Principal Component Analysis And Linear Discriminant Analysis Research Articles

Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction

Nasopharyngeal cancer (NPC) is a malignant tumor with high prevalence in Southeast Asia and highly invasive and metastatic characteristics. Radiotherapy is the primary strategy for NPC treatment, however there is still lack of effect method for predicting the radioresistance that is the main reason for treatment failure. Herein, the molecular profiles of patient plasma from NPC with radiotherapy sensitivity and resistance groups as well as healthy group, respectively, were explored by label-free surface enhanced Raman spectroscopy (SERS) based on surface plasmon resonance for the first time. Especially, the components with different molecular weight sizes were analyzed via the separation process, helping to avoid the possible missing of diagnostic information due to the competitive adsorption. Following that, robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was employed to extract the feature of blood-SERS data and establish an effective predictive model with the accuracy of 96.7% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100% for identifying the NPC subjects from healthy ones. This work demonstrates the potential of molecular separation-assisted label-free SERS combined with machine learning for NPC screening and treatment strategy guidance in clinical scenario.

Prediction of technological properties of wheat flour by combination of UV-VIS-NIR spectroscopy and multivariate analysis methods

Over the last decades, optical spectroscopy methods that do not require complex sample preparation have been widely used to identify and control the composition of food products. In the present study, the possibility of using UV-VIS-NIR spectroscopy combined with multivariate analysis for grading wheat flour into groups differing in technological properties was analyzed. UV-VIS-NIR spectra contain information on the combination and intensity of absorption bands assigned to functional groups of the composition components and determining the technological properties of wheat flour. The database of UV-VIS-NIR spectra of wheat flour samples differing by technological properties was formed into three groups: the first group — wheat flour samples with good baking properties, the second group — with reduced baking properties, the third group — with low baking properties. The visible range of UV-VIS-NIR diffuse reflectance spectrum was used to calculate the color coordinates in the CIE colorimetric system L*a*b*. The greatest difference among the groups in the color coordinates of the samples was found for the coordinate b*, which is associated with the different content of coloring pigments. The spectra database was used to build a classification model for grading wheat flour into quality groups using a combination of principal component analysis and linear discriminant analysis (PCA-LDA) methods. The achieved results indicate that the classification model built on the training sample is able to distinguish wheat flour spectra by quality groups with an accuracy of 96.49%. The effective- ness of the model is verified using a test set of spectra of wheat flour samples. The present study confirms that the combina- tion of UV-VIS-NIR spectroscopy in conjunction with the PCA-LDA method has significant potential for determining a quality group of wheat flour based on technological properties.

Aquaphotomics based screening of tomato powder extracts reveals susceptibility to bulking and coloring agents

Gaining prominence as indispensable culinary ingredients, tomato powders aren't exempt from fraudulent practices. While the complexity of conventional authentication methods hinders their adoption as routine assessment tools, our study examines the efficiency of aquaphotomics as a potential alternative, that entails limited preparation steps and involves no solvent extractions. Tested across three tomato varieties, aqueous blends comprising concentrations in the range [0%–20%] of two adulterant types (bulking agent, coloring agent), added separately or concurrently, were considered. Upon scanning with an XDS Near infrared spectrometer operating in transmission mode, the data was subjected to chemometrics based analysis over the 1st and 2nd water overtone ranges. While attempting to perform classification based on the level of adulteration, running hybrid principal component analysis and linear discriminant analysis (PCA-LDA) qualitative modeling on single adulterant mixtures allowed for recognition and prediction rates varying from [97.76%–100%] and [88.59%–100%], respectively. Slightly lower accuracies were obtained when coupling the bulking and coloring agent. Partial least squares regression (PLSR) models gave good estimates of the extent of adulteration, with respective R2 and RMSECV values reaching at best, 0.98 and 0.45%w/w. The aquagrams revealed distinctive spectral patterns highlighting the gradual level of adulteration. Assuredly, these findings ascertain the contributions aquaphotomics promises in ensuring the authenticity of tomato powders, when analyzed in their liquid state.

