Diagnosing Hirschsprung disease by detecting intestinal ganglion cells using label-free hyperspectral microscopy

AbstractHirschsprung disease (HD) is a congenital disorder in the distal colon that is characterized by the absence of nerve ganglion cells in the diseased tissue. The primary treatment for HD is surgical intervention with resection of the aganglionic bowel. The accurate identification of the aganglionic segment depends on the histologic evaluation of multiple biopsies to determine the absence of ganglion cells in the tissue, which can be a time-consuming procedure. We investigate the feasibility of using a combination of label-free optical modalities, second harmonic generation (SHG); two-photon excitation autofluorescence (2PAF); and Raman spectroscopy (RS), to accurately locate and identify ganglion cells in murine intestinal tissue without the use of exogenous labels or dyes. We show that the image contrast provided by SHG and 2PAF signals allows for the visualization of the overall tissue morphology and localization of regions that may contain ganglion cells, while RS provides detailed multiplexed molecular information that can be used to accurately identify specific ganglion cells. Support vector machine, principal component analysis and linear discriminant analysis classification models were applied to the hyperspectral Raman data and showed that ganglion cells can be identified with a classification accuracy higher than