A pilot study on colonic mucosal tissues by fluorescence spectroscopy technique: Discrimination by principal component analysis (PCA) and artificial neural network (ANN) analysis

Pulsed laser‐induced autofluorescence spectra of pathologically certified normal and malignant colonic mucosal tissues were recorded at 325 nm excitation. The spectra were analysed using three different methods for discrimination purposes. First, all the spectra were subjected to the principal component analysis (PCA) and the discrimination between normal and malignant cases were achieved using parameters like, spectral residuals, Mahalanobis distance and scores of factors. Second, to understand the changes in tissue composition between the two classes (normal, and malignant), difference spectrum was constructed by subtracting mean spectrum of calibration set samples from simulated mean of all spectra of any one class (normal/malignant) and in third, artificial neural network (ANN) analysis was carried out on the same set of spectral data by training the network with spectral features like, mean, median, spectral residual, energy, standard deviation, number of peaks for different thresholds (100, 250 and 500) after carrying out 1st‐order differentiation of the training set samples and discrimination between normal and malignant conditions were achieved. The specificity and sensitivity were determined in PCA and ANN analyses and they were found to be 100 and 91.3% in PCA, and 100 and 93.47% in ANN, respectively. Copyright © 2008 John Wiley & Sons, Ltd.     

In and ex vivo breast disease study by Raman spectroscopy

In this work, Raman spectra in the 900?C1,800?cm^?1 wavenumber region of in vivo and ex vivo breast tissues of both healthy mice (normal) and mice with induced mammary gland tumors (abnormal) were measured. In the case of the in vivo tissues, the Raman spectra were collected for both transcutaneous (with skin) and skin-removed tissues. To identify the spectral differences between normal and cancer breast tissue, the paired t-test was carried out for each wavenumber using the whole spectral range from both groups. Quadratic discriminate analysis based on principal component analysis (PCA) was also used to determine and evaluate differences in the Raman spectra for the various samples as a basis for diagnostic purposes. The differences in the Raman spectra of the samples were due to biochemical changes at the molecular, cellular and tissue levels. The sensitivity and specificity of the classification scheme based on the differences in the Raman spectra obtained by PCA were evaluated using the receiver operating characteristic (ROC) curve. The in vivo transcutaneous normal and abnormal tissues were correctly classified based on their measured Raman spectra with a discriminant proportion of 73%, while the in vivo skin-removed normal and abnormal tissues were correctly classified again based on their measured Raman spectra with a discriminant proportion of 86%. This result reveals a strong influence due to the skin of the breast, which decreased the specificity by 11%. Finally, the results from ex vivo measurements gave the highest specificity and sensitivity: 96 and 97%, respectively, as well as a largest percentage for correct discrimination: 94%. Now that the important bands have been experimentally determined in this and other works, what remains is for first principles molecular-level simulations to determine whether the changes are simply due to conformational changes, due to aggregation, due to changes in the environment, or complex interactions of all of the above.     

Native fluorescence spectroscopy of blood plasma in the characterization of oral malignancy

Native fluorescence characteristics of blood plasma were studied in the visible spectral region, at two different excitation wavelengths, 405 and 420 nm, to discriminate patients with different stages of oral malignancy from healthy subjects. The fluorescence spectra of blood plasma of oral malignant subjects exhibit characteristic spectral differences with respect to normal subjects. Different ratios were calculated using the fluorescence intensity values at those emission wavelengths that give characteristic spectral features of each group of experimental subjects studied. These fluorescence intensity ratios were used as input variables for a multiple linear discriminant analysis across different groups. Leave-one out cross-validation was used to check the reliability of each discriminant analysis performed. The discriminant analysis performed across normal and oral cancerous subjects classified 94.7% of the original grouped cases and 93.7% of the cross-validated grouped cases. A classification algorithm was developed on the basis of the score of the discriminant functions (discriminant score) resulted in the analyses. The diagnostic potentiality of the present technique was also estimated in the discrimination of malignant subjects from normal and nonmalignant diseased subjects such as liver diseases. In the discriminant analysis performed across the three groups, normal, oral malignancy (including early and advanced stages) and liver diseases, 99% of the original grouped cases and 95.9% of the cross-validated grouped cases were correctly classified. Similar analysis performed across normal, early stage of oral malignancy, advanced oral malignancy and liver diseases correctly classified 94.9% of the original grouped cases and 91.8% of the cross-validated grouped cases.     

