Principal Component Analysis Score Plot Research Articles

Estimation for Raw Material Plants of a Henna Product Using LC-High Resolution MS and Multivariate Analysis.

Henna is a plant-based dye obtained from the powdered leaf of the pigmented plant Lawsonia inermis, and has often been used for grey hair dyeing, treatment, and body painting. As a henna product, the leaves of Indigofera tinctoria and Cassia auriculata can be blended to produce different colour variations. Although allergy from henna products attributed to p-phenylenediamine, which is added to enhance the dye, is reported occasionally, raw material plants of henna products could also contribute to the allergy. In this study, we reported that raw material plants of commercial henna products distributed in Japan can be estimated by LC-high resolution MS (LC-HRMS) and multivariate analysis. Principal Component Analysis (PCA) score plot clearly separated 17 samples into three groups [I; henna, II; blended henna primarily comprising Indigofera tinctoria, III; Cassia auriculata]. This grouping was consistent with the ingredient lists of products except that one sample listed as henna was classified as Group III, indicating that its ingredient label may differ from the actual formulation. The ingredients characteristic to Groups I, II, and III by PCA were lawsone (1), indirubin (2), and rutin (3), respectively, which were reported to be contained in each plant as ingredients. Therefore, henna products can be considered to have been manufactured from these plants. This study is the first to estimate raw material plants used in commercial plant-based dye by LC-HRMS and multivariate analysis.

Nanoparticle Uptake and Bioaccumulation in Pisum sativum L. (Green Pea) Analyzed via Dark-Field Microscopy, Infrared Spectroscopy, and Principal Component Analysis Combined with Machine Learning

The green pea (Pisum sativum L.) is an economically, nutritionally, and culturally important legume. It is a crop that is subject to various investigations due to its popularity with the development of various protocols in different topics, except for nano-biotechnological studies. This work was carried out to evaluate the uptake, distribution, translocation, and bioaccumulation of the single-walled carbon nanotubes (CNTs) and gold nanoparticles (AuNPs) within the economically important plant Pisum sativum morphologically and anatomically with a dark-field microscopy system. Data were analyzed for morphological parameters such as stem, tendril, root length, number, shape, width-length of the stipules, and root-stem-stipule. Our results proved the stimulation for growth and anatomical parameters such as CNTs aggregates and AuNPs particles at paranchyma, cortex, spongia cells, starch formation and accumulation in lenticels, stoma cells, and stomatal pores. In this study, we compared the utilization of the entire available Attenuated Total Reflectance—Fourier Transform Infrared Spectroscopy (ATR-FTIR) spectral range (525–4000 cm−1) for conducting principal component analysis (PCA) without excluding any specific spectral wavenumbers with the spectral range chosen based on larger PCA loadings. The results demonstrate that for both chosen spectral ranges of the PCA score plots, utilizing only the first three principal components (PCs), we effectively visually separated three groups: (1) plants treated with Au NPs, (2) plants treated with CNTs, and (3) control plants without nanoparticle treatment using ATR-FTIR spectral data from combined samples of root, stem, and leaves from the Pisum sativum plant. Our investigation shows that green pea, a species of the Fabaceae family, is low-cost, fast, and non-toxic and requires an environmentally safe process in the area of nanotechnology in bio-application regarding the green synthesis of nanoparticles; it is a step for green mining, phytoremediation, delivering drugs, and biomolecules. Our findings show that green pea and the Fabaceae family have more advantages for the biological synthesis of C-Au nanoparticles and guide soil health, agricultural development, pharmaceuticals, drug delivery science, and other types of medicinal investigations with a new approach, while a lot of economic plants in the Fabaceae family will be available for the green synthesis of more NPs with single and rapid protocols and will be a popular family in nano-biotechnological studies in the next few decades.