Label-free SERS detection of prostate cancer based on multi-layer perceptron surrogate model method

Prior surface-enhanced Raman spectroscopy (SERS) research has shown that pre-processing is necessary before analysis. Pre-processing also typically serves the dual purposes of removing the auto-fluorescence background and minimizing data volatility. This method allows for a more accurate comparison of spectral traits and relative SERS peak strength. However, because there are so many different kinds of samples, it can take a long time, and there is no assurance that the approach chosen will work well with a particular kind of sample. Therefore, this study employed a deep learning technique called multi-layer perceptron (MLP) to simplify the pre-processing of blood plasma SERS samples in patients with prostate cancer (PC), as well as to enhance the sensitivity and specificity of diagnosis using SERS technology. First of all, significant variations in peak intensity can be observed in the difference spectra, facilitating differentiation between PC and normal groups. Second, the data analysis was carried out in three different stages (raw data, defluorescenced data, and normalized data) using principal component analysis and linear discriminant analysis (PCA-LDA), as well as PCA-multi-layer perceptron (PCA-MLP). Finally, when SERS data was analyzed using PCA-LDA, there were significant differences in classification accuracy across each stage (The classification accuracy of three different stages were 76.90%, 85.60%, 95.20%, respectively). However, when PCA-MLP was utilized for SERS data analysis, the classification accuracy remained consistently high and stable (The classification accuracy of three different stages were 92.00%, 92.40%, 96.70%, respectively). The experimental results of PCA-MLP for classifying specific SERS data indicate that analyzing raw data directly can simplify the experimental process and enhance the efficacy of SERS analysis.

Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis

AbstractThe occurrence of weeds is one of the main factors limiting agricultural productivity. Studies on new techniques for the identification of these species can contribute to the development of proximal sensors, which in the future might be coupled to machines to optimize the performance of species-specific weed management. Thus, the objective of this study was to use near-infrared (NIR) spectroscopy and multivariate analysis to discriminate three morningglory species (Ipomoea spp.). The NIR spectra were collected from the leaves of the three weed species at the vegetative stage (up to five leaves), within the spectral band of 4,000 to 10,000 cm−1. The discrimination models were selected according to accuracy, sensitivity, specificity, and Youden’s index and were analyzed with a validation data set (n = 135). The best results occurred when the selection of spectral bands associated with the use of preprocessing was performed. It was possible to obtain an accuracy of 99.3%, 98.5%, and 98.7% for ivyleaf morningglory (Ipomoea hederifolia L.), Japanese morningglory [Ipomoea nil (L.) Roth], and hairy woodrose [Merremia aegyptia (L.) Urb.], respectively. NIR spectroscopy associated with principal component analysis and linear discriminant analysis (PC-LDA) or partial least-squares regression with discriminant analysis (PLS-DA) can be used to discriminate Ipomoea spp.

Rapid label-free detection of cholangiocarcinoma from human serum using Raman spectroscopy.

Cholangiocarcinoma (CCA) is highly prevalent in the northeastern region of Thailand. Current diagnostic methods for CCA are often expensive, time-consuming, and require medical professionals. Thus, there is a need for a simple and low-cost CCA screening method. This work developed a rapid label-free technique by Raman spectroscopy combined with the multivariate statistical methods of principal component analysis and linear discriminant analysis (PCA-LDA), aiming to analyze and classify between CCA (n = 30) and healthy (n = 30) serum specimens. The model’s classification performance was validated using k-fold cross validation (k = 5). Serum levels of cholesterol (548, 700 cm-1), tryptophan (878 cm-1), and amide III (1248,1265 cm-1) were found to be statistically significantly higher in the CCA patients, whereas serum beta-carotene (1158, 1524 cm-1) levels were significantly lower. The peak heights of these identified Raman marker bands were input into an LDA model, achieving a cross-validated diagnostic sensitivity and specificity of 71.33% and 90.00% in distinguishing the CCA from healthy specimens. The PCA-LDA technique provided a higher cross-validated sensitivity and specificity of 86.67% and 96.67%. To conclude, this work demonstrated the feasibility of using Raman spectroscopy combined with PCA-LDA as a helpful tool for cholangiocarcinoma serum-based screening.

