Principal Component Analysis Loading Plots Research Articles

Laser-induced breakdown spectroscopy analysis of tourmaline: protocols, procedures, and predicaments

Abstract. Laser-induced breakdown spectroscopy (LIBS) is an appropriate choice of analytical tool for analysis of complex minerals because it is rapid, requires little sample preparation, and acquires major and trace element compositional information on all naturally occurring elements at concentrations above their intrinsic levels of detection for the specific analyte material. Tourmaline, a complex borosilicate mineral supergroup, was chosen as a test mineral due to the complexity of its major and minor element composition. Four analytical issues were investigated during project development: (1) the spacing between analytical laser shots to avoid analysis of the recast from previous laser ablations, (2) the efficacy of using a cleaning shot prior to data acquisition, (3) the number of ablations required to collect an average spectrum that is representative of the tourmaline sample, and (4) the effect of spectrometer drift on principal component analysis (PCA) when using the entire LIBS spectra to model the compositional variations within the sample suite. The minimum spacing between locations of analysis was determined to be 800 µm for the analytical conditions used in this study by examining spectra acquired in a 2×2 grid across a quartz–tourmaline boundary. At a spacing of 100 µm, twice the diameter of the laser beam, the intensity of the boron I peaks at 249.68 and 249.77 nm was very low in the first location (quartz) but quite high in the fourth location (quartz) due to deposition of tourmaline-composition recast by laser shots in the second and third locations (both on tourmaline). Increasing the distance between locations to 800 µm ensured that the area analyzed largely avoided the recast layer from previous ablations. Given that the distribution of recast was taken into account, no cleaning shots were collected. PCA score plots calculated using successively larger numbers of spectra from the same sample show that a total of 64 spots, or 16 2×2 grids, are needed to acquire a representative average analysis of tourmaline. Spectrometer drift was recognized in PCA loading plots by a characteristic splitting of element peaks; half the peak indicates positive loading and the other half of the peak indicates negative loading. Drift correction was aligned by placing the Ca II peak at 393.34 nm in the 393.398 bin; this correction eliminated split peaks in loading plots. The resolution of these issues yielded LIBS spectra suitable for multivariate statistical analysis that can be applied to understanding geologic processes. These results contribute to the application of rapid LIBS analysis of complex geomaterials.

Unveiling the impact of harvest time on Dioscorea opposita Thunb. cv. Tiegun maturity by NMR-based metabolomics and LC-MS/MS analysis.

Dioscorea opposita Thunb. cv. Tiegun maturity (DM) is an important factor influencing its quality. However, there are few studies on the impact of harvest time on its maturation. In the present study, a NMR-based metabolomics approach was applied to investigate the dynamic metabolic changes of D. opposita Thunb. cv. Tiegun at six different harvest stages: stage 1 (S1), stage 2 (S2), Stage 3 (S3), stage 4 (S4), stage 5 (S5) and stage 6 (S6). Principal component analysis showed distinct segregation of samples obtained from S1, S2 and S3 compared to those derived from S4, S5 and S6. Interestingly, these samples from the two periods were obtained before and after frost, indicating that frost descent might be important for DM. Eight differential metabolites responsible for good separation of different groups were identified by the principal component analysis loading plot and partial least squares-discriminant analysis. In addition, quantitative analysis of these metabolites using liquid chromatography-tandem mass spectrometry determined the effects of harvest time on these metabolite contents, two of which, sucrose and allantoin, were considered as potential biomarkers to determine DM. The present study demonstrated that NMR-based metabolomics approach could serve as a powerful tool to identify differential metabolites during harvesting processes, also offering a fresh insight into understanding the DM and the potential mechanism of quality formation. © 2024 Society of Chemical Industry.

Early colorectal cancer detection: a serum analysis platform combining SERS and machine learning.

