ライフサイエンスコーパス: least squares

1 and PLS-DA (Discriminant Analysis by Partial Least Squares).

2 -squares (OLS) linear regression and 2-stage least-squares (2SLS) regression (MR analysis).

3 Sparse partial least squares, a variable selection approach, was used t

4 re generated with a regularized non-negative least squares algorithm from multiecho spin-echo MR imag

5 gative matrix factorization with alternating least-squares algorithm (NMF-ALS) to solve spectral over

6 xible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by ta

7 by means of multivariate data-driven partial least squares analyses.

8 A multilevel, within-group, sparse partial least squares analysis of covariation of microbial, infl

9 results were correlated by means of partial least squares analysis.

10 ent observables through a weighted nonlinear least-squares analysis of a constrained model.

11 Our workflow consists of nonlinear least-squares analysis of steady-state spectroscopic mea

12 Multivariable ordinary least squares and probit models were used to estimate th

13 e sites, and express the problem as a linear least squares and solve it in polynomial time.

14 tochastic search variable selection, partial least squares, and support vector machines using the rad

15 component and discriminant analysis, partial least-squares, and principal component regression.

16 A quasi-least squares approach was used to examine the relations

17 ria for model fitting, such as the method of least squares, are modified by imposing a penalty for ea

18 nation of general baseline (using asymmetric least squares (AsLS)), removing spots shift and concavit

19 ctor analysis (PARAFAC) and unfolded-partial least squares coupled to residual bilinearization (U-PLS

20 Multivariate partial least squares discriminant analyses were applied to dete

21 f mussels was analysed by Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) which reve

22 ponent analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA).

23 rallel factor analysis (PARAFAC) and Partial least squares Discriminant Analysis (PLS DA) were used f

24 MR) spectroscopy, and analysed using partial least squares discriminant analysis (PLS-DA) and partial

25 mid-infrared (MIR) spectroscopy and partial least squares discriminant analysis (PLS-DA) as a means

26 The prediction errors using partial least squares discriminant analysis (PLS-DA) discriminat

27 Object-wise partial least squares discriminant analysis (PLS-DA) models were

28 Four different partial least squares discriminant analysis (PLS-DA) models were

29 fication models were developed using partial least squares discriminant analysis (PLS-DA) to distingu

30 Partial least squares discriminant analysis (PLS-DA) was used to

31 Using partial least squares discriminant analysis (PLS-DA), it was pos

32 ised pattern recognition techniques, Partial Least Squares Discriminant Analysis (PLS-DA), Soft Indep

33 incipal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), the purees

34 incipal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA).

35 riate supervised classification with Partial Least Squares Discriminant Analysis (PLS-DA).

36 f seasonality was obtained using the partial least squares discriminant analysis (PLSDA) algorithm.

37 Here, we presented a partial least squares discriminant analysis (PLSDA) method based

38 Partial least squares discriminant analysis also allowed predict

39 Multivariate partial least squares discriminant analysis demonstrated similar

40 A partial least squares discriminant analysis was employed to iden

41 Orthogonal partial least squares discriminant analysis was used to build mo

42 ording to Banff criteria for AMR and partial least squares discriminant analysis was used to identify

43 tatistical analysis, such as PLS-DA (Partial Least Squares Discriminant Analysis) and LDA (Linear Dis

44 Component Analysis) and supervised (Partial Least Squares Discriminant Analysis) multiparametric sta

45 ediction using significance testing, partial least squares discriminant analysis, and receiver operat

46 The partial least-squares discriminant analysis (PLS-DA) model built

47 We report partial least-squares discriminant analysis (PLS-DA) models of s

48 Using partial least-squares discriminant analysis (PLS-DA) to compare

49 building classification models with partial least-squares discriminant analysis (PLSDA) and obtainin

50 based partial least-squares (GAPLS), partial least-squares discriminant analysis (PLSDA), K nearest n

51 incipal-component analysis (PCA) and partial least-squares discriminant analysis (PLSDA), were perfor

52 mportance measurements obtained from partial least-squares discriminant analysis models.

