PCA-LDA Analysis Research Articles

Forensic identification and differentiation of some protected timber species using ATR-FTIR spectroscopy and chemometrics

Identifying an unascertained timber species is essential to stop illegal logging of the protected species. Timber forensics involves the identification of an unknown timber species to link to its source or to authenticate the timber and its products. This paper anticipates a quick, robust, non-destructive, and environment-friendly proof-of-concept study using ATR-FTIR spectroscopy and chemometric interpretation to identify and discriminate economically important and legitimately protected timber species. The chemometric methods used included partial least square discriminant analysis (PLS-DA), principal component analysis (PCA), and linear discriminant analysis (LDA). The mid-IR spectral bands indicated the presence of timber constituents such as cellulose, lignin, and hemicellulose. PLS-DA successfully discriminated between hardwoods and softwoods with 100% accuracy. PCA-LDA analysis of softwoods and hardwoods was done separately. LDA for softwoods resulted in a training and validation accuracy of 87.5%. Similarly, LDA analysis of hardwoods showed 82.22% training and 80% validation accuracies. The results of the blind test showed that all the blind samples could be correctly identified using this approach with 100% accuracy. All these approaches delivered significant findings to identify and discriminate timber samples. It is believed that this study will offer great opportunities to withstand illegal logging quickly and non-destructively.

Label-free screening of common urinary system tumors from blood plasma based on surface-enhanced Raman spectroscopy

BackgroundThe incidence of common urinary system tumors has been rising rapidly in recent years, and most urinary system-derived tumors lack specific biomarkers. ObjectivesTo explore the efficacy of surface-enhanced Raman spectroscopy (SERS) of blood plasma in screening three common urinary system tumors, including bladder cancer (BC), prostate cancer (PCa), and renal cell carcinoma (RCC). MethodsSERS plasma spectra from 125 plasma samples, including 25 PCa, 38 RCC, 24 BC patients, and 38 normal volunteers, were collected. All candidates had no other comorbidities. The Diagnosis was based on the combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and the effectiveness of the diagnostic algorithms was verified using the Receiver Operating Characteristic Curve (ROC). ResultsThere are significant differences in SERS signals between PCa, BC, RCC, and normal plasma, especially at 639, 889, 1010, 1136, and 1205 cm−1. The PCA-LDA results show that high sensitivity (100 %), specificity (100 %), and accuracy (100 %) could be achieved for screening the PCa, RCC, BC group vs. the normal group, the PCa group vs. the BC and RCC group, respectively. The diagnostic sensitivity, specificity, and accuracy for the BC group vs. the RCC group are 79.2 %, 71.1 %, and 75.15 %, respectively. The integrated area under the ROC curve (AUC) is 1.0, 1.0, and 1.0 for the PCa, RCC, and BC group vs. the normal group, respectively. The AUC of the PCa group vs. the BC group and RCC group and the BC group vs. the RCC group are 1.0, 1.0, and 0.842, respectively. ConclusionsLabel-free plasma-SERS technology with PCA-LDA analysis could be a useful screening method for detecting urinary system tumors (PCa, RCC, and BC) in this exploratory study.

Types of Polyunsaturated Fatty Acids in Epididymal Adipose Tissue Are Distinguishable Using Raman Spectroscopy and Multivariate Analysis

Biological polyunsaturated fatty acids (PUFAs) are important precursors of secondary messengers that modulate inflammatory responses, cellular growth, and cholesterol metabolism. The optimal n-6/n-3 ratio is extremely important for maintaining normal homeostasis because n-3 and n-6 PUFAs are competitively metabolized. To date, a widely accepted analytical method to determine the biological n-6/n-3 ratio is gas chromatography-mass spectrometry (GC-MS) on dried whole blood samples. However, this technique has several drawbacks, including the intrusive nature of collecting blood samples, high expenses involved, and length of time required to use the GC/MS instrument. To overcome these limitations, we introduced Raman spectroscopy (RS) to distinguish PUFAs present in the epididymal adipose tissue (EAT) isolated from experimental rats that were fed three different high-fat diets (HFDs) with multivariate analysis, including principal component analysis (PCA) and linear discriminant analysis (LDA). The diets comprised HFD, HFD + perilla oil (HFD + PO [n-3 rich oil]), and HFD + corn oil (HFD + CO [n-6 rich oil]). This method allows for quantitative, label-free, noninvasive, and rapid monitoring of biochemical changes in the EAT with high sensitivity. In RS, the Raman bands of the EAT from three different diet groups (HFD, HFD + PO, and HFD + CO) detected and distinguished peaks at 1079 (C-C stretching vibration), 1300 (CH2 deformation), 1439 (CH2 deformation), 1654 (amide I), 1746 (C = O stretching vibration), and 2879 cm-1 (-C-H stretching vibration). The PCA-LDA analysis results showed that PUFAs in the EAT of animals receiving the three different dietary interventions can be determined according to the three groups (HFD, HFD + PO, and HFD + CO). In conclusion, we investigated the possibility of determining PUFA profiles in specimens using RS.

