Spectral Data Sets Research Articles

Raman Spectroscopy and Machine Learning Reveals Early Tumor Microenvironmental Changes Induced by Immunotherapy.

Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, and identifying these patients is difficult due to a lack of reliable biomarkers for prediction and evaluation of treatment response. Here, we demonstrate the first application of label-free Raman spectroscopy for elucidating biomolecular changes induced by anti-CTLA4 and anti-PD-L1 immune checkpoint inhibitors (ICI) in the tumor microenvironment (TME) of colorectal tumor xenografts. Multivariate curve resolution-alternating least squares (MCR-ALS) decomposition of Raman spectral datasets revealed early changes in lipid, nucleic acid, and collagen content following therapy. Support vector machine classifiers and random forests analysis provided excellent prediction accuracies for response to both ICIs and delineated spectral markers specific to each therapy, consistent with their differential mechanisms of action. Corroborated by proteomics analysis, our observation of biomolecular changes in the TME should catalyze detailed investigations for translating such markers and label-free Raman spectroscopy for clinical monitoring of immunotherapy response in cancer patients. SIGNIFICANCE: This study provides first-in-class evidence that optical spectroscopy allows sensitive detection of early changes in the biomolecular composition of tumors that predict response to immunotherapy with immune checkpoint inhibitors.

Lifting the limitations of Gaussian mixture regression through coupling with principal component analysis and deep autoencoding

The mathematical modeling of correlations between target properties and their factors of influence, particularly that allowing inverse analysis, is an essential part of molecular, material, and process designs. In contrast to approaches employing pseudo-inverse analysis, Gaussian mixture regression (GMR), which assumes that the relationships between variables can be represented as a mixture of Gaussian distributions, allows for direct inverse analysis. However, as this model optimizes the means and variance–covariance matrices of all variables, parameter estimation becomes increasingly difficult with the increasing number of variables. Herein, this drawback is addressed by the transformation of explanatory variables X into latent variables Z before GMR modeling. As the inverse (Z to X) transformation is necessary for direct inverse analysis, principal component analysis (PCA) and deep autoencoding (DAE) are employed as dimensionality reduction methods. After X is transformed to Z with the help of PCA or DAE, a GMR model is constructed with Z and objective variables Y, and the proposed method is therefore denoted as PCA-GMR or DAE-GMR, respectively. As Z values can be predicted by inputting Y values into the GMR model and can be transformed to X values, direct inverse analysis is also possible. Given that unlabeled data can be employed to construct PCA and DAE models, the proposed methods can also be used as semi-supervised learning techniques. The predictive abilities of PCA-GMR and DAE-GMR are verified using molecular, material, and spectral datasets and surpass that of traditional GMR on all datasets, with the maximum reduction of prediction errors equaling 63%.

Using leaf-based hyperspectral reflectance for genotype classification within a soybean germplasm collection assessed under different levels of water availability

ABSTRACT Because of the major international issue of intellectual property rights over seed technology, time-efficient methodologies that could characterize and monitor soybean genotypes are increasingly needed as they contribute to royalty collection and better management of seed genetics, leading to more efficient production strategies. This research aims at developing models capable of spectrally classifying soybean genotypes, at leaf scale, under different levels of water availability. Leaf reflectance spectra were collected using the Fieldspec 3 Jr. spectroradiometer, a hyperspectral proximal sensor. Two experiments were conducted at Embrapa Soja (Brazilian Agricultural Research Corporation). Experiment I was carried out in a plant growth chamber, using 12 soybean genotypes with different tolerance levels to drought, submitted to irrigation and water deficit treatments, where 214 spectral samples were collected. Experiment II was conducted in a greenhouse, using 164 soybean genotypes (from 15 different countries) with different responses to drought, submitted to irrigation and water deficit treatments, where 6,046 spectral samples were collected. Principal component analysis (PCA) was used to explore the qualitative spectral differences among the evaluated genotypes within each spectral dataset, and the stepwise procedure was carried out to select the wavelengths that best discriminate the soybean genotypes. Linear discriminant analysis (LDA) was used to obtain a model of classification of each leaf-based reflectance spectrum for each soybean genotype by its spectral behaviour. The acquired leaf reflectance was similar among soybean genotypes. The PCA explained over 96% of the spectral variance in the first three principal components, and the stepwise procedure selected 70 and 410 spectral bands in each dataset. In Experiment I, LDA presented an accuracy of 100%. In Experiment II, the LDA classified 149 genotypes with an accuracy of over 90%. The obtained results demonstrate the great potential of the spectral classification of soybean genotypes at leaf scale, regardless of the water status of the plants.

