Identification of gallbladder cancer by direct near-infrared measurement of raw bile combined with two-trace two-dimensional correlation analysis.

We demonstrated the utility of direct near-infrared (NIR) bile analysis for the identification of gallbladder (GB) cancer by employing two-trace two-dimensional (2T2D) correlation analysis to recognize dissimilar spectral features among diverse bile samples for potential improvement of discrimination accuracy. To represent more diverse clinical cases for reliable assessment, bile samples obtained from five normal, 44 gallstone, 25 GB polyp, six hepatocellular cancer (HCC), and eight GB cancer subjects were analyzed. Due to the altered metabolic pathways by carcinogenesis, the NIR spectral features of GB cancer samples, including intensity ratios of main peaks, were different from those of other sample groups. The differentiation of GB cancer in the principal component (PC) score domain was mediocre and subsequent discrimination accuracy based on linear discriminant analysis (LDA) was 88.5%. When 2T2D slice spectra were obtained using a reference spectrum constructed by the linear combination of the spectra of five pure representative bile metabolites and employed, the accuracy was improved to 95.6%. The sensitive recognition of dissimilar spectral features in GB cancer by 2T2D correlation analysis was responsible for the enhanced discrimination.

Screening of gall bladder cancer through infrared analysis of bile and examination of varied bile constituent composition by the disease.

To demonstrate the infrared (IR)-based bile analysis as a reliable screening tool for gall bladder (GB) cancer, we analyzed a sample set of 37 diverse bile samples (five normal, 18 GB polyp, six hepatocellular carcinoma (HCC), and eight GB cancer subjects). Bile samples of normal subjects (control) and HCC patients were newly included to examine if IR-based bile analysis could be expanded to identify HCC. Concentrations of three bile acids and eight bile salts in the aqueous phase samples were determined in parallel and lipidomic analysis of nine lipid classes in the organic phase samples was performed using liquid chromatography-mass spectrometry. Concentrations of bile salts were lower and relative abundances of bile salts were dissimilar between GB cancer samples and remained group samples. Also, the levels of lipids such as phosphatidylcholines and phosphatidylethanolamines were again lower and their relative abundances in the organic phase of GB cancer samples were different from those of other samples. IR spectral features of the aqueous, organic, and amphiphilic aggregate phases were individually characteristic, while not descriptive enough for the thorough identification of GB cancer. Nonetheless, since they were mutually complementary to represent different metabolites in bile, the use of three phase-merged spectra was synergetic to yield the superior discrimination of GB cancer.

A weighted twin support vector machine as a potential discriminant analysis tool and evaluation of its performance for near-infrared spectroscopic discrimination of the geographical origins of diverse agricultural products.

A weighted twin support vector machine (wTWSVM) was proposed as a potential discriminant analysis tool and its utility was evaluated for near-infrared (NIR) spectroscopic identification of the geographical origins of 12 different agricultural products including black soybean and garlic. In the wTWSVM, weights were applied on each variable in the sample spectra to highlight detailed NIR spectral features and the optimal weights to minimize the discrimination error were iteratively searched. Then, the weighted spectra were employed to determine the samples' geographical origins using a TWSVM adopting two non-parallel hyperplanes for the discrimination. For the performance evaluation, SVM, TWSVM, and wTWSVM were separately used for the two-group discriminations and their accuracies were comparatively analyzed. When the SVM and TWSVM accuracies were compared, the improvements by using the TWSVM were significant (95% confidence level) for 10 out of the 12 products. Moreover, the accuracy improvements with the wTWSVM against SVM were significant for all the 12 products. In the case of the TWSVM-wTWSVM accuracy comparison, the improvements by the wTWSVM were also significant for 10 products, thereby demonstrating superior discrimination performance of wTWSVM. Based on the overall results, the wTWSVM could be a potential chemometric tool for discriminant analysis and expandable to other areas such as spectroscopy-based biomedical disease diagnosis and forensic analysis.

Enhanced identification of defective pre-coated paints on metal through simple temperature-perturbed infrared measurement in conjunction with two-trace two-dimensional correlation analysis.

A promising infrared (IR) spectroscopic method able to effectively identify defective pre-coated metal (PCM), a pre-painted metal panel, has been demonstrated. A temperature-perturbed IR measurement in conjunction with a two-trace two-dimensional (2T2D) correlation analysis was proposed as a strategy for enhancing defect identification. Our objectives were to induce dissimilar temperature-driven structural variations of base paints and added components, to recognize dissimilarities by 2T2D correlation analysis, and to use subsequent 2T2D correlation features to identify sample defects. For the exploratory examination, three defect cases were studied: 1) grey-silver PCMs with and without phosphate epoxy (2.0%), 2) normal and violet colorant-contaminated (0.2%) black PCMs, and 3) normal, violet (0.5%), and yellow colorant-contaminated (0.1%) white PCMs. The IR spectral features of the PCMs collected at 20 and 50 °C were different due to the temperature-dependent structural variations. Initial measurements at 50 °C allowed discrimination of normal and violet colorant-contaminated black PCMs. When using 2T2D slice spectra obtained from 2T2D correlation analysis using the spectra measured at the two temperatures, violet- as well as yellow colorant-contaminated white PCMs were identified, while these were unclear in the measurements at either 20 or 50 °C. The effective capture of dissimilar temperature-driven spectral variations of base paint and colorants (contaminants) by 2T2D correlation analysis was responsible for the improved defect identification.