New Insights into the Multivariate Analysis of SER Spectra Collected on Blood Samples for Prostate Cancer Detection: Towards a Better Understanding of the Role Played by Different Biomolecules on Cancer Screening: A Preliminary Study.

Simple SummaryIn recent years, research on biofluids using Raman and SERS has expanded dramatically, indicating the enormous promise of this technology as a high-throughput tool for identifying cancer and other disorders. In the investigations thus far, researchers have concentrated on a specific illness or condition, but the techniques employed to acquire experimental spectra prevent direct comparison of the data. This necessitates comparative research of a variety of diseases and an increase in scientific cooperation to standardize experimental conditions. In our study, positive results were reached by applying a combined SERS multivariate analysis (MVA) to the urgent problem of prostate cancer diagnosis that was directly linked to real-world settings in healthcare. Moreover, in comparison to the prostate-specific antigen (PSA) test, which has a high sensitivity but limited specificity, our combined SERS-MVA method has greater specificity, which may assist in preventing the overtreatment of patients.It is possible to obtain diagnostically relevant data on the changes in biochemical elements brought on by cancer via the use of multivariate analysis of vibrational spectra recorded on biological fluids. Prostate cancer and control groups included in this research generated almost similar SERS spectra, which means that the values of peak intensities present in SERS spectra can only give unspecific and limited information for distinguishing between the two groups. Our diagnostic algorithm for prostate cancer (PCa) differentiation was built using principal component analysis and linear discriminant analysis (PCA-LDA) analysis of spectral data, which has been widely used in spectral data management in many studies and has shown promising results so far. In order to fully utilize the entire SERS spectrum and automatically determine the most meaningful spectral features that can be used to differentiate PCa from healthy patients, we perform a multivariate analysis on both the entire and specific spectral intervals. Using the PCA-LDA model, the prostate cancer and control groups are clearly distinguished in our investigation. The separability of the following two data sets is also evaluated using two alternative discrimination techniques: principal least squares discriminant analysis (PLS-DA) and principal component analysis—support vector machine (PCA-SVM).

A comparative study based on serum SERS spectra in and on the coffee ring for high precision breast cancer detection

AbstractHuman blood contains a variety of biomolecules that are highly related to the occurrence, progression, and prognosis of diseases, including cancer. The development of rapid and accurate methods for the detection of these biomarkers with extremely low concentration is vital for improving cancer screening. Herein, a label‐free surface‐enhanced Raman scattering (SERS) technology was employed to detect human serum samples from healthy volunteers (n = 51) and breast cancer patients (n = 58) with the aim of exploring the differences in bio‐components between them. Moreover, the influence of measurement location during SERS assay was evaluated due to the occurrence of “coffee‐ring (CR)” effect associated with this work. Results showed that the SERS spectra obtained from the CR area can better reflect the overall biochemical information of serum samples and exhibited excellent diagnostic sensitivity (94.8%) and specificity (98%) for identifying breast cancer from healthy groups using principal component analysis and linear discriminant analysis (PCA‐LDA) algorithm. This work showed a preliminary study of the serum SERS method for breast cancer detection, providing a potential rapid and accurate liquid biopsy strategy for clinical cancer screening.

Surface-Enhanced Raman Spectroscopy of Pretreated Plasma Samples Predicts Disease Recurrence in Muscle-Invasive Bladder Cancer Patients Undergoing Neoadjuvant Chemotherapy and Radical Cystectomy.

