Rapid determination of triglyceride and glucose levels in Drosophila melanogaster induced by high-sugar or high-fat diets based on near-infrared spectroscopy

Triglyceride and glucose levels are important indicators for determining metabolic syndrome, one of the leading public-health burdens worldwide. Drosophila melanogaster is an ideal model for investigating metabolic diseases because it has 70% homology to human genes and its regulatory mechanism of energy metabolism homeostasis is highly similar to that of mammals. However, traditional analytical methods of triglyceride and glucose are time-consuming, laborious, and costly. In this study, a simple, practical, and reliable near-infrared (NIR) spectroscopic analysis method was developed for the rapid determination of glucose and triglyceride levels in an in vivo model of metabolic disorders using Drosophila induced by high-sugar or high-fat diets. The partial least squares (PLS) model was constructed and optimized using different spectral regions and spectral pretreatment methods. The overall results had satisfactory prediction performance. For Drosophila induced by high-sugar diets, the correlation coefficient (R_P) and root mean square error of prediction (RMSEP) were 0.919 and 0.228 mmoL gprot^-1 for triglyceride and 0.913 and 0.143 mmoL gprot^-1 for glucose respectively; for Drosophila induced by high-fat diets, the R_P and RMSEP were 0.871 and 0.097 mmoL gprot^-1 for triglyceride and 0.853 and 0.154 mmoL gprot^-1 for glucose, respectively. This study demonstrated the potential of using NIR spectroscopy combined with PLS in the determination of triglyceride and glucose levels in Drosophila, providing a rapid and effective method for monitoring metabolite levels during disease development and a possibility for evaluating metabolic diseases in humans in clinical practice.

Near-infrared spectra of liquid and gas samples by diffuse reflectance employing benchtop and handheld spectrophotometers

This paper describes a new method to obtain NIR spectra of liquid and gas samples by diffuse reflectance, which is especially suitable for handheld spectrophotometers, since most of these instruments are designed to acquire spectrum using this geometry. The core of the method is a diffuse reflectance cell, which consists of a vial containing a mixture of the liquid or gas sample (rare medium) and a powder (dense medium). Using this strategy, no adaptation is required to measure spectra with most portable NIR spectrometers. This new method was used to obtain NIR spectra of several liquids and gases, which were compared with traditional transmittance spectra. As a proof of concept, measurements of biodiesel/vegetable oil/diesel blends were used to build multivariate calibrations to predict the contents of biodiesel and vegetable oil in diesel blends using benchtop and handheld FT-NIR spectrophotometers. This low-cost method was demonstrated to be suitable for overcoming problems related to the handling of viscous samples and expand the applications with portable NIR instruments.

Monitoring photolysis and (solar photo)-Fenton of enrofloxacin by a methodology involving EEM-PARAFAC and bioassays: Role of pH and water matrix

The degradation of enrofloxacin (ENR) by direct photolysis, Fenton and solar photo-Fenton processes has been studied in different water matrices, such as ultra-pure water (MQ), tap water (TW) and highly saline water (SW). Reactions have been conducted at initial pH 2.8 and 5.0. At pH = 2.8, HPLC analyses showed a fast removal of ENR by (solar photo)-Fenton treatments in all studied water matrices, whereas a 40% removal was observed after 120 min of photolysis. However, TOC measurements showed that only solar photo-Fenton was able to produce significant mineralization (80% after 120 min of treatment); differences between ENR removal and mineralization can be attributed to the release of important amounts of reaction by-products. Excitation-emission matrices (EEMs) combined with parallel factor analysis (PARAFAC) were employed to gain further insight into the nature of these by-products and their time-course profile, obtaining a 5-component model. EEM-PARAFAC results indicated that photolysis is not able to produce important changes in the fluoroquinolone structure, in sharp contrast with (solar photo)-Fenton, where decrease of the components associated with fluoroquinolone core was observed. Agar diffusion tests employing E. coli and S, aureus showed that the antibiotic activity decreased in parallel with the destruction of the fluoroquinolone core.

