Unsupervised Pattern Recognition Methods Research Articles

Plasmonic Photocatalysis Driven by Indirect Gold Excitation Via Upconversion Nanoparticle Emission Monitored In Situ by Surface-Enhanced Raman Spectroscopy

Lanthanide-doped upconversion nanoparticles (UCNPs) absorb low-energy photons, in the near-infrared region (NIR), and emit high-energy photons, in the visible or ultraviolet region (UV/Vis). The higher energy radiation emissions can be used to promote plasmonic photocatalytic reactions by coupling UCNPs with plasmonic nanoparticles. Thus, in this work, highly crystalline β-NaYF4:Yb3+,Er3+@NaYF4:Nd3+@NaYF4 UCNPs capped with an amino-modified nanosized silica shell were coupled with Au nanospheres (13 ± 2 nm). The plasmonic-driven dimerization of 4-aminothiophenol (4-ATP) to 4,4′-dimercaptoazobenzene (DMAB) was carried out solely by using the UCNP visible emissions to excite the Au nanoparticle localized surface plasmon resonance band. The reaction was monitored in situ through surface-enhanced Raman spectroscopy (SERS) and optimal conditions were achieved in order to eliminate secondary influences during spectra acquisition. The SERS substrates containing either UCNPs@SiO2-NH2/Au or SiO2-NH2/Au nanoparticles were prepared by the drop-cast method in silicon substrates. In the studied reaction conditions, plasmonic photocatalytic activity was observed only in the substrates containing UCNPs and Au nanoparticles. The plasmonic-driven reaction was achieved by UCNPs excitation with a 980 nm laser (25.7 mW), aligned in a Raman spectrometer, only after 5 s of exposition. Interactive document mapping (IDMAP), which is an unsupervised pattern recognition method, was applied to analyze the numerous SERS spectra acquired during the plasmonic photocatalytic measurements. The high values found for the silhouette coefficient (0.88 for the Si_SiO2-NH2/Au and 0.76 for the Si_NYF@SiO2-NH/Au substrate) indicate that strong discrimination among the Raman spectra was achieved. Thus, through IDMAP, it was observed that the SERS spectra obtained for the substrates containing either UCNPs@SiO2-NH2/Au or SiO2-NH2/Au corresponded to spectral features, respectively, from the DMAB product and the 4-ATP reagent, evidencing that, in the optimized reaction conditions, the dimerization occurred only through the interaction of the UCNPs with the Au nanospheres.

Influence of the use of sulfur dioxide, the distillation method, the oak wood type and the aging time on the production of brandies

Brandies are spirits produced from wine spirit and wine distillates. The original wines selected to be distilled to produce the wine spirits as well as the distillation method used determine, to a large extent, the organoleptic characteristics of the final products. The young wine spirits evolve during their aging in oak casks, this being another key stage that affects the chemical and sensorial characteristics of the final brandy. In this work, seven different brandies have been studied. They were obtained from wine produced with and without the addition of sulfur dioxide, during their fermentation, using different distillation methods (single, double or serial distillation using pot stills and continuous column distillation) and aged for 14 or 28 months in three different types of oak wood (Quercus alba, Quercus robur and Quercus petraea) previously toasted to two different grades (medium or light).The use of unsupervised pattern recognition methods (HCA and FA) determined that the addition of sulfur dioxide during the fermentation of the base wine has a major influence on the aromatic and phenolic profile of the aged distillates. On the other hand, by means of supervised pattern recognition methods such as LDA and ANNs, the most significant variables that would allow to discriminate between the classes of brandies identified in the study were evaluated. Thus, the results obtained should cast some light on the most significant variables to be taken into account regarding Brandy production processes if a better control over these production processes is to be achieved, so that more exclusive and better quality products are obtained.

Performance Analysis and Architecture of a Clustering Hybrid Algorithm Called FA+GA-DBSCAN Using Artificial Datasets.

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a widely used algorithm for exploratory clustering applications. Despite the DBSCAN algorithm being considered an unsupervised pattern recognition method, it has two parameters that must be tuned prior to the clustering process in order to reduce uncertainties, the minimum number of points in a clustering segmentation MinPts, and the radii around selected points from a specific dataset Eps. This article presents the performance of a clustering hybrid algorithm for automatically grouping datasets into a two-dimensional space using the well-known algorithm DBSCAN. Here, the function nearest neighbor and a genetic algorithm were used for the automation of parameters MinPts and Eps. Furthermore, the Factor Analysis (FA) method was defined for pre-processing through a dimensionality reduction of high-dimensional datasets with dimensions greater than two. Finally, the performance of the clustering algorithm called FA+GA-DBSCAN was evaluated using artificial datasets. In addition, the precision and Entropy of the clustering hybrid algorithm were measured, which showed there was less probability of error in clustering the most condensed datasets.