ObjectiveTo explore the value of surface-enhanced Raman spectroscopy analysis of pretreated plasma samples in prediction of bladder cancer (BCa) recurrence after neoadjuvant chemotherapy (NAC) and radical cystectomy (RC).Patients and MethodsSERS was used to analyze plasma samples collected before biopsy and treatment in BCa patients undergoing NAC and RC. The value of clinicopathological parameters and distinctive SERS peaks in the prediction of disease recurrence were analyzed in Cox regression proportional hazard analysis and Log rank test. Principal component analysis and linear discriminant analysis (PCA-LDA) were employed to process spectral data and construct diagnostic algorithms.ResultsA total of 88 patients with 440 plasma SERS spectra were collected. The SRES spectra from recurrent patients were compared with patients who remained recurrence free. The SERS demonstrated higher levels of circulating free nucleic acid components in recurrent population, which is represented by significantly higher intensities at SERS peaks of 725 cm−1, 1328 cm−1 and 1455 cm−1. The SERS also detected significantly lower levels of tryptophan shown as lower significantly intensities at the 1558 cm−1, which is proved to be an independent predictor of BCa recurrence. The addition of SERS peaks of 1558 cm−1 to classic clinicopathological predictors including pathological tumor stage, lymph node metastasis and pathological downstaging can significantly enhance the power of the predictive model from 0.66 to 0.76 in the area under curve (AUC) of receiver operating characteristic (ROC) curves. Meanwhile, the PCA-LDA diagnostic model based on SERS spectra reveals a high accuracy of 85.2% in prediction of disease recurrence and the AUC of 0.92 in the ROC curve. When validated in the leave-one-out cross-validation method, the accuracy of the model remained 84.1%.ConclusionWe show that SERS analysis of plasma before NAC treatment can accurately classify patients with different risks of disease recurrence after surgery and improve the power of clinicopathological predictive models, thus refining clinical decision-making.

Diagnosis of Dental Fluorosis Using Micro-Raman Spectroscopy Applying a Principal Component-Linear Discriminant Analysis

Dental fluorosis is an irreversible condition caused by excessive fluoride consumption during tooth formation and is considered a public health problem in several world regions. The objective of this study was to evaluate the capability of micro-Raman spectroscopy to classify teeth of different fluorosis severities, applying principal component analysis and linear discriminant analysis (PCA-LDA), and estimate the model cross-validation accuracy. Forty teeth of different fluorosis severities and a control group were analyzed. Ten spectra were captured from each tooth and a total of 400 micro-Raman spectra were acquired in the wavenumber range of 250 to 1200 cm−1, including the bands corresponding to stretching and bending internal vibrational modes ν1, ν2, ν3, and ν4 (PO43−). From the analysis of the micro-Raman spectra an increase in B-type carbonate ion substitution into the phosphate site of the hydroxyapatite as fluorosis severity increases was identified. The PCA-LDA model showed a sensitivity and specificity higher than 94% and 93% for the different fluorosis severity groups, respectively. The cross-validation accuracy was higher than 90%. Micro-Raman spectroscopy combined with PCA-LDA provides an adequate tool for the diagnosis of fluorosis severity. This is a non-invasive and non-destructive technique with promising applications in clinical and epidemiological fields.

Multivariate color scale image analysis – Thin layer chromatography for comprehensive evaluation of complex samples fingerprint

A chemometric evaluation of the information provided by different color scale fingerprints in thin layer chromatographic analysis of complex samples is proposed for the correct classification of a set of medicinal plant extracts. The fingerprints of the samples were acquired on HPTLC Silica gel 60 F254 and HPTLC Silica gel 60 plates using multiple levels of visualization under UV light. Images processing on red (R), green (G), blue (B) and respectively grey (K) color scale selection was used in order to evaluate the complete chromatographic profile of the extracts. Combination of Principal Component Analysis (PCA) and Factor Analysis (FA) method was applied in order to reveal the individual contribution of each color scales in the analysis of chromatographic fingerprints. The suggested technique provides an applicable strategy to screen for efficacy-associated color scale for grouping/classification of the extracts exploiting the information provided by HPTLC fingerprints. The principal component analysis and linear discriminant analysis (PCA-LDA) method was applied for the evaluation of numerical data provided by color scale fingerprints digitization and for samples classification. A correct classification of the analyzed extracts according to the plants phylum was revealed by color scale fingerprints analysis. The proposed methodology could be considered as a promising tool with future applications in plant material investigations even from the taxonomic perspective classification.