53 ignificant perturbations [orthogonal partial least-squares discriminant analysis Q(2)(Y) of 0.728] in

54 Partial least-squares discriminant analysis was performed on the

55 NMR) profiles were analyzed by using partial least-squares discriminant analysis, and the results wer

56 By applying the tested Partial Least Squares - Discriminant Analysis multiclass model (

57 Our algorithm, based on partial least squares-discriminant analysis (PLS-DA) and cross-v

58 MS) based metabolomics combined with partial least squares-discriminant analysis (PLS-DA) multivariat

59 mid-level data fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was

60 Discriminant Analysis (PCA-LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applie

61 ies of mass spectra was subjected to partial least squares-discriminant analysis (PLS-DA), a multivar

62 r transform infrared (FTIR) data and partial least squares-discriminant analysis (PLS1-DA) approach.

63 sform, mask construction, and sparse-partial least squares-discriminant analysis (s-PLS-DA) allow dat

64 Partial least squares-discriminant analysis also showed that the

65 Several Orthogonal Partial Least Squares-Discriminant Analysis models were generate

66 Orthogonal partial least squares-discriminant analysis was performed to sel

67 Using partial least-squares-discriminant analysis (PLS-DA), 87 route-s

68 of the biologics, we have developed partial least-squares-discriminant analysis derived decision alg

69 h two groups were obtained using the partial least squares discriminate analysis of 9 lipid metabolit

70 ouped distinctly for ENS and IUGR by partial least-squares discriminate analysis (PLS-DA; P < 0.01),

71 tivariate supervised classification (partial least squares discrimination analysis - PLSDA).

72 incipal component analysis (PCA) and partial least squares-discrimination analysis (PLS-DA) identifie

73 sis (LDA), and discriminant unfolded partial least-squares (DU-PLS).

74 roadening are simultaneously determined by a least-squares fit of simulated to measured absorption pr

75 With this model, a precise, global nonlinear least-squares fit was achieved simultaneously on the tem

76 e patterns, and three error distributions on least-squares fits were considered (in total, 144 simula

77 nitial guesses, which are further refined by least squares fitting.

78 nd a recently developed iterative, nonlinear least-squares fitting algorithm were combined to allow d

79 nd from the frequency domain using nonlinear least-squares fitting of spectral impedance.

80 on rate constants were obtained by nonlinear least-squares fitting of the instantaneous comonomer con

81 channels and transporters were estimated by least-squares fitting of the model predictions to experi

82 method of continuous variation and nonlinear least-squares fitting reveal that the peptides form a mi

83 n the detector allowing the application of a least-squares fitting with external analytical tools.

84 rd quadratic data mismatch terms that define least-squares fitting, we motivate a regularization term

85 by UV-Vis spectroscopy combined with partial least squares for discriminant analysis (PLS-DA).

86 flectance spectroscopy combined with partial least squares for monitoring the stability of phenolic c

87 ch we refer to as scPLS (single cell partial least squares), for robust and accurate inference of con

88 ares (iPLS), genetic algorithm-based partial least-squares (GAPLS), partial least-squares discriminan

89 ons were determined from percent changes and least-squares geometric means (LSGMs) of sCOT concentrat

90 sca River Basin (ARB) with (i) a generalized least-squares (GLS) regression analysis of the trend and

91 ent, and using circular dichroism and matrix least-squares Henderson-Hasselbalch global fitting, unra

92 Weighted partial least squares (hybrid method) was used to derive an ener

93 sed an Image Downsampling Expedited Adaptive Least-squares (IDEAL) fitting algorithm that quantifies

94 wey, Terza, bootstrap, and corrected 2-stage least squares (in the linear case) standard errors gave

95 A), Fisher-ratio (F-ratio), interval partial least-squares (iPLS), genetic algorithm-based partial le

96 ordinate descent based iteratively reweighed least squares (IRLS) algorithm has been proposed.

97 Furthermore, kernel partial least squares is used to predict adaptive behavior, as m

98 reatment effect is estimated using a 2-stage least squares IV approach that excludes IV-confounders w

99 (-1) because it gave better fit quality than least squares linear regression.