Composition of Volatile Compounds in Heat-Treated Meat from Polish Native Sheep Breeds Using HS-SPME-GC/MS – Chemometric Classification Based on Breed and Age

Abstract In order to characterize the volatile compounds composition of lamb meat, 3 groups of baked leg muscle samples from two Polish native sheep breeds: Świniarka aged 9 months (S9), Wrzosówka aged 9 months (W9), and Wrzosówka aged 7 months (W7), were analyzed by HS-SPME-GC/MS, followed by multivariate statistics comprising the F-ratio method for variables pre-selection, and PCA-LDA analysis. Ninety seven volatile compounds were determined, out of which 74 were identified. The largest classes of volatile compounds were aldehydes and furans followed by alcohols, hydrocarbons, ketones, and sulfur and nitrogen compounds. Statistically significant differences between the S9, W9, and W7 groups were observed for 11 volatiles, i.e.: pentanal; hexanal; 1,3-octadiene; benzaldehyde; 3-ethyl-2-methyl-1,3-hexadiene; 3-octen-2-one; 2-octenal; 2-hexylfuran; tetradecanal; pentadecanal; hexadecanal. The HS-SPME-GC/MS method coupled with chemometrics, based on the relative intensity spectral data of these volatiles, proved to be an effective tool for the discrimination of lambs according to breed and age. The classification accuracy value for the S9, W9, and W7 groups was 100%.

Investigation of the discrimination and characterization of blood serum structure in patients with opioid use disorder using IR spectroscopy and PCA-LDA analysis

Harmful illicit drug use, such as opioid use disorder (OUD), causes multiple diseases that result in physiological, pathological, and structural changes in serum biochemical parameters based on the period of use. Fourier-transform infrared (FTIR) spectrometry is a noninvasive optical technique that can provide accurate evidence about the biochemical compounds of analytical samples. This technique is based on the detection of functional groups and the spectral analysis of the region of the selected bands, which provides a reliable and accurate tool for evaluating changes in the biochemical parameters of OUD patients. In the present study, the Attenuated Total Reflection (ATR)-FTIR technique and clinical laboratory biochemical results were used to investigate the phospholipid–protein balance in the blood serum of participants with OUD by comparing their data to that of healthy controls. To compare the biochemical laboratory results with serum vibrational spectroscopy, we used infrared (IR) spectroscopy to distinguish the serum of the OUD patients, who had an average duration of use of 7.31 ± 3.8 years (ranging from 6 to 15 years). We aimed to compare the clinical reports with findings from IR spectroscopy coupled with chemometrics analysis, principal component analysis (PCA), and linear discriminant analysis (LDA). The serum samples of the OUD male patients (n = 20) and healthy male individuals (n = 14) were evaluated using FTIR spectroscopy (range of 4000 cm−1 – 400 cm−1). We focused on the areas where the results showed significant band differences and significant chemometric differences at the fingerprint region (1800 cm−1– 900 cm−1), Amide I (1700 cm−1–1600 cm−1), C–H stretching band (3000 cm−1–2800 cm−1), triglyceride (Tg) levels and cholesterol esters (1800 cm−1–1700 cm−1), and total protein region (1700 cm−1 –1350 cm−1). The intensity of these band areas was significantly different (p < 0.01) between OUD patients and healthy controls. Moreover, different bands on the serum spectrum of the OUD patients were explored. The results successfully specified the distinctions between OUD and the healthy controls (HCs). We compared the results with biochemical markers, such as albumin (Alb), Tg, and total cholesterol (Tc) levels of the patients, as well as the data of the healthy subjects obtained from the hospital. Additionally, we found that the Tg, Tc, and Alb levels decreased as the duration of heroin use increased based on the biochemical markers of the OUD patients. The laboratory biochemical reports and the vibrational spectroscopic analysis were correlated. The confidence of specificity, sensitivity, and accuracy was 100%, 92.85%, and 97.06% in the second derivative, respectively. Thus, we demonstrated that IR spectroscopy, multivariate data analysis, and clinical reports are consistent and correlated. Furthermore, FTIR is a simple and readily available diagnostic test that can successfully differentiate the serum samples of OUD patients from those of healthy subjects.

