Near-infrared spectroscopy for metabolite quantification and species identification

Age, sex, and mating status discrimination in the sand fly Lutzomyia longipalpis using near infra-red spectroscopy (NIRS)

BACKGROUND

Understanding aspects related to the physiology and capacity of vectors is essential for effectively controlling vector-borne diseases. The sand fly Lutzomyia longipalpis has great importance in medical entomology for disseminating Leishmania parasites, the causative agent of Leishmaniasis, one of the main neglected diseases listed by the World Health Organization (WHO). In this respect, it is necessary to evaluate the transmission potential of this species and the success of vector control interventions. Near-infrared spectroscopy (NIRS) has been used to estimate the age of mosquitoes in different conditions (laboratory, semi-field, and conservation), taxonomic analysis, and infection detection. However, no studies are using NIRS for sand flies.

METHODS

In this study, we developed analytic models to estimate the age of L. longipalpis adults under laboratory conditions, identify their copulation state, and evaluate their gonotrophic cycle and diet.

RESULTS

Sand flies were classified with an accuracy of 58-82% in 3 age groups and 82-92% when separating them into young (<8 days) or old (>8 days) insects. The classification between mated and non-mated sandflies was 98-100% accurate, while the percentage of hits of females that had already passed the first gonotrophic cycle was only 59%.

CONCLUSIONS

We consider the age and copula estimation results very promising, as they provide essential aspects of vector capacity assessment, which can be obtained quickly and at a lower cost with NIRS.

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.

High-throughput infrared spectroscopy for quantification of peptides in drug discovery

Peptides have gained an increasing importance in drug discovery as potential therapeutics. Discovery efforts toward finding new, efficacious peptide-based therapeutics have increased the throughput of peptide development, allowing the rapid generation of unique and pure peptide samples. However, high-throughput analysis of peptides may be still challenging and can encumber a high-throughput drug discovery campaign. We report herein a fit-for-purpose method to quantify peptide concentrations using high-throughput infrared spectroscopy (HT-IR). Through the development of this method, multiple critical method parameters were optimized including solvent composition, droplet deposition size, plate drying procedures, sample concentration, and internal standard. The relative absorbance of the amide region (1600-1750 cm^-1) to the internal standard, K₃Fe(CN)₆ (2140 cm^-1), was determined to be most effective at providing lowest interference for measuring peptide concentration. The best sample deposition was achieved by dissolving samples in a 50:50 v/v allyl alcohol/water mixture. The developed method was used on 96-well plates and analyzed at a rate of 22 min per plate. Calibration curves to measure sample concentration versus response relationship displayed sufficient linearity (R² > 0.95). The repeatability and scope of detection was demonstrated with eighteen peptide samples that were measured with most values below 20% relative standard deviation. The linear dynamic range of the method was determined to be between 1 and 5 mg/mL. This developed HT-IR methodology could be a useful tool in peptide drug candidate lead identification and optimization processes.

Novel frontier in wildlife monitoring: Identification of small rodent species from fecal pellets using near-infrared reflectance spectroscopy (NIRS)

Small rodents are prevalent and functionally important across the world's biomes, making their monitoring salient for ecosystem management, conservation, forestry, and agriculture. There is a growing need for cost-effective and noninvasive methods for large-scale, intensive sampling. Fecal pellet counts readily provide relative abundance indices, and given suitable analytical methods, feces could also allow for the determination of multiple ecological and physiological variables, including community composition. In this context, we developed calibration models for rodent taxonomic determination using fecal near-infrared reflectance spectroscopy (fNIRS). Our results demonstrate fNIRS as an accurate and robust method for predicting genus and species identity of five coexisting subarctic microtine rodent species. We show that sample exposure to weathering increases the method's accuracy, indicating its suitability for samples collected from the field. Diet was not a major determinant of species prediction accuracy in our samples, as diet exhibited large variation and overlap between species. fNIRS could also be applied across regions, as calibration models including samples from two regions provided a good prediction accuracy for both regions. We show fNIRS as a fast and cost-efficient high-throughput method for rodent taxonomic determination, with the potential for cross-regional calibrations and the use on field-collected samples. Importantly, appeal lies in the versatility of fNIRS. In addition to rodent population censuses, fNIRS can provide information on demography, fecal nutrients, stress hormones, and even disease. Given the development of such calibration models, fNIRS analytics could complement novel genetic methods and greatly support ecosystem- and interaction-based approaches to monitoring.