Colorectal cancer (CRC) is one of the deadliest malignancies globally, with high incidence and mortality rates. Early detection is crucial for improving treatment success rates and patient survival. However, due to the difficulty in detecting early symptoms, many cases are diagnosed at advanced stages, necessitating more sensitive and accurate detection methods. This study proposes a novel approach combining the Principal Component Analysis (PCA)-Dynamic Weighted Nearest Neighbor (DWNN) model with Surface-Enhanced Raman Scattering (SERS) technology to detect the serum of CRC mice at different stages. Establishing the CRC mice model, serum samples were collected for further analysis. An Au Nanocluster (AuNC) substrate was synthesized to ensure optimal SERS enhancement. The PCA-DWNN recognition model was constructed to classify the SERS spectra of CRC at different stages. The synthesized AuNC substrate has high sensitivity, good reproducibility, uniformity, and stability, making it a high-performance nanomaterial. The PCA-DWNN model has significant advantages in identifying high-dimensional and complex SERS spectra, offering excellent classification accuracy and robustness, with an accuracy rate of 97.5%. By analyzing the PCA loading plot, it was observed that as CRC progressed, the content and structure of proteins, lipids, amino acids, and carbohydrates in the serum changed, reflected in different characteristic peaks in the SERS spectra. This study suggests that SERS combined with PCA-DWNN has potential in the early detection of CRC, possibly providing a novel approach for clinical diagnostics.

A machine learning-driven SERS platform for precise detection and analysis of vascular calcification.

Vascular calcification (VC) significantly increases the incidence and mortality rates of cardiovascular diseases, severely threatening public health as a global issue. Currently, there are no effective methods to prevent and treat vascular calcification. This study proposes a machine learning-assisted surface-enhanced Raman scattering (SERS) technique for label-free, highly sensitive analysis of VC rat serum. We prepared gold nanobipyramid (GNBP) substrates using seed-mediated and liquid-liquid interface self-assembly methods and measured the SERS spectra of the serum. The collected spectral data were processed using a Principal Component Analysis (PCA)-Linear Discriminant Analysis (LDA) model to achieve effective sample differentiation. In this analysis model, GNBP substrates enabled rapid, sensitive, and label-free serum spectral detection, achieving classification accuracy, sensitivity, and specificity of 96.0%, and an AUC value of 0.98, significantly outperforming currently used machine learning methods. By analyzing the PCA loading plots, key spectral features that distinguished VC were successfully captured. This study demonstrates that combining SERS technology with machine learning provides a new method and foundation for real-time diagnosis and identification of VC, showcasing the significant advantages of GNBP substrates in high-sensitivity and high-specificity detection, potentially improving the early diagnosis and treatment of VC significantly.

Yield attributing traits of high zinc rice (Oryza sativa L.) genotypes with special reference to principal component analysis

Total 21 high zinc rice genotypes were evaluated under five different locations for 14 different yield attributing traits, including grain yield/plant (gm) to determine the pattern of variation, the relationship among the individuals and their characteristics through Principal Component Analysis (PCA) during the Kharif-2017. PCA was done for all the locations individually as well as pooled analysis for all locations using R software. Out of the 14 PCs, the initial four PCs contributed more to the total variability. The highest cumulative variability of the first four PCs found at Bhikaripur (81.11%) followed by BHU Agriculture research farm-II (79.23%) etc. and Pooled variability was 76.61%. Pooled data analysis indicates PCA biplot or loading plot of first two principal components revealed that days to maturity, days to 1st flowering date and days to 50% flowering loaded more on the first component and number of spikelets per panicles, number of grains/panicles, grain weight per panicle, grain yield/plant accounted more variation in the second component compared to the other parameters. Thus, the pooled analysis of principal component analysis revealed the characters contributing to the variation and genetic variability that exists in these rice genotypes. This is because the genotypes BRRIdhan 72, Sambamahsuri and Swarna were identified in different principle components related to grain yield and grain quality, and were also located farthest away from biplot origin in individual PCA based biplot. So they may be employed to improve yield attributing factors like total effective tiller number. PC1, PC2 and PC3 have days to first flowering and days to 50% flowering, hence their genotypes may be valuable in producing early maturing cultivars. Thus, the results revealed that wide range of variability was shown by different traits of the genotypes which can be utilized in rice improvement programmes.

Evaluating the physiological and biochemical responses of melon plants to NaCl salinity stress using supervised and unsupervised statistical analysis