Classification of normal and malignant human gastric mucosa tissue with confocal Raman microspectroscopy and wavelet analysis

Thirty-two samples from the human gastric mucosa tissue, including 13 normal and 19 malignant tissue samples were measured by confocal Raman microspectroscopy. The low signal-to-background ratio spectra from human gastric mucosa tissues were obtained by this technique without any sample preparation. Raman spectral interferences include a broad featureless sloping background due to fluorescence and noise. They mask most Raman spectral feature and lead to problems with precision and quantitation of the original spectral information. A preprocessed algorithm based on wavelet analysis was used to reduce noise and eliminate background/baseline of Raman spectra. Comparing preprocessed spectra of malignant gastric mucosa tissues with those of counterpart normal ones, there were obvious spectral changes, including intensity increase at approximately 1156cm(-1) and intensity decrease at approximately 1587cm(-1). The quantitative criterion based upon the intensity ratio of the approximately 1156 and approximately 1587cm(-1) was extracted for classification of the normal and malignant gastric mucosa tissue samples. This could result in a new diagnostic method, which would assist the early diagnosis of gastric cancer.     

Three-dimensional total synchronous luminescence spectroscopy criteria for discrimination between normal and malignant breast tissues

Specimens of malignant and normal female human breast tissues were analyzed after surgery by means of synchronous luminescence spectroscopy. Measurements were performed in the ranges of excitation wavelengths from 330 to 650 nm and synchronous wavelengths from 30 to 120 nm to obtain ordinary and first derivative three-dimensional total synchronous luminescence spectra (3d-TSLS) of each specimen. Arithmetic mean of these spectra has been calculated for normal and malignant specimens and analyzed to establish criteria for tissue differentiation. Spectral domain volumes (volumes below luminescence intensity surface) and mean spectral slopes have been calculated and also analyzed as tissue discrimination criteria. The obtained results are discussed in view of the possible relevance of synchronous luminescence spectroscopy in discrimination between normal and malignant breast tissue.     

Matching fluorescence spectra of oil spills with spectra from suspect sources

Fluorescence spectroscopy is ideally suited to the analysis of oil spills as it allows chemical information of polycyclic aromatic hydrocarbons to be acquired quickly, sensitively and selectively. Unlike infrared spectra which have detailed peak information, many fluorescence spectra have only a few broad peaks. Nine different samples of crude and diesel oils were used for testing point-to-point matching across the spectral range. Five of them were discriminated by point-to-point matching algorithms and the other four very similar samples were not. Principal components analysis (PCA) did successfully discriminate among all similar samples. PCA could also distinguish the extent of weathering of different samples, an important factor in matching environmental spills.     

新鲜乳腺癌组织的近红外拉曼光谱分析

采用便携式拉曼光谱仪对正常、良性和恶性的乳腺癌组织进行检测,通过对其拉曼光谱的指认,归纳了其主要区别和特征. 在3类乳腺组织中有明显的脂类的特征峰（1230,1268,1301,1440和1743 cm^-1）,而在良性和恶性的组织中,则出现了较为明显的蛋白（1246,1271,1315和1364 cm^-1）和核酸（1340 cm^-1）的特征峰. 良性和恶性组织的区别在于恶性组织特有的特征峰（1340 cm^-1）,而良性组织所特有的特征峰则应归属为蛋白. 在数据分析过程中,选择能够反映样本化学本质的特征峰,利用高斯过程的机器学习对特征峰值建立模型. 特异性（0.94）、灵敏度（0.95）和Matthews相关系数（0.86）表明在模型中3种组织有比较良好的辨别度,对于应用拉曼光谱方法辨别正常和患病乳腺组织具有参考价值.     

