Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

Fourier Transform Infrared Spectroscopy (FTIR) is a ubiquitous spectroscopic technique. Spectral interpretation is a time-consuming process, but it yields important information about functional groups present in compounds and in complex substances. We develop a generalizable model via a machine learning (ML) algorithm using Convolutional Neural Networks (CNNs) to identify the presence of functional groups in gas phase FTIR spectra. The ML models will reduce the amount of time required to analyze functional groups and facilitate interpretation of FTIR spectra. Through web scraping, we acquire intensity-frequency data from 8728 gas phase organic molecules within the NIST spectral database and transform the data into images. We successfully train models for 15 of the most common organic functional groups, which we then determine via identification from previously untrained spectra. These models serve to expand the application of FTIR measurements for facile analysis of organic samples. Our approach was done such that we have broad functional group models that inference in tandem to provide full interpretation of a spectrum. We present the first implementation of ML using image-based CNNs for predicting functional groups from a spectroscopic method.</p

Noncredible Performance in Individuals with External Incentives: Empirical Derivation and Cross-Validation of the Psychosocial Distress Scale (PDS).

Using a known groups design, a new Minnesota Multiphasic Personality Inventory (MMPI-2) subscale, the 20-item Psychosocial Distress Scale (PDS), was empirically derived and cross-validated. The PDS demonstrated good classification accuracy between subjects under external incentive vs. no incentive conditions. In the initial calibration sample (N = 84) a cut score of ≥10 on the PDS was associated with good classification accuracy (85.7%), high specificity (90.0%), and adequate sensitivity (81.8%). Under cross-validation conditions (N = 83) a cut score of ≥10 on the PDS was also associated with nearly identical classification accuracy (86.5%), specificity (91.89%), and sensitivity (82.61%). A cut score of ≥12 was associated with 100% positive predictive power; that is, no false-positive errors in both the initial calibration sample and the subsequent cross-validation sample. The current study suggests that in addition to noncredible cognitive performance, civil litigants and disability claimants may overreport psychosocial complaints that can be identified and that the scale may generalize to other settings and patient groups

Saccharide Concentration Prediction from Proxy Ocean Samples Analyzed Via Infrared Spectroscopy and Quantitative Machine Learning

Solvated organics in the ocean are present in relatively small concentrations but contribute largely to ocean chemical diversity and complexity. Existing in the ocean as dissolved organic carbon (DOC) and enriched within the sea surface microlayer (SSML), these compounds have large impacts on atmospheric chemistry through their contributions to cloud nucleation, ice formation, and other climatological processes. The ability to quantify the concentrations of organics in ocean samples is critical to understanding these marine processes. The work presented herein details an investigation to develop a machine learning (ML) methodology utilizing infrared spectroscopy data to accurately estimate saccharide concentrations in complex solutions. We evaluated multivariate linear regression (MLR), K-nearest neighbors (KNN), decision trees (DT), gradient-boosted regressors (GBR), multilayer perceptrons (MLP), and support vector regressors (SVR) toward this goal. SVR models are shown to best predict accurate generalized saccharide concentrations. Our work presents an application combining fast spectroscopic techniques with ML to analyze organic composition in proxy ocean samples. As a result, we target a generalized method for analyzing field marine samples more efficiently without sacrificing accuracy or precision

A conceptual map of invasion biology: Integrating hypotheses into a consensus network

14 páginas.. 2 figuras.- 1 tabla.- referencias.-Background and aims Since its emergence in the mid‐20th century, invasion biology has matured into a productive research field addressing questions of fundamental and applied importance. Not only has the number of empirical studies increased through time, but also has the number of competing, overlapping and, in some cases, contradictory hypotheses about biological invasions. To make these contradictions and redundancies explicit, and to gain insight into the field’s current theoretical structure, we developed and applied a Delphi approach to create a consensus network of 39 existing invasion hypotheses. Results The resulting network was analysed with a link‐clustering algorithm that revealed five concept clusters (resource availability, biotic interaction, propagule, trait and Darwin’s clusters) representing complementary areas in the theory of invasion biology. The network also displays hypotheses that link two or more clusters, called connecting hypotheses, which are important in determining network structure. The network indicates hypotheses that are logically linked either positively (77 connections of support) or negatively (that is, they contradict each other; 6 connections). Significance The network visually synthesizes how invasion biology’s predominant hypotheses are conceptually related to each other, and thus, reveals an emergent structure – a conceptual map – that can serve as a navigation tool for scholars, practitioners and students, both inside and outside of the field of invasion biology, and guide the development of a more coherent foundation of theory. Additionally, the outlined approach can be more widely applied to create a conceptual map for the larger fields of ecology and biogeography.Financial support was provided by the Foundation of German Business (sdw) to ME; by the German Federal Ministry of Education and Research (BMBF) within the collaborative project ‘Bridging in Biodiversity Science—BIBS’ (funding number 01LC1501A‐H) to ME, AS, CM, CS, MB‐V and TH; and by the Deutsche Forschungsgemeinschaft (DFG, JE 288/9‐2) to JMJ. SK, FAY and W‐CS acknowledge support from University Stellenbosch's DST‐NRF Centre of Excellence for Invasion Biology (C•I•B), MV from Belmont Forum‐BiodivERsA project InvasiBES (PCI2018‐092939) funded by the Spanish Ministerio de Ciencia, Innovación y Universidades. SK acknowledges financial support from the South African National Department of Environment Affairs through its funding to the Invasive Species Programme of the South African National Biodiversity Institute. AN and PP were supported by EXPRO grant no. 19‐28807X (Czech Science Foundation) and the long‐term research development project RVO 67985939 (The Czech Academy of Sciences). LG‐A acknowledges support from the MICINN project INTERCAPA (CGL‐2014‐ 56739‐R), FE and JMJ from the BiodivERsA‐Belmont Forum Project ‘Alien Scenarios’ (FWF project no I 4011‐B32, BMBF project 01LC1807B), AM from the Natural Environmental Research Council (grant number NE/L002531/1) and AR from the Natural Sciences and Engineering Research Council of Canada.Peer reviewe