Principal Component Loadings Research Articles

Evaluation of soil quality in major grain-producing region of the North China Plain: Integrating minimum data set and established critical limits

Abstract Understanding of the nexus between soil quality (SQ) and productivity, the heterogeneity of SQ and the restricting indicators that affect crop productivity is essential for a specific region to encourage improving of SQ while contributing to profitable crop production. The objectives of this study were to develop minimum dataset (MDS) variable selection methods that reflects the total dataset (TDS) of soil properties for SQ assessment and crop yield prediction in Yucheng County, a major grain-producing region in the North China Plain. Critical values of soil properties for standardized scoring functions were established based on local crop yields in field condition and the key restricting indicators limiting crop productivity were evaluated. In this study, 99 topsoil (0–20 cm) samples were collected and 14 soil properties including pH, electrical conductivity, clay content, soil organic matter, cation exchangeable capacity, total nitrogen, phosphorus (P) and potassium (K), available P, K, Cu, Mn, Mo and Zn were analyzed. Three MDSs were created by different variable selection methods using principal component analysis (PCA) of soil properties. Among these MDSs, the indicator of norm values derived from the total principal component loadings was considered to be the most suitable method for evaluating SQ. The MDS-based soil quality index (SQI) had higher correlations with the TDS-based SQI and crop yield. The critical values of soil properties were identified by regressing soil variables with local yields of maize and wheat in the field condition. The SQIs calculated using measured critical values were more accurate than that based on published empirical values. The evaluation results revealed that the calculated SQI accounted for 28% of the variation in crop yields, and there probably are some strong limitations for crop yield through soil characteristics (soil texture, salinization, and nutrient supply, etc.). Thus, management practices and amelioration of the above soil-limiting factors should be comprehensively considered in agricultural production. The finding can enable policy making and policy implementation in relation to SQ in the future.

Aging-Sensitive Networks Within the Human Structural Connectome Are Implicated in Late-Life Cognitive Declines

BackgroundAging-related cognitive decline is a primary risk factor for Alzheimer’s disease and related dementias. More precise identification of the neurobiological bases of cognitive decline in aging populations may provide critical insights into the precursors of late-life dementias. MethodsUsing structural and diffusion brain magnetic resonance imaging data from the UK Biobank (n = 8185; age range, 45–78 years), we examined aging of regional gray matter volumes (nodes) and white matter structural connectivity (edges) within 9 well-characterized networks of interest in the human brain connectome. In the independent Lothian Birth Cohort 1936 (n = 534; all 73 years of age), we tested whether aging-sensitive connectome elements are enriched for key domains of cognitive function before and after controlling for early-life cognitive ability. ResultsIn the UK Biobank, age differences in individual connectome elements corresponded closely with principal component loadings reflecting connectome-wide integrity (|rnodes| = .420; |redges| = .583), suggesting that connectome aging occurs on broad dimensions of variation in brain architecture. In the Lothian Birth Cohort 1936, composite indices of node integrity were predictive of all domains of cognitive function, whereas composite indices of edge integrity were associated specifically with processing speed. Elements within the central executive network were disproportionately predictive of late-life cognitive function relative to the network’s small size. Associations with processing speed and visuospatial ability remained after controlling for childhood cognitive ability. ConclusionsThese results implicate global dimensions of variation in the human structural connectome in aging-related cognitive decline. The central executive network may demarcate a constellation of elements that are centrally important to age-related cognitive impairments.

The fuzzy comprehensive evaluation (FCE) and the principal component analysis (PCA) model simulation and its applications in water quality assessment of Nansi Lake Basin, China