Nanoparticle Uptake and Bioaccumulation in Pisum sativum L. (Green Pea) Analyzed via Dark-Field Microscopy, Infrared Spectroscopy, and Principal Component Analysis Combined with Machine Learning

The green pea (Pisum sativum L.) is an economically, nutritionally, and culturally important legume. It is a crop that is subject to various investigations due to its popularity with the development of various protocols in different topics, except for nano-biotechnological studies. This work was carried out to evaluate the uptake, distribution, translocation, and bioaccumulation of the single-walled carbon nanotubes (CNTs) and gold nanoparticles (AuNPs) within the economically important plant Pisum sativum morphologically and anatomically with a dark-field microscopy system. Data were analyzed for morphological parameters such as stem, tendril, root length, number, shape, width-length of the stipules, and root-stem-stipule. Our results proved the stimulation for growth and anatomical parameters such as CNTs aggregates and AuNPs particles at paranchyma, cortex, spongia cells, starch formation and accumulation in lenticels, stoma cells, and stomatal pores. In this study, we compared the utilization of the entire available Attenuated Total Reflectance—Fourier Transform Infrared Spectroscopy (ATR-FTIR) spectral range (525–4000 cm−1) for conducting principal component analysis (PCA) without excluding any specific spectral wavenumbers with the spectral range chosen based on larger PCA loadings. The results demonstrate that for both chosen spectral ranges of the PCA score plots, utilizing only the first three principal components (PCs), we effectively visually separated three groups: (1) plants treated with Au NPs, (2) plants treated with CNTs, and (3) control plants without nanoparticle treatment using ATR-FTIR spectral data from combined samples of root, stem, and leaves from the Pisum sativum plant. Our investigation shows that green pea, a species of the Fabaceae family, is low-cost, fast, and non-toxic and requires an environmentally safe process in the area of nanotechnology in bio-application regarding the green synthesis of nanoparticles; it is a step for green mining, phytoremediation, delivering drugs, and biomolecules. Our findings show that green pea and the Fabaceae family have more advantages for the biological synthesis of C-Au nanoparticles and guide soil health, agricultural development, pharmaceuticals, drug delivery science, and other types of medicinal investigations with a new approach, while a lot of economic plants in the Fabaceae family will be available for the green synthesis of more NPs with single and rapid protocols and will be a popular family in nano-biotechnological studies in the next few decades.

Proteome-wide Characterization and Pathophysiology Correlation in Non-ischemic Cardiomyopathies.

Although the clinical consequences of advanced heart failure (HF) may be similar across different etiologies of cardiomyopathies, their proteomic expression may show substantial differences in relation to underlying pathophysiology. We aimed to identify myocardial tissue-based proteomic characteristics and the underlying molecular pathophysiology in non-ischemic cardiomyopathy with different etiologies. Comparative extensive proteomic analysis of the myocardium was performed in nine patients with biopsy-proven non-ischemic cardiomyopathies (3 dilated cardiomyopathy [DCM], 2 hypertrophic cardiomyopathy [HCM], and 4 myocarditis) as well as five controls using tandem mass tags combined with liquid chromatography-mass spectrometry. Differential protein expression analysis, Gene Ontology (GO) analysis, and Ingenuity Pathway Analysis (IPA) were performed to identify proteomic differences and molecular mechanisms in each cardiomyopathy type compared to the control. Proteomic characteristics were further evaluated in accordance with clinical and pathological findings. The principal component analysis score plot showed that the controls, DCM, and HCM clustered well. However, myocarditis samples exhibited scattered distribution. IPA revealed the downregulation of oxidative phosphorylation and upregulation of the sirtuin signaling pathway in both DCM and HCM. Various inflammatory pathways were upregulated in myocarditis with the downregulation of Rho GDP dissociation inhibitors. The molecular pathophysiology identified by extensive proteomic analysis represented the clinical and pathological properties of each cardiomyopathy with abundant proteomes. Different etiologies of non-ischemic cardiomyopathies in advanced HF exhibit distinct proteomic expression despite shared pathologic findings. The benefit of tailored management strategies considering the different proteomic expressions in non-ischemic advanced HF requires further investigation.

