Near infrared spectroscopy and multivariate calibration for simultaneous determination of glucose, triglycerides and high-density lipoprotein in animal plasma

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.

Application of Multivariate Statistical Analysis to Simultaneous Spectrophotometric Enzymatic Determination of Glucose and Cholesterol in Serum Samples

A method using UV-Vis spectroscopy and multivariate tools for simultaneous determination of glucose and cholesterol was developed in this paper. The method is based on the development of the reaction between the analytes (cholesterol and glucose) and enzymatic reagents. The spectra were analyzed by partial least squares regression and artificial neural networks. The precision estimated between nominal and calculate concentration demonstrate that artificial neural network model was adequate to quantify both analytes in serum samples, since the % relative error obtained was in the interval from 5.1 to 8.3. The proposed model was applied to analyze blood serum samples, and the results are similar compared to those obtained employing the reference method.

Near-infrared spectroscopy for metabolite quantification and species identification

Near-infrared (NIR) spectroscopy is a high-throughput method to analyze the near-infrared region of the electromagnetic spectrum. It detects the absorption of light by molecular bonds and can be used with live insects. In this study, we investigate the accuracy of NIR spectroscopy in determining triglyceride level and species of wild-caught Drosophila. We employ the chemometric approach to produce a multivariate calibration model. The multivariate calibration model is the mathematical relationship between the changes in NIR spectra and the property of interest as determined by the reference analytical method. Once the calibration model was developed, we used an independent set to validate the accuracy of the calibration model. The optimized calibration model for triglyceride quantification yielded coefficients of determination of 0.73 for the calibration test set and 0.70 for the independent test set. Simultaneously, we used NIR spectroscopy to discriminate two species of Drosophila. Flies from independent sets were correctly classified into Drosophila melanogaster and Drosophila simulans with accuracy higher than 80%. These results suggest that NIRS has the potential to be used as a high-throughput screening method to assess a live individual insect's triglyceride level and taxonomic status.

Qualitative and quantitative analysis of ochratoxin A contamination in green coffee beans using Fourier transform near infrared spectroscopy

BACKGROUND

Ochratoxin A (OTA) contamination is highly prevalent in a variety of agricultural products including the commercially important coffee bean. As such, rapid and accurate detection methods are considered necessary for the identification of OTA in green coffee beans. The goal of this research was to apply Fourier transform near infrared spectroscopy to detect and classify OTA contamination in green coffee beans in both a quantitative and qualitative manner.

RESULTS

PLSR models were generated using pretreated spectroscopic data to predict the OTA concentration. The best model displayed a correlation coefficient (r) of 0.814, a standard error of prediction (SEP and bias of 1.965 µg kg^-1 and 0.358 µg kg^-1 , respectively. Additionally, a PLS-DA model was also generated, displaying a classification accuracy of 96.83% for a non-OTA contaminated model and 80.95% for an OTA contaminated model, with an overall classification accuracy of 88.89%.

CONCLUSION

The results demonstrate that the developed model could be used for detecting OTA contamination in green coffee beans in either a quantitative or qualitative manner. © 2016 Society of Chemical Industry.

From near-infrared and Raman to surface-enhanced Raman spectroscopy: progress, limitations and perspectives in bioanalysis

Over recent decades, spreading environmental concern entailed the expansion of green chemistry analytical tools. Vibrational spectroscopy, belonging to this class of analytical tool, is particularly interesting taking into account its numerous advantages such as fast data acquisition and no sample preparation. In this context, near-infrared, Raman and mainly surface-enhanced Raman spectroscopy (SERS) have thus gained interest in many fields including bioanalysis. The two former techniques only ensure the analysis of concentrated compounds in simple matrices, whereas the emergence of SERS improved the performances of vibrational spectroscopy to very sensitive and selective analyses. Complex SERS substrates were also developed enabling biomarker measurements, paving the way for SERS immunoassays. Therefore, in this paper, the strengths and weaknesses of these techniques will be highlighted with a focus on recent progress.

Degree of deacetylation of chitosan by infrared spectroscopy and partial least squares

The determination of the degree of deacetylation of highly deacetylated chitosan by infrared (IR) spectroscopy was significantly improved with the use of partial least squares (PLS). The IR spectral region from 1500 to 1800 cm(-1) was taken as the dataset. Different PLS models resulting from various data pre-treatments were evaluated and compared. The PLS model that gave excellent internal and external validation performance came from the data that were corrected for the baseline and that was normalized relative to the maximum corrected absorbance. Analysis of the PLS loadings plot showed that the important variables in the spectral region came from the absorption maxima related to the amide bands at 1660 and 1550 cm(-1) and amine band at 1600 cm(-1). IR-PLS results were comparable to the results obtained by potentiometric titration. IR-PLS results were found to be more precise and rugged compared to the usual IR absorbance ratio method. This is consistent with the fact that IR spectral resolution is not really high and that the absorption at a single wavelength is influenced by other factors like hydrogen bonding and the presence of water.