100 mated by the SD of the residuals of ordinary least squares linear regressions of SAP mean deviation (

101 Partial least squares-linear discriminant analysis (PLS-DA) prov

102 all fibroblast strains combined, the partial least squares-linear discriminant analysis (PLS-LDA) mod

103 The least squares (LS) mean change in nasal polyp score was

104 Least squares (LS) mean improvements at week 4 in ADHD R

105 BE days per week decreased with the 50-mg/d (least squares [LS] mean [SE] change, -1.49 [0.066]; P =

106 per h following placebo treatment (ratio of least-squares [LS] means 0.67, 95% CI 0.48-0.94, p=0.024

107 An approach using locally weighted partial least squares (LW-PLS) was followed to build the regress

108 he multivariate curve resolution alternating least squares (MCR-ALS) method is applied to previously

109 nd multivariate curve resolution-alternating least squares (MCR-ALS) was applied to the data to obtai

110 ied out by multicurve resolution alternating least-squares (MCR-ALS) algorithm provides reliable resu

111 Multivariate curve resolution-alternating least-squares (MCR-ALS) was applied to LC-DAD, LC-FLD, a

112 Multivariate curve resolution-alternating least-squares (MCR-ALS) was applied to the UVRR spectra,

113 d, multivariate curve resolution-alternating least-squares (MCR-ALS), to circumvent this issue while

114 of multivariate curve resolution-alternating least-squares (MCR-ALS).

115 Least squares mean (95% CI) treatment differences for ch

116 Mean diurnal IOP at week 6 (least squares mean +/- standard error) was 17.6 +/- 0.4

117 t week 24 from baseline in SGRQ total score (least squares mean [SE] change from baseline -15.6 (1.0)

118 he primary endpoint was calculated using the least squares mean at each timepoint from a generalised

119 Least squares mean average 24-h pain score at 16 weeks i

120 r (P < 0.001) to the monthly regimen, with a least squares mean BCVA change from baseline of 6.2 vers

121 At the end of the double-blind phase, the least squares mean change (SE) in off-time was -64.5 (14

122 The least squares mean change at 48 weeks was -6.33 in the t

123 ms and overall illness severity, assessed by least squares mean change at week 6 in the MADRS and CGI

124 tly greater improvements in WPAI-PSO scores (least squares mean change from baseline [SE]) relative t

125 Least squares mean change from baseline to week 6 in AIM

126 The least squares mean change in modified Rodnan skin score

127 Use of cariprazine led to a greater least squares mean change in PANSS-FSNS from baseline to

128 ions in serum phosphate level from baseline (least squares mean change: tenapanor =0.47-1.98 mg/dl; p

129 The least squares mean changes (SE) from baseline to day 2 i

130 cariprazine vs -7.44 points for risperidone; least squares mean difference -1.46, 95% CI -2.39 to -0.

131 was reduced by 72% from baseline to week 24 (least squares mean difference -2.4 mumol/L [SE 0.4], 95%

132 ith pitavastatin and 20.9% with pravastatin (least squares mean difference -9.8%, 95% CI -13.8 to -5.

133 The least squares mean difference compared with placebo in a

134 ance on the MCCB composite score at week 12 (least squares mean difference from placebo, 1.3 and 1.5

135 At Week 4 (after the first treatment), the least squares mean difference in the AE-QoL and DLQI sco

136 nd 15.0 (13.6) for the placebo group, with a least squares mean difference of -30.0 (95% CI -67.9 to

137 ndpoint of sweat chloride concentration, the least squares mean difference versus placebo was -20.8 m

138 isits up to and including the week 24 visit, least squares mean difference was -1.09 units (95% CI -1

139 aseline to week 6 compared with placebo; the least squares mean difference was -4.0 (95% CI=-6.3, -1.

140 cance was observed on the CGI-S (1.5 mg/day: least squares mean difference=-0.4, 95% CI=-0.6, -0.1; 3

141 ean difference=5.5, SE=1.9), working memory (least squares mean difference=5.4, SE=2.0), and attentio

142 red at final assessment for verbal learning (least squares mean difference=5.5, SE=1.9), working memo

143 rence=5.4, SE=2.0), and attention/vigilance (least squares mean difference=8.7, SE=2.5).

144 The least squares mean differences from placebo in ChBLAUCRT

145 Least squares mean percentage change in SCr from baselin

146 gnificance over placebo in the 50-mug group (least squares mean, -0.23; 26% improvement; P = .015).

147 ent differences were significant in stage 1 (least squares mean, -1.5; 95% CI, -2.3 to -0.7; P<.001).