Optical diagnosis of peritoneal metastases by infrared microscopic imaging

Fourier transform infrared (FTIR) spectroscopy is nowadays widely accepted as a technique with high potential for diagnosis of cancerous tissues. This study presents an example of the investigation of peritoneal metastases by FTIR microimaging. Peritoneal malignancies are generally secondary localizations of primary visceral cancers such as ovarian, stomach or colon cancers. By analysing simultaneously both formalin-fixed paraffin-embedded and frozen specimens, we examined malignant and non-malignant (i.e. fibrotic and cicatricial) peritoneal lesions. Paraffin-embedded tissues were analysed without any previous dewaxing. Multivariate statistical approaches, based on the classification of infrared data by hierarchical cluster analysis, allowed the discrimination of these various samples. Microimaging also permits the revelation of the heterogeneity of the tissue: it was possible to localize precisely the cancerous areas, and to distinguish, on the basis of their spectral signatures, the peritumoral neighbouring connective tissue close to the carcinomatous areas from the connective tissue distant from the cancerous areas. These spectral differences could be useful as complementary information to study molecular changes associated with the malignancy.     

FTIR investigation of spray-dried milk protein concentrate powders

Fourier transform infrared (FTIR) spectroscopy was used to examine the conformation of proteins in spray-dried milk protein concentrate (MPC) powders and to determine if the spectral changes could be related to nitrogen solubility of these powders. MPC samples (83–92% protein, dry basis) were prepared using a range of processing conditions and stored for 4 weeks at 21 °C. FTIR spectra were collected in the mid infrared (MIR) region between 4000 and 600 cm⁻¹. FTIR data was pre-processed to remove physical effects causing discrimination between samples using firstly second derivatives and normalization and secondly the extended multiplicative scatter correction (EMSC) technique. The FTIR spectral changes were subsequently assessed using second derivative spectroscopy and principal components analysis (PCA) in the amide I and II regions (1700–1400 cm⁻¹) and the fingerprint region (1800–700 cm⁻¹). PCA analysis showed that the different powder preparations could be separated on scores plots but the separation was not related to nitrogen solubility per se. However, changes in nitrogen solubility of individual MPC powders during storage could be correlated to changes in FTIR spectra. PCA analysis of FTIR spectra could generally discriminate between MPC powders that had lost significant nitrogen solubility (9–20%) and those in which nitrogen solubility was preserved on storage. There were changes in intensity and/or position of bands at 1630 cm⁻¹ when the solubility of a stored sample decreased substantially. The results of this work also show that EMSC data pre-processing for these samples gives comparable results when compared with more complicated data pre-processing for the removal of physical effects.     

人体乳腺癌组织红外光谱特征的研究

利用红外光谱法对正常乳腺组织、良性肿瘤和乳腺癌组织进行了对比研究.与正常组织的光谱相比,癌组织中蛋白质的某些氨基酸残基的ν_C-O(H)谱带位置明显向高波数位移,表明组织中该基团中的氢键大部分被破坏.蛋白质分子的氢键化的ν_NH谱带位置向低波数位移,且谱带形状也有所改变,说明NH…O=C氢键化程度增强.核酸的磷酸二酯基团的ν_s,PO₂^-谱带吸收强度明显增强,反映癌细胞内DNA相对含量增加;位于970cm^-1附近的ν_s,PO₃^{-谱带强度增加,提示细胞中磷酸化蛋白含量增加.而胶原蛋白的特征谱带强度减弱,说明其相对含量减少.研究证明,红外光谱可在分子水平上揭示乳腺肿瘤的特征.}     

基于稳健统计的新鲜乳腺组织拉曼光谱分析

采用便携式拉曼光谱仪对新鲜乳腺正常组织、良性组织和恶性组织进行检测,通过稳健统计方法对拉曼光谱数据进行分析处理,建立乳腺组织拉曼光谱标准图谱,根据标准图谱特征峰归纳3类组织的主要区别和特征.在3类乳腺组织中,正常组织有明显的脂类特征峰(1078,1297,1437,1653,1746 cm-1),而在良性和恶性组织中则出现了较明显的蛋白特征峰(1259,1530,1650 cm-1),正常、良性和恶性组织的主要区别集中在1340和1534 cm-1处,应归属为蛋白和类胡萝卜素,这一结果并不能由经典统计方法得出.基于稳健统计建立的新鲜乳腺组织拉曼光谱标准图谱为构建数学模型来鉴别乳腺病灶的性质奠定了基础.     