An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection

Detecting PV system faults in a timely fashion is important to ensure the safe operation of equipment and reduce their impact on the economy of the PV systems. It is necessary to further improve the time-sensitive performance evaluation of the system. However, the hourly weather scenario segmentations are seldom considered during the hour-level online monitoring process. Therefore, a hybrid method based on unsupervised hourly weather status pattern recognition and blending fitting model is proposed for hourly fault detection to improve the performance evaluation of PV systems. The proposed solution includes three parts, firstly, in the data preprocessing stage, the measured power with the errors and noise under normal operation situation caused by the environment changes is corrected by monthly linear fitting. Secondly, an unsupervised hourly weather status pattern recognition method is constructed using the measured radiation data, including unsupervised clustering and the Multiclass-GBDT-LR classification process. Finally, after eliminating the anomalies and errors, the blending fitting model of the hourly sub-weather status is established. Through the analysis of power plants in Australia and China, the proposed solutions are validated and evaluated to be superior to existing data-driven solutions in terms of fitting accuracy, detection validity, and response time. Numerical results of case studies indicate that the developed methodology under sub-weather has improved the detection accuracy up to 97.71% and 99.29% compared to benchmark models.

Nondestructive Characterization of Citrus Fruit by near-Infrared Diffuse Reflectance Spectroscopy (NIRDRS) with Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (FLDA)

The nondestructive characterization of citrus varieties (Egyptian sweet orange, Lane Late navel orange, Australian orange, and Blood orange) was developed based on near-infrared diffuse reflectance spectroscopy (NIRDRS) together with principal component analysis (PCA) and Fisher linear discriminant analysis (FLDA). An experiment for the penetration of NIRDRS into the peel was designed and the effects of different spectral acquisition points were investigated. Pretreatments were used to eliminate the spectral interferences. As an unsupervised pattern recognition method, PCA was used to establish the characterization models. Furthermore, supervised pattern recognition based on PCA and FLDA was employed to enhance the accuracy. The results of the penetration experiments show that near-infrared light enters the citrus peel and is able to characterize the internal composition. Even with the optimized spectral pretreatment, accurate characterization of citrus varieties was not achieved by PCA. However, the accurate characterization of citrus varieties was provided by PCA-FLDA. The accuracies of four spectral acquisition points are 95%, while the characterization accuracies of six spectral acquisition points are 100% combined with optimized spectral pretreatment. Therefore, NIRDRS with PCA-FLDA is suitable for the rapid and nondestructive characterization of citrus varieties.

Metabolomics-based profiling of 4 avocado varieties using HPLC\u2013MS/MS and GC/MS and evaluation of their antidiabetic activity

Seven avocado “Persea americana” seeds belonging to 4 varieties, collected from different localities across the world, were profiled using HPLC–MS/MS and GC/MS to explore the metabolic makeup variabilities and antidiabetic potential. For the first time, 51 metabolites were tentatively-identified via HPLC–MS/MS, belonging to different classes including flavonoids, biflavonoids, naphthodianthrones, dihydrochalcones, phloroglucinols and phenolic acids while 68 un-saponified and 26 saponified compounds were identified by GC/MS analysis. The primary metabolic variabilities existing among the different varieties were revealed via GC/MS-based metabolomics assisted by unsupervised pattern recognition methods. Fatty acid accumulations were proved as competent, and varietal-discriminatory metabolites. The antidiabetic potential of the different samples was explored using in-vitro amylase and glucosidase inhibition assays, which pointed out to Gwen (KG) as the most potent antidiabetic sample. This could be attributed to its enriched content of poly-unsaturated fatty acids and polyphenolics. Molecular docking was then performed to predict the most promising phytoligands in KG variety to be posed as antidiabetic drug leads. The highest in-silico α-amylase inhibition was observed with chrysoeriol-4′-O-pentoside-7-O-rutinoside, apigenin-7-glucuronide and neoeriocitrin which might serve as potential drug leads for the discovery of new antidiabetic remedies.

