Multivariate data such as spectra frequently contain measured variables that are uninformative, and removal of such variables requires the use of methods that can be used to select informative variables. Partial least squares (PLS) regression may incorporate information from uninformative measured variables, and so it is important to select variables before performing the PLS regression. Elastic net (EN) regression can be used to perform variable selection automatically. An EN regression can be used to select groups of correlated variables or to select either sparse or nonsparse sets of variables. However, the predictive performance of the EN regression can be significantly worse than competing 1-step variable selection methods such as variable importance in projection (VIP). In the present work, the use of the EN to select variables, followed by conventional PLS regression on the selected variables (EN-PLS), has been investigated. Variable selection by using EN-PLS was compared with that from EN regression, sparse PLS regression, VIP, and from selectivity ratio selection on 2 data sets of visible/near-infrared spectra. In all cases, the wavelengths selected were compared with reference data. The variables selected by using EN-PLS offered advantages in interpretability and gave more robust prediction performance as compared with those obtained from full-spectrum PLS and the other variable selection methods. This paper reports a method for variable selection by using an EN regression prior to a second regression by using PLS, a 2-step method termed EN-PLS. Variables selected by using EN-PLS are compared with variables selected from the EN regression, as well as VIP, selectivity ratio, and the sparse PLS regression, 3 commonly used methods for variable selection in chemometrics. The EN-PLS is shown to select variables that were more easily interpreted. In addition, EN-PLS performed more robustly than a PLS regression performed on all variables, as well as reduced PLS regressions by using variables selected from either the sparse PLS regression algorithm or a VIP variable selection followed by PLS modeling.
ObjectiveStress can play a role in the onset and exacerbation of psoriasis. Psychological interventions to reduce stress have been shown to improve psychological and psoriasis-related outcomes. This pilot randomised study investigated the feasibility of a brief interaction with a Paro robot to reduce stress and improve skin parameters, after a stressor, in patients with psoriasis.MethodsAround 25 patients with psoriasis participated in a laboratory stress task, before being randomised to either interact with a Paro robot or sit quietly (control condition) for 30 min. Raman spectroscopy and trans-epidermal water loss were measured at baseline, after the stressor and after the intervention as indexes of acute skin changes. Psychological variables, including self-reported stress and affect, were also measured at the three time-points.ResultsNo statistically significant differences between the two conditions were found for any of the outcomes measured. However, effect sizes suggest significance could be possible with a larger sample size. Changes in the psychological and Raman spectroscopy outcomes across the experimental session were found, indicating the feasibility of the procedures.ConclusionThis pilot study showed that a brief interaction with a Paro robot was a feasible intervention for patients with psoriasis, but future trials should broaden the inclusion criteria to try to increase recruitment rates. Studying people who are highly stressed, depressed or who are stress-responders may increase the power of the intervention to show effects using a longer-term intervention.
Mid-infrared spectroscopy has been developed as a reliable and rapid tool for routine analysis of fat, protein, lactose and other components in liquid milk. However, variations within and between FTIR instruments, even within the same milk testing laboratory, present a challenge to the accuracy of measurement of particularly minor components in the milk, such as individual fatty acids or proteins. In this study we have used Analysis of variance–Simultaneous Component Analysis (ASCA), to monitor the spectral variation between and within each of four different FOSS FTIR spectrometers over each week in an independent milk testing laboratory over 4 years, between August 2017 and March 2021 (223 weeks). On everyday of each week, spectra of the same pilot milk sample were recorded approximately every hour on each of the four instruments. Overall, variations between instruments had the largest effect on spectral variation over each week, making a significant contribution every week. Within each instrument, day-to-day variations over the week were also significant for all but two of the weeks measured, however it contributed less to the variance overall. At certain times other factors not explained by weekday variation or inter-instrument variation dominated the variance in the spectra. Examination of the scores and loadings of the weekly ASCA analysis allowed identification of changes in the spectral regions affected by drifts in each instrument over time. This was found to particularly affect some of the fatty acid predictions.
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