Fingerprint and multicomponent quantitative analysis for the quality evaluation of Sibiraea angustata leaves by HPLC-DAD and HPLC-ESI-MS/MS combined with chemometrics

Sibiraea angustata leaves, known as a traditional Tibetan medicine, have been specially used in the treatment of indigestion and obesity. In the study, a simple and sensitive high-performance liquid chromatography (HPLC) method with a diode array detector (DAD) was established to solve the problem of lacking quality standard of S. angustata leaves, including the fingerprint analysis and quantification of six characteristic components. The analytical method was validated for linearity, repeatability, stability, recovery, and specificity. Seventeen raw samples and 1 processed sample of S. angustata leaves were collected from different locations of China to establish the fingerprint. The chemometric methods, including similarity analysis (SA), principal component analysis (PCA), and hierarchical clustering analysis (HCA), were applied to distinguish the 18 batches of S. angustata samples. The results successfully sorted these samples into five clusters and kept in line with each other. According to the result of the fingerprint analysis, 21 peaks were extracted to be the common peaks and most of them were identified by mass spectrometry (MS) with electron-spray ionization (ESI) in the negative mode. Meanwhile, the loading plot of PCA further indicated that the peaks of neochlorogenic acid, chlorogenic acid, ferulic acid, rutin, hyperin, and isoquercitrin played a greater role in the discrimination among the 21 peaks. So the six components mentioned above were investigated as index constituents to evaluate the quality of S. angustata leaves from different locations. The study demonstrated that the developed new method was a beneficial approach for authentication and quality evaluation of S. angustata leaves.     

HPLC Fingerprint with Multi-components Analysis for Quality Consistency Evaluation of Traditional Chinese Medicine Si-Mo-Tang Oral Liquid Preparation

Si-Mo-Tang(SMT) oral liquid preparation, a traditional Chinese medicine, was prepared from four crude herbal drugs, Fructus Aurantii Submaturus, Radix Aucklandiae, Semen Arecae and Radix Linderae Aggregatae. A combinative method using HPLC fingerprint and quantitative analysis was developed and validated for quality consistency evaluation of SMT. Individual HPLC chromatograms were evaluated against the mean chromatogram generated via a similarity evaluation computer program. Data from chromatographic fingerprints were also processed with principal component analysis(PCA) and hierarchical cluster analysis(HCA). Additionally, six components (naringin, isonaringin, hesperidin, neohesperidin, norisoboldine and potassium sorbate) in SMT were simultaneously determined to interpret the quality consistency. For fingerprint analysis, 20 peaks were selected as the characteristic peaks to evaluate the similarities of 26 SMT collected from different manufacturers. Among the 20 characteristic peaks, 10 peaks were assigned to be naringin, hesperidin, neohesperidin, isonaringin, neoeriocitrin, tangeretin, nobiletin, norisoboldine, 5-(ethoxymethyl)furan-2-carbaldehyde and potassium sorbate, respectively. The results of similarity analysis, PCA and HCA, indicate that the samples from different manufacturers were consistent with each other in composition. The results from the quantitative data show that the contents of six compounds were significantly different in SMT oral liquid preparations from different manufacturers. The combinative method of chromatographic fingerprint with quantitative analysis developed here offered an efficient way for the quality consistency evaluation of the traditional Chinese medicine SMT.     