On crack tip localisation in quasi-statically loaded, adhesively bonded double cantilever beam specimens by acoustic emission

The feasibility of acoustic emission structural health monitoring to detect, localise and monitor crack propagation during quasi-static mode I loading of adhesively bonded joints was studied. Unsupervised artificial intelligence pattern recognition methods (Self-Organised maps and K-means) were used to classify acoustic emission raw data as either background noise or relevant information. After that, three different time-of-arrival picking algorithms were considered and implemented to determine the acoustic emission source’s location, and their accuracy was discussed. Localised acoustic emission events were divided into well-defined groups with different energy levels and compared to Digital Image Correlation and visual evaluation results. It was possible to conclude that the highest energetic group allows the assessment of the onset of plasticisation ahead of the crack-tip within the studied adhesive, bringing novel standpoints to the use of acoustic emission as a structural health monitoring method for adhesively bonded joints.

Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) spectra of salivary glands carcinoma, tumor and healthy tissues and their homogenates analyzed by chemometry: Towards development of the novel tool for clinical diagnosis

In this study, two approaches to salivary glands studies are presented: Raman imaging (RI) of tissue cross-section and surface-enhanced Raman spectroscopy (SERS) of tissue homogenates prepared according to elaborated protocol. Collected and analyzed data demonstrate the significant potential of SERS combined with multivariate analysis for distinguishing carcinoma or tumor from the normal salivary gland tissues as a rapid, label-free tool in cancer detection in oncological diagnostics. Raman imaging allows a detailed analysis of the cell wall's chemical composition; thus, the compound's distribution can be semi-quantitatively analyzed, while SERS of tissue homogenates allow for detailed analysis of all moieties forming these tissues. In this sense, SERS is more sensitive and reliable to study any changes in the area of infected tissues. Principal component analysis (PCA), as an unsupervised pattern recognition method, was used to identify the differences in the SERS salivary glands homogenates. The partial least squares-discriminant analysis (PLS-DA), the supervised pattern classification technique, was also used to strengthen further the computed model based on the latent variables in the SERS spectra. Moreover, the chemometric quantification of obtained data was analyzed using principal component regression (PCR) multivariate calibration. The presented data prove that the PCA algorithm allows for 91% in seven following components and the determination between healthy and tumor salivary gland homogenates. The PCR and PLS-DA methods predict 90% and 95% of the variance between the studied groups (in 6 components and 4 factors, respectively). Moreover, according to calculated RMSEC (RMSEP), R2C (R2P) values and correlation accuracy (based on the ROC curve), the PLS-DA model fits better for the studied data. Thus, SERS methods combined with PLS-DA analysis can be used to differentiate healthy, neoplastic, and mixed tissues as a competitive tool in relation to the commonly used method of histopathological staining of tumor tissue.

UV spectroscopy and HPLC combined with chemometrics for rapid discrimination and quantification of Curcumae Rhizoma from three botanical origins

Curcumae Rhizoma (Ezhu in Chinese) is a multi-origin herbal medicine with excellent clinical efficacy. For fast discrimination and quantification analysis of Ezhu from three botanical origins (Curcuma kwangsiensis, Curcuma phaeocaulis, and Curcuma wenyujin), ultra-violet (UV) spectroscopy and high performance liquid chromatography (HPLC) combined with chemometric tools were employed in this study. Firstly, the analysis method for the simultaneous determination of eleven compounds in Ezhu was developed by HPLC, and the UV spectra of thirty-eight batches of Ezhu were acquired. Then, principal component analysis (PCA), an unsupervised pattern recognition method, was applied on the HPLC and UV spectral data. PCA did not show a clear separation between C. phaeocaulis and C. wenyujin samples with HPLC data. By contrast, the supervised techniques, decision tree (DT) and linear discriminant analysis (LDA), achieved the complete discrimination for the three species of Ezhu with 100 % correct classification rate (CCR), showing excellent performance. Based on UV spectral data, PCA presented good performance for discriminating the three species of Ezhu. LDA, support vector machine (SVM) and k-nearest neighbors (KNN) models provided 96.3 % CCR for the calibration set and 100 % CCR for the validation set. Moreover, the partial least squares (PLS) and back propagation-artificial neural network (BP-ANN) quantitative models established on UV spectral data were satisfactory in predicting the contents of zederone, curdione and 3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane. The residual predictive deviation (RPD) for zederone, curdione and 3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane of PLS models were 3.169, 1.502 and 1.735, and that of BP-ANN models were 3.467, 2.481 and 2.370, respectively. The present work proposed a rapid and reliable method for the discrimination of Ezhu from three botanical origins and the prediction of zederone, curdione and 3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane contents in Ezhu, which will help a lot in the quality control of Ezhu and other multi-origin herbal medicines.