The Fuzzy Comprehensive Evaluation (FCE) and the Principal Component Analysis (PCA) were simulated to assess water quality of the Nansi Lake Basin, China. The membership functions were established via the Nor-Half Sinusoidal Distribution Method, and the weight was calculated via the Exceeding Standard Multiple Method. To enhance the efficiency of extracting principal pollutant, the eigenequation was solved through the Jacobi Method, and the principal components were extracted based on eigenvalue, contribution ratio, accumulating contribution ratio, principal component loading and score. Water quality classification based on “National Surface Water Environmental Quality Standards of China (GB3838-2002) was used to assess the water quality. Considering the difference of the temporal and spatial distribution in average, water quality of Level I was 28.9%, 28.1%, 25.1%, 25.6%, respectively in spring, summer, autumn, and winter, which suggested that water quality in spring and summer was better than in autumn and winter. The order of water quality was Zhaoyang Lake (Level I) > Nanyang Lake (Level I) > Dushan Lake (Level III) > Weishan Lake (Level III and IV). There were four extracted principal components that can replace the fourteen pollutant indexes for assessing water quality. According to the annual mean data of the 1st principal components, the most important pollutions were heavy metals, including As (0.933), Hg (0.931), Cd (0.929), Cr(VI) (0.926), Pb (0.925), and Cu (0.534). It is proved that the combined FCE-PCA model could provide valuable information in the water quality assessment for the Nansi Lake Basin.

Fine scale surface climate in complex terrain using machine learning

AbstractAccurate and high spatial resolution (<100 m) surface climate information is crucial for process‐based modelling in hydrology, ecology, agriculture, urban studies etc, especially in complex terrain landscapes where coarse grid resolution information (∼10 km) is inadequate to represent pronounced local variability. We used a machine learning‐based workflow to predict high resolution (30 m) and sub‐daily atmospheric variables fields of near‐surface air temperature and humidity, and wind speed. The method used the Principal Component Analysis (PCA) decomposition applied on ground stations observations or Global Climate Model (GCM) residual error, in a sequence with bias correction and statistical models (Linear Regression‐LR, Artificial Neural Network model‐ANN and Empirical Quantile Mapping‐EQM) to provide downscaling from large scale atmospheric conditions to complex terrain variability. The predictions described relationships of Principal Component (PC) scores dependent on GCM temporal variability on 6‐hourly basis (with LR or ANN or EQM), and PC loadings dependent on topographic indexes to help providing horizontal sub‐grid extrapolation. The methods were validated with a 1‐year dataset from a dense weather stations network deployed in a complex terrain basin in tropical climate of Southeast Brazil. We present an exhaustive description of the PC modes daily/seasonal variability for each variable, and their spatial variability associated to the topography and thermal driven circulations. The predictions in general substantially improved accuracy when compared to GCM outputs, especially near the valley and in sheltered area where local effects are mandatories. Specially, ANN and EQM significantly improved the predictions at the variability of extreme events, such as the formation of strong cold air pooling or wetting in the valley.

Fast and robust ancestry prediction using principal component analysis.

Population stratification (PS) is a major confounder in genome-wide association studies (GWAS) and can lead to false-positive associations. To adjust for PS, principal component analysis (PCA)-based ancestry prediction has been widely used. Simple projection (SP) based on principal component loadings and the recently developed data augmentation, decomposition and Procrustes (ADP) transformation, such as LASER and TRACE, are popular methods for predicting PC scores. However, the predicted PC scores from SP can be biased toward NULL. On the other hand, ADP has a high computation cost because it requires running PCA separately for each study sample on the augmented dataset. We develop and propose two alternative approaches: bias-adjusted projection (AP) and online ADP (OADP). Using random matrix theory, AP asymptotically estimates and adjusts for the bias of SP. OADP uses a computationally efficient online singular value decomposition algorithm, which can greatly reduce the computation cost of ADP. We carried out extensive simulation studies to show that these alternative approaches are unbiased and the computation speed can be 16-16 000 times faster than ADP. We applied our approaches to the UK Biobank data of 488 366 study samples with 2492 samples from the 1000 Genomes data as the reference. AP and OADP required 0.82 and 21 CPU hours, respectively, while the projected computation time of ADP was 1628 CPU hours. Furthermore, when inferring sub-European ancestry, SP clearly showed bias, unlike the proposed approaches. The OADP and AP methods, as well as SP and ADP, have been implemented in the open-source Python software FRAPOSA, available at github.com/daviddaiweizhang/fraposa. leeshawn@umich.edu. Supplementary data are available at Bioinformatics online.

Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging.

Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with Rp2 = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that Rp2 was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork.

Craniofacial Analysis May Indicate Co-Occurrence of Skeletal Malocclusions and Associated Risks in Development of Cleft Lip and Palate.

