Local partial least squares based on global PLS scores

Shen, Guanghui; Lesnoff, Matthieu; Baeten, Vincent; Dardenne, Pierre; Davrieux, Fabrice; Ceballos, Hernán; Belalcázar, John; Dufour, Dominique; Yang, Zengling; Han, Lujia; Pierna, Juan Antonio Fernández

doi:10.1002/cem.3117

Cited by 30 publications

(23 citation statements)

References 27 publications

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“…Furthermore, in a bid to support the genetic improvement of crops such as cassava, large databases of biophysical and NIRS data are developed, to establish robust calibrations based on diverse sample sets . Specific references such as Davrieux et al (2016) and Shen et al (2019) provide procedures on how large databases can be handled using several methods for the development of accurate predictions for various traits.…”

Section: Spectroscopic Techniquesmentioning

confidence: 99%

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Near‐infrared spectroscopy applications for high‐throughput phenotyping for cassava and yam: A review

Alamu

Nuwamanya

Cornet

et al. 2020

Int J of Food Sci Tech

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The review aimed to identify the different high-throughput phenotyping (HTP) techniques that used for quality evaluation in cassava and yam breeding programmes, and this has provided insights towards the development of metrics and their application in cassava and yam improvements. A systematic review of the published research articles involved the use of NIRS in analysing the quality traits of cassava and yam was carried out, and Scopus, Science Direct, Web of Sciences and Google Scholar were searched. The results of the review established that NIRS could be used in understanding the chemical constituents (carbohydrate, protein, vitamins, minerals, carotenoids, moisture, starch, etc.) for high-throughput phenotyping. This study provides preliminary evidence of the application of NIRS as an efficient and affordable procedure for HTP. However, the feasibility of using mid-infrared spectroscopy (MIRS) and hyperspectral imaging (HSI) in combination with the NIRS could be further studied for quality traits phenotyping.

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Section: Spectroscopic Techniquesmentioning

confidence: 99%

“…(2016) and Shen et al . (2019) provide procedures on how large databases can be handled using several methods for the development of accurate predictions for various traits.…”

Section: Nirs and Hsi Techniques For High‐throughput Phenotyping For mentioning

confidence: 99%

Near‐infrared spectroscopy applications for high‐throughput phenotyping for cassava and yam: A review

Alamu

Nuwamanya

Cornet

et al. 2020

Int J of Food Sci Tech

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“…Near‐infrared spectroscopy (NIRS) is a fast and nondestructive analytical method for predicting chemical compositions, currently used in many agronomic contexts . The partial least squared regression (PLSR) is very efficient for NIRS predictions when the relationship between the spectral information and the response is linear.…”

Section: Introductionmentioning

confidence: 99%

Comparison of locally weighted PLS strategies for regression and discrimination on agronomic NIR data

Lesnoff

Metz

Roger

2020

Journal of Chemometrics

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In multivariate calibrations, locally weighted partial least squared regression (LWPLSR) is an efficient prediction method when heterogeneity of data generates nonlinear relations (curvatures and clustering) between the response and the explicative variables. This is frequent in agronomic data sets that gather materials of different natures or origins. LWPLSR is a particular case of weighted PLSR (WPLSR; ie, a statistical weight different from the standard 1/n is given to each of the n calibration observations for calculating the PLS scores/loadings and the predictions). In LWPLSR, the weights depend from the dissimilarity (which has to be defined and calculated) to the new observation to predict. This article compares two strategies of LWPLSR: (a) "LW": the usual strategy where, for each new observation to predict, a WPLSR is applied to the n calibration observations (ie, entire calibration set) vs (b) "KNN-LW": a number of k nearest neighbors to the observation to predict are preliminary selected in the training set and WPLSR is applied only to this selected KNN set. On three illustrating agronomic data sets (quantitative and discrimination predictions), both strategies overpassed the standard PLSR. LW and KNN-LW had close prediction performances, but KNN-LW was much faster in computation time. KNN-LW strategy is therefore recommended for large data sets. The article also presents a new algorithm for WPLSR, on the basis of the "improved kernel #1" algorithm, which is competitor and in general faster to the already published weighted PLS nonlinear iterative partial least squares (NIPALS). K E Y W O R D Sdiscrimination, locally weighted calibration, near-infrared spectroscopy, partial least squares, regression

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“…Next, supervised PLS-DA modeling was applied to improve class discrimination between the two groups (infected versus control). PLS-DA utilizes prior information of class membership and hence optimizes separation [ 21 ]. The validity of the model was evaluated using R 2 and Q 2 values, where R 2 represents the proportion of variance in the data explained by the model and suggests goodness of fit, and Q 2 shows the predictability of the model.…”

Section: Methodsmentioning

confidence: 99%

Structural, Functional, and Metabolic Alterations in Human Cerebrovascular Endothelial Cells during Toxoplasma gondii Infection and Amelioration by Verapamil In Vitro

et al. 2020

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Toxoplasma gondii (T. gondii), the causative agent of toxoplasmosis, is a frequent cause of brain infection. Despite its known ability to invade the brain, there is still a dire need to better understand the mechanisms by which this parasite interacts with and crosses the blood–brain barrier (BBB). The present study revealed structural and functional changes associated with infection and replication of T. gondii within human brain microvascular endothelial cells (BMECs) in vitro. T. gondii proliferated within the BMECs and disrupted the integrity of the cerebrovascular barrier through diminishing the cellular viability, disruption of the intercellular junctions and increasing permeability of the BMEC monolayer, as well as altering lipid homeostasis. Proton nuclear magnetic resonance (1H NMR)-based metabolomics combined with multivariate data analysis revealed profiles that can be attributed to infection and variations in the amounts of certain metabolites (e.g., amino acids, fatty acids) in the extracts of infected compared to control cells. Notably, treatment with the Ca2+ channel blocker verapamil rescued BMEC barrier integrity and restricted intracellular replication of the tachyzoites regardless of the time of treatment application (i.e., prior to infection, early- and late-infection). This study provides new insights into the structural and functional changes that accompany T. gondii infection of the BMECs, and sheds light upon the ability of verapamil to inhibit the parasite proliferation and to ameliorate the adverse effects caused by T. gondii infection.

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Local partial least squares based on global PLS scores

Cited by 30 publications

References 27 publications

Near‐infrared spectroscopy applications for high‐throughput phenotyping for cassava and yam: A review

Near‐infrared spectroscopy applications for high‐throughput phenotyping for cassava and yam: A review

Comparison of locally weighted PLS strategies for regression and discrimination on agronomic NIR data

Structural, Functional, and Metabolic Alterations in Human Cerebrovascular Endothelial Cells during Toxoplasma gondii Infection and Amelioration by Verapamil In Vitro

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