Tensor electrical impedance myography identifies clinically relevant features in amyotrophic lateral sclerosis

Objective. Electrical impedance myography (EIM) shows promise as an effective biomarker in amyotrophic lateral sclerosis (ALS). EIM applies multiple input frequencies to characterise muscle properties, often via multiple electrode configurations. Herein, we assess if non-negative tensor factorisation (NTF) can provide a framework for identifying clinically relevant features within a high dimensional EIM dataset. Approach. EIM data were recorded from the tongue of healthy and ALS diseased individuals. Resistivity and reactivity measurements were made for 14 frequencies, in three electrode configurations. This gives 84 (2 × 14 × 3) distinct data points per participant. NTF was applied to the dataset for dimensionality reduction, termed tensor EIM. Significance tests, symptom correlation and classification approaches were explored to compare NTF to using all raw data and feature selection. Main Results. Tensor EIM provides highly significant differentiation between healthy and ALS patients (p < 0.001, AUROC = 0.78). Similarly tensor EIM differentiates between mild and severe disease states (p < 0.001, AUROC = 0.75) and significantly correlates with symptoms (ρ = 0.7, p < 0.001). A trend of centre frequency shifting to the right was identified in diseased spectra, which is in line with the electrical changes expected following muscle atrophy. Significance. Tensor EIM provides clinically relevant metrics for identifying ALS-related muscle disease. This procedure has the advantage of using the whole spectral dataset, with reduced risk of overfitting. The process identifies spectral shapes specific to disease allowing for a deeper clinical interpretation.

Nondestructive Detection of Weight Loss Rate, Surface Color, Vitamin C Content, and Firmness in Mini-Chinese Cabbage with Nanopackaging by Fourier Transform-Near Infrared Spectroscopy.

A nondestructive optical method is described for the quality assessment of mini-Chinese cabbage with nanopackaging during its storage, using Fourier transform-near infrared (FT-NIR) spectroscopy. The sample quality attributes measured included weight loss rate, surface color index, vitamin C content, and firmness. The level of freshness of the mini-Chinese cabbage during storage was divided into three categories. Partial least squares regression (PLSR) and the least squares support vector machine were applied to spectral datasets in order to develop prediction models for each quality attribute. For a comparative analysis of performance, the five preprocessing methods applied were standard normal variable (SNV), first derivative (lst), second derivative (2nd), multiplicative scattering correction (MSC), and auto scale. The SNV-PLSR model exhibited the best prediction performance for weight loss rate (Rp2 = 0.96, RMSEP = 1.432%). The 1st-PLSR model showed the best prediction performance for L* value (Rp2 = 0.89, RMSEP = 3.25 mg/100 g), but also the lowest accuracy for firmness (Rp2 = 0.60, RMSEP = 2.453). The best classification model was able to predict freshness levels with 88.8% accuracy in mini-Chinese cabbage by supported vector classification (SVC). This study illustrates that the spectral profile obtained by FT-NIR spectroscopy could potentially be implemented for integral assessments of the internal and external quality attributes of mini-Chinese cabbage with nanopacking during storage.

Rapid prediction of soil available sulphur using visible near-infrared reflectance spectroscopy

Rapid and accurate prediction of soil available S, an important secondary nutrient, is crucial for its site-specific management in a cultivated region. Although traditional chemical analysis of any nutrient is an accurate method, but often costly, time-consuming and destructive in nature. Recently visible near-infrared (VIS-NIR) reflectance spectroscopic technique has gained its popularity for rapid, non-destructive and cost-effective assessment of soil nutrients. Hence, a study was carried out in an intensively cultivated region of Katol block of Nagpur, Maharashtra, during 2018-20 for rapid prediction of soil available S using spectroscopic technique. Both spectroscopic and chemical analyses were carried out using 132 georeferenced surface soil samples (0-15 cm depth). The descriptive statistical analysis showed that the available S content varied from 1.09 to 47.88 mg/kg. Multivariate models namely partial least square regression (PLSR) and random forest (RF) were applied to develop spectral models for S prediction from spectral dataset. Several statistical diagnostics like coefficient of determination (R2), root mean square error (RMSE), ratio of performance deviation (RPD) and ratio of performance to interquartile distance (RPIQ) were used to evaluate the performances of two models. The best prediction of S was achieved from nonlinear RF model (R2 = 0.71, RMSE = 8.86, RPD =1.18, RPIQ = 1.69) as compared to linear PLSR model (R2 = 0.53, RMSE = 9.04, RPD = 1.16, RPIQ = 1.66) datasets. Therefore, the result suggested applying non-linear multivariate model (RF) for obtaining best predictability for S from spectroscopic technique.

