Redundancy Analysis to Reduce the High-Dimensional Near-Infrared Spectral Information to Improve the Authentication of Olive Oil

Abstract

The high price of marketing of extra virgin olive oil (EVOO) requires the introduction of cost-effective and sustainable procedures that facilitate its authentication, avoiding fraud in the sector. Contrary to classical techniques (such as chromatography), near-infrared (NIR) spectroscopy does not need derivatization of the sample with proper integration of separated peaks and is more reliable, rapid, and cost-effective. In this work, principal component analysis (PCA) and then redundancy analysis (RDA)�which can be seen as a constrained version of PCA�are used to summarize the high-dimensional NIR spectral information. Then PCA and RDA factors are contemplated as explanatory variables in models to authenticate oils from qualitative or quantitative analysis, in particular, in the prediction of the percentage of EVOO in blended oils or in the classification of EVOO or other vegetable oils (sunflower, hazelnut, corn, or linseed oil) by the use of some machine learning algorithms. As a conclusion, the results highlight the potential of RDA factors in prediction and classification because they appreciably improve the results obtained from PCA factors in calibration and validation.

Storage Time Detection of Torreya grandis Kernels Using Near Infrared Spectroscopy

“…PCA and redundancy analysis (RDA) techniques were used to qualitatively or quantitatively verify the authenticity of olive oil, predicting the percentage of EVOO in mixed oil or pure EVOO. The results showed the potential of RDA factors for predicting and classifying, significantly improving the calibration and validation results obtained from the PCA factors [27].…”

“…NIR troscopy was also used to discriminate the authenticity of extra virgin olive oil (E PCA and redundancy analysis (RDA) techniques were used to qualitatively or qu tively verify the authenticity of olive oil, predicting the percentage of EVOO in mi or pure EVOO. The results showed the potential of RDA factors for predicting and fying, significantly improving the calibration and validation results obtained fr PCA factors [27]. The first and second principal components were used for plotting the results of the PCA, and the classification results are presented in Figure 3.…”

Quantitative analysis of potentially toxic elements in soil by XRF based on efficient reinforcement learning and sparse partial least squares

“…Consequently, the use of pre-processing steps to enhance the validity and reliability of XRF spectral variables before quantitative analysis is necessary. 6 Variable selection is an effective pre-processing algorithm that lters out complex interference in high-dimensional spectra. 7,8 It aims to remove irrelevant variables from the properties of interest to optimize the performance of subsequent quantitative analysis tasks.…”

“…Consequently, the use of pre-processing steps to enhance the validity and reliability of XRF spectral variables before quantitative analysis is necessary. 6…”

Research on the wavelength intersection union fusion selection method for near-infrared spectroscopy analysis of apple sugar degree

“…With the significant advantages of fast, non-destructive, and environmentally friendly, the application of near-infrared spectroscopy analysis technology has expanded from the initial detection of moisture content in grains to various stages in the cultivation, harvesting, storage, processing, and market supervision of crops [1][2][3][4][5][6][7] . In recent years, both spectroscopy instruments and chemometrics have experienced rapid development.…”