EVALUATION OF GEOMETRIC MORPHOMETRIC APPROACH FOR ETHNICITIES DISCRIMINATION USING HANDWRITTEN NUMERAL CHARACTERS

Handwriting evidence is a valuable source for authorship identification, an important aspect in investigating crimes such as murder, suicide, illegal drug trafficking, kidnapping, and document forgery. It relies heavily on the examination of written characters that make the document. However, specific studies on the handwritten numeral characters are scarce despite being crucial in assisting investigators in solving crimes. Hence, this study is aimed to gauge the possibility to discriminate authors according to their ethnicities by means of their handwritten numeral characters using a novel Geometric Morphometric (GMM) technique. Handwritten numeral characters collected from 30 individuals from three main different ethnic groups in Malaysia; Malay, Chinese and Indian were first digitised and landmarked using GMM software. Cluster patterns can be observed in the Principal Component Analysis (PCA) score plots, belonging exclusively to the three different ethnic groups. Significant differences (p<0.0001) were discovered in handwritten numerals characters 3, 4, 5, 7 and 9 amongst the three ethnicities when tested using Procrustes ANOVA, which signifying that it is possible to discriminate authors according to their ethnicities using their handwritten numeral characters. However, more sophisticated meta-analyses are needed in order to find the most effective technique for determining and discriminating the author's ethnicity.

#1043 Metabolomics and proteomics study on kidney function post-liver transplantation

Abstract Background and Aims Hepatorenal syndrome (HRS) is a serious complication characterized by kidney dysfunction in patients with advanced liver disease. The pathophysiology of HRS is poorly understood and involves a combination of hemodynamic, circulatory, inflammatory, and hormonal factors. While some patients with HRS restore kidney function after liver transplantation (LT), others continue to have impaired kidney function. Understanding the mechanisms associated with persistent kidney dysfunction is essential for developing effective treatments and stratifying patients for combined liver-kidney transplantation versus LT only. Method We analyzed serum samples from 10 patients of the multicenter prospective study (INAID CTOT14). Five of them had no HRS and maintained an eGFR > 50 ml/min post-LT. One patient with HRS recovered kidney function post-LT. Four patients with HRS failed to regain kidney function at one- and two-months post-LT (eGFR < 30 ml/min). We categorized the five patients without HRS and one patient with recovered eGFR as Normal Kidney Function (NKF) group and the other four patients as Impaired Kidney Function (IKF) group. Serum samples were collected at one (visit 1) and two months (visit 2) post-LT and stored at −80°C until analysis. Both untargeted metabolomics and proteomics analysis were conducted using quantitative liquid chromatography tandem mass spectroscopy (LC-MS/MS) on Agilent® Q-TOF 6546. Data was analyzed by Agilent® Qualitative Workflow 10, Agilent® Profinder 10, MetaboAnalyst (V6.0), and Spectrum Mill MS software. Results A total of 150 metabolites were identified by LC-MS/MS based metabolomics. Principal component analysis (PCA) did not show significant differences between visit 1 and visit 2 samples (Fig. 1A). Therefore, visit 1 and visit 2 samples were analyzed together. With 12 samples in NKF and 8 samples in IKF group, the PCA score plot showed distinct clustering of the two groups (Fig. 1B). Volcano plot analysis was conducted to analyze metabolite differences between NKF and IKF. P-values were calculated using a Student's t-test, and differential metabolites were identified with a P-value cut-off of < .05 and a fold change > 2.0. The result revealed 37 downregulated and 13 upregulated metabolites when comparing NKF and IKF (Fig. 1C). Notably, creatinine was among the downregulated metabolites in the NKF group, validating the accuracy of our analysis. Untargeted proteomics analysis was also performed on all 20 samples. Using a P-value cut-off of .05 and a fold change >1.25, 43 proteins were found to be upregulated, and four proteins to be downregulated when comparing NKF to IKF. Fig. 2A lists the top 10 regulated proteins, with beta-2-microglobulin as the most significantly changed. Reactome® pathway analysis of these 47 proteins highlighted potential impacts on various biological pathways (Fig. 2B), providing potential valuable insights into the molecular alterations associated with kidney function post-LT. Conclusion Our comprehensive metabolomics and proteomics study characterized the molecular alterations associated with kidney function post-LT. The identified metabolites and proteins may provide valuable insights for understanding persistent kidney damage. Future research is warranted to confirm and correlate these findings with clinical outcomes, enhancing the applicability and significance of our results in the broader context of HRS and LT.