Salinity is a major environmental issue affecting plant production, particularly in the Middle East. Multivariate data analysis including principal component analysis (PCA), hierarchical clustering analysis (HCA), and orthogonal projections to latent structures discriminant analysis (OPLS-DA) are powerful statistical modeling tools that can extract important information from large complex datasets and explain the relationship between different variables. The combined use of supervised and unsupervised statistical analysis thus provides a valuable understanding of tendencies (PCA) and sample grouping (OPLS-DA). In the current study, PCA, HCA, and OPLS-DA as well as analysis of variance were used to assess the physiological and biochemical response of three melon genotypes (Ghobadlu, Suski-e-Sabz, and Galia F1) to NaCl concentrations (0, 50, and 100 mM) after 15 and 30 days of treatment in a greenhouse. PCA results and loading plots showed that axis 1 principal component 1 was negatively correlated with intrinsic water use efficiency, instantaneous water use efficiency, abscisic acid, and flavonol content at 15 and 30 days of treatment. In other words, these parameters increased under salinity conditions in the studied genotypes, which could be of interest as a stress tolerance indicator at both sampling dates. These findings are in accordance with the mean comparison results of traits in three melon genotypes under NaCl concentrations. In addition, the HCA heatmap showed that all measured parameters can be grouped into two distinct clusters at the two time points. Further, supervised analysis with OPLS-DA easily discriminates samples according to genotype and salinity. In conclusion, multivariate and univariate statistics can be useful for revealing patterns and thus selecting melon genotypes with high salt tolerance that can be used in breeding programs. Genotypes Galia F1 and Ghobadlu were more tolerant of salinity than Suski-e-Sabz, as both had a better performance in stomatal and non-stomatal parameters at the two sampling dates.

Nanomechanical and Molecular Characterization of Aging in Dentinal Collagen

Methylglyoxal (MGO) is an important molecule derived from glucose metabolism with the capacity of attaching to collagen and generating advanced glycation end products (AGEs), which accumulate in tissues over time and are associated with aging and diseases. However, the accumulation of MGO-derived AGEs in dentin and their effect on the nanomechanical properties of dentinal collagen remain unknown. Thus, the aim of the present study was to quantify MGO-based AGEs in the organic matrix of human dentin as a function of age and associate these changes with alterations in the nanomechanical and ultrastructural properties of dentinal collagen. For this, 12 healthy teeth from <26-y-old and >50-y-old patients were collected and prepared to obtain crown and root dentin discs. Following demineralization, MGO-derived AGEs were quantified with a competitive ELISA. In addition, atomic force microscopy nanoindentation was utilized to measure changes in elastic modulus in peritubular and intertubular collagen fibrils. Finally, principal component analysis was carried out to determine aging profiles for crown and root dentin. Results showed an increased presence of MGO AGEs in the organic matrix of dentin in the >50-y-old specimens as compared with the <26-y-old specimens in crown and root. Furthermore, an increase in peritubular and intertubular collagen elasticity was observed in the >50-y-old group associated with ultrastructural changes in the organic matrix as determined by atomic force microscopy analysis. Furthermore, principal component analysis loading plots suggested different “aging profiles” in crown and root dentin, which could have important therapeutic implications in restorative and adhesive dentistry approaches. Overall, these results demonstrate that the organic matrix of human dentin undergoes aging-related changes due to MGO-derived AGEs with important changes in the nanomechanical behavior of collagen that may affect diagnostic and restorative procedures in older people.

INVESTIGATION OF ANTIOXIDANT ACTIVITY AND CHEMICAL FINGERPRINT OF MARINE POLYCHAETE BASED ON ATR-FTIR METABOLOMICS

Marine polychaete is an important component in marine benthic communities and can be widely found in Malaysia. To date, the information regarding its chemicals and biological activities of marine polychaete is still limited. This study aims to evaluate the chemical fingerprint of the marine polychaetes Marphysa moribidii and Diopatra claparedii using Attenuated Total Reflectance Fourier-Transform Infrared (ATR FTIR) metabolomics. The antioxidant activity including total phenolic content (TPC) and DPPH free radical scavenging activity was also evaluated. The results showed that D. claparedii has a higher amount of TPC (0.47±.0.03 mg GAE/g) compared to M. moribidii (0.30± 0.01 mg GAE/g). The DPPH activity tested at the concentration 5000 μg/ml showed percentage of inhibition expressed by D. claparedii and M. moribidii extracts were 38.80±11.70 and 25.54±7.35 respectively. The principal component analysis (PCA) score plot showed distinct clusters between M. moribidii and D. claparedii. Further investigation via PCA loading plot showed that the FTIR signals at 3340, 1090, 1047 and 880 cm-1 were contributed by D. claparedii. Meanwhile, the partial least square (PLS) analysis revealed several signals were correlated with the TPC including stretching vibration of C-H at 2854 cm−1, C=C bonds at 1640 cm-1 and C=C bending at 1726 cm-1 showing the presence of aromatic ring deformations. This study provides the key important chemical fingerprint of different marine polychaetes (D. claparedii and M. moribidii sp.) that might be useful for the discovery of bioactive compounds from natural resources.

