the multidisciplinary readership at which it is aimed.
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