Rapid intraoperative diagnosis of gynecological cancer by ATR-FTIR spectroscopy of fresh tissue biopsy.

A rapid and reliable intraoperative diagnostic technique to support clinical decisions was developed using Fourier-transform infrared (FTIR) spectroscopy. Twenty-six fresh tissue samples were collected intraoperatively from patients undergoing gynecological surgeries. Frozen section (FS) histopathology aimed to discriminate between malignant and benign tumors was performed, and attenuated total reflection (ATR) FTIR spectra were collected from these samples. Digital dehydration and principal component analysis and linear discriminant analysis (PCA-LDA) models were developed to classify samples into malignant and benign groups. Two validation schemes were employed: k-fold and "leave one out." FTIR absorption spectrum of a fresh tissue sample was obtained in less than 5 minutes. The fingerprint spectral region of malignant tumors was consistently different from that of benign tumors. The PCA-LDA discrimination model correctly classified the samples into malignant and benign groups with accuracies of 96% and 93% for the k-fold and "leave one out" validation schemes, respectively. We showed that a simple tissue preparation followed by ATR-FTIR spectroscopy provides accurate means for very rapid tumor classification into malignant and benign gynecological tumors. With further development, the proposed method has high potential to be used as an adjunct to the intraoperative FS histopathology technique.

Label-free detection of multiple genitourinary cancers from urine by surface-enhanced Raman spectroscopy.

Detecting cancers through testing biological fluids, namely, "liquid biopsy", is noninvasive and shows great promise in cancer diagnosis, surveillance and screening. Many metabolites that may reflect cancer specificity are concentrated in and excreted through urine. In this study, urine samples were collected from healthy subjects and patients with bladder or prostate cancer. By using surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles, urine sample spectra from 500-1800cm-1 were obtained. The spectra were classified by principal component analysis and linear discriminant analysis (PCA-LDA). The results showed that the classification accuracy of the model for healthy individuals, bladder cancer patients and prostate cancer patients was 91.9%, and the classification accuracy of the test set was 89%, which indicated that SERS combined with the PCA-LDA diagnostic algorithm could be used as a classification and diagnostic tool to detect and distinguish bladder cancer and prostate cancer through testing urine.

Surface-enhanced Raman spectroscopy of preoperative serum samples predicts Gleason grade group upgrade in biopsy Gleason grade group 1 prostate cancer

PurposeTo predict Gleason grade group (GG) upgrade in biopsy Gleason grade group 1 (GG1) prostate cancer (CaP) patients using surface-enhanced Raman spectroscopy (SERS). Materials and methodsPreoperative serum samples of patients with biopsy GG1 and subsequent radical prostatectomy were analyzed using SERS. The role of clinical variables and distinctive SERS spectra in the prediction of GG upgrade were evaluated. Principal component analysis and linear discriminant analysis (PCA-LDA) were used to manage spectral data and develop diagnostic algorithms. ResultsA total of 342 preoperative serum SERS spectra from 114 patients were obtained. SERS detected a higher level of circulating free nucleic acid bases and a lower level of lipids in patients with GG upgrade to GG3 and higher, presenting as SERS spectral peaks of 728 cm−1 and 1,655 cm−1, respectively. Both spectral peaks were independent predictors of GG upgrade and their addition to clinical predictors of PSA and positive core percent significantly improved predictive power of the logistic regression model with area under curve improved from 0.65 to 0.80 (P = 0.0045). Meanwhile, PCA-LDA diagnostic model based on serum SERS spectra showed a high accuracy of 91.2% in predicted groups and remained stable with a sensitivity, specificity, and accuracy of 65%, 97.3%, 86.0%, respectively when validated by leave-one-out cross-validation method. ConclusionsBy analyzing preoperative serum samples, SERS combined with PCA-LDA model could be a promising tool for prediction of Gleason GG upgrade in biopsy GG1 CaP and assist in treatment decision-making in clinical practice.