Alicyclobacillus spp. in fruit-based products: Isolation, identification, quantitative assessment (SPME/GC–MS) of spoilage compounds and spore's resistance to thermal shocks

Alicyclobacillus spp. is the cause of great concern for the food industry due to their spores' resistance (thermal and chemical) and the spoilage potential of some species. Despite this, not all Alicyclobacillus strains can spoil fruit juices. Thus, this study aimed to identify Alicyclobacillus spp. strains isolated from fruit-based products produced in Argentina, Brazil, and Italy by DNA sequencing. All Alicyclobacillus isolates were tested for guaiacol production by the peroxidase method. Positive strains for guaiacol production were individually inoculated at concentration of 103 CFU/mL in 10 mL of orange (pH 3.90) and apple (pH 3.50) juices adjusted to 11°Brix, following incubation at 45 °C for at least 5 days to induce the production of the following spoilage compounds: Guaiacol, 2,6-dichlorophenol (2,6-DCP) and 2,6-dibromophenol (2,6-DBP). The techniques of micro-solid phase extraction by headspace (HS-SPME) and gas-chromatography with mass spectrometry (GC–MS) were used to identify and quantify the spoilage compounds. All GC–MS data was analyzed by principal component analysis (PCA). The effects of different thermal shock conditions on the recovery of Alicyclobacillus spores inoculated in orange and apple juice (11°Brix) were also tested. A total of 484 strains were isolated from 48 brands, and the species A. acidocaldarius and A. acidoterrestris were the most found among all samples analyzed. In some samples from Argentina, the species A. vulcanalis and A. mali were also identified. The incidence of these two main species of Alicyclobacillus in this study was mainly in products from pear (n = 108; 22.3 %), peach (n = 99; 20.5 %), apple (n = 86; 17.8 %), and tomato (n = 63; 13 %). The results indicated that from the total isolates from Argentina (n = 414), Brazil (n = 54) and Italy (n = 16) were able to produce guaiacol: 107 (25.8 %), 33 (61.1 %) and 13 (81.2 %) isolates from each country, respectively. The PCA score plot indicated that the Argentina and Brazil isolates correlate with higher production of guaiacol and 2,6-DCP/2,6-DBP, respectively. Heatmaps of cell survival after heat shock demonstrated that strains with different levels of guaiacol production present different resistances according to spoilage ability. None of the Alicyclobacillus isolates survived heat shocks at 120 °C for 3 min. This work provides insights into the incidence, spoilage potential, and thermal shock resistance of Alicyclobacillus strains isolated from fruit-based products.

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.

Impact of ultracentrifugal milling on the thermo-rheological, functional, and physicochemical properties of yellow and green pea flour