The Use of Chemometrics for Classification of Sidaguri (&lt;i&gt;Sida rhombifolia&lt;/i&gt;) Based on FTIR Spectra and Antiradical Activities

Sidaguri (Sida rhombifolia) is one of the herbal components used in traditional medicine. The application of chemometrics in the standardization of herbal medicine is common. The objective of this study was to classify Sidaguri from different regions based on FTIR spectra with chemometrics of principal component analysis (PCA) and to correlate the antioxidant activities with FTIR spectra using the multivariate calibration of partial least square regression (PLSR). The extraction of Sidaguri powder was performed using ultrasound-assisted extraction (UAE) at optimum conditions. The obtained extracts were subjected to antiradical scavenging activities using DPPH (2,2’-diphenyl-1-picrylhydrazyl) and ABTS (2,2′-azinobis-3-ethylbenzothiazoline-6-sulfonic acid) radicals. The PCA result shows that Sidaguri from different regions could be separated using 14 wavenumbers of FTIR spectra based on the PCA's loading plot. PLSR regression using the second derivative FTIR spectra at wavenumbers of 3662–659 cm–1 could predict radical scavenging activities (RSA) of Sidaguri with R2 values of 0.9636 and 0.9024 for calibration and validation models, with RMSEC and RMSEP values of 1.45% and 2.65%, respectively. It can be concluded that FTIR spectra treated by PCA were reliable for classifying Sidaguri from different regions. At the same time, PLSR was accurate and precise enough to predict the RSA of Sidaguri.

Preharvest 1-methylcyclopropene treatment effects on fruit quality attributes and targeted metabolites in ‘Wonhwang’ pears stored at room temperature after cold storage

Preharvest 1-methylcyclopropene (1-MCP) spray treatment effects on fruit quality attributes and targeted metabolites were investigated in ‘Wonhwang’ Asian pears during the shelf-life period following cold storage. The preharvest 1-MCP treatment was sprayed 1 week before harvest, and then the pears were stored at 1°C for up to 3 months and then transferred to room-temperature conditions for the shelf-life period. The soluble solids content (SSC) was higher in the preharvest 1-MCP-treated fruit than in untreated fruit at harvest and during the second half of the shelf-life period after cold storage. However, preharvest 1-MCP treatment aggravated the incidence and severity of flesh browning. The contents of asparagine, histidine, valine, and isoleucine were higher in the preharvest 1-MCP-treated fruit than in the untreated fruit during the shelf-life period after cold storage. The results of principal component analysis loading plot indicated that preharvest 1-MCP treatment was strongly associated with leucine, tryptophan, histidine, valine, and isoleucine. In addition, the results of Pearson's correlation coefficient networking indicated that preharvest 1-MCP treatment affected the differential responses of fruit quality attributes and targeted metabolites during the shelf-life period after cold storage. Therefore, the results suggested that preharvest 1-MCP treatment could alter SSC levels and certain targeted metabolites along with internal tissue colour variables in ‘Wonhwang’ pears during the shelf-life period after short-term cold storage.

Discrimination of pistachio cultivars based on multi-elemental fingerprinting by pattern recognition methods

The potential of multi-elemental fingerprinting based on inductively coupled plasma optical emission measurements (ICP OES) was examined to classify pistachio cultivars. Five pistachio cultivar samples were collected during the harvesting period 2017–2018 from Kerman in Iran. The ability of multivariate data analysis approaches, such as principal component analysis (PCA) and principal component analysis-linear discriminant analysis (PCA-LDA) have been investigated in order to achieve discrimination of different cultivars. Eighteen variables i.e. the contents of Al, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Pb, Sr and Zn at mg g−1 level, determined by ICP OES were used. The results showed that the five pistachio cultivars can be identified based on the multi-elemental fingerprints. The loading plot of PCA illustrated that Pb, Cr, Co, Cd, and Ba have the lowest contributions in discrimination of the different pistachio cultivars. Almost all samples were correctly classified by the PCA-LDA model using cross validation (99.0%). The mean sensitivity and specificity were 98.0% and 99.6%, respectively, indicating the satisfactory performance of the model. The results demonstrate multi-elemental fingerprinting combined with multivariate data analysis methods can be employed as an effective and feasible method for classification of Iranian pistachio based on their cultivars.

