Iterative re-weighted multilinear partial least squares modelling for robust predictive modelling

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A new formula for fast computation of segmented cross validation residuals in linear regression modelling -- providing efficient regularisation parameter estimation in Ridge Regression and the Tikhonov Regularisation Framework

In the present paper we prove a new theorem, resulting in an exact updating formula for linear regression model residuals to calculate the segmented cross-validation residuals for any choice of cross-validation strategy without model refitting. The required matrix inversions are limited by the cross-validation segment sizes and can be executed with high efficiency in parallel. The well-known formula for leave-one-out cross-validation follows as a special case of our theorem. In situations where the cross-validation segments consist of small groups of repeated measurements, we suggest a heuristic strategy for fast serial approximations of the cross-validated residuals and associated PRESS statistic. We also suggest strategies for quick estimation of the exact minimum PRESS value and full PRESS function over a selected interval of regularisation values. The computational effectiveness of the parameter selection for Ridge-/Tikhonov regression modelling resulting from our theoretical findings and heuristic arguments is demonstrated for several practical applications.Comment: 33 pages, 10 figure, 8 table

Orders of magnitude speed increase in Partial Least Squares feature selection with new simple indexing technique for very tall data sets

Feature selection is a challenging combinatorial optimization problem that tends to require a large number of candidate feature subsets to be evaluated before a satisfying solution is obtained. Because of the computational cost associated with estimating the regression coefficients for each subset, feature selection can be an immensely time-consuming process and is often left inadequately explored. Here, we propose a simple modification to the conventional sequence of calculations involved when fitting a number of feature subsets to the same response data with partial least squares (PLS) model fitting. The modification consists in establishing the covariance matrix for the full set of features by an initial calculation and then deriving the covariance of all subsequent feature subsets solely by indexing into the original covariance matrix. By choosing this approach, which is primarily suitable for tall design matrices with significantly more rows than columns, we avoid redundant (identical) recalculations in the evaluation of different feature subsets. By benchmarking the time required to solve regression problems of various sizes, we demonstrate that the introduced technique outperforms traditional approaches by several orders of magnitude when used in conjunction with PLS modeling. In the supplementary material, we provide code for implementing the concept with kernel PLS regression.acceptedVersio

Multitemporal Feature-Level Fusion on Hyperspectral and LiDAR Data in the Urban Environment

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Encoder–decoder neural networks for predicting future FTIR spectra – application to enzymatic protein hydrolysis

In the process of converting food-processing by-products to value-addedingredients, fine grained control of the rawmaterials, enzymes and process conditionsensures the best possible yield and eco-nomic return. However, when raw mate-rial batches lack good characterization andcontain high batch variation, online or at-line monitoring of the enzymatic reac-tions would be beneficial. We investigate the potential of deep neural networks inpredicting the future state of enzymatic hydrolysis as described by Fourier-trans-form infrared spectra of the hydrolysates. Combined with predictions of averagemolecular weight, this provides a flexible and transparent tool for process moni-toring and control, enabling proactive adaption of process parameters.publishedVersio

Suitability of FTIR to distinguish pure cultures of problematic mould species from closely related species in the meat industry

Aims: The aim of the study was to apply Fourier Transform Infrared spectroscopy (FTIR) as a rapid screening method for moulds in a specific food production environment (cured meat) and to evaluate whether the method was sufficiently accurate to distinguish Penicillium species that constitute a hazard for the food quality and safety (Penicillium solitum and Penicillium nordicum) from closely related species. Methods and Results: FTIR was applied to classify the indigenous mycobiota of two production sites for dried and cured meat products in Norway. Results showed that FTIR was suitable to analyse large amounts of data. While correct classification rates varied depending on the species, overall results indicated that FTIR was able to distinguish the undesired mould species P. solitum and P. nordicum from other species and may hence present an option for rapid screening of large numbers of samples to identify changes in mould composition on site. Conclusions: FTIR presents a potential method for detecting changes in levels of undesired fungi in meat-processing environments. Significance and Impact of the study: This is the first study that applies FTIR to a specific food production environment and it increases the knowledge on both possibilities and limitations of the method in classification of fungi.publishedVersio

Selection of principal variables through a modified Gram–Schmidt process with and without supervision

