Evaluation of MEMS NIR Spectrometers for On-Farm Analysis of Raw Milk Composition

Indahl

Skogholt

et al. 2022

Multiway datasets arise in various situations, typically from specialised measurement technologies, as a result of measuring data over varying conditions in multiple dimensions or simply as sets of possibly multichannel images. When such measurements are intended for predicting some external properties, the amount of methods available is limited. The multilinear partial least squares (PLS) is among the few available options. In the present work, we generalise the canonical partial least squares framework to handle multiway data. We demonstrate the resulting multiway data analysis method to be capable of building parsimonious models, encompassing continuous and categorical responses—both single and multiple—in a unifying framework. This also enables inclusion of additional responses/information that can contribute to more parsimonious models. Finally, we achieve a considerable advantage in computational speed without sacrificing numerical precision by deflating the responses and orthogonalising scores rather than the more costly deflations of the predictor data.

Section: Data Setsmentioning

confidence: 99%

The canonical partial least squares approach to analysing multiway datasets—N‐CPLS

Indahl

Skogholt

et al. 2022

“…The milk dataset is a perfect example of multiblock multiway multiple response data set, hence suitable to show all the capabilities of the SKCovSel method. The milk data set has spectral, protein and fat measurements performed on 296 milk samples 31 . Three portable spectral sensors were used to collected spectral data on milk samples: NIRONE 1.4 (1100 to 1400 nm), NIRONE 2.0 (1550 to 1950 nm) and NIRONE 2.5 (2000 to 2450 nm) from Spectral Engines (Helsinki, Finland).…”

Section: Datamentioning

confidence: 99%

“…Due to the extra measurement in the reflection mode, the data from NIRONE 2.0 can be considered as 3‐way data (samples

\times

spectral variable

\times

measurement mode). More information on the data set and reference protein and fat analysis protocol can be obtained in the earlier study 31 . Data are summarised in Table 1.…”

Section: Datamentioning

confidence: 99%

“…The milk data set has spectral, protein and fat measurements performed on 296 milk samples. 31 Three portable spectral sensors were used to collected spectral data on milk samples: NIRONE 1.4 (1100 to 1400 nm), NIRONE 2.0 (1550 to 1950 nm) and NIRONE 2.5 (2000 to 2450 nm) from Spectral Engines (Helsinki, Finland). All measurements were performed in transmission mode except for the NIRONE 2.0, for which more measurements of the same samples were performed in reflectance mode.…”

Section: Comments On the Skcovsel Algorithmmentioning

confidence: 99%

“…More information on the data set and reference protein and fat analysis protocol can be obtained in the earlier study. 31 Data are summarised in Table 1. Data were partitioned into calibration (60%) and test (40%) set to show the capability of the extracted features and the regression coefficients to predict the multiple responses in the milk.…”

Section: Comments On the Skcovsel Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Swiss knife covariates selection: A unified algorithm for covariates selection in single block, multiblock, multiway, multiway multiblock cases including multiple responses

Mishra

Indahl

2022

A novel unified covariates selection algorithm called Swiss knife covariates selection (SKCovSel) is presented. It is suitable for selecting covariates in a wide range of data scenarios such as a single two‐way data block, two‐way multiblock, multiway, multiway multiblock, selection of covariates along different modes for multiway data blocks and for selecting covariates for all mentioned cases in multiple response scenarios. In the multiblock case, the method can be scale and data block order‐independent depending on the preference of the user. For multiway scenarios, the method can be multiway mode order independent, depending on the preference of the user. The proposed SKCovSel algorithm generalises the recent speed improvements from faster CovSel to all mentioned data block cases. It also reformulates the multiway case to do proper deflation and rank one slab selections. Particularly, for modelling of multiblock data sets, the SKCovSel follows the “winner takes all” strategy of the stepwise response‐oriented sequential alternation modelling. In the case of multiway data, the SKCovSel strategy considers multiway loading weights after decomposition of a high‐dimensional squared covariance matrix to select features across different modes. The algorithmic steps of the methods are presented, and cases of modelling different data types such as single block, multiblock, multiway multiblock, modes selection for multiway data and multiple responses modelling are shown. The method incorporates all popular covariates selection algorithms existing in the chemometric literature.

Iterative re‐weighted covariates selection for robust feature selection modelling in the presence of outliers (irCovSel)

Mishra

2022

A new method for feature selective modelling in the presence of outliers is presented. The method is a combination of iterative re-weighted partial least squares and the covariates selection approach. The method relies on iterative down-weighting of the outlying samples prior to estimating the squared covariance for covariates selection. In this way, the outlying samples carrying low weights have minimal influence on the squared covariance estimation, while the inliers have the maximum influence on the squared covariance estimation.The method allows selecting robust features, and models based on such features in general perform better in terms of prediction accuracy (lower error) than selecting features using equal sample weights for all samples. The algorithm description and tests of the method in single and multiple response scenarios are presented. Method performance is also demonstrated on a real spectral data set.