Pea seeds conversion into flour enhance their incorporation into diverse food systems. However, milling impacts changes in the flour's physicochemical and technofunctional properties. In this study, the impact of ultracentrifugal milling on the thermal, rheological, functional and physicochemical properties of flour from yellow and green pea varieties was investigated. Pea seeds were milled to maximum particle sizes of 0.25, 0.50 and 1.00 mm based on different screen aperture fitted to the ultracentrifugal mill. Scanning electron microscopy showed that flour samples milled with the 0.25 mm screen aperture had higher disentangled protein particles but more damaged starch particles compared to the 0.50 and 1.00 mm screens. The FTIR analysis indicated similar spectra with varying functional groups at corresponding wavelengths but different absorbance intensity. At 2853 cm−1 wavelength, additional peak for cellulose was detected in 0.25 mm milling condition but absent in other samples. Flour milled to 0.25 and 0.50 mm maximum particle size had the most desirable outcomes to rheological and thermal attributes. Principal component analysis score plots elucidated relationship between milling conditions, flour functionality and pea variety with PC1 at 46.64% and PC2 at 34.53%. These findings demonstrate that milling conditions can be a potential tool to manipulate pea flour functionality.

Fecal Carriage of Colibactin-Encoding Escherichia coli Associated With Colorectal Cancer Among a Student Populace.

Fecal carriage of the colibactin (clb) gene in Escherichia coli is described as a source that could promote carcinogenesis, progressing to colorectal cancer. The present study investigated the demographic, dietary, and antibiotic consumption variables as correlates for fecal carriage of clb+/E coli among the student populace. In a randomized cross-sectional survey, E coli (N = 136) from the fecal samples of eligible students were characterized and evaluated for antibiotic resistance, β-lactamase (blm), biofilm, virulence factor production, and strain tryptophan reverse mutagenic activity. The encoded clb+/E coli were analyzed for correlates with principal component analysis. Of all the E coli strains, a low rate of 2 clb+/E coli (1.5%) and higher rates of biofilm (13.2%) and blm producers (11.8%) were recorded among the mutant strains as compared with the nonmutant types. All the clb+/E coli showed complete resistance to amoxicillin, Augmentin (amoxicillin and clavulanate), gentamicin, and trimethoprim/sulfamethoxazole. The fecal clb-encoded E coli (1.5%) were not associated with demographic status, fiber-based food (odds ratio [OR], 1.03; 95% CI, 56.74-138.7; P = .213), alcohol (OR, 1.27; 95% CI, 61.74-147.1; P = .221), antibiotic consumptions (OR, 1.11; 95% CI, 61.29-145.3; P = .222), and handwashing (OR, 1.17; 95% CI, 60.19-145.5; P = .216). The hierarchical cluster of blm+/E coli revealed high-level resistance with a multiantibiotic resistance index ≥0.2 (P < .05). Only 12% of all strains were tryptophan mutant/blm+, and 1.5% of clb+/ECblm+ were observed in fecal samples with a 452-base pair size. Trimethoprim/sulfamethoxazole and biofilm production positively regressed with clb expression (P > .05). Principal component analysis score plot indicated an association of clb+/ECblm+ with dietary pattern, alcohol, blm, and hemolysin production. The combined activity of blm and biofilm production in the gut microbiota could promote clb+/E coli colonization, facilitating genotoxin production and possible colorectal cancer induction.

Gold nanoclusters-based fluorescence sensor array for herbicides qualitative and quantitative analysis

Herbicides have been extensively used around the world, which poses a potential hazard to humans and wildlife. Accurate detection of herbicides is crucial for the environment and human health. Herein, a simple and sensitive fluorescence sensor array was constructed for discrimination and identification of herbicides. Fluorescent gold nanoclusters modified with 11-mercaptoundecanoic acid or reduced glutathione were prepared, respectively. Metal ions quenched the fluorescence of nanoclusters through coordination and leading to the aggregation of gold nanoclusters. The addition of auxin herbicides (2,4-dichlorophenoxyacetic acid, 2-methyl-4-chlorophenoxyacetic acid, decamba, picloram, quinclorac) restored the fluorescence of nanoclusters with different degrees. The mechanism study showed auxin herbicides can bind with metal ions and re-disperse the gold nanoclusters from the aggregation state. The "on-off-on" fluorescent sensor array was constructed basic on above detection mechanism. Combined with principal component analysis (PCA) and hierarchical cluster analysis (HCA) methods, auxin herbicides are well separated on 2D/3D PCA score plots and HCA dendrogram in the range of 40–500 μm. In addition, the fluorescence sensor array performed successful in detecting real samples and blind samples. The developed sensor system shows a promising in practical detection of herbicides.