Autofluorescence spectroscopy of normal and malignant human breast cell lines

The fluorescence of tryptophan, reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD) were characterized in normal human breast cells as well as in malignant human breast cells of similar and dissimilar genetic origins. Fluorescence measurements of each cell line were made over a wide range of cell concentrations, and the fluorescence per cell was determined from the slope in the linear range of the fluorescence intensity vs cell concentration plot. All of the malignant cells showed a statistically significant decrease in the tryptophan fluorescence per cell relative to that of the normal cells. No statistically significant differences were observed in the NAD(P)H or FAD fluorescence per cell between the normal and any of the malignant cell types. NAD(P)H fluorescence was also imaged from monolayers of the normal and malignant cells (of similar genetic origin) using two-photon fluorescence microscopy. A statistically significant decrease in the NAD(P)H fluorescence with malignancy was observed, suggesting that fluorescence imaging of single cells or the cell monolayer preparation may provide more contrast than volume-averaged fluorescence measurements of cells in suspension. In conclusion, the differences in normal and malignant human breast tissue fluorescence spectra may be attributed in part to differences in the intrinsic cellular fluorescence of normal and malignant breast epithelial cells.     

Metabolomic investigation of gastric cancer tissue using gas chromatography/mass spectrometry

Gastric cancer screening or diagnosis is mainly based on endoscopy and biopsy. The aim of this study was to identify the difference of metabolomic profile between normal and malignant gastric tissue, and to further explore tumor biomarkers. Chemical derivatization together with gas chromatography/mass spectrometry (GC/MS) was utilized to obtain the metabolomic information of the malignant and non-malignant tissues of gastric mucosae in 18 gastric cancer patients. Acquired metabolomic data was analyzed using the Wilcoxon rank sum test to find the tissue metabolic biomarkers for gastric cancer. A diagnostic model for gastric cancer was constructed using principal component analysis (PCA), and was assessed with receiver-operating characteristic (ROC) curves. Results showed that 18 metabolites were detected differently between the malignant tissues and the adjacent non-malignant tissues of gastric mucosa. Five metabolites were also detected differently between the non-invasive tumors and the invasive tumors. The diagnostic model could discriminate tumors from normal mucosae with an area under the curve (AUC) value of 0.9629, and another diagnostic model constructed for clinical staging was assessed with an AUC value of 0.969. We conclude that the metabolomic profile of malignant gastric tissue was different from normal, and that the selected tissue metabolites could probably be applied for clinical diagnosis or staging for gastric cancer.     

Non-destructive analysis of the two subspecies of African elephants, mammoth, hippopotamus, and sperm whale ivories by visible and short-wave near infrared spectroscopy and chemometrics

Visible (VIS) and short-wave near infrared (SW-NIR) spectroscopy was used for non-destructive analysis of ivories. VIS-SW-NIR (500-1000 nm) spectra were measured in situ for five kinds of ivories, that is two subspecies of African elephants, mammoth, hippopotamus, and sperm whale. Chemometrics analyses were carried out for the spectral data from 500 to 1000 nm region. The five kinds of ivories were clearly discriminated from each other on the scores plot of two principal components (PCs) obtained by principal component analysis (PCA). It was noteworthy that the ivories of the two subspecies of African elephants were discriminated by the scores of PC 1. The loadings plot for PC 1 showed that the discrimination relies on the intensity changes in bands due to collagenous proteins and water interacting with proteins. It was found that the scores plot of PC 2 is useful to distinguish between the ivories of the two subspecies of African elephants and the other ivories. We also developed a calibration model that predicted the specific gravity of five kinds of ivories from their VIS-SW-NIR spectral data using partial least squares (PLS)-1 regression. The correlation coefficient and root mean square error of cross validation (RMSECV) of this model were 0.960 and 0.037, respectively.     

Feasibility study for diagnosis of stomach adenoma and cancer using IR spectroscopy

