Reduction of repeatability error for analysis of variance-Simultaneous Component Analysis (REP-ASCA): Application to NIR spectroscopy on coffee sample

Ryckewaert, Maxime; Gorretta, Nathalie; Henriot, Fabienne; Marini, Federico; Roger, Jean‐Michel

doi:10.1016/j.aca.2019.12.024

Cited by 14 publications

(7 citation statements)

References 25 publications

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“…An analysis of variance method called Reduction of Repeatability Error-Analysis of Variance-Simultaneous Component Analysis (REP-ASCA) [ 24 ] was used (1) to assess whether the genotype has a significant impact on speckle measurement, (2) to identify variables involved in this differentiation.…”

Section: Methodsmentioning

confidence: 99%

“…To analyze a set of speckle measurements associated with a design of experiments, methods of analysis of variance must be adapted to multivariate data. The method called Reduction of Repeatability Error-Analysis of Variance-Simultaneous Component Analysis (REP-ASCA) [ 24 ] is suitable to study the influence of identified factors while reducing error due to the lack of repeatability of measurements.…”

Section: Introductionmentioning

confidence: 99%

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A New Optical Sensor Based on Laser Speckle and Chemometrics for Precision Agriculture: Application to Sunflower Plant-Breeding

Ryckewaert¹,

Heran²,

Faur³

et al. 2020

Sensors

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New instruments to characterize vegetation must meet cost constraints while providing accurate information. In this paper, we study the potential of a laser speckle system as a low-cost solution for non-destructive phenotyping. The objective is to assess an original approach combining laser speckle with chemometrics to describe scattering and absorption properties of sunflower leaves, related to their chemical composition or internal structure. A laser diode system at two wavelengths 660 nm and 785 nm combined with polarization has been set up to differentiate four sunflower genotypes. REP-ASCA was used as a method to analyze parameters extracted from speckle patterns by reducing sources of measurement error. First findings have shown that measurement errors are mostly due to unwilling residual specular reflections. Moreover, results outlined that the genotype significantly impacts measurements. The variables involved in genotype dissociation are mainly related to scattering properties within the leaf. Moreover, an example of genotype classification using REP-ASCA outcomes is given and classify genotypes with an average error of about 20%. These encouraging results indicate that a laser speckle system is a promising tool to compare sunflower genotypes. Furthermore, an autonomous low-cost sensor based on this approach could be used directly in the field.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Optical Sensor Based on Laser Speckle and Chemometrics for Precision Agriculture: Application to Sunflower Plant-Breeding

Ryckewaert¹,

Heran²,

Faur³

et al. 2020

Sensors

Self Cite

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show abstract

“…In this way, the effect of the studied factor is compared with its distribution under the null hypothesis as estimated by the permutations. 17,18 Then, a bilinear decomposition of each effect matrix is performed using simultaneous component analysis (SCA). In the context of ASCA (under the constraints of ANOVA), this reduces to PCA, as the goal is to model the variability linked to each of the factors.…”

Section: Anova-ascamentioning

confidence: 99%

Methodologies based on ASCA to elucidate the influence of a subprocess: Vinification as a case of study

Schorn-García

Giussani

Busto

et al. 2023

Journal of Chemometrics

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In food manufacturing and processing, food matrix complexity usually makes it difficult to detect unwanted subprocesses, which can impact the quality of the final product. In the case of wine alcoholic fermentation, the main process is the conversion of sugars into ethanol and carbon dioxide, but the presence of some unwanted microorganisms could lead to wine contamination by production of undesired minor compounds. In the study we present, an intentional contamination of the vinification process by the addition of acetic acid bacteria was studied using a portable Fourier transform infrared (FT-IR) spectrometer. ANOVA simultaneous component analysis (ASCA) was used to unravel these minor variability sources. However, as the subprocess is two orders of magnitude lower in concentration than the main process, different methodologies were used to enhance the ASCA results, such as to select a specific spectral region related to acetic acid bacteria metabolism, to divide the process in time intervals related to the different phases, or to unfold the data matrix in different ways. In addition, spectral preprocessing was optimized to scale up small peaks related to the subprocess. Our results show that several methodologies to build ASCA models can be applied to emphasize and better characterize bacteria contamination subprocesses.

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“…It is interesting to note that if the entire spectrum is considered rather than absorbance or reflectance at given wavelengths, such corrections are related to the orthogonalization approaches from the chemometrics literature. Indeed, recently Ryckeweart et al [31] proposed to make use of spectra replicates, typically obtained when characterizing plants under genetic trials, to reduce the repeatability error. They developed a new preprocessing technique based on orthogonalization after an ANOVA-simultaneous component analysis (REP-ASCA).…”

Section: Preprocessing Nir Spectramentioning

confidence: 99%

Phenomic Selection: A New and Efficient Alternative to Genomic Selection

Robert

Brault

Rincent

et al. 2022

Methods in Molecular Biology

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Recently, it has been proposed to switch molecular markers to near-infrared (NIR) spectra for inferring relationships between individuals and further performing phenomic selection (PS), analogous to genomic selection (GS). The PS concept is similar to genomic-like omics-based (GLOB) selection, in which molecular markers are replaced by endophenotypes, such as metabolites or transcript levels, except that the phenomic information obtained for instance by near-infrared spectroscopy (NIRS) has usually a much lower cost than other omics. Though NIRS has been routinely used in breeding for several decades, especially to deal with end-product quality traits, its use to predict other traits of interest and further make selections is new. Since the seminal paper on PS, several publications have advocated the use of spectral acquisition (including NIRS and hyperspectral imaging) in plant breeding towards PS, potentially providing a scope of what is possible. In the present chapter, we first come back to the concept of PS as originally proposed and provide a classification of selected papers related to the use of phenomics in breeding. We further provide a review of the selected literature concerning the type of technology used, the preprocessing of the spectra, and the statistical modeling to make predictions. We discuss the factors that likely affect the efficiency of PS and compare it to GS in terms of predictive ability. Finally, we propose several prospects for future work and application of PS in the context of plant breeding.

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Reduction of repeatability error for analysis of variance-Simultaneous Component Analysis (REP-ASCA): Application to NIR spectroscopy on coffee sample

Cited by 14 publications

References 25 publications

A New Optical Sensor Based on Laser Speckle and Chemometrics for Precision Agriculture: Application to Sunflower Plant-Breeding

A New Optical Sensor Based on Laser Speckle and Chemometrics for Precision Agriculture: Application to Sunflower Plant-Breeding

Methodologies based on ASCA to elucidate the influence of a subprocess: Vinification as a case of study

Phenomic Selection: A New and Efficient Alternative to Genomic Selection

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