Using Raman Spectroscopy to Investigate the Molecular Level Characteristics of Endometriosis-=SUP=-*-=/SUP=-

Endometriosis is a benign gynecologic disorder. It is particularly common among young women and may make pregnancy difficult. In this study molecular level characterization of endometriosis tissues were performed using Raman spectroscopy in combination with multivariate statistical analysis. Three hundred sixty-six Raman spectra recorded from different points of seventy two tissue samples, taken from the cyst walls of twelve patients were examined. Principle Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) were performed on the Raman data, and the samples were then classified into three groups; severe, moderate, and weak endometriosis. In the severe endometriosis group, the relative band intensities of DNA were increased. Moreover, increase in pyrrole moieties and kynurenine were seen. The results show that endometriosis severity correlates to increase in DNA concentration, and degradation of tryptophan due to increased indoleamine-pyrrole 2, 3-dioxygenase (IDO) activity, and an increase in kynurenine concentration and pyrrole intermediate. It is concluded that Raman spectroscopy is capable of providing a quick diagnosis, ahead of the pathology result being reported. Keywords: Endometriosis; Raman spectra, PCA-LDA analysis.

Investigations of the Neolithic potteries of 6th millennium BC from Göytepe-Azerbaijan by vibrational spectroscopy and chemometric techniques

This article presents the results of the vibrational spectroscopic and chemometric analyses of Neolithic pottery remains excavated in Göytepe (Azerbaijan), a typical Shomutepe-Shulaveri culture settlement. Fifty-five pottery fragments, that were unearthed in the excavations of Göytepe during the 2009–2013 years, were investigated using Fourier Transform Infrared (FTIR) and micro-Raman spectroscopy. X-Ray Diffraction (XRD) was used as a complementary technique. The firing-temperature and -conditions were inferred from the mineral phases obtained from the vibrational spectra of the samples and were estimated to be between 600 °C and 750 °C in oxidizing atmosphere. As chemometrics, Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA), were applied to the FTIR spectral data, in order to examine the possible classification of those findings, and to extract the most discriminant features. Pottery fragments were identified and characterized, depending on the excavation levels, by PCA-LDA analysis.

Label-free detection of liver cancer based on silver nanoparticles coated tissue surface-enhanced Raman spectroscopy

Surface-enhanced Raman spectroscopy (SERS) was employed to obtain hepatocellular carcinoma (HCC) tissue spectra with coated silver nanoparticles. Principal component analysis (PCA) combined with linear discriminate analysis (LDA) was introduced to analyze the obtained spectra. The sensitivity and specificity of discrimination for HCC and normal group is 93.8% and 100%. While that is 79.2% and 93.8% for HCC and adjacent HCC group, this study suggests a great potential method for early HCC detection by combination of silver nanoparticles based tissue SERS and PCA-LDA analysis.

Application of FT-IR spectroscopy on breast cancer serum analysis

Breast cancer is regarded as the most malignant tumor among women throughout the world. Therefore, early detection and proper diagnostic methods have been known to help save women's lives. Fourier Transform Infrared (FT-IR) spectroscopy, coupled with PCA-LDA analysis, is a new technique to investigate the characteristics of serum in breast cancer. In this study, 43 breast cancer and 43 healthy serum samples were collected, and the FT-IR spectra were recorded for each one. Then, PCA analysis and linear discriminant analysis (LDA) were used to analyze the spectral data. The results showed that there were differences between the spectra of the two groups. Discriminating wavenumbers were associated with several spectral differences over the 950‐1200cm‐1(sugar), 1190‐1350cm‐1 (collagen), 1475‐1710cm‐1 (protein), 1710‐1760cm‐1 (ester), 2800‐3000cm‐1 (stretching motions of –CH2 & ‐CH3), and 3090‐3700cm‐1 (NH stretching) regions. PCA-LDA performance on serum IR could recognize changes between the control and the breast cancer cases. The diagnostic accuracy, sensitivity, and specificity of PCA-LDA analysis for 3000‐3600cm‐1 (NH stretching) were found to be 83%, 84%, 74% for the control and 80%, 76%, 72% for the breast cancer cases, respectively. The results showed that the major spectral differences between the two groups were related to the differences in protein conformation in serum samples. It can be concluded that FT-IR spectroscopy, together with multivariate data analysis, is able to discriminate between breast cancer and healthy serum samples.