Non-syndromic orofacial clefts encompass a range of morphological changes affecting the oral cavity and the craniofacial skeleton, of which the genetic and epigenetic etiologic factors remain largely unknown. The objective of this study is to explore the contribution of underlying dentofacial deformities (also known as skeletal malocclusions) in the craniofacial morphology of non-syndromic cleft lip and palate patients (nsCLP). For that purpose, geometric morphometric analysis was performed using full skull cone beam computed tomography (CBCT) images of patients with nsCLP (n = 30), normocephalic controls (n = 60), as well as to sex- and ethnicity- matched patients with an equivalent dentofacial deformity (n = 30). Our outcome measures were shape differences among the groups quantified via principal component analysis and associated principal component loadings, as well as mean shape differences quantified via a Procrustes distance among groups. According to our results, despite the shape differences among all three groups, the nsCLP group shares many morphological similarities in the maxilla and mandible with the dentofacial deformity group. Therefore, the dentoskeletal phenotype in nsCLP could be the result of the cleft and the coexisting dentofacial deformity and not simply the impact of the cleft.

Geochemical data handling, using multivariate statistical methods for environmental monitoring and pollution studies

Abstract This study utilized multivariate statistics such as hierarchical cluster and principal component analyses to monitor water and stream sediments pollution. Three Principal components loadings: PC1, PC2 and PC3 were generated from water samples data yielding eigen values of 20.563, 8.477, and 7.635 respectively. PC1 explained 28.56% of the total variance with high positive loadings for EC, turbidity, Br, Cl, SO 4 2 − , Ce, Dy, Er, Eu, La, Lu, Nd, Pr, Sm, Tb, Tm, Yb, Al, Ba, Bi, Co, Co, Cs, Mn, Th, Ti, U, V and Y. PC2 explained 11.77% of the total variance with high positive loadings for Hf and high negative factor loadings for Hg. PC3 explained 10.60% of the total variance and positively loaded for P, NO3, Cd, Ge, Ni, Pb, Sn, and Zn. PC1 is related to saline waters intrusion and geological nature of the aquifer. PC2 is controlled by high field strength element (HFSE) Hofnium (Hf) due to mineralization. PC3 contributed to pollution due to anthropogenic activities caused by chemical nitrogenous and phosphate fertilizer applications, to the soil and mineralogical constituents of the aquifer rock. Four principal components were identified in stream sediments and possessed eigen values of 12.29, 5.47, 3.19 and 2.10 respectively. PC1 explained 39.65% of the total variance and had significant positive loadings for Mo, Cu, Pb, Ni, Zn, Fe, U, Th, V, Ba, Ti, Sn, Nb, Be, Sc, Li, Zr, Rb and Hf. This suggest the occurrence of heavy mineral phases such as garnet, barite, zircon and monazite in mafic and ultramafic rocks and resistive heavy minerals. PC2 accounted for 17.65% of the total variance with moderate positive loadings for Mo, Cu, As and Ba, but with moderate negative loadings for Th, La and Ba. This component shows lithogeochemical input representative of weathering and mobility of sediments related mineralization. PC3 accounted for 10.29% of the total variance with positive high loadings for Mn, Bi and Y. PC3 may be related to anthropogenic activities such as agriculture, industry and urban life in the study area. PC4 accounted for 6.78% of the total variance with positive high loading for Ta. PC4 indicates the mineralization acquired by this component.

Investigating aquaphotomics for temperature-independent prediction of soluble solids content of pure apple juice