Attenuated Total Reflection Fourier Transform Infrared Spectroscopy combined with chemometric modelling for the classification of clinically relevant Enterococci.

Attenuated Total Reflection Fourier Transform Infrared (ATR-FT-IR) Spectroscopy and chemometric modelling, including soft independent modelling by class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM), were applied to attempt to discriminate 60 clinical isolates of Enterococcus faecium and Enterococcus faecalis and hence evaluate the performance of the spectroscopic approach in identifying enterococci infections. The bacterial samples were identified by polymerize chain reaction (PCR) amplification and their ATR-FT-IR spectra acquired. Spectra were processed to the second derivative using the Savitzky-Golay algorithm and normalized using extended multiplicative signal correction employing the UnscramblerX (CAMO, Norway) software package. Multivariate classification models and their performance were evaluated using Cohen's Kappa coefficient. Principal component analysis (PCA) score plots showed separate clusters of spectra related to membership to E. faecium and E. faecalis, with this explained by bands assigned to PO2 (1230cm-1 ), P-O-C (1114cm-1 ), monosubstituted alkene (997, 987cm-1 ) and C-O (1070, 1055, 1036cm-1 ) corresponding to teichoic acids, polysaccharides and peptidoglycan from the cell wall in PCA and PLS-DA loading plots. The best classification model for E. faecium and E. faecalis is SVM, indicating via highest Kappa score. The classification coefficient between SIMCA, PLS-DA, SVM and PCR as reference method were 0·59, 0·9 and 1, respectively, shown as the Kappa scores. The main spectral differences observed between the two clinically relevant enterococci species were associated with changes in the teichoic acid content of cell walls. With regard to the binary classification method, SVM was found to be the best performing classification model, providing the highest correlation with the PCR results. The study shows that ATR-FT-IR spectroscopy in combination with chemometric modelling can be applied for the phenotypic identification and discrimination of clinically relevant and similar enterococcal species.

Fruit quality and major primary metabolites differ across production systems in cold-stored figs (Ficus carica L.)

The greenhouse production system is becoming popular in fig cultivation as a means to extend the harvest window and season of fig fruit. The aim of this study was to evaluate the comparative effects of two production systems (open field vs. greenhouse) on the physiological and biochemical responses in terms of fruit quality attributes and the major primary metabolites in ‘Masui Dauphine’ fig fruit stored at 0.5 °C for up to 30 days. Weight loss increased with storage duration and was much greater for fruits grown in the greenhouse than those grown in the open field; fruit length and diameter at the peduncle-end region decreased with storage duration and were much shorter in the greenhouse fruit than in the open field fruit; fruit respiration rate was higher in the open field fruit than in the greenhouse fruit during cold storage. The colour variables gradually decreased in peel tissues regardless of the tissue region but were not consistent in the cortex tissues during cold storage; and the glucose and fructose levels increased with storage duration, regardless of the production system. The organic acid contents were not consistent during cold storage—citric acid content was higher but fumaric acid content was lower in the open field fruit than in the greenhouse fruit. Aspartic acid, arginine, γ-aminobutyric acid, methionine, tryptophan, phenylalanine, leucine, and lysine levels were higher in the open field fruits than in greenhouse fruits, and vice-versa for glutamic acid. The results of multivariate data analyses, including a normalised heatmap matrix system, principal component analysis loading plot, and correlation coefficient matrix, indicated that fruit quality attributes and major metabolites responded differentially to production systems during cold storage. Overall, the results suggested that production systems could affect fig fruit quality and storability as well as the concentrations of certain major metabolites during cold storage.

Quantitative and chemical fingerprint analysis for quality control of Zingiber zerumbet based on HPTLC combined with chemometric methods