Quantification of Coffea arabica and Coffea canephora var. robusta concentration in blends by means of synchronous fluorescence and UV-Vis spectroscopies

The potential of fluorescence, UV-Vis spectroscopies as well as the low- and mid-level data fusion of both spectroscopies for the quantification of concentrations of roasted Coffea arabica and Coffea canephora var. robusta in coffee blends was investigated. Principal component analysis was used to reduce data multidimensionality. To calculate the level of undeclared addition, multiple linear regression (PCA-MLR) models were used with lowest root mean square error of calibration (RMSEC) of 3.6% and root mean square error of cross-validation (RMSECV) of 7.9%. LDA analysis was applied to fluorescence intensities and UV spectra of Coffea arabica, canephora samples, and their mixtures in order to examine classification ability. The best performance of PCA-LDA analysis was observed for data fusion of UV and fluorescence intensity measurements at wavelength interval of 60nm. LDA showed that data fusion can achieve over 96% of correct classifications (sensitivity) in the test set and 100% of correct classifications in the training set, with low-level data fusion. The corresponding results for individual spectroscopies ranged from 90% (UV-Vis spectroscopy) to 77% (synchronous fluorescence) in the test set, and from 93% to 97% in the training set. The results demonstrate that fluorescence, UV, and visible spectroscopies complement each other, giving a complementary effect for the quantification of roasted Coffea arabica and Coffea canephora var. robusta concentration in blends.

PCA-LDA Analysis of Human and Canine Blood Based on Non-contact Raman Spectroscopy†

PCA-LDA Analysis of Human and Canine Blood Based on Non-contact Raman Spectroscopy†

Spectrally resolved infrared microscopy and chemometric tools to reveal the interaction between blue light (470 nm) and methicillin-resistant Staphylococcus aureus

Blue light inactivates methicillin-resistant Staphylococcus aureus (MRSA), a Gram-positive antibiotic resistant bacterium that leads to fatal infections; however, the mechanism of bacterial death remains unclear. In this paper, to uncover the mechanism underlying the bactericidal effect of blue light, a combination of Fourier transform infrared (FTIR) spectroscopy and chemometric tools is employed to detect the photoreactivity of MRSA and its distinctive pathway toward apoptosis after treatment. The mechanism of action of UV light and vancomycin against MRSA is also investigated to support the findings. Principal component analysis followed by linear discriminant analysis (PCA- LDA) is employed to reveal clustering of five groups of MRSA samples, namely untreated (control I), untreated and incubated at ambient air (control II), irradiated with 470nm blue light, irradiated with 253.5 UV light, and vancomycin-treated MRSA. Loadings plot from PCA-LDA analysis reveals important functional groups in proteins (1683, 1656, 1596, 1542cm−1), lipids (1743, 1409cm−1), and nucleic acids region of the spectrum (1060, 1087cm−1) that are responsible for the classification of blue light irradiated spectra and control spectra. Cluster vector plots and scores plot reveals that UV light-irradiated spectra are the most biochemically similar to blue light- irradiated spectra; however, some wavenumbers experience a shift. The shifts between blue light and UV light irradiated loadings plot at νasym PO2− band (from 1228 to 1238cm−1), DNA backbone (from 970 to 966cm−1) and base pairing vibration of DNA (from 1717 to 1712cm−1) suggest distinctive changes in DNA conformation in response to irradiation. Our findings indicate that irradiation of MRSA with 470nm light induces A-DNA cleavage and that B-DNA is more resistant to damage by blue light. Blue light and UV light treatment of MRSA are complementary and distinct from the known antimicrobial effect of vancomycin. Moreover, it is known that UV-induced cleavage of DNA predominantly targets B-DNA, which is in agreement with the FTIR findings. Overall the results suggest that the combination of light and vancomycin could be a more robust approach in treating MRSA infections

Dosage- and time-dependent antibacterial effect of zinc oxide nanoparticles determined by a highly uniform SERS negating undesired spectral variation.