Colorimetric aggregation assay based on array of gold and silver nanoparticles for simultaneous analysis of aflatoxins, ochratoxin and zearalenone by using chemometric analysis and paper based analytical devices.

A paper based sensor array ispresented to discriminate and determine five mycotoxins classified into three categories, namely aflatoxins, ochratoxins and zearalenone. The gold and silver nanoparticles, synthesized by three different reducing or capping agents, were employed as sensing elements of thefabricated device. These nanoparticles were poured onto hydrophilic circular zones embedded on the hydrophobic substrate. The response of the assay is dependent on the aggregation of nanoparticles for interaction with mycotoxins. Due to aggregation, the gold and silver nanoparticles changed to purple and brown, respectively. Color changes provide unique colorimetric signatures conducive to recognizing the type of mycotoxin, identifying its chemical structure, and finding the fungi that produce it. The discrimination ability of the assay was investigated by both supervised (linear discriminate analysis) and unsupervised (principle component analysis and hierarchical cluster analysis) pattern recognition methods. The assay was applied to the point of need determination of aflatoxin B1, aflatoxin G1, aflatoxin M1, ochratoxin A and zearalenone with a detection limit of 2.7, 7.3, 2.1, 3.3 and 7.0ng.mL-1, respectively. The fabricated device has highpotential of simultaneously determining the mycotoxins in pistachio, wheat, coffee and milk with the help of partial least square method. The root mean square errors for prediction of PLS model were 5.7, 5.2, 1.5, 7.2 and 2.9 for aflatoxin B1, aflatoxin G1, aflatoxin M1, ochratoxin A and zearalenone, respectively. Graphical abstractSchematic representation of paper based colorimetric sensor array based on gold and silver nanoparticles for both qualitative and quantitative analysis of aflatoxins, ochratoxin and zearalenone.

Pattern recognition-based Raman spectroscopy for non-destructive detection of pomegranates during maturity.

In this study, the feasibility of utilizing Fourier transform Raman spectroscopy, combined with supervised and unsupervised pattern recognition methods was considered, to distinguish the maturity stage of pomegranate "Ashraf variety" during four distinct maturity stages between 88 and 143days after full bloom. Principal component analysis (PCA) as an unsupervised pattern recognition method was performed to verify the possibility of clustering of the pomegranate samples into four groups. Two supervised pattern recognition techniques namely, partial least squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA) were also used as powerful supervised pattern recognition methods to classify the samples. The results showed that in all groups of samples, the Raman spectra of the samples were correctly clustered using PCA. The accuracy of the SIMCA classification for differentiation of four pomegranate groups was 82%. Also, the overall discriminant power of PLS-DA classes was about 96%, and 95% for calibration and validation sample sets, respectively. Due to the misclassification among different classes of immature pomegranates, that was lower than the expected, it was not possible to discriminate all the immature samples in individual classes. However, when considering only the two main categories of "immature" and "mature", a reasonable separation between the classes were obtained using supervised pattern recognition methods of SIMCA and PLS-DA. The SIMCA based on PCA modeling could correctly categorize the samples in two classes of immature and mature with classification accuracy of 100%.

Fractography combined with unsupervised pattern recognition of acoustic emission signals for a better understanding of crack propagation in adhesively bonded wood

In this paper, acoustic emission (AE) signals obtained during quasi-static crack propagation in adhesively bonded beech wood were classified using an unsupervised pattern recognition method. Two ductile one-component polyurethane (1C-PUR) adhesives with the same formulation except for one system being reinforced with short polyamide (~ 1 mm long) fibers were compared to a relative brittle phenol–resorcinol–formaldehyde (PRF) adhesive. Using only localized AE signals, it was shown that the signals originating from the crack propagation could be classified into two different clusters. Comparing the AE signals with a new fractography method, it was estimated that different clusters are due to distinct failure mechanisms, with signals of cluster 1 being associated with wood failure and signals of cluster 2 with adhesive failure. The obtained results suggest that for the PRF adhesive the wood fibers help to slow down the crack propagation. A similar but lesser effect was noted for the polyamide fibers added to the 1C-PUR adhesive matrix.