Adulteration identification of Hainan camellia (Camellia oleifera Abel.) oil based on comprehensive two‐dimensional gas chromatography and quadrupole time‐of‐flight mass coupled with chemometrics spectrometry

SummaryThe nutritional value of Hainan camellia oil is incomparable to other places, and Hainan camellia oil is expensive, so unscrupulous businessmen often mix other edible oils into them for illegal profit. This study detected the changes of volatile components in Hainan camellia oil adulterated with five edible oils (peanut, soybean, corn, rapeseed, and sunflower oils) using comprehensive two‐dimensional gas chromatography and quadrupole time‐of‐flight mass spectrometry (HS‐SPME‐GC × GC‐Q‐TOFMS). The principal component analysis (PCA) score plot showed that pure Hainan camellia oil (PHCO) and Hainan camellia oil samples were adulterated with other edible oils clearly to identify the adulteration separated from each other. The adulteration of PHCO could be accurately predicted using the developed PCA and OPLS‐DA models for concentrations as low as 5%. More importantly, several potential markers were found to identify the impersonate PHCO. Therefore, the volatile compound determination based on HS‐SPME‐GC × GC‐Q‐TOFMS proved to be an effective method for detecting adulteration of other edible oils in PHCO.

Predicting Egg Storage Time with a Portable Near-Infrared Instrument: Effects of Temperature and Production System.

Determining egg freshness is critical for ensuring food safety and security and as such, different methods have been evaluated and implemented to accurately measure and predict it. In this study, a portable near-infrared (NIR) instrument combined with chemometrics was used to monitor and predict the storage time of eggs under two storage conditions-room temperature (RT) and cold (CT) storage-from two production systems: cage and free-range. A total of 700 egg samples were analyzed, using principal component analysis (PCA) and partial least squares (PLS) regression to analyze the NIR spectra. The PCA score plot did not show any clear separation between egg samples from the two production systems; however, some egg samples were grouped according to storage conditions. The cross-validation statistics for predicting storage time were as follows: for cage and RT eggs, the coefficient of determination in cross validation (R2CV) was 0.67, with a standard error in cross-validation (SECV) of 7.64 days and residual predictive deviation (RPD) of 1.8; for CT cage eggs, R2CV of 0.84, SECV of 5.38 days and RPD of 3.2; for CT free-range eggs, R2CV of 0.83, SECV of 5.52 days and RPD of 3.2; and for RT free-range eggs, R2CV of 0.82, SECV of 5.61 days, and RPD of 3.0. This study demonstrated that NIR spectroscopy can predict storage time non-destructively in intact egg samples. Even though the results of the present study are promising, further research is still needed to further extend these results to other production systems, as well as to explore the potential of this technique to predict other egg quality parameters associated with freshness.

Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms.

Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky-Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices.

Identification of marine microplastics based on laser-induced fluorescence and principal component analysis

Although the laser-induced fluorescence method shows great potential for microplastic particle detection, overlapping fluorescence signals make accurate type and proportion identification difficult. This paper presents the identification of marine microplastics based on laser-induced fluorescence and principal component analysis. This method works by measuring the fluorescence spectra of water-containing microplastic samples irradiated with a 405-nm laser, which are then analyzed using the principal component analysis (PCA) method. The nine types of microplastics were differentiated based on their positions in the PCA score plot. The mixed sample was positioned between the pure microplastic samples. The component ratio determines its position relative to that of the pure microplastic samples. The first two principal components of the mixed microplastics were linearly dependent. Natural seawater had less influence on the detection, and a mass concentration as low as 0.03% was detected.