A simple, reliable high-performance thin-layer chromatography (HPTLC) method was developed for chemical fingerprinting of Zingiber zerumbet and quantitative estimation of zerumbone. Thirty-six batches of Z. zerumbet were collected from five eco-regions of eastern India. Zerumbone content varied from 52.4 to 214.6 mg/g (dry weight) in methanolic extract of Z. zerumbet rhizomes. Zerumbone content was in the following order: moist deciduous forests of the lower Gangetic plains > Brahmaputra valley evergreen forest > Odisha semi-evergreen forests > Sundarbans freshwater swamp forests > moist deciduous forest of the eastern highlands. Relative Standard Deviation (RSD) of the relative peak areas (RPA) and relative retention times (RRT) of eight characteristic peaks in repeatability and stability test were <3%, and the fingerprinting method was confirmed to be suitable for Z. zerumbet rhizomes. Chemometric approaches like hierarchical cluster analysis (HCA) and principal component analysis (PCA) were employed to classify Z. zerumbet samples based upon their eco-region. Consistent results were achieved showing Z. zerumbet samples could be effectively grouped according to their eco-region. The PCA loading plots identified three probable chemical markers, which might be useful in discriminating the samples. This combinative approach could be used for quality assessment of Z. zerumbet and for the formulations containing zerumbone.

FTIR Microspectroscopy for the Assessment of Mycoplasmas in HepG2 Cell Culture

To assess the presence and absence of mycoplasma contamination in cell culture, Fourier transform infrared (FTIR) microspectroscopy, coupled with multivariate analysis, was deployed to determine the biomolecular changes in hepatocellular carcinoma cells, HepG2, before and after mycoplasma contamination. The contaminated HepG2 cells were treated with antibiotic BM-Cyclin to decontaminate the mycoplasma, and polymerase chain reaction (PCR) was then performed to confirm the presence or the absence of mycoplasma contamination. The contaminated and decontaminated HepG2 cells were analyzed by FTIR microspectroscopy with principal component analysis (PCA) and peak integral area analysis. The results showed that the FTIR spectra of contaminated HepG2 cells demonstrated the alteration in the IR spectra corresponding to the lipid, protein, and nucleic acid regions. PCA analysis distinguished the spectral differences between the groups of mycoplasma-contaminated and -decontaminated cells. The PCA loading plots suggest that lipid and protein are the main contributed molecules for the difference between these two cell groups. Peak integral area analysis illustrated the increase of lipid and nucleic acid and the decrease of protein contents in the contaminated HepG2 cells. FTIR microspectroscopy is, therefore, proven to be a potential tool for assessing mycoplasma removal by monitoring biomolecular alterations in cell culture.

PiTMaP: A New Analytical Platform for High-Throughput Direct Metabolome Analysis by Probe Electrospray Ionization/Tandem Mass Spectrometry Using an R Software-Based Data Pipeline.

A new analytical platform called PiTMaP was developed for high-throughput direct metabolome analysis by probe electrospray ionization/tandem mass spectrometry (PESI/MS/MS) using an R software-based data pipeline. PESI/MS/MS was used as the data acquisition technique, applying a scheduled-selected reaction monitoring method to expand the targeted metabolites. Seventy-two metabolites mainly related to the central energy metabolism were selected; data acquisition time was optimized using mouse liver and brain samples, indicating that the 2.4 min data acquisition method had a higher repeatability than the 1.2 and 4.8 min methods. A data pipeline was constructed using the R software, and it was proven that it can (i) automatically generate box-and-whisker plots for all metabolites, (ii) perform multivariate analyses such as principal component analysis (PCA) and projection to latent structures-discriminant analysis (PLS-DA), (iii) generate score and loading plots of PCA and PLS-DA, (iv) calculate variable importance of projection (VIP) values, (v) determine a statistical family by VIP value criterion, (vi) perform tests of significance with the false discovery rate (FDR) correction method, and (vii) draw box-and-whisker plots only for significantly changed metabolites. These tasks could be completed within ca. 1 min. Finally, PiTMaP was applied to two cases: (1) an acetaminophen-induced acute liver injury model and control mice and (2) human meningioma samples with different grades (G1-G3), demonstrating the feasibility of PiTMaP. PiTMaP was found to perform data acquisition without tedious sample preparation and a posthoc data analysis within ca. 1 min. Thus, it would be a universal platform to perform rapid metabolic profiling of biological samples.

Tree species identification via 1H NMR fingerprinting of supercritical carbon dioxide wood extractives

Six tree species were examined using 1H NMR spectroscopy of sap extracted by supercritical CO2. A metabolomic approach was developed to evaluate the sap extracted from sapwood of Norway spruce (Picea abies), Sitka spruce (Picea sitchensis), radiata pine (Pinus radiata), macrocarpa (Cupressus macrocarpa), and two Eucalyptus species—shining gum and mountain ash (Eucalyptus nitens and Eucalyptus regnans. The sap extraction patterns in the different species were visualised using 1H magnetic resonance imaging. In softwoods with distinct annual rings, water was first removed from the latewood bands, and then gradually from the earlywood bands. In the case of the hardwood species an almost random water redistribution, rather than water expulsion, was observed. Analysis of the principal component analysis loading plots showed that the significant differences in the sap between each species were due to the carbohydrate region. Key discriminators were identified as pinitol, sucrose, glucose, and fructose.