High Throughput Screening of Serum γ-Glutamyl Dipeptides for Risk Assessment of Nonalcoholic Steatohepatitis with Impaired Glutathione Salvage Pathway.

Nonalcoholic fatty liver disease (NAFLD) is the most common preventable chronic liver disorder in developed countries, the prevalence of which is increasing worldwide due to its association with obesity and type 2 diabetes. However, the exact mechanisms of NAFLD pathophysiology remain poorly understood including its progression to the more severe nonalcoholic steatohepatitis (NASH). New advances for early detection and monitoring of NASH progression are limited due to the lack of specific blood biomarkers, thus requiring invasive liver biopsies for histopathology. Herein, multisegment injection-capillary electrophoresis-tandem mass spectrometry (MSI-CE-MS/MS) is validated as a high throughput, robust, and quantitative platform for targeted analysis of a panel of 16 serum γ-glutamyl dipeptides from a cohort of NASH adult patients from Japan (median age = 53 years, median BMI = 27 kg/m2, n = 116). Multiplexed separations based on MSI-CE-MS/MS enable the design of unique data workflows that rely on customizable serial sample injection formats for accurate determination of γ-glutamyl dipeptides with quality control. Also, the introduction of a liquid coolant device to the capillary outlet improves long-term migration time stability in CE. Unsupervised pattern recognition methods revealed two distinctive NASH subgroups based on their contrasting γ-glutamyl dipeptide status despite patients having similar clinical phenotypes and NASH activity scores (median NAS ≈ 6.0). There was an inverse correlation between serum γ-glutamyl dipeptide concentrations and γ-glutamyltransferease (GGT) enzyme activity (r = -0.46; p = 2.5 × 10-7), which was indicative of a low-risk (n = 64) as compared to a high-risk (n = 52) patient subgroup with impaired glutathione salvage pathway and likely poor clinical prognosis. Our findings highlight the key role of defects in the γ-glutamyl cycle for differentiation of NASH patients, which may enable better risk assessment of long-term survivorship as a complement to standard liver enzyme screens and histopathology.

Determination of Forming Limits in Sheet Metal Forming Using Deep Learning.

The forming limit curve (FLC) is used to model the onset of sheet metal instability during forming processes e.g., in the area of finite element analysis, and is usually determined by evaluation of strain distributions, derived with optical measurement systems during Nakajima tests. Current methods comprise of the standardized DIN EN ISO 12004-2 or time-dependent approaches that heuristically limit the evaluation area to a fraction of the available information and show weaknesses in the context of brittle materials without a pronounced necking phase. To address these limitations, supervised and unsupervised pattern recognition methods were introduced recently. However, these approaches are still dependent on prior knowledge, time, and localization information. This study overcomes these limitations by adopting a Siamese convolutional neural network (CNN), as a feature extractor. Suitable features are automatically learned using the extreme cases of the homogeneous and inhomogeneous forming phase in a supervised setup. Using robust Student’s t mixture models, the learned features are clustered into three distributions in an unsupervised manner that cover the complete forming process. Due to the location and time independency of the method, the knowledge learned from formed specimen up until fracture can be transferred on to other forming processes that were prematurely stopped and assessed using metallographic examinations, enabling probabilistic cluster membership assignments for each frame of the forming sequence. The generalization of the method to unseen materials is evaluated in multiple experiments, and additionally tested on an aluminum alloy AA5182, which is characterized by Portevin-LE Chatlier effects.

Near Infrared Spectroscopy Based on Supervised Pattern Recognition Methods for Rapid Identification of Adulterated Edible Gelatin

The aim of this work is to identify the adulteration of edible gelatin using near-infrared (NIR) spectroscopy combined with supervised pattern recognition methods. The spectral data obtained from a total of 144 samples consisting of six kinds of adulterated gelatin gels with different mixture ratios were processed with multiplicative scatter correction (MSC), Savitzky–Golay (SG) smoothing, and min-max normalization. Principal component analysis (PCA) was first carried out for spectral analysis, while the six gelatin categories could not be clearly distinguished. Further, linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA), backpropagation neural network (BPNN), and support vector machine (SVM) were introduced to establish discrimination models for identifying the adulterated gelatin gels, which gave a total correct recognition rate of 97.44%, 100%, 97.44%, and 100%, respectively. For the SIMCA model with significant level α = 0.05, sample overlapping clustering appeared; thus, the SVM model presents the best recognition ability among these four discrimination models for the classification of edible gelatin adulteration. The results demonstrate that NIR spectroscopy combined with unsupervised pattern recognition methods can quickly and accurately identify edible gelatin with different adulteration levels, providing a new possibility for the detection of industrial gelatin illegally added into food products.