Microstructural Analysis of Whey/Soy Protein Isolate Mixed Gels Using Confocal Raman Microscopy.

This work explores the potential of confocal Raman microscopy to investigate the microstructure of mixed protein gel systems. Heat-set protein gels were prepared using whey protein isolate (WPI), soy protein isolate (SPI), and mixtures thereof, with a total of five different whey-to-soy protein ratios (100, 75, 50, 25, and 0%). These were analysed using confocal Raman microscopy, and different data analysis approaches were used to maximize the amount of structural and compositional information extracted from the spectral datasets generated, including both univariate and multivariate analysis methods. Small spectral differences were found between pure WPI and SPI gels, mainly attributed to conformational differences (amide bands), but SPI exhibited considerably greater auto-fluorescence than WPI. The univariate analysis method allowed for a rapid microstructural analysis, successfully mapping the distribution of protein and water in the gels. The greater fluorescence of the capsule-like structures found in the mixed gels, compared to other regions rich in proteins, suggested that these may be enriched in soy proteins. Further analysis, using a multivariate approach, allowed us to distinguish proteins with different levels of hydration within the gels and to detect non-proteinaceous compounds. Raman microscopy proved to be particularly useful to detect the presence of residual lipids in protein gels.

Quantification of soil organic carbon at regional scale: Benefits of fusing vis-NIR and MIR diffuse reflectance data are greater for in situ than for laboratory-based modelling approaches

Benefits of fusion approaches for visible to near (vis-NIR) and mid-infrared (MIR) chemometric modelling have been studied to some extent for laboratory-based soil studies, but little is known about the usefulness and limitations for in situ studies. Objectives were to compare laboratory-based and in situ vis-NIR and MIR partial least squares (PLS) and bagging-PLS regression approaches and to explore the potentials of combining both types of spectral data for the quantification of soil organic carbon (SOC). We applied different established low-level (spectra concatenation, outer product fusion approach) and high-level (averaging of vis-NIR and MIR modelling results) data fusion methods. The studied set comprised a total of 186 soil samples collected in Saxony-Anhalt and northern Saxony, Central Germany. One subset (Querfurt Plateau) covered 90 finely-textured soils originating from the Chernozem soil region, another (Düben Heath) with 96 samples was characterized by a wider pedological variety. Vis-NIR and MIR diffuse reflectance spectra were measured in situ on the soil surface and in the laboratory on pre-treated (dried and finely ground) soil material with the ASDFieldSpec4 and the Agilent 4300 Handheld FTIR instruments. We found a regionally stratified approach to be beneficial for accurate estimations for both laboratory and in situ data. For laboratory spectra, MIR outperformed vis-NIR data in both regions (Querfurt Plateau: r2 = 0.85 vs. 0.65, RMSE = 0.11 (in % SOC) vs. 0.17; Düben Heath: 0.77 vs. 0.69 (r2) and 0.27 vs. 0.40 (RMSE)). Ranking for in situ data was the same, but accuracies decreased markedly. With MIR, r2 amounted to 0.58 and RMSE was 0.20 for the Querfurt Plateau (vis-NIR: r2 = 0.26, RMSE = 0.27); for Düben Heath, r2 was 0.60 and RMSE was 0.39 for MIR data, while vis-NIR resulted in an r2 of 0.53 and an RMSE of 0.43. For the studied samples, which had medium to low water contents (0.68 to 16.8 wt%, median at 5.4 wt%), we found accuracies with both spectral datasets to be similarly affected by in situ conditions. Model ensemble averaging based on bagging-PLS regression was the most efficient approach to improve SOC estimation accuracies with in situ spectral data, whereas model averaging was in general of little effect for laboratory data. Improvements were most marked for the in situ data of the Düben Heath region, where r2 increased to a value of 0.77 and RMSE decreased to 0.28. Low-level data fusion methods did not yield any improvements compared to model ensemble averaging. For the latter, we identified averaging with weights derived sample-wise from uncertainties in the bootstrap-based modelling as being most accurate, but with little benefits compared to a simple (unweighted) averaging of vis-NIR and MIR estimates. Our results suggest that already a simple averaging procedure has the potential to advance multi-sensor applications integrating vis-NIR and MIR data for in situ or on-site soil spectroscopy. This applies especially to regions with heterogeneous soil conditions, tied to spectral variablity, as this increases the probability of complementary vis-NIR and MIR information and their prospective fusion.