The methods of aquaphotomics were explored as an aid to improve near infrared spectroscopic predictive modelling of the soluble solids content of pure apple juice at different temperatures. The study focussed on the first overtone region of the O–H stretching vibration of water (1300–1600 nm). A transmission-based FT-NIR (Fourier transform near infrared) spectrometer was used to acquire 103 spectra of freshly expressed juice samples from individual ‘Braeburn’ apples over the wavelength range of 870–1800 nm with a 1 mm cuvette at three temperatures, 20, 25 and 30°C. The aquagram of the first overtone water region showed a trend of increasing bound water absorption with rising soluble solids content, from 7.3 to 13.7°Brix, and increasing free water absorption with rising temperature from 20 to 30°C. Predictive models for apple juice soluble solids content at 25°C were developed using partial least squares regression with spectral pre-processing by standard normal variate (SNV) followed by second derivative transformation (SNV + 2D) or no pre-processing on absorbance spectra at all. The best result, with lowest standard error of prediction of 0.38°Brix, was obtained using the first overtone water region with partial least squares regression on the SNV + 2D spectra. The method of extended multiplicative scatter correction was used, as an additional pre-processing step, to improve apple juice soluble solids content prediction at different temperatures. The interference component selected for the extended multiplicative scatter correction method was the first principal component loading measured using pure water samples taken at the same three temperatures (20, 25 and 30°C). Such extended multiplicative scatter correction pre-processing greatly reduced the soluble solids content prediction bias, when applying the partial least squares regression model developed at 20°C to samples measured at 25 and 30°C, from 0.23 to 0.08 and 0.36 to 0.13°Brix, respectively. Model precision (in terms of standard error of prediction) was also slightly improved by 0.02°Brix in each case, from 0.40 to 0.38 and 0.46 to 0.44°Brix at 25 and 30°C respectively.

Effect of scanning samples through polypropylene film on predicting nitrogen content of forage using handheld NIR

This study examined the effect of collecting near infrared (NIR) spectra of forage samples through a transparent polypropylene (PP) plastic film instead of glass cups on calibrating two handheld NIR spectrometers to nitrogen content (N). The first device was a transportable spectrometer (H1) covering 790–2500 nm at 1 nm interval, while the second device was a smartphone spectrometer (H2) covering 900–1700 nm at 4 nm interval. The spectra from each spectrometer were subjected to principal component analysis (PCA) to identify wavebands for PP packaging that would interfere in subsequent partial least squares (PLS) regression modeling to predict N. PCA results showed that the loadings of the first principal component (PC1) of the first derivative of the spectra from H1 and loadings of the second principal component (PC2) of the second derivative of the spectra from H2 were useful in identifying wavebands due to PP film. Regression models for H1 had better prediction performance when spectra were collected through glass than through PP films, in terms of coefficient of determination (r² = 0.958), standard error of prediction (SEP = 0.96 g kg^-1), and ratio of performance to deviation (RPD) = 4.93 vs. (r² = 0.942, SEP = 1.13 g kg^-1, and RPD = 4.17). Similar results were obtained for H2 using spectra collected through glass (r² = 0.821, SEP = 1.73 g kg^-1, and RPD = 2.72) than through PP (r² = 0.499, SEP = 2.99 g kg^-1, and RPD = 1.57). Removing peaks due to PP in the sample spectra improved the PLS models for H1 (r² = 0.959, SEP = 0.94 g kg^-1, and RPD = 5.02), but not for H2 (r² = 0.521, SEP = 3.17 g kg^-1, and RPD = 1.49). Hence, scanning samples through PP films can reduce the accuracy of predicting N, but for some handheld NIR spectrometers, this could be overcome by excluding wavebands due to PP.

Assessment model of regions’ economy in the context of their sustainable development

Currently realizing the new active role of the region as a sustainable development entity is a strategic direction for regional policy’s development. Assessing the sustainable development of regions is an important part of such a policy and facilitates the timely identification of internal and external threats, the development of the necessary stabilization measures to prevent their negative impact, and the formation of strategies aimed at the sustainable functioning of regional systems. The economic system is an important subsystem of the region. The article proposes an approach to assess the level of regions economic development in the context of ensuring its sustainable development. It is based on comprehensive assessment technology. The sustainable economic development composite index is calculated by a weighted additive convolution of partial indicators. A feature of the proposed approach is the simultaneous use of both metric and nonmetric indicators. The metric component is used to calculate the composite index values. Weight coefficients are calculated by the principal component method using the factor loadings of the first principal component. The non-metric part of the initial data is used to refine these weights. The article describes the algorithm for calculating a composite index. The practical testing of the proposed approach is presented for the case Ukraine’s regions. The results lead to the conclusion about significant problems in ensuring sustainable development of the regional economy. Outcomes obtained are very helpful for the public administration bodies to develop and revise the appropriate policy for solving the sustainable development problems in each region.