148 ifferences were also significant in stage 2 (least squares mean, -1.6; 95% CI, -2.9 to -0.3; P=.02).

149 rovided no protection against FEV1 decrease (least squares mean: CNTO3157 [n = 30] = -7.08% [SE, 8.15

150 The least-squares mean (+/-SE) reduction in the average numb

151 At week 12, the least-squares mean (SE) change in the Unified Dyskinesia

152 postintervention.Over time, whole-body mass (least-squares mean +/- SE: -7.9 +/- 0.6 kg), whole-body

153 From baseline to week 12, the least-squares mean AIMS score improved by -3.3 points (S

154 Least-squares mean baseline-to-week-15 change in QLQ-C30

155 The least-squares mean change for ataluren versus placebo in

156 The least-squares mean change from baseline was significantl

157 n patients at week 48 compared with placebo (least-squares mean change from baseline: Q4W group 0.106

158 The least-squares mean change in 6MWD from baseline to week

159 to 1.01; P = .002) and both recent and past (least-squares mean change score, 0.37; 95% CI, 0.04 to 0

160 atients in groups that reported recent only (least-squares mean change score, 0.62; 95% CI, 0.23 to 1

161 a symptoms were improved by the Q8W regimen (least-squares mean difference -0.25, 95% CI -0.45 to -0.

162 en the CYT003 and placebo groups at week 12 (least-squares mean difference 0.3 mg: -0.027 [95% confid

163 ll difference in glycated hemoglobin levels (least-squares mean difference for sitagliptin vs. placeb

164 The least-squares mean difference versus placebo with respec

165 p with baseline ppFEV1 levels lower than 40 (least-squares mean difference vs placebo was 3.7 percent

166 icagrelor (27.6) versus clopidogrel (211.2); least-squares mean difference was -183.6 (95% confidence

167 (95% CI 0-150; p=0.04) greater improvement (least-squares mean difference) in prebronchodilator FEV1

168 not significantly different between groups (least-squares mean difference, -0.7 [95% CI, -1.6 to 0.2

169 arger in the high- vs moderate-volume group (least-squares mean difference, -1.0% [95% CI, -1.6% to -

170 significantly more in the high-volume group (least-squares mean difference, -10.8 [95% CI, -19.5 to -

171 acebo, from a baseline of 9.06 (2.50) hours (least-squares mean difference, 0.96 hour; 95% CI, 0.56-1

172 QS (2008-2011) and to 2) calculate adjusted least-squares mean outcomes across quartiles of protein

173 dupilumab dose regimens based on EASI score least-squares mean percentage change (SE) from baseline

174 At 48 weeks, the least-squares mean percentage reduction in LDL cholester

175 Least-squares mean PRU at 2 hours post loading dose was

176 t difference between eculizumab and placebo (least-squares mean rank 56.6 [SEM 4.5] vs 68.3 [4.5]; ra

177 e reduced the LDL cholesterol level (up to a least-squares mean reduction of 50.6% from baseline).

178 line to day 84) and LDL cholesterol (up to a least-squares mean reduction of 59.7% from baseline to d

179 mg or more reduced the PCSK9 level (up to a least-squares mean reduction of 74.5% from baseline to d

180 egimens reduced the levels of PCSK9 (up to a least-squares mean reduction of 83.8% from baseline to d

181 At day 180, the least-squares mean reductions in LDL cholesterol levels

182 ebo group in the alkaline phosphatase level (least-squares mean, -113 and -130 U per liter, respectiv

183 The ratio of geometric least squares means (CT-P10/rituximab) was 102.25% (90%

184 excreted 41% less alpha-CEHC (all values are least-squares means +/- SEMs: 0.6 +/- 0.1 compared with

185 The least-squares means +/- SEs for changes in dietary index

186 e regression was performed using the partial least squares method to quantify the starch in the sprea

187 w QSRR model based on a Kernel-based partial least-squares method for predicting UPLC retention times

188 nm were spectrally unmixed by using a linear least-squares method to obtain sPA images.

189 analysis of the unfolded spectra by partial least squares methods (PLS1 and PLS2) revealed quantitat

190 strong connections with popular regularized least-squares methods, and the use of such numerical rec

191 Least-squares minimization of log K = LA + LB with the d

192 In the orthogonal partial least-squares model, VNIR spectra of surface-sediment sa

193 ney characterization was achieved by partial least squares modeling (PLS).