Studying the pathological and biochemical features in breast cancer progression by confocal Raman microspectral imaging of excised tissue samples.

Confocal Raman microspectral imaging (CRMI) has been used to detect the spectra-pathological features of ductal carcinoma in situ (DCIS) and lobular hyperplasia (LH) compared with the heathy (H) breast tissue. A total of 15-20 spectra were measured from healthy tissue, LH tissue, and DCIS tissue. One-way ANOVA and Tukey's honest significant difference (HSD) post hoc multiple tests were used to evaluate the peak intensity variations in all three tissue types. Besides that, linear discrimination analysis (LDA) algorithm was adopted in combination with principal component analysis (PCA) to classify the spectral features from tissues at different stages along the continuum to breast cancer. Moreover, by using the point-by-point scanning methodology, spectral datasets were obtained and reconstructed for further pathologic visualization by multivariate imaging methods, including K-mean clustering analysis (KCA) and PCA. Univariate imaging of individual Raman bands was also used to describe the differences in the distribution of specific molecular components in the scanning area. After a detailed spectral feature analysis from 800 to 1800cm-1 and 2800 to 3000cm-1 for all the three tissue types, the histopathological features were visualized based on the content and structural variations of lipids, proteins, phenylalanine, carotenoids and collagen, as well as the calcification phenomena. The results obtained not only allowed a detailed Raman spectroscopy-based understanding of the malignant transformation process of breast cancer, but also provided a solid spectral data support for developing Raman based breast cancer clinical diagnostic techniques.

A transfer function to predict soil surface reflectance from laboratory soil spectral libraries

Spectral-based models extracted from laboratory reflectance in the 400–2500 nm spectral range to predict soil attributes may not be applicable to soil spectra acquired in the field. This is because laboratory sampling procedures disturb the natural soil surface's status. We investigated this issue by using the soil surface-dependent property of water-infiltration rate (WIR). We created a dataset with 114 samples collected from six fields with varying textures located in three different Mediterranean countries (Israel, Greece, Italy). Using the field and laboratory spectral datasets, we demonstrated that WIR is better predicted by field vs. laboratory measurements (R2 = 0.92 and 0.56, respectively). We also developed a transfer function (TF) to predict the field spectral measurements from the laboratory spectra. Use of the TF-processed dataset considerably improved the WIR prediction using laboratory information (from R2 = 0.56 to 0.76). It was concluded that soil surface reflectance values can be estimated based on laboratory spectra using a TF. The generated TF enables exploiting soil spectral libraries for remote-sensing views and for assessing surface-related soil properties.

Adapting confocal Raman microscopy for in situ studies of redox transformations at electrode-electrolyte interfaces

Confocal Raman microscopy was applied to quantify redox species present within the diffusion layer adjacent to an electrode surface under potentiostatic control. A glass microscope coverslip with a thin indium tin oxide (ITO) coating served as both the working electrode and optical window for a microscope-stage mountable spectroelectrochemical cell. A high numerical aperture objective mounted in an inverted microscope frame just below the stage brought excitation radiation through the coverslip window and to a tight focus a few micrometers above the ITO film surface. Species diffusing into the confocal probe volume defined by the excitation beam focus and the collected light region were detected, identified and quantified based on their Raman scattering frequencies and intensities. In measurements that interrogated the interconversion of ferrocyanide and ferricyanide ions as a function of applied voltage, least-squares regression analysis of spectral datasets predicted the formal potential and relative surface concentrations of the ions in good agreement with the expected Nernstian response. Preliminary studies of methyl viologen reduction at an ITO film/Nafion membrane interface were conducted and show the possibility for estimation of mass transport coefficients of redox species within ionic polymer materials.