Stacked ensemble extreme learning machine coupled with Partial Least Squares-based weighting strategy for nonlinear multivariate calibration

With its simple theory and strong implementation, extreme learning machine (ELM) becomes a competitive single hidden layer feed forward networks for nonlinear multivariate calibration in chemometrics. To improve the generalization and robustness of ELM further, stacked generalization is introduced into ELM to construct a modified ELM model called stacked ensemble ELM (SE-ELM). The SE-ELM is to create a set of sub-models by applying ELM repeatedly to different sub-regions of the spectra and then combine the predictions of those sub-models according to a weighting strategy. Three different weighting strategies are explored to implement the proposed SE-ELM, such as the Winner-takes-all (WTA) weighting strategy, the constraint non-negative least squares (CNNLS) weighing strategy and the partial least squares (PLS) weighting strategy. Furthermore, PLS is suggested to be selected as the optimal weighting method that can handle the multi-colinearity among the predictions yielded by all the sub-models. The experimental assessment of the three SE-ELM models with different weighting strategies is carried out on six real spectroscopic datasets and compared with ELM, back-propagation neural network (BPNN) and Radial basis function neural network (RBFNN), statistically tested by the Wilcoxon signed rank test. The obtained experimental results suggest that, in general, all the SE-ELM models are more robust and more accurate than traditional ELM. In particular, the proposed PLS-based weighting strategy is at least statistically not worse than, and frequently better than the other two weighting strategies, BPNN, and RBFNN.

Calibration sets selection strategy for the construction of robust PLS models for prediction of biodiesel/diesel blends physico-chemical properties using NIR spectroscopy

Even when the feasibility of using near infrared (NIR) spectroscopy combined with partial least squares (PLS) regression for prediction of physico-chemical properties of biodiesel/diesel blends has been widely demonstrated, inclusion in the calibration sets of the whole variability of diesel samples from diverse production origins still remains as an important challenge when constructing the models. This work presents a useful strategy for the systematic selection of calibration sets of samples of biodiesel/diesel blends from diverse origins, based on a binary code, principal components analysis (PCA) and the Kennard-Stones algorithm. Results show that using this methodology the models can keep their robustness over time. PLS calculations have been done using a specialized chemometric software as well as the software of the NIR instrument installed in plant, and both produced RMSEP under reproducibility values of the reference methods. The models have been proved for on-line simultaneous determination of seven properties: density, cetane index, fatty acid methyl esters (FAME) content, cloud point, boiling point at 95% of recovery, flash point and sulphur.

Classification of biodiesel and fuel blends using gas chromatography - differential mobility spectrometry with cluster analysis and isolation of C18:3 me by dual ion filtering

Fatty acid alkyl esters (FAAEs) were determined at 10-100mg/L in biodiesel and blends with petrodiesel without sample pre-treatment using gas chromatography with a tandem differential mobility detector. Selectivity was provided through chromatographic separations and atmospheric pressure chemical ionization reactions in the detector with mobility characterization of gas ions. Limits of detection were ~0.5ng with an average of 2.98% RSD for peak area precision, ≤1.3% RSD for retention time precision, and ≤9.2% RSD for compensation voltage precision. Biodiesel blends were classified using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Unsupervised cluster analysis captured 52.72% of variance in a single PC while supervised analysis captured 71.64% of variance using Fisher ratio feature selection. Test set predictions showed successful clustering according to source or feedstock when regressed onto the training set model. Detection of the regulated substance methyl linolenate (C18:3 me) was achieved in 6-10s with a 1m long capillary column using dual ion filtering in the tandem differential mobility detector.

Simultaneous determination of multiple components in explosives using ultraviolet spectrophotometry and a partial least squares method

We used UV spectrophotometry and a chemometric method to develop a novel method for the simultaneous determination of multiple components in explosives.