194 ring 2012-2013, we estimated pooled ordinary least-squares models, clustered at the household level,

195 te tryptophan composition data are required, least-squares nonlinear regression is the best approach

196 riant using Lasso for selection and ordinary least squares (OLS) for estimation performs particularly

197 Ordinary least squares (OLS) regression was used to explore facto

198 nd DBP was assessed by conventional ordinary least-squares (OLS) linear regression and 2-stage least-

199 We used segmented ordinary least-squares (OLS) regression using Newey-West standard

200 ivative order, and choice of method (partial least-squares or principal component regression), which

201 Notably, using partial least-squares-path modeling we found that WM insult with

202 such as principal component analysis-inverse least-squares (PCA-ILS), has become standard for signal

203 distribution between the porphyrin-porphyrin least-squares planes.

204 A), k nearest neighbours (kappa-NN), partial least squares (PLS) analysis and probabilistic neural ne

205 NIR and XRF spectra, combined with partial least squares (PLS) data treatment, were used to develop

206 Partial least squares (PLS) identified patterns of relationships

207 Partial least squares (PLS) modelling of the anti-trypanosomal a

208 In order to generate partial least squares (PLS) models from the MIR/NIR spectral dat

209 iarrhea, and death was assessed with partial least squares (PLS) path modeling.

210 We built a Partial Least Squares (PLS) predictive model to quantify the rel

211 Projection to latent structures by partial least squares (PLS) regression analysis showed the volat

212 ibration models were built using the Partial Least Squares (PLS) regression method to determine solub

213 ted total reflectance (FTIR-ATR) and partial least squares (PLS) regression model for the prediction

214 kernel spectra were used to develop partial least squares (PLS) regression models for protein predic

215 To do it, Partial Least Squares (PLS) regression models were applied to co

216 Also, partial least squares (PLS) regression models were constructed o

217 Partial Least Squares (PLS) regression was used to make the pred

218 ion peroxide value established using partial least squares (PLS) regression were characterized for MI

219 Here, we apply the method of partial least squares (PLS) to extract the encoded features of m

220 on of gluten in wheat flour based on partial least squares (PLS) treatment of FT-Raman data is descri

221 specific chemometrics tools, such as partial least squares (PLS), interval-PLS, synergy interval-PLS

222 Partial Least Squares (PLS)-based techniques were applied to com

223 ear-infrared spectroscopy (NIRS) and partial least squares (PLS).

224 ne approach to enable a quantitative partial least-squares (PLS) chemometric model to measure and mon

225 use of a 0-100% concentration range partial least-squares (PLS) regression model to estimate concent

226 tein content, which were built using partial least-squares (PLS) regression, exhibit satisfactory pre

227 -predictive statistical models using partial least-squares (PLS) regression.

228 Partial least-squares (PLS)-discriminant analysis (DA) was emplo

229 Comparing robust-variance ordinary least squares, random-effects regression, fixed-effects

230 hocyanins and flavanols) by modified partial least squares regression (MPLS) using a number of spectr

231 Calibrations were performed by partial least squares regression (MPLS) using a number of spectr

232 ins, cellulose and hemicelluloses by partial least squares regression (PLS) analysis on the basis of

233 alibration curves were obtained with partial least squares regression (PLS).

234 incipal Component Analysis (PCA) and Partial Least Squares Regression (PLS).

235 With the help of partial least squares regression (PLS-R), we could link the sens

236 s discriminant analysis (PLS-DA) and partial least squares regression (PLS-R).

237 using a portable infrared system and partial least squares regression (PLSR) calibration models were

238 leaf age and develop a spectra-based partial least squares regression (PLSR) model to predict age usi

239 Partial least squares regression (PLSR) revealed that ethyl capr

240 study, peak area integration (PAI), Partial Least Squares Regression (PLSR), and Principal Component

241 rithms: deep belief networks (DBNs), partial least squares regression (PLSR), principal component ana

242 utationally tractable formulation of partial least squares regression (PLSR).

243 Partial least squares regression analysis appears as the most co

244 rmation or fibrotic gene expression, partial least squares regression analysis was applied.