Discrimination between Dementia Groups and Healthy Elderlies Using Scalp-Recorded-EEG-Based Brain Functional Connectivity Networks

Objective: To establish a practical method for discriminating dementia groups and healthy elderlies, by using scalp-recorded electroencephalograms (EEGs). Methods: 16-ch EEGs were recorded during resting state for 39 dementia groups and 11 healthy elderlies. The connectivity between any two electrodes was estimated by synchronization likelihood (SL). The brain networks were constructed by normalized SL values. The present leave-one-out cross validation (LOOCV) required the Euclidean distance between any two subjects having 120-dimensional vectors concerned with the SL values for six frequency bands. In order to investigate factors which would affect the LOOCV results, principal component analysis (PCA) was applied to all the subjects. Results: The accuracy for the upper alpha yielded more than 80% and 70% in the dementia groups and the healthy elderlies, respectively. The LOOCV result could be explained in terms of brain networks such as executive control network (ECN) and default mode network (DMN) characterized by factor loadings of principal components. Conclusions: Dementia groups and healthy elderlies could be characterized by principal components of SL values between all the electrode pairs, even less connections, which revealed disruption and preservation of DMN and ECN. Significance: This study will provide a simple and practical method for discriminating dementia groups from healthy elderlies by scalp-recorded EEGs.

Comprehensive expression patterns of inflammatory cytokines in aqueous humor of patients with neovascular age-related macular degeneration

Neovascular age-related macular degeneration (nAMD) is a complex and multi-factorial disease, and low-grade inflammation is associated with pathogenesis of nAMD. Aqueous humor could reflect intraocular immune environments in various eye diseases. The research so far used aqueous humor samples and revealed that inflammation is involved in pathophysiology of nAMD, although immunological roles of cytokines were evaluated inadequately with aspect to individual effects. Here we used 27 kinds of cytokines covering general immunologic reactions, examined specific expression patterns of cytokines, and assessed relationships between inflammation and pathophysiology of nAMD by multivariate analyses. In nAMD eyes, principal component analysis showed that IL-7, MCP-1, MIP-1β and VEGF had high principal component loadings of over 0.6 in the first principal component constituting 32.6% of all variability of the data. In exploratory factor analysis, IL-6, MCP-1 and MIP-1β had high factor loadings (FL) of over 0.5 in Factor 1 constituting 32.6% of all variability, while VEGF had FL of over 1.0 in Factor 3 constituting 10.7% of all variability. In hierarchical cluster analysis, MCP-1 and VEGF were located in the cluster of first proximate mutual distance to central retinal thickness. These data could suggest that low-grade inflammation is a principal contributor in nAMD.

Identifying the principal factors influencing traffic safety on interstate highways

This study aims at identifying the principal factors influencing fatal, nonfatal injury and non-injury traffic crashes on urban and rural interstate highway segments using a statistical approach called principal component analysis. Initially, fourteen explanatory variables including segment length, annual average daily traffic (AADT), weekday/weekend, hour of the day, urban–rural designation of the segment, median type, pavement surface condition, roadway geometric characteristics, weather, number of lanes, and drivers’ age and gender, and the accident year were considered. Separate principal component analyses are performed for the three crash categories and the aggregate dataset. The results of the analyses show that, regardless of the crash categories used, seven principal components accounting for over 70% of the variances in the original datasets were retained. In addition to the overall dimensional reduction of the original dataset by 50%, the results suggest that the key variables contributing to the crashes across the categories of the accident types observed on the freeway segments are insignificant. The retained principal component loadings of the factors PC1, PC2, PC3, and PC5 revealed the fact that the number of lanes, the median type, and the AADT of the segments are highly correlated and represented by the first factor (PC1). Similarly, other interrelated factors such as the prevailing weather and the pavement surface condition (wet, dry, snow), the hour of the day and the lighting condition, the drivers’ age and gender are well represented by PC2, PC3, and PC5 respectively.

Anatomical Variability of Intercondylar Fossa Geometry in Patients Diagnosed with Primary Anterior Cruciate Ligament Rupture.