Biofluid analysis and classification using IR and 2D-IR spectroscopy

Vibrational spectroscopy has produced valuable information for biomedical research owing to its label-free and high-throughput capabilities. However, the complexity of and large number of variables of spectral datasets has seen the increasing application of multivariate analysis (MVA) and machine learning algorithms in recent years. In particular, the use of these techniques applied to the analysis of IR spectra of biological samples has been demonstrated as a powerful tool for the rapid sample analysis and diagnosis of disease. In this article, we review a variety of classification techniques employed for the analysis of infrared (IR) spectral datasets of biofluids, quoting prediction accuracies to demonstrate their effectiveness. With the advent of new technologies, two-dimensional infrared spectroscopy (2D-IR) has recently been applied to biomedical problems and shows potential future applications in biofluid analysis, however with complex multi-dimensional datasets there is a desire for advanced analytical techniques. As the application of 2D-IR to biofluids and physiological protein samples is in its infancy, large spectral datasets of biofluids suitable for classification are not readily available. It is imperative to establish in what way 2D-IR datasets respond to pre-processing and analytical methods. For the first time we draw on the classification techniques applied to IR datasets discussed in this review and relevant 2D-IR studies to discuss the future of machine learning algorithms in 2D-IR spectroscopy.

Deep learning based spectral CT imaging

Spectral computed tomography (CT) has attracted much attention in radiation dose reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray energy spectrum is divided into several bins, each energy-bin-specific projection has a low signal-noise-ratio (SNR) than the current-integrating counterpart, which makes image reconstruction a unique challenge. Traditional wisdom is to use prior knowledge based iterative methods. However, this kind of methods demands a great computational cost. Inspired by deep learning, here we first develop a deep learning based reconstruction method; i.e., U-net with Lpp-norm, Total variation, Residual learning, and Anisotropic adaption (ULTRA). Specifically, we emphasize the various multi-scale feature fusion and multichannel filtering enhancement with a denser connection encoding architecture for residual learning and feature fusion. To address the image deblurring problem associated with the L22- loss, we propose a general Lpp-loss, p>0. Furthermore, the images from different energy bins share similar structures of the same object, the regularization characterizing correlations of different energy bins is incorporated into the Lpp- loss function, which helps unify the deep learning based methods with traditional compressed sensing based methods. Finally, the anisotropically weighted total variation is employed to characterize the sparsity in the spatial–spectral domain to regularize the proposed network In particular, we validate our ULTRA networks on three large-scale spectral CT datasets, and obtain excellent results relative to the competing algorithms. In conclusion, our quantitative and qualitative results in numerical simulation and preclinical experiments demonstrate that our proposed approach is accurate, efficient and robust for high-quality spectral CT image reconstruction.

Application of Raman spectroscopy for characterization of the functional polarization of macrophages into M1 and M2 cells

Macrophages are key cells in the immune inflammatory response that can be differentiated into M1 and M2 phenotypes. Polarization has a critical therapeutic value, especially in diseases in which an M1/M2 imbalance plays a pathophysiological role. Raman spectroscopy has proven to be a promising bioanalytical technique for discriminating different cell types. However, to our knowledge, its application to identify the functional polarization of macrophages into M1 or M2 cells is yet to be investigated. In this work, Raman spectroscopy was applied to the analysis of macrophage polarization, and the spectral datasets were analyzed using principal component analysis (PCA). In vitro, resting J774.1 macrophages were treated with LPS/IFN-γ to induce the M1 phenotype or with IL-4 to induce the M2 phenotype. The resulting Raman spectra showed sufficient biochemical information to distinguish between M1 and M2 phenotypes when analyzed by PCA, reflecting the changes in cell markers caused by differentiation. The Raman spectra collected from LPS-stimulated M1 and M2 macrophages were more intense. The functional phenotype of M1 macrophages was confirmed by IL-6 secretion and TNF-α mRNA expression, while M2 macrophages produced IL-10 and Arg-1 mRNA, as well as by the morphological changes observed by scanning electron microscopy. Taken together, the results indicate that Raman spectroscopy combined with PCA analysis is a useful tool to identify the functional phenotypes of macrophages, providing an alternative way to distinguish between cells in distinct differentiation stages.