Classification of condom lubricants in cyanoacrylate treated fingerprints by desorption electrospray ionization mass spectrometry

Traces of condom lubricants in fingerprints can be valuable information in cases of sexual assault. Ideally, not only confirmation of the presence of the condom but also determination of the type of condom brand used can be retrieved. Previous studies have shown to be able to retrieve information about the condom brand and type from fingerprints containing lubricants using various analytical techniques. However, in practice fingerprints often appear latent and need to be detected first, which is often achieved by cyanoacrylate fuming. In this study, we developed a desorption electrospray ionization mass spectrometry (DESI-MS) method which, combined with principal component analysis and linear discriminant analysis (PCA-LDA), allows for high accuracy classification of condom brands and types from fingerprints containing condom lubricant traces. The developed method is compatible with cyanoacrylate (CA) fuming. We collected and analyzed a representative dataset for the Netherlands comprising 32 different condoms. Distinctive lubricant components such as polyethylene glycol (PEG), polydimethylsiloxane (PDMS), octoxynol-9 and nonoxynol-9 were readily detected using the DESI-MS method. Based on the analysis of lubricant spots, a 99.0% classification accuracy was achieved. When analyzing lubricant containing fingerprints, an overall accuracy of 90.9% was obtained. Full chemical images could be generated from fingerprints, showing the distribution of lubricant components such as PEG and PDMS throughout the fingerprint, while still allowing for classification. The developed method shows potential for the development of DESI-MS based analyses of CA treated exogenous compounds from fingerprints for use in forensic science.

Surface-enhanced raman spectroscopy: A non invasive alternative procedure for early detection in childhood asthma biomarkers in saliva.

The early detection of bronchial inflammation in asthma, through a non-invasive, simple method and under a subclinical state, could lead to a more effective control of this condition. The aim of this study was to identify biomarkers of bronchial inflammation in the saliva of children with asthma through immunoassay and Surface Enhanced Raman Spectroscopy (SERS). We conducted an analytical cross-sectional study in 44 children ages 6-12; the diagnosis of asthma was made according to Global Initiative for Asthma (GINA) standards. The children's saliva was analyzed by immunoassay for the quantification of 37 cytokines, as well as SERS analysis in a confocal Raman microscope at 785 nm. We found a significant association between bronchial obstruction and IL-8 (p = 0.004), IL-10 (p = 0.008) and sCD163 (p = 0.003). The Raman spectra showed significant amplification in the region of 760 to 1750 cm-1. The Principal Component Analysis and Linear Discriminant Analysis (PCA-LDA) method has a sensitivity of 85%, specificity of 82% and an accuracy of 84% for the diagnosis of asthma. These results demonstrate the presence of a subclinical inflammatory state, suggestive of bronchial remodeling in the population studied. The SERS method is a potential tool for identifying bronchial inflammation and its endotype, allowing for a highly sensitive and specific diagnosis.

Recognition of Heavy Metal Contamination Using Tegillarca Granosa LIBS Data

The problem of heavy metal pollution in food and the environment has drawn increasing public concern, because of its irreversible damage. Traditional methods of detecting heavy metals require complicated laboratory conditions and skilled experimenters, and cannot meet cost-effectiveness, rapidity and ease of use. In this paper, we propose a principal component analysis and linear discriminant analysis (PCA-LDA) pattern recognition algorithm for rapid detection of heavy metals in tegillarca granosa by using laser-induced breakdown spectroscopy (LIBS) data. First, the principal component analysis (PCA) algorithm is used to reduce the dimension of spectral data from the previous 2811 dimension to 16 dimensions. Second, the linear discriminant analysis (LDA) algorithm is used for classification. Results show that PCA-LDA has the best classification accuracy compared with the partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA) and support vector machine (SVM) algorithms, and achieves a discrimination accuracy of 87%. This work indicates that by combining pattern recognition with LIBS technology, healthy tegillarca granosa can be quickly distinguished from heavy metal polluted samples, which offers possibility of environmental monitoring and in field food detection.