Exploring two-trace two-dimensional (2T2D) correlation spectroscopy as an effective approach to improve accuracy of discriminant analysis by highlighting asynchronous features in two separate spectra of a sample.

This study aims to demonstrate two-trace two-dimensional (2T2D) correlation spectroscopy as an effective tool for improving the accuracy of discriminant analysis. Because 2T2D correlation analysis allows sensitive capturing of asynchronous spectral behaviors between two compared spectra of a sample, the subsequent asynchronous correlation features are expected to reveal more sample-to-sample characteristics and discriminants than the original spectral feature. Initially, near-infrared (NIR) spectroscopic authentication of pure olive oil was performed using the spectra collected at 20 °C and 41 °C. When the 2T2D slice spectra of the samples were used for the discriminant analysis, the authentication accuracy reached to 100%, while became degraded in the cases of using the spectra collected either at 20 °C or 41 °C. Furthermore, a simple strategy of utilizing the average spectrum of one sample group as the reference spectrum in the 2T2D correlation analysis was proposed for two-group discrimination and evaluated for the NIR identification of the geographical origins of agricultural products (milk vetch root (MVR) and perilla seed samples). Because the average spectrum of one sample group was used for comparison, dissimilar constituent compositions of the samples in another group were better observed, thereby improving the accuracy of discrimination of the geographical origins of the samples in both cases. The overall results demonstrated that 2T2D correlation analysis is effective for highlighting the minute asynchronous spectral features of a sample and can be expanded for diverse vibrational spectroscopy-based discriminant analyses.

Feasibility of discrimination of gall bladder (GB) stone and GB polyp using voltage-applied SERS measurement of bile juice samples in conjunction with two-trace two-dimensional (2T2D) correlation analysis.

Voltage-applied SERS measurement of bile juice in conjunction with two-trace two-dimensional (2T2D) correlation analysis was demonstrated as a potential tool to enhance discrimination of gall bladder (GB) stone and GB polyp. When SERS spectra of the aqueous phases extracted from raw bile juice samples were measured with applying external voltage from -300 to +300 mV (100 mV intervals), subsequent spectral variations of the adsorbed components (bilirubin-containing compounds) on the SERS substrate were minute, and discrimination of the two GB diseases in a principal component score domain was difficult. Therefore, 2T2D correlation analysis, effectively identifying asynchronous (dissimilar) spectral behaviors in the voltage-induced SERS spectra, was used to improve the discrimination. When two spectra of a sample collected with application of +100 and +300 mV were adopted, the features of subsequent 2T2D slice spectra were characteristic, and discrimination of GB stone and GB polyp substantially improved. External voltage application and recognition of the voltage-induced spectral features by 2T2D correlation analysis were key factors for the improvement. Since the demonstrated method relied on only a few SERS-active compounds, infrared (IR) spectroscopy featuring all the present components in the samples was also evaluated for comparison. However, the IR-based discrimination was inferior because the metabolite compositions in the samples between the GB diseases were not noticeably different.

Evaluation of an autoencoder as a feature extraction tool for near-infrared spectroscopic discriminant analysis.

The utility of an autoencoder (AE) as a feature extraction tool for near-infrared (NIR) spectroscopy-based discrimination analysis has been explored and the discrimination of the geographic origins of 8 different agricultural products has been performed as the case study. The sample spectral features were broad and insufficient for component distinction due to considerable overlap of individual bands, so AE enabling of extracting the sample-descriptive features in the spectra would help to improve discrimination accuracy. For comparison, four different inputs of AE-extracted features, raw NIR spectra, principal component (PC) scores, and features extracted using locally linear embedding were employed for sample discrimination using support vector machine. The use of AE-extracted feature improved the accuracy in the discrimination of samples in all 8 products. The improvement was more substantial when the sample spectral features were indistinct. It demonstrates that AE is expandable for vibrational spectroscopic discriminant analysis of other samples with complex composition.

Incorporation of two-dimensional correlation analysis into discriminant analysis as a potential tool for improving discrimination accuracy: Near-infrared spectroscopic discrimination of adulterated olive oils.

A strategy of combining temperature-induced spectral variation and two-dimensional correlation (2D-COS) analysis as a potential tool to improve accuracy of sample discrimination is suggested. The potential application of this method was evaluated using near-infrared (NIR) spectroscopic discrimination of adulterated olive oils. Rather than utilizing static spectral information at a certain temperature, dynamic spectral features induced by an external perturbation such as temperature change would be more informative for sample discrimination, and 2D-COS analysis was a reliable choice to characterize temperature-induced spectral variation. For evaluation, NIR spectra of 9 pure olive oils and 90 olive oils adulterated with canola, soybean, and corn oils (adulteration rate: 5%) were collected at four different temperatures (20, 27, 34, 41 °C). In constant-temperature measurements, the scores of pure and adulterated samples obtained by principal component analysis (PCA) were considerably overlapped. When 2D-COS analysis was performed using temperature-varied (20-41 °C) spectra and the resulting power spectra from 2D synchronous correlation spectra were used for PCA, identification of the two groups was noticeably enhanced and subsequent k-nearest neighbor (k-NN)-based discrimination accuracy substantially improved to 86.4%. While, the accuracies resulted in the constant-temperature measurements ranged only from 50.9 to 55.8%. The dynamic temperature-induced spectral variation of the samples effectively featured by 2D-COS analysis was ultimately more informative and allowed improvement in accuracy.