The mechanisms and prediction of non-structural carbohydrates accretion and depletion after mechanical wounding in slash pine (Pinus elliottii) using near-infrared reflectance spectroscopy

BACKGROUND

The allocation of non-structural carbohydrates (NSCs) plays a critical role in the physiology and metabolism of tree growth and survival defense. However, little is known about the allocation of NSC after continuous mechanical wounding of pine by resin tapping during tree growth.

RESULTS

Here, we examine the NSC allocation in plant tissues after 3 year lasting resin tapping, and also investigate the use of near-infrared reflectance (NIR) spectroscopy to quantify the NSC, starch and free sugar (e.g., sucrose, glucose, and fructose) concentrations in different plant tissues of slash pine. Spectral measurements on pine needle, branch, trunk phloem, and root were obtained before starch and free sugar concentrations were measured in the laboratory. The variation of NSC, starch and free sugars in different plant tissues after resin tapping was analyzed. Partial least squares regression was applied to calibrate prediction models, models were simulated 100 times for model performance and error estimation. More NSC, starch and free sugars were stored in winter than summer both in tapped and control trees. The position of resin tapping significantly influenced the NSCs allocation in plant tissues: more NSCs were transformed into free sugars for defensive resin synthesis close to the tapping wound rather than induced distal systemic responses. Models for predicting NSC and free sugars of plant tissues showed promising results for the whole tree for fructose (R²_CV = 0.72), glucose (R²_CV = 0.67), NSCs (R²_CV = 0.66) and starch (R²_CV = 0.58) estimates based on NIR models. Models for individual plant tissues also showed reasonable predictive ability: the best model for NSCs and starch prediction was found in root. The significance multivariate correlation algorithm for variable selection significantly reduced the number of variables. Important variables were identified, including features at 1021-1290 nm, 1480, 1748, 1941, 2020, 2123 and 2355 nm, which are highly related to NSC, starch, fructose, glucose and sucrose.

CONCLUSIONS

NIR spectroscopy provided a rapid and cost-effective method to monitor NSC, starch and free sugar concentrations after continuous resin tapping. It can be used for studying the trade-off between growth and production of defensive metabolites.

Application and interpretation of deep learning methods for the geographical origin identification of Radix Glycyrrhizae using hyperspectral imaging

Radix Glycyrrhizae is used as a functional food and traditional medicine. The geographical origin of Radix Glycyrrhizae is a determinant factor influencing the chemical and physical properties as well as its medicinal and health effects. The visible/near-infrared (Vis/NIR) (376–1044 nm) and near-infrared (NIR) hyperspectral imaging (915–1699 nm) were used to identify the geographical origin of Radix Glycyrrhizae. Convolutional neural network (CNN) and recurrent neural network (RNN) models in deep learning methods were built using extracted spectra, with logistic regression (LR) and support vector machine (SVM) models as comparisons. For both spectral ranges, the deep learning methods, LR and SVM all exhibited good results. The classification accuracy was over 90% for the calibration, validation, and prediction sets by the LR, CNN, and RNN models. Slight differences in classification performances existed between the two spectral ranges. Further, interpretation of the CNN model was conducted to identify the important wavelengths, and the wavelengths with high contribution rates that affected the discriminant analysis were consistent with the spectral differences. Thus, the overall results illustrate that hyperspectral imaging with deep learning methods can be used to identify the geographical origin of Radix Glycyrrhizae, which provides a new basis for related research.

Hyperspectral imaging provides an effective way to identify the geographical origin of Radix Glycyrrhizae to assess its quality.