Massive production of nanomaterials posesa high risk to environmental ecology and human health. However, comprehensive understanding of nanotoxicity is still a major challenge due to the limitations of assessment methods, especially at the molecular level. We developed a new, sensitive, and robust fingerprinting surface-enhanced Raman spectroscopy (SERS) approach to interrogate both dose- and time-dependent phenotypic bacterial responses to zinc oxide nanoparticles (ZnO NPs). SERS enhancement was provided by biocompatible Au NPs. Additionally, a novel vacuum filtration-based strategy was adopted to fabricate bacterial samples with highly uniform SERS signals, ensuring the acquisition of robust and independent spectral changes from ZnO NPs-impacted bacteria without undesirable spectral variations. Combined with multivariate analysis, clear and informative spectral alteration profiles were obtained. Much greater alterations were found in low-dose ranges than high-dose ranges, indicating a reduction in the bioavailability of ZnO NPs with doses. Time-resolved bacterial responses provided important information on toxic dynamics, i.e., rapid action of ZnO NPs within 0.5h was identified, and ZnO NPs at low doses and long exposure time exerted similar effects to high doses, indicating the concerns associated with low-dose exposure. Further analysis of biochemical changes revealed metabolic activity decrease over both incubation time and doses. Meanwhile, a short-term protection strategy of bacteria by producing lipid-containing outer membrane vesicles to mitigate the cell of toxic NPs was suggested. Finally, Zn(2+) ions released from NPs were demonstrated to be irrelevant to bacterial responses on both dose and time scales. The new SERS methodology can potentially profile a large variety of toxic NPs and advance our understanding of nanotoxicity. Graphical Abstract A highly uniform SERS signal of bacteria negating undesired spectral variation via a novel vacuum filtration-based strategy, combined with multivariate PCA-LDA analysis, was utilized to interrogate both dose- and time-dependent antibacterial effect of zinc oxide nanoparticles, and can be extended to a variety of other toxic nanoparticles.

FT-IR spectroscopy supported by PCA–LDA analysis for the study of embryonic stem cell differentiation

As recently pointed out in the literature, Fourier transform infrared (FT-IR) spectroscopy is emerging as a power- ful tool in stem cell research. In this work we characterized in situ by FT-IR microspectroscopy the differentiation of murine embryonic stem cells (ES) to monitor possible changes in the cell macromolecular content during the early stages of differen- tiation. Undifferentiated and differentiating cells at 4, 7, 9 and 14 days were measured. Data were analyzed by the principal component and subsequent linear discriminant analyses (PCA-LDA) that enabled us to segregate ES cell spectra into well sep- arate clusters and to identify the most significant spectral changes. Important changes in the lipid (3050-2800 cm −1 ), protein (1700-1600 cm −1 ) and in the nucleic acid (1050-850 cm −1 ) absorption regions were observed between days 4 to 7 of dif- ferentiation, indicating the appearance - at day 7 - of the new phenotype into cardiomyocyte precursors. Also the presence of DNA/RNA hybrid bands (954 cm −1 and 899 cm −1 ) suggests that the transcriptional switch of the genome started at this stage of differentiation. Particularly noteworthy, we suggest that the 2936 cm −1 shoulder we observed could reflect methyl group vibrations thus allowing the detection of variations in methylation levels of the stem cell during differentiation. These infrared results were found to be in agreement with the biochemical characterization of these differentiating cells, underlying the great potential of FT-IR spectroscopy in stem cell research.

基于NIR-SERS技术结合PCA-LDA统计分析肝癌术后恢复状况

基于NIR-SERS技术结合PCA-LDA统计分析肝癌术后恢复状况

Automated Quantification of Tumor Viability in a Rabbit Liver Tumor Model after Chemoembolization Using Infrared Imaging

The rabbit VX2 tumor is a fast-growing carcinoma model commonly used to study new therapeutic devices, such as catheter-based therapies for patients with inoperable hepatocellular carcinoma. The evaluation of tumor viability after such locoregional therapies is essential to directing hepatocellular carcinoma management. We used infrared microspectroscopy for the automatic characterization and quantification of the VX2 liver tumor viability after drug-eluting beads transarterial chemoembolization (DEB-TACE). The protocol consisted of K-means clustering followed by principal component analysis (PCA) and linear discriminant analysis (LDA). The K-means clustering was used to classify the spectra from the infrared images of control or treated tumors and to build a database of many tissue spectra. On the basis of this reference library, the PCA-LDA analysis was used to build a predictive model to identify and quantify automatically tumor viability on unknown tissue sections. For the DEB group, the LDA model determined that the surface of tumor necrosis represented 91.6% ± 8.9% (control group: 33.1% ± 19.6%; Mann-Whitney P = 0.0004) and the viable tumor 2.6% ± 4% (control group: 62.2% ± 15.2%; Mann-Whitney P = 0.0004). Tissue quantification measurements correlated well with tumor necrosis (r = 0.827, P < 0.0001) and viable tumor (r = 0.840, P < 0.0001). Infrared imaging and PCA-LDA analysis could be helpful for easily assessing tumor viability.