Wood properties of mature loblolly pine grown in Hawaii, North Carolina, and Mississippi: impacts of climate conditions on specific gravity and chemical composition

Higher productivities for loblolly pine (Pinus taeda L.) growing at exotic locations (e.g., Hawaii, Brazil) indicate that the full growth potential of this southern pine species has yet to be reached in plantations across the southeastern United States (US). The higher productivity of Hawaii-grown loblolly pine has been attributed to more favorable climate conditions. To date, physical, anatomical, and chemical property data for loblolly pine wood from exotic locations are scarce. Mid-infrared spectroscopy coupled with multivariate analysis was used to screen for chemical differences between wood samples from Hawaii (HI), North Carolina (NC), and Mississippi (MS). The principal component analysis scores plot showed that the MS samples formed a distinct cluster apart from overlapping clusters for the other two sites. Using standard wet chemistry procedures, we found that the mean lignin content for the MS site (30.1%) was significantly lower than that determined for the NC (34.5%) and HI (33.9%) sites; said lower lignin value was offset by significantly higher glucose and mannose contents. Results on the US mainland were seemingly consistent with the greater formation of latewood at sites with longer growing seasons, and the lower lignin content of latewood relative to earlywood shown for pines. Given the low percent latewood for the HI site (37.2%) relative to both mainland sites (MS, 54.8%; NC, 53.1%), yet similar lignin contents between the NC and HI sites, it appears that the relative lignin value for these exotically-grown loblolly pine trees cannot be solely attributed to the proportion of latewood.

Comparative Study of Essential Oils from Different Organs of Syzygium cumini (Pamposia) Based on GC/MS Chemical Profiling and In Vitro Antiaging Activity

Syzygium cumini L. is an evergreen tree belonging to family Myrtaceae, employed for different traditional uses like diabetes, inflammation, and fever. The current study aimed to compare the chemical compositions of the essential oils (EOs) isolated from different organs of Syzygium cumini (leaves (Scl), fruits (Scf), seeds (Scs), and bark (Scb)) using a GC/MS analysis. Also, a chemometric analysis was applied to explore the main similarities and differences among different organs using a Principal Component Analysis (PCA) and a hierarchal cluster analysis (HCA). Furthermore, in vitro antiaging activities were investigated via anti-collagenase, anti-elastase, and anti-hyaluronidase assays. The GC-MS analysis revealed 82 compounds representing 92.13%, 99.42%, 100%, and 92.97% in Scl, Scf, Scs, and Scb, respectively. The predominant components were α-pinene, β-pinene, (E)-β-caryophyllene, α-caryophyllene, caryophyllene oxide, and α-humulene epoxide II with variable percentages. All EOs were positioned on positive PC1, except for Scs, which was positioned on the negative side in a separate quadrant. The HCA dendrogram displayed the closeness of Scl and Scb, which was not clearly recognized in the PCA score plot. Moreover, the Scs oils were totally discriminated from other parts. The Scl and Scs oils showed superior anti-collagenase, anti-elastase, and anti-hyaluronidase activities. Thus, S. cumini oils should be considered for cosmetic preparations to retard skin aging manifestations.

Classification of Listeria species using near infrared hyperspectral imaging

Near infrared (NIR) hyperspectral imaging and multivariate data analysis was evaluated for its potential to detect and classify Listeria species. Three Listeria species, namely L. monocytogenes (ATCC 23074), L. innocua (ATCC 33090) and L. ivanovii (ATCC 19119) were grown for single colonies on Brain Heart Infusion agar and imaged in the NIR range of 950–2500 nm. Principal component analysis (PCA) was used for data exploration and to establish pattern recognition. Images were pre-processed with standard normal variate correction and the Savitzky-Golay smoothing technique (third order polynomial with 15 points). Two approaches to data analysis, that is object-wise and pixel-wise analysis, were investigated for discriminant analysis. The PCA score plot showed slight separation between the three groups with L. monocytogenes and L. ivanovii grouping close together. It was possible to visualise separation along PC3 (5.64% sum of squares (SS)) and PC4 (3.44% SS). Based on the loadings, differences in bacteria were attributed to teichoic acids, protein, and carbohydrate composition in the bacterial cell wall within the wavelength range 1000–1900 nm. Using extracted spectral data from the hypercubes, partial least squares discriminant analysis was employed for further classification. Classification accuracies above 90% were achieved for L. monocytogenes, L. innocua and L. ivanovii. This was true for data analysed using both pixel-wise analysis and object-wise analysis. The results demonstrated that hyperspectral imaging has notable potential to classify bacteria within the Listeria genus. Nonetheless, in order to improve model efficiency, model optimisation and incorporation of more bacterial strains need to be investigated in further research.