Pattern recognition-based optical technique for non-destructive detection of Ectomyelois ceratoniae infestation in pomegranates during hidden activity of the larvae.

In this research, the feasibility of utilizing visible/near-infrared (Vis/NIR) spectroscopy as an optical non-destructive technique combined with both supervised and unsupervised pattern recognition methods was assessed for detection of Ectomyelois ceratoniae, carob moth, infestation in pomegranates during hidden activity of the larvae. To this end, some fruits were artificially contaminated to the carob moth larvae. Vis/NIR spectra of the blank samples and the contaminated pomegranates without and with external visual symptoms of larvae infestation were analyzed one and two weeks after contaminating the samples as three groups of "Healthy", "Unhealthy-A" and "Unhealthy-B", respectively. Principal component analysis (PCA) as unsupervised pattern recognition method was used to verify the possibility of clustering of the pomegranate samples into the three groups. Discriminant analysis (DA) based on PCA was also used as a powerful supervised pattern recognition method to classify the samples. The calibration models of linear, quadratic and Mahalanobis discriminant analyses were developed based on different spectral pre-processing techniques. The best PCA-DA model was obtained using Mahalanobis distance method and first derivative (D1) pre-processing. The total percentage of correctly classified samples with the best calibration model was 97.9%. The developed model could also predict unknown samples with total percentage of correctly classified samples of 90.6%. It was concluded that Vis/NIR spectroscopy combined with pattern recognition method of PCA-DA can be an appropriate and rapid technology for non-destructively screening the pomegranates for detection of carob moth infestation during hidden activity of the larvae.

Detection of sunn pest-damaged wheat samples using visible/near-infrared spectroscopy based on pattern recognition

The presence of sunn pest-damaged grains in wheat mass reduces the quality of flour and bread produced from it. Therefore, it is essential to assess the quality of the samples in collecting and storage centers of wheat and flour mills. In this research, the capability of visible/near-infrared (Vis/NIR) spectroscopy combined with pattern recognition methods was investigated for discrimination of wheat samples with different percentages of sunn pest-damaged. To this end, various samples belonging to five classes (healthy and 5%, 10%, 15% and 20% unhealthy) were analyzed using Vis/NIR spectroscopy (wavelength range of 350–1000 nm) based on both supervised and unsupervised pattern recognition methods. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the unsupervised techniques and soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) as supervised methods were used. The results showed that Vis/NIR spectra of healthy samples were correctly clustered using both PCA and HCA. Due to the high overlapping between the four unhealthy classes (5%, 10%, 15% and 20%), it was not possible to discriminate all the unhealthy samples in individual classes. However, when considering only the two main categories of healthy and unhealthy, an acceptable degree of separation between the classes can be obtained after classification with supervised pattern recognition methods of SIMCA and PLS-DA. SIMCA based on PCA modeling correctly classified samples in two classes of healthy and unhealthy with classification accuracy of 100%. Moreover, the power of the wavelengths of 839 nm, 918 nm and 995 nm were more than other wavelengths to discriminate two classes of healthy and unhealthy. It was also concluded that PLS-DA provides excellent classification results of healthy and unhealthy samples (R2 = 0.973 and RMSECV = 0.057). Therefore, Vis/NIR spectroscopy based on pattern recognition techniques can be useful for rapid distinguishing the healthy wheat samples from those damaged by sunn pest in the maintenance and processing centers.