245 successfully integrated RSM and the partial least squares regression method to optimise the PLR extr

246 ed into three-way arrays using N-way partial least squares regression methods (NPLS1 and NPLS2) and a

247 Ordinary least squares regression methods were used to investigat

248 The residuals of a least squares regression model are defined as the observ

249 cell behaviors were used to create a partial least squares regression model to predict the hierarchy

250 coupled with descriptive sensory and partial least squares regression modelling can help unravel inte

251 Next, we test a series of ordinary least squares regression models predicting low ECDI scor

252 tion was a significant slope of the ordinary least squares regression of a simulated patient's mean d

253 the stronger the correlation) from weighted least squares regression of trial-specific hazard ratios

254 Partial least squares regression showed that alcohol-induced cha

255 Instead, multivariate partial least squares regression showed that DOM composition was

256 ort, the "Children of 1997." We used partial least squares regression to account for colinearity betw

257 tified AD-associated cytokines using partial least squares regression to correlate cytokine expressio

258 py combined with analytical data and partial least squares regression to quantify the carbon content

259 Partial least squares regression was employed to design calibrat

260 Partial least squares regression with a longitudinal clinical de

261 on was established based on modified partial least squares regression with reference values of HPLC.

262 ental variable (IV) analysis using two stage least squares regression with the rs4820599 in the GGT1

263 ean substitution, k-nearest neighbors, local least squares regression, Bayesian principal components

264 onsider several multi-label learning partial least squares regression, canonical correlation analysis

265 Partial Least Squares regression, Discriminant Analysis and Arti

266 two sets of genes jointly using the partial least squares regression, scPLS is capable of making ful

267 In a multivariate ordinary least squares regression, the analysis estimated the log

268 ponse by using cubic splines and generalized least squares regression.

269 brations were developed resorting to partial least squares regression.

270 made using bootstrapping methods and partial least squares regression.

271 nary function were examined by using partial least squares regression.

272 troscopic data, called Durbin-Watson partial least-squares regression (dwPLS), is proposed in this pa

273 ncipal components analysis (PCA) and partial least-squares regression (PLS) of 87 nutrients.

274 Five partial least-squares regression (PLSR) models were constructed

275 The feasibility of using partial least-squares regression (PLSR) to predict the success o

276 inant function analysis (PC-DFA) and partial least-squares regression (PLSR) were employed to investi

277 ew approach and classified by sparse partial least-squares regression (PLSR).

278 lity attributes were predicted using partial least-squares regression (PLSR).

279 ass spectrometry (UHPLC-ESI-MS), and partial least-squares regression (PLSR).

280 l, binary logistic regression model, partial least-squares regression model, artificial neural networ

281 Canonical correlation analysis and partial least-squares regression modeling were employed to explo

282 l series, and by applying generalized linear least-squares regression modelling to components of the

283 Generalized least-squares regression models were used to assess the

284 rates of change were estimated with ordinary least-squares regression, and linear mixed effects model

285 near multivariate calibration, i.e., partial least-squares regression, specifically adapted to neural

286 AC components were used to develop a partial least-squares regression-based model (r(2) = 0.53; Nash-

287 ic covariates that was reduced using partial least-squares regression.

288 ormulae agree with the estimates obtained by least-squares regression.

289 d using two strategies, linear and nonlinear least squares regressions, with the latter accounting fo

290 ate species sensitivity to listed taxa using least-squares regressions of the sensitivity of a surrog

291 as assessed using random-effects generalized least squares spline models.

292 Principal component analysis (PCA), least squares-support vector machines (LS-SVM) and PCA-b

293 Ordinary least squares tended to overstate the significance of he

294 crete model and use the method of non-linear least squares to estimate the age-specific annual rate o

295 an instrumental variable, we used two-stage least squares to estimate the causal effect of years of

296 Generalized least-squares trend estimation model was used to estimat

297 ed on alternating non-negativity-constrained least squares which accounts for the spatial correlation

298 ate calibration methods, as unfolded-partial least squares with residual bilinearization (U-PLS/RBL)

299 of multivariate curve resolution-alternating least-squares with an additional sparse regression step

300 ion (U-PLS/RBL) and multidimensional-partial least-squares with residual bilinearization (N-PLS/RBL),