The aims of this study were to (1) describe the three-dimensional characteristics and sources of anatomical variability in the geometry of the intercondylar fossa ("notch") in an anterior cruciate ligament (ACL)-injured sample and (2) assess the relationship between patient factors and anatomical variability of the fossa in the context of impingement risk. A retrospective analysis of preoperative magnetic resonance imaging (MRI) for 49 patients with ACL rupture was performed. Scans were examined in the axial plane using an online picture archiving and communication system (PACS) viewer and fossa width and angle assessed at multiple slices, as well as anteroposterior depth, fossa height, and calculated total volume. Principal component analysis was performed to prioritize the sources of variability. A multivariate linear regression was performed to assess relationships between different patient factors, controlling for imaging parameters and principal component loadings. Geometric properties were normally distributed for all but fossa volume, height, and distal angle. Three principal components (PCs) were identified explaining 80% of total variance, shape (PC1), size in the coronal plane (PC2), and size in the sagittal plane (PC3). Patient factors were significantly (P < 0.05) related to PC loadings; however, a substantial amount of variance in each model remained unexplained. Intercondylar fossa characteristics vary considerably within ACL-injury patients with shape and size in coronal and axial planes, explaining most of the variance. Although patient factors are associated with anatomical characteristics, further work is required to identify the correct combination of factors accurately predicting geometry of the fossa for planning ACL reconstruction. Clin. Anat. 33:610-618, 2020. © 2019 Wiley Periodicals, Inc.

Determination of the bruise degree for cherry using Vis-NIR reflection spectroscopy coupled with multivariate analysis.

Determination and classification of the bruise degree for cherry can improve consumer satisfaction with cherry quality and enhance the industry’s competiveness and profitability. In this study, visible and near infrared (Vis-NIR) reflection spectroscopy was used for identifying bruise degree of cherry in 350–2500 nm. Sampling spectral data were extracted from normal, slight and severe bruise samples. Principal component analysis (PCA) was implemented to determine the first few principal components (PCs) for cluster analysis among samples. Optimal wavelengths were selected by loadings of PCs from PCA and successive projection algorithm (SPA) method, respectively. Afterwards, these optimal wavelengths were empolyed to establish the classification models as inputs of least square-support vector machine (LS-SVM). Better performance for qualitative discrimination of the bruise degree for cherry was emerged in LS-SVM model based on five optimal wavelengths (603, 633, 679, 1083, and 1803 nm) selected directly by SPA, which showed acceptable results with the classification accuracy of 93.3%. Confusion matrix illustrated misclassification generally occurred in normal and slight bruise samples. Furthermore, the latent relation between spectral property of cherries in varying bruise degree and its firmness and soluble solids content (SSC) was analyzed. The result showed both colour, firmness and SSC were consistent with the Vis-NIR reflectance of cherries. Overall, this study revealed that Vis-NIR reflection spectroscopy integrated with multivariate analysis can be used as a rapid, intact method to determine the bruise degree of cherry, laying a foundation for cherry sorting and postharvest quality control.

Fabrication of SERS active Langmuir–Blodgett Film substrate for screening human cancer cell lines: Experimental observations supported by multivariate data analyses

This paper reports fabrication of efficient and reproducible Surface Enhance Raman Scattering active substrate through integration of self-assembly and Langmuir-Blodgett technique for the detection of normal and cancerous cell lines associated with prostate and breast cancers. Morphology of the substrate has been visualized using FESEM and AFM images. In vitro cytotoxicity test was performed to check the biocompatibility of the substrate. SERS sensing ability of the substrate has been tested with the probe 4-Mercaptopyridine molecule. The as-prepared substrate has been employed for the first time to detect normal and cancerous cell lines linked with prostate and breast cancers. Multivariate statistical approaches like principal component analysis (PCA) and loading spectra were further explored for segregation and identification of the cancer cells from their normal counterparts. Intriguingly, the peak positions of the loading spectra nearly correspond to the difference SERS spectra between the normal and cancerous cell lines of prostate and breast cancers. To our knowledge, such one to one correspondence between the peak positions of loading and differences in the SERS spectra has been noticed for the first time and can act as a diagnostic platform to identify the biomarkers for cancerous cell lines in the molecular scale.