Colours and pigments in late ukiyo-e art works: A preliminary non-invasive study of Japanese woodblock prints to interpret hyperspectral images using in-situ point-by-point diffuse reflectance spectroscopy

Dyes and pigments have been used in traditional Japanese woodblock printings in the ukiyo-e style for several centuries. However, the possible introduction of new European pigments and the extraordinary quality of the works of the later period led to take a keen interest in the analytical study of these ukiyo-e prints manufactured at the end of the 19th century. The present research discusses the analytical results of the series Bijin jūni kagetsu (“Beauties in the Twelve Months”) by Shuntei, dated in 1898–1899. Due to the characteristics of this type of artworks, diffuse reflectance spectroscopy (DRS) has been chosen as a portable, non-invasive analytical technique to identify pigments and colorants used in the woodblock prints. The analytical possibilities offered by the in-situ point-by-point DRS as a preliminary tool to better interpret hyperspectral imaging (HSI) data were highlighted. Reflectance spectra of more than 190 points sampled throughout the artwork were recorded between 360 and 740nm, and characterised. Throughout an statistical classification of the spectral data using unsupervised pattern recognition methods, twenty-two different colour composition was confirmed. These groups of reflectance spectra were then identified according the presence of characteristic maxima, minima and inflection points in every spectrum. Some micro-samples extracted from the pigments still remained in a woodblock (used for a 20th century print) were analysed by FESEM-EDS to better interpret some reflectance spectra. Traditional Japanese pigments (like vermilion, red lead, and indigo) and synthetic materials introduced in the 18th and 19th centuries (Prussian blue, synthetic arsenic sulphides, eosin, and methyl violet or crystal violet) were identified in the Shuntei's series colours, together with some mixtures of pigments to prepare blue, green, orange and purple. Based on these twenty-two reflectance patterns, the point-by-point spectra make easier the task of interpreting the very complex HSI data, and reduce the time of treatment and interpretation required by HSI.

Automatic Detection and Quantitative DCE-MRI Scoring of Prostate Cancer Aggressiveness

To develop a robust and clinically applicable automated method for analyzing Dynamic Contrast Enhanced (DCE-) MRI of the prostate as a guide for targeted biopsies and treatments. An unsupervised pattern recognition (PR) method was used to analyze prostate DCE-MRI from 71 sequential radiotherapy patients. Identified regions of interest (ROIs) with increased perfusion were assigned either to the peripheral (PZ) or transition zone (TZ). Six quantitative features, associated with the washin and washout part of the weighted average DCE curve from the ROI, were calculated. The associations between the assigned DCE-scores and Gleason Score (GS) were investigated. A heatmap of tumor aggressiveness covering the entire prostate was generated and validated with histopathology from MRI-ultrasound fused (MRI-US) targeted biopsies. The volumes of the PR-identified ROI's were significantly correlated with the highest GS from the biopsy session for each patient. Following normalization (and only after normalization) with gluteus maximus muscle's DCE signal, the quantitative features in PZ were significantly correlated with GS. These correlations straightened in subset of patients with available MRI-US biopsies when GS from the individual biopsies were used. Area under the receiver operating characteristics curve for discrimination between indolent vs aggressive cancer for the significant quantitative features reached 0.88-0.95. When DCE-scores were calculated in normal appearing tissues, the features were highly discriminative for cancer vs no cancer both in PZ and TZ. The generated heatmap of tumor aggressiveness coincided with the location and GS of the MRI-US biopsies. A quantitative approach for DCE-MRI analysis was developed. The resultant map of aggressiveness correlated well with tumor location and GS and is applicable for integration in radiotherapy/radiology imaging software for clinical translation.

Qualitative and quantitative analysis of toxic materials in adulterated fruit pickle samples by a colorimetric sensor array

A simple and low cost method was presented for detection and determination of two major toxic materials including alum and synthetic acetic acid in fraud pickles based on a novel and sensitive colorimetric sensor array. This sensor was composed of a (4×5) array of pH and redox indicators. The color change profiles were individual fingerprints for each specifics analytes and can be monitored with an ordinary flatbed scanner followed by unsupervised pattern recognition method such as principal component analysis (PCA) and hierarchical clustering analysis (HCA). The produced color patterns were dependent on the type of fruit used for producing of pickle and hence they used for discrimination of the vinegar based on the type of fruits they originated. Also, the responses of the sensors were dependent on the amounts of alum and synthetic acetic acid added to the pickles. Partial least square (PLS) regression as a multivariate calibration method was used to estimate the content of alum and synthetic acetic acid in pickle samples through image analysis. A root mean square error for calibration and prediction of 0.469 and 0.446 for alum and also 1.34 and 0.933 for acetic acid were obtained, respectively. This colorimetric sensor array demonstrates excellent potential for qualitative and quantitative control of fruit pickle samples.