In various situations requiring empirical model building from highly multivariate measurements, modelling based on partial least squares regression (PLSR) may often provide efficient low-dimensional model solutions. In unsupervised situations, the same may be true for principal component analysis (PCA). In both cases, however, it is also of interest to identify subsets of the measured variables useful for obtaining sparser but still comparable models without significant loss of information and performance. In the present paper, we propose a voting approach for sparse overall maximisation of variance analogous to PCA and a similar alternative for deriving sparse regression models influenced closely related to the PLSR method. Both cases yield pivoting strategies for a modified Gram–Schmidt process and its corresponding (partial) QRfactorisation of the underlying data matrix to manage the variable selection process. The proposed methods include score and loading plot possibilities that are acknowledged for providing efficient interpretations of the related PCA and PLS models in chemometric applications.Selection of principal variables through a modified Gram–Schmidt process with and without supervisionpublishedVersio

RENT—Repeated Elastic Net Technique for Feature Selection

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The effect of bio-electro-magnetic-energyregulation therapy on sleep duration and sleep quality among elite players in Norwegian women’s football

The current study investigated if physical loads peak on game days and if Bio- Electro-Magnetic-Energy-Regulation (BEMER) therapy is affecting sleep duration and sleep quality on nights related to game nights among elite players in Norwegian women’s elite football. The sample included 21 female football players from an elite top series club with a mean age of ~24 years (± 2.8). Sleep was measured every day over a period of 273 consecutive days with a Somnofy sleep monitor based on ultra-wideband (IR-UWB) pulse radar and Doppler technology. The current study was conducted as a quasi-experiment, where each player was their own control based on a control period that lasted for 3 months, and an experimental period that lasted for 5 months. Accordantly, the time each player spent with BEMER therapy was used as a control variable. Multivariate analyses of variance using FFMANOVA and univariate ANOVA with False Discovery Rate adjusted p-values show that physical performance (total distance, distance per minute, sprint meters >22.5 kmh, accelerations and decelerations) significantly peak on game day compared with ordinary training days and days related to game days. The results also show that sleep quantity and quality are significantly reduced on game night, which indicate disturbed sleep caused by the peak in physical load. Most sleep variables significantly increased in the experiment period, where BEMER therapy was used, compared to the control period before the introduction of BEMER therapy. Further, the analyses show that players who spent BEMER therapy >440 h had the most positive effects on their sleep, and that these effects were significantly compared to the players who used BEMER therapy <440 h. The findings are discussed based on the function of sleep and the different sleep stages have on recovery

Microwave-assisted production of biodiesel

ncreasing popularity of sour beer urges the development of novel solutions for controlled fermentations both for fast acidification and consistency in product flavor and quality. One possible approach is the use of Saccharomyces cerevisiae in co-fermentation with Lactobacillus species, which produce lactic acid as a major end-product of carbohydrate catabolism. The ability of lactobacilli to ferment beer is determined by their capacity to sustain brewing-related stresses, including hop iso-α acids, low pH and ethanol. Here, we evaluated the tolerance of Lactobacillus brevis BSO464 and Lactobacillus buchneri CD034 to beer conditions and different fermentation strategies as well as their use in the brewing process in mixed fermentation with a brewer’s yeast, S. cerevisiae US-05. Results were compared with those obtained with a commercial Lactobacillus plantarum (WildBrewTM Sour Pitch), a strain commonly used for kettle souring. In pure cultures, the three strains showed varying susceptibility to stresses, with L. brevis being the most resistant and L. plantarum displaying the lowest stress tolerance. When in co-fermentation with S. cerevisiae, both L. plantarum and L. brevis were able to generate sour beer in as little as 21 days, and their presence positively influenced the composition of flavor-active compounds. Both sour beers were sensorially different from each other and from a reference beer fermented by S. cerevisiae alone. While the beer produced with L. plantarum had an increased intensity in fruity odor and dried fruit odor, the L. brevis beer had a higher total flavor intensity, acidic taste and astringency. Remarkably, the beer generated with L. brevis was perceived as comparable to a commercial sour beer in multiple sensory attributes. Taken together, this study demonstrates the feasibility of using L. brevis BSO464 and L. plantarum in co-fermentation with S. cerevisiae for controlled sour beer production with shortened production time.publishedVersio