The feasibility of infrared (IR) spectroscopy as a biomedical analysis tool for the diagnosis of stomach malignancy including adenoma and cancer has been studied using unstained biopsy samples. Biopsy samples were acquired from 11 subjects. IR spectra were collected for these samples using a microscope (aperture: 25 μm × 25 μm). The samples were stained again and the spots where the IR spectra were collected were re-examined by a pathologist to ensure the spectra represented the correct diagnostic information. The spectral features were compared among the averaged spectra of normal and malignant tissues. The spectral contrasts could be correlated to the differences in the molecular structure of the membrane lipids of the two tissue types as well as the variation in their glycogen contents. However, the spectral features between the adenoma and cancer tissues could not be distinguished. Initially we used principal component analysis (PCA) to examine the degree of separation between tissue types. Soft independent modeling of class analogies (SIMCA) was employed to evaluate the prediction accuracy of IR spectroscopy for the diagnosis of stomach adenoma and cancer. The prediction accuracies for normal, adenoma and cancer tissues were 77%, 30% and 87%, respectively, using SIMCA. IR microscopy successfully differentiated normal and malignant tissues. However, a more sophisticated algorithm will be required in order to effectively extract relevant information for the differentiation between stomach adenoma and cancer.     

High-wavenumber FT-Raman spectroscopy for in vivo and ex vivo measurements of breast cancer

The identification of normal and cancer breast tissue of rats was investigated using high-frequency (HF) FT-Raman spectroscopy with a near-infrared excitation source on in vivo and ex vivo measurements. Significant differences in the Raman intensities of prominent Raman bands of lipids and proteins structures (2,800?C3,100?cm^?1) as well as in the broad band of water (3,100?C3,550?cm^?1) were observed in mean normal and cancer tissue spectra. The multivariate statistical analysis methods of principal components analysis (PCA) and linear discriminant analysis (LDA) were performed on all high-frequency Raman spectra of normal and cancer tissues. LDA results with the leave-one-out cross-validation option yielded a discrimination accuracy of 77.2, 83.3, and 100% for in vivo transcutaneous, in vivo skin-removed, and ex vivo biopsy HF Raman spectra. Despite the lower discrimination value for the in vivo transcutaneous measurements, which could be explained by the breathing movement and skin influences, our results showed good accuracy in discriminating between normal and cancer breast tissue samples. To support this, the calculated integration areas from the receiver-operating characteristic (ROC) curve yielded 0.86, 0.94, and 1.0 for in vivo transcutaneous, in vivo skin-removed, and ex vivo biopsy measurements, respectively. The feasibility of using HF Raman spectroscopy as a clinical diagnostic tool for breast cancer detection and monitoring is due to no interfering contribution from the optical fiber in the HF Raman region, the shorter acquisition time due to a more intense signal in the HF Raman region, and the ability to distinguish between normal and cancerous tissues.     

FTIR and Raman microspectroscopy of normal,benign, and malignant formalin-fixed ovarian tissues

Ovarian cancer is the sixth most common cancer among women worldwide, and mortality rates from this cancer are higher than for other gynecological cancers. This is attributed to a lack of reliable screening methods and the inadequacy of treatment modalities for the advanced stages of the disease. FTIR and Raman spectroscopic studies of formalin-fixed normal, benign, and malignant ovarian tissues have been undertaken in order to investigate and attempt to understand the underlying biochemical changes associated with the disease, and to explore the feasibility of discriminating between these different tissue types. Raman spectra of normal tissues indicate the dominance of proteins and lower contents of DNA and lipids compared to malignant tissues. Among the pathological tissues studied, spectra from benign tissues seem to contain more proteins and less DNA and lipids compared to malignant tissue spectra. FTIR studies corroborate these findings. FTIR and Raman spectra of both normal and benign tissues showed more similarities than those of malignant tissues. Cluster analysis of first-derivative Raman spectra in the 700–1700 cm⁻¹ range gave two clear groups, one corresponding to malignant and the other to normal+benign tissues. At a lower heterogeneity level, the normal+benign cluster gave three nonoverlapping subclusters, one corresponding to normal and two for benign tissues. Cluster analysis of second-derivative FTIR spectra in the combined spectral regions of 1540–1680 and 1720–1780 cm⁻¹ resulted into two clear clusters corresponding to malignant and normal+benign tissues. The cluster corresponding to normal+benign tissues produced nonoverlapping subclusters for normal and benign tissues at a lower heterogeneity level. The findings of this study demonstrate the feasibility of Raman and FTIR microspectroscopic discrimination of formalin-fixed normal, benign, and malignant ovarian tissues.