Diagnosis of Esophageal Tissue Based on Surface-Enhanced Raman Spectroscopy

Gold nanoparticle based surface-enhanced Raman spectroscopy (SERS) was first applied to analyze esophageal cancer tissue and normal tissue in this paper. SERS measurements were performed on 62 tissue samples (31 esophageal carcinoma tissues and 31 normal tissues) obtained from 31 patients. Principal components analysis (PCA) combined with linear discriminant analysis (LDA) were employed to analyze and classify the tissue SERS spectra acquired from esophageal cancer and normal tissues. The diagnostic algorithms based on PCA-LDA yielded high diagnostic sensitivity (90.3%) and specificity (90.3%). Receiver operating characteristic (ROC) curve was employed to confirm the effectiveness of PCA-LDA multivariate analysis. Tentative assignments of the tissue SERS spectra suggested some changes in protein structure, a decrease in the relative amounts of phospholipids, an increase in the percentage of tryptophan, histone, tyrosine and phenylalanine contents in tumor tissue as compared to that of normal subject. The results from our exploratory study demonstrated that gold nanoparticle based tissue SERS spectroscopy in conjunction with PCA-LDA analysis can differentiate esophageal cancer from normal esophageal tissue samples with high accuracy. Tissue SERS spectroscopy may be a potentially clinically useful tool for the early diagnosis of esophageal cancer.

FT-IR spectroscopy supported by PCA–LDA analysis for the study of embryonic stem cell differentiation

As recently pointed out in the literature, Fourier transform infrared (FT-IR) spectroscopy is emerging as a powerful tool in stem cell research. In this work we characterizedin situby FT-IR microspectroscopy the differentiation of murine embryonic stem cells (ES) to monitor possible changes in the cell macromolecular content during the early stages of differentiation. Undifferentiated and differentiating cells at 4, 7, 9 and 14 days were measured. Data were analyzed by the principal component and subsequent linear discriminant analyses (PCA–LDA) that enabled us to segregate ES cell spectra into well separate clusters and to identify the most significant spectral changes. Important changes in the lipid (3050–2800 cm–1), protein (1700–1600 cm–1) and in the nucleic acid (1050–850 cm–1) absorption regions were observed between days 4 to 7 of differentiation, indicating the appearance – at day 7 – of the new phenotype into cardiomyocyte precursors. Also the presence of DNA/RNA hybrid bands (954 cm–1and 899 cm–1) suggests that the transcriptional switch of the genome started at this stage of differentiation. Particularly noteworthy, we suggest that the 2936 cm–1shoulder we observed could reflect methyl group vibrations thus allowing the detection of variations in methylation levels of the stem cell during differentiation. These infrared results were found to be in agreement with the biochemical characterization of these differentiating cells, underlying the great potential of FT-IR spectroscopy in stem cell research.

Discrimination between ricin and sulphur mustard toxicity in vitro using Raman spectroscopy

A Raman spectroscopy cell-based biosensor has been proposed for rapid detection of toxic agents, identification of the type of toxin and prediction of the concentration used. This technology allows the monitoring of the biochemical properties of living cells over long periods of time by measuring the Raman spectra of the cells non-invasively, rapidly and without use of labels (Notingher et al. 2004 doi:10.1016/j.bios.2004.04.008). Here we show that this technology can be used to distinguish between changes induced in A549 lung cells by the toxin ricin and the chemical warfare agent sulphur mustard. A multivariate model based on principal component analysis (PCA) and linear discriminant analysis (LDA) was used for the analysis of the Raman spectra of the cells. The leave-one-out cross-validation of the PCA-LDA model showed that the damaged cells can be detected with high sensitivity (98.9%) and high specificity (87.7%). High accuracy in identifying the toxic agent was also found: 88.6% for sulphur mustard and 71.4% for ricin. The prediction errors were observed mostly for the ricin treated cells and the cells exposed to the lower concentration of sulphur mustard, as they induced similar biochemical changes, as indicated by cytotoxicity assays. The concentrations of sulphur mustard used were also identified with high accuracy: 93% for 200 microM and 500 microM, and 100% for 1,000 microM. Thus, biological Raman microspectroscopy and PCA-LDA analysis not only distinguishes between viable and damaged cells, but can also discriminate between toxic challenges based on the cellular biochemical and structural changes induced by these agents and the eventual mode of cell death.