Surface-enhanced Raman spectroscopy before radical prostatectomy predicts biochemical recurrence better than CAPRA-S.

ObjectiveThe objective of this study was to evaluate the performance of surface-enhanced Raman spectroscopy (SERS) in the prediction of early biochemical recurrence after radical prostatectomy (RP).Patients and methodsWe synthesized monodisperse gold nanoparticles as SERS-enhanced substrates and analyzed preoperative plasma samples of patients who underwent RP. The roles of clinical risk model (Cancer of the Prostate Risk Assessment [CAPRA] score) and distinctive SERS spectra on prediction of early biochemical recurrence were evaluated. The principal component analysis and linear discriminant analysis (PCA-LDA) were used to manage the spectral data and develop diagnostic algorithm.ResultsA total of 306 preoperative plasma Raman spectra from 102 patients were collected. SERS spectrum from those who developed early biochemical recurrence were compared to those who remained biochemical recurrence-free. The SERS detected more abundant circulating free nucleic acid bases in biochemical recurrence population, presenting significant stronger intensities at SERS spectral bands 725 and 1,328 cm−1. The addition of Raman spectral peak 1,328 cm−1 to CAPRA postsurgical (CAPRA-S) score significantly improved the predictive power of logistic regression model compared to simple CAPRA score (P<0.001). Meanwhile, the leave-one-out cross-validation method was used to validate the PCA-LDA model and revealed the sensitivity, specificity, and accuracy of 65.8%, 87.5%, and 79.4%, respectively. The receiver operating characteristic (ROC) curve was used to evaluate the performance of different models. Area under the ROC curve of the CAPRA-S score model alone was 0.77, however, when combined with Raman spectral peak 1,328 cm−1, it improved to 0.81.ConclusionOur primary results suggested that SERS could be a meaningful technique for prediction of early biochemical recurrence in prostate cancer.

Chemometric Assessment of Chromatographic Methods for Herbal Medicines Authentication and Fingerprinting.

Nowadays, increasingly more individuals turn to supplementation of the diet with herbal medicines and many such products are marketed lately. Thus the problem that this article focuses on is that these products are not subjected to rigorous quality control like synthetic drugs are, which rises a constant debate whether the supplements actually contain the herb or mixture of herbs that the manufacturer claims they do. As a solution, micellar electrokinetic chromatography and high performance liquid chromatography were investigated in order to fingerprint and authenticate herbal medicines. For this purpose, minimal sample pre-treatment was applied to several fruit based herbal medicines, which were compared with the ethanolic extract of the respective fruit. The holistic evaluation of the electropherograms and chromatograms was made by using appropriate chemometric tools, such as principal component analysis (PCA), cluster analysis and a combination of PCA and linear discriminant analysis (PCA-LDA). The results suggest that the developed method was able to successfully discriminate between different herbal medicines, based on their raw material content. Moreover, this simple and efficient methodology might also be used for routine screening and authenticity control of different products and could be implemented in any quality control laboratory.

Study on PCA-LDA for Fast Identifying the Type of Coal Mine Water with LIF Technology

Identifying the type of coal mine water is the foundation of coal mine hydrogeological research and has great significance for coal mine safe production. Considering the time consuming of the conventional method, we propose a new Laser Induced Fluorescence (LIF) combining with Principal Component Analysis and Linear Discriminant Analysis (PCA-LDA) method for coal source identification. Firstly, in order to obtain the valid bands from spectra results, LIF system is used to stimulate 405nm laser for the 400-800nm fluorescence spectra of tested water. Then, PCA is applied to reduce the dimension of spectral data. Finally, according to the different number of principal component with different pre-treatments the LDA identifications are implemented. Experiment results indicate that after valid bands selection, wavelet pre-processing, and PCA dimension reduction, the identification effect of spectra data with LDA method can be excellent as the number of principal component sets 6, and the correct recognition rate can reach to 100%. Thus, PCA-LDA algorithm with LIF tech is an effective identification method for quickly identification of the type of coal mine water.