Do Spectra Live in the Matrix? A Brief Tutorial on Applications of Factor Analysis to Resolving Spectral Datasets of Mixtures

In spite of a rapid growth of data processing software, that has allowed for a huge advancement in many fields of chemistry, some research issues still remain problematic. A standard example of a troublesome challenge is the analysis of multi-component mixtures. The classical approach to such a problem consists of separating each component from a sample and performing individual measurements. The advent of computers, however, gave rise to a relatively new domain of data processing – chemometry – focused on decomposing signal recorded for the sample rather than the sample itself. Regrettably, still a very few chemometric methods are practically used in everyday laboratory routines. The Authors believe that a brief ‘user-friendly’ guide-like article on several ‘flagship’ algorithms of chemometrics may, at least partly, stimulate an increased interest in the use of these techniques among researchers specializing in many fields of chemistry. In the paper, five different techniques of factor analysis are used for the analysis of a three-component system of fluorophores. These algorithms, applied on the excitation-emission spectra, recorded for the ‘unknown’ mixture, allowed to unambiguously determine its composition without the need for physical separation of the components. An example of using chemometric methods for physical chemistry research is also provided. For each presented technique of the data analysis, a short description of its theoretical background followed by an example of its practical performance is given. In addition, the Reader is supplemented with a basic information on matrix algebra, detailed experimental ‘recipes’, reference specialist literature and ready-to-use MATLAB codes.Graphical abstract

Soil properties: Their prediction and feature extraction from the LUCAS spectral library using deep convolutional neural networks

Soil, as a non-renewable resource, should be monitored continuously to prevent its degradation and promote sustainable agriculture. Soil spectroscopy in the visible-near infrared range is a fast and cost-effective analytical technique to predict soil properties. Although traditional machine learning methods are widely used for modeling soil spectral data, large spectral datasets may require better analytical methods for big data. Here, we explored the modeling potential of deep convolutional neural networks (DCNNs) for soil properties based on a large soil spectral library. The European topsoil dataset provided by the Land Use/Cover Area frame Survey (LUCAS) was used for DCNN modeling with the original absorbance spectra. Two single-task 16-layer DCNN models (LucasResNet-16 and LucasVGGNet-16) were used to make regression predictions of seven soil properties and classification predictions of soil texture. The effects of data pre-processing on single-task and multi-task DCNN modeling were assessed. The SHapley Additive exPlanations method was used to interpret the output of a DCNN model (LucasResNet-16). The DCNN models produced accurate predictions for most soil properties, and were superior to a single-task shallow convolutional neural network and traditional machine learning methods. Spectral transformation was effective for predicting some soil properties, while spectral downsampling led to a reduction in the modeling accuracy. The performance of a multi-task DCNN model built on the basis of LucasResNet-16 was improved compared with the performance of the single-task model. Soil organic carbon content, nitrogen content, cation exchange capacity, pH, and calcium carbonate content were well predicted, with the root mean squared error of 19.130 g∙kg−1, 0.971 g∙kg−1, 6.614 cmol(+)∙kg−1, 0.326, and 24.526 g∙kg−1, respectively. The overall classification accuracy of soil texture was 0.749 (four groups) and 0.566 (12 levels). The position of feature wavelengths differed among the soil properties, for which multiple characteristic peaks were common. This study fully demonstrates the modeling potential of deep learning with soil ultraspectral data, which could enhance precision agriculture.

Spectral sparse recovery form a single RGB image

An efficient procedure for spectral reflectance recovery using a new compressive sensing-based spectral sparse recovery method is proposed by using the optimized sparse basis functions to recover spectral reflectance from a single RGB image. Unlike the conventional spectral recovery methods, the sparse basis functions of the proposed method are an optimal solution for obtaining the information needed to consider the characteristics of the RGB response values. The novelty of the proposed method is to obtain the optimized sparse basis functions considering the influence of the spectral reflectance dataset and the sample’s colorimetric characteristic, which meets the minimum of the spectral recovery error. Spectral and colorimetric accuracy of the proposed method is studied to verify the effectiveness compared to existing methods. As the spectral recovery results show, the proposed method is extremely effective in recovering spectral reflectance.

Direct infusion–tandem mass spectrometry combining with data mining strategies enables rapid chemome characterization of medicinal plants: A case study of Polygala tenuifolia

Data-independent MS2 spectrum acquisition after fragmenting the precursor ion cohort with 1 Da bin, termed as MS/MSALL ®, offers an opportunity to achieve rapid chemome characterization when being coupled with direct infusion (DI). Some post-acquisition data processing strategies, such as mass defect filtering (MDF), diagnostic fragment ion filtering (DFIF), and neutral loss filtering (NLF), facilitate data extraction from massive dataset, and moreover, molecular weight (MW) imprinting allows rapid capturing those reported components. Here, DI–MS/MSALL ® was employed to acquire cubic spectral dataset, and the strategies such as MW imprinting, MDF, DFIF, and NLF, were subsequently applied to filter the structural information. The integrated pipeline was utilized for the chemome characterization of Polygala tenuifolia, a famous edible medicinal plant. To aid information filtering, an in-house chemical library was built by comprehensively collecting structural information from some available databases. A single analytical run was completed within 5 min. For MS1 spectrum processing, MW imprinting was firstly applied to capture the compounds in the chemical library, and “five-point” MDF frames were employed to pursue saponins, oligosaccharide esters, and xanthones. Regarding MS2 spectral plot, DFIF and NLF were deployed to search information-of-interest. Structural identification was accomplished by carefully correlating precursor ions and MS2 spectra, applying the well-defined mass cracking rules, and referring to literature information as well as available databases. A total of 109 compounds, mainly saponins (40 ones), oligosaccharide esters (29 ones), and xanthones (19 ones), were captured and structurally annotated. MS1 spectra were also implemented for chemome comparison between Polygala tenuifolia and several similar plants belonging to Polygala genus, resulting in the observation of significant inter- and intra-species differences. Above all, DI–MS/MSALL ® is a promising choice for high-throughput chemome profiling of, but not limited to, medicinal plants, in particular when being integrated with post-acquisition data processing strategies.

An exploratory MALDI-TOF MS library based on SARAMIS superspectra for rapid identification of Aspergillus section Nigri.

Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is a broadly used technique for identification and typing of microorganisms. However, its application to filamentous fungi has been delayed. The objective of this study was to establish a data library for rapid identification of the genus Aspergillus sect. Nigri using MALDI-TOF MS. With respect to sample preparation, we compared the utility of using mature mycelia, including conidial structures, to accumulate a wider range of proteins versus the conventional method relying on young hyphae. Mass spectral datasets obtained for 61 strains of 17 species were subjected to cluster analysis and compared with a phylogenetic tree based on calmodulin gene sequences. Specific and frequent mass spectral peaks corresponding to each phylogenetic group were selected (superspectra for the SARAMIS system). Fifteen superspectra representing nine species were ultimately created. The percentage of correct identification for 217 spectra was improved from 36.41% to 86.64% using the revised library. Additionally, 2.76% of the spectra were assigned to candidates that comprised several related species, including the correct species.

Theoretical Simulation of Near-Infrared Spectrum of Piperine: Insight into Band Origins and the Features of Regression Models.

We investigated the near-infrared spectrum of piperine using quantum mechanical calculations. We evaluated two efficient approaches, DVPT2//PM6 and DVPT2//ONIOM [PM6:B3LYP/6-311++G(2df, 2pd)] that yielded a simulated spectrum with varying accuracy versus computing time factor. We performed vibrational assignments and unveiled complex nature of the near-infrared spectrum of piperine, resulting from a high level of band convolution. The most meaningful contribution to the near-infrared absorption of piperine results from binary combination bands. With the available detailed near-infrared assignment of piperine, we interpreted the properties of partial least square regression models constructed in our earlier study to describe the piperine content in black pepper samples. Two models were compared with spectral data sets obtained with a benchtop and a miniaturized spectrometer. The two spectrometers implement distinct technology which leads to a profound instrumental difference and discrepancy in the predictive performance when analyzing piperine content. We concluded that the sensitivity of the two instruments to certain types of piperine vibrations is different and that the benchtop spectrometer unveiled higher selectivity. Such difference in obtaining chemical information from a sample can be one of the reasons why the benchtop spectrometer performs better in analyzing the piperine content of black pepper. This evidenced direct correspondence between the features critical for applied near-infrared spectroscopic routine and the underlying vibrational properties of the analyzed constituent in a complex sample.