Aroma-based discrimination of Egyptian versus Indian guava fruits and in response to probiotics as analyzed via SPME/GC–MS and chemometric tools

Guava tree (Psidium guajava L., Myrtaceae) is an economic grown worldwide, particularly in tropical and subtropical regions. Guavas encompass numerous cultivars (cvs.) that were discriminated in previous studies based on leaf morphological features and profile of volatile organic compounds (VOCs). Nevertheless, fruit VOCs have also shown outstanding potential for discrimination of other plant taxa, which has not been utilized in guava. Hence, the current study investigates the various guava cvs. harvested from India and Egypt. A total of 5 samples were analyzed by solid phase microextraction coupled to gas chromatography/mass spectrometry. Results led to the detection of 42 VOCs belonging to aldehydes, alcohols, esters, ketones, aliphatic and aromatic hydrocarbons, in addition to monoterpene and sesquiterpene hydrocarbons. Butylated hydroxytoluene and β-caryophyllene were predominant reaching 77% and 41% in Egyptian and Indian guava, respectively. The impact of probiotic fermentation, i.e., Lactobacillus acidophilus and L. plantarum on aroma profile was not significantly different (p > 0.05). Multivariate data analyses were further applied for samples classification and markers determination, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). PCA score plot showed clear segregation of Egyptian from Indian specimens, whereas OPLS-DA revealed that β-caryophyllene was associated with white fruit versus 3-butenyl isothiocyanate and muurolol in red fruit type in the case of Indian guava. The richness of Egyptian guava in butylated hydroxytoluene in addition to the presence of vitamin C may potentiate its antioxidant activity, to be followed in subsequent studies regarding its health effects.

Differentiating Between Three Licorice Species Using SFC-TOF/MS Analysis With Principal Component Analysis

Objective Although the discrimination of crude drugs by metabolomics analysis using liquid chromatography–mass spectrometry (LC–MS) has been widely reported, there are few reports on natural products analysis using supercritical fluid chromatography (SFC)–MS. This study aimed to compare the results of metabolomic analysis of licorice obtained using SFC–MS and LC–MS. Methods We analyzed 14 CHCl3 extracts of licorice roots ( Glycyrrhiza glabra, G. uralensis, and G. inflata) using SFC-time-of-flight (TOF)/MS and LC-TOF/MS in conjunction with principal component analysis (PCA). The three species were assessed for discrimination based on the PCA results. The characteristic constituents of each variety were purified, and their chemical structures were elucidated using various spectroscopic analyses. Results The PCA score plots of the results of both SFC and LC classified the licorice samples into three groups. Seven compounds (six known compounds and one new phenolic compound, glyglabone) were isolated from G. glabra, five known compounds were isolated from G. uralensis, and four known compounds were isolated from G. inflata. Conclusions The 16 isolated compounds may be characteristic compounds in the three licorice species. The SFC and LC methods showed comparable results in distinguishing the three species. SFC was highly effective for directly analyzing CHCl3 extracts, reducing analysis time and organic solvent consumption by over 50% compared to the LC analysis. Thus, SFC-TOF/MS in combination with PCA can be used to differentiate between G. glabra, G. uralensis, and G. inflata.