Differential responses of fruit quality and major targeted metabolites in three different cultivars of cold-stored figs (Ficus carica L.)

Fig fruit are highly perishable after harvest. To elucidate the relationship between fruit physiological performance and major targeted metabolites during cold storage, fig fruit of three major cultivars, ‘Masui Dauphine’, ‘Bongresi’, and ‘Banane’, were stored at 0.5 °C for up to 30 d, followed by 1 d at 20 °C. Fruit fresh weight loss increased and fruit length and diameter decreased during cold storage, regardless of cultivars. Lightness and chroma from peel and cortex tissues decreased, although differences were detected among cultivars. Hue angle was differently responsive according to cultivars, fruit regions, and tissues. Soluble solids content (SSC), titratable acidity (TA), and the levels of glucose and fructose increased, irrespective of fig cultivars but individual organic acids remained unchanged during cold storage. The contents of aspartic acid and glutamic acid increased, but the levels of other free amino acids decreased, regardless of cultivars during cold storage. The overall heatmap responses were highest at 30 d, compared with the other removal times during cold storage. Principal component analysis (PCA) scores plot. indicated the biochemical and physiological fruit responses of the three fig cultivars were clearly separated and diverged based on storage times and cultivars. Based on the results of the PCA loading plot, SSC, TA, glucose, fructose, and fruit fresh weight loss were closely linked with glutamate and aspartate. Therefore, the results indicated the physiological and biochemical responses of fig fruit quality attributes and major targeted metabolites could be affected either by storage duration or by fig cultivar.

Diagnosis of malignant pleural mesothelioma from pleural fluid by Fourier transform-infrared spectroscopy coupled with chemometrics.

This study was conducted to differentiate malignant pleural mesothelioma (MPM) from lung cancer (LC) and benign pleural effusion (BPE) from pleural fluids using the diagnostic power of Fourier transform-infrared spectroscopy with attenuated total reflectance mode coupled with chemometrics. Infrared spectra of MPM (n = 24), LC (n = 20), and BPE (n = 25) were collected, and hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to their spectra. HCA results indicated that MPM was differentiated from LC with 100% sensitivity and 100% specificity and from BPE, with 100% sensitivity and 88% specificity, which were also confirmed by PCA score plots. PCA loading plots indicated that these separations originated mainly from lipids, proteins, and nucleic acids-related spectral bands. There was significantly higher lipid, protein, nucleic acid, and glucose contents in the MPM and LC. However, the significant changes in triglyceride and cholesterol ester content, protein and nucleic acid structure, a lower membrane fluidity, and higher membrane order were only observed in the MPM. To check the classification success of some test samples/each group, soft independent modeling of class analogies was performed and 96.2% overall classification success was obtained. This approach can provide a rapid and inexpensive methodology for the efficient differentiation of MPM from other pleural effusions.

Use of Infrared Spectroscopy and Near Infrared Hyperspectral Images to Evaluate Effects of Different Chemical Agents on PET Bottle Surface

PET - poly (ethylene terephthalate) - bottle surfaces were exposed to chemical agents and evaluated with middle infrared spectroscopy (MIR) and near infrared hyperspectral imaging (HSI-NIR). Images (928-2.524 nm) were acquired at different times from bottle surfaces exposed to alkaline, acidic and ethanolic media. Savitzky-Golay smoothing (2nd order polynomial and 5-point window) combined with Standard Normal Variate (SNV) was used for pre-processing data. Scores from principal component analysis (PCA) on images showed discrimination of PET according to exposure time, particularly when employing NaOH. After the first two hours of chemical exposure, significant contribution of the (-C-O + OH) combination band indicated formation of glycol ethylene on the PET surface, which was confirmed from PCA loading plots. MIR reflectance spectra ranged from 4.000 to 650 cm-1. Changes in spectral intensities could be associated with depolymerization of PET when exposed to NaOH. These were confirmed with PCA and attributed to the carboxylate group of terephthalate.