Identification of wheat grain in different states based on hyperspectral imaging technology

Nondestructive identification of wheat grains in different states plays an important role in improving the quality of wheat products. This study investigated the possibility of using hyperspectral imaging techniques to discriminate healthy wheat grain, germinated wheat grain, mildewed wheat grain, and shriveled wheat grain (wheat grain infected with fusarium head blight). Both sides of individual wheat kernels were subjected to hyperspectral imaging (866.4–1701.0 nm) to acquire hyperspectral cube data. Spectral data were preprocessed by using standardization and multiple scattering correction. In addition, the principal component loading method was used to extract the characteristic wavelengths of both sides of wheat grains. The sample is divided into calibration set, test set, and validation set. The data of the calibration set are used to train the partial least squares discriminant analysis model, K-nearest neighbor model, and the support vector machine model, and the test set data are used to test the model. The results show that spectral data of both sides can achieve good classification results, while the reverse spectral data perform better. By comparing with each other, the support vector machine model is selected as the best classification model. Finally, using two hyperspectral images (reverse side) that are not involved in training and testing to verify the accuracy of the established support vector machine model, and the classification effect maps of the four wheat grains were visualized. The results indicate that nondestructive classification of wheat grains in different states is feasible based on hyperspectral imaging technology.

Chemotaxonomic differentiation of Clerodendrum species based on high-performance thin-layer chromatographic fingerprinting of key secondary metabolites and chemometric data analysis

Clerodendrum viscosum leaves are used in indigenous systems of medicines of mainland and maritime Southeast Asian countries for the treatment of fever, pain, dysentery, colic, and removal of Ascarids. The Clerodendrum species under study exhibit various phytochemical and morphological similarities. Therefore, it is very challenging to distinguish raw powdered materials used for therapeutic purposes. A validated high-performance thin-layer chromatography (HPTLC) method with 4 key markers, viz., 24β-ethylcholesta-5,22E,25-triene-3β-O-D-glucoside, clerodinin-A, 24β-ethylcholesta-5,22E,25-triene-3β-ol, and lupeol coupled with a chemometric analysis was used to distinguish 3 closely related Clerodendrum species, viz., C. inerme, C. multiforum, and C. viscosum. PRISMA approach was applied for effective HPTLC fingerprint development. The HPTLC—densitometry method was validated following the current International Conference on Harmonisation (ICH) guidelines. Taxonomic differentiation was established by fingerprint-based similarity analysis, a chemotaxonomic study using hierarchical clustering analysis (HCA), and principal component analysis (PCA) was done. HPTLC chromatogram similarity was calculated as correlation coefficient and congruence coefficient values, demonstrating poor similarities (0.26–0.86). However, PCA has resulted in 2 principal component (PC) loadings. PC1 separated C. multiforum, explaining 85.48% of variance mainly due to distribution of 2 triterpenoids. The present HPTLC method is coupled to marker-based quality determination of raw plants as well as discrimination of Clerodendrum species. Chemometric analysis based on 4 metabolites clearly establishes a practical identification of Clerodendrum species intended for therapeutic use.

Non-Destructive Trace Detection of Explosives Using Pushbroom Scanning Hyperspectral Imaging System.

The aim of this study was to investigate the potential of the non-destructive hyperspectral imaging system (HSI) and accuracy of the model developed using Support Vector Machine (SVM) for determining trace detection of explosives. Raman spectroscopy has been used in similar studies, but no study has been published which is based on measurement of reflectance from hyperspectral sensor for trace detection of explosives. HSI used in this study has an advantage over existing techniques due to its combination of imaging system and spectroscopy, along with being contactless and non-destructive in nature. Hyperspectral images of the chemical were collected using the BaySpec hyperspectral sensor which operated in the spectral range of 400–1000 nm (144 bands). Image processing was applied on the acquired hyperspectral image to select the region of interest (ROI) and to extract the spectral reflectance of the chemicals which were stored as spectral library. Principal Component Analysis (PCA) and first derivative was applied to reduce the high dimensionality of the image and to determine the optimal wavelengths between 400 and 1000 nm. In total, 22 out of 144 wavelengths were selected by analysing the loadings of principal components (PC). SVM was used to develop the classification model. SVM model established on the whole spectrum from 400 to 1000 nm achieved an accuracy of 81.11%, whereas an accuracy of 77.17% with less computational load was achieved when SVM model was established on the optimal wavelengths selected. The results of the study demonstrate that the hyperspectral imaging system along with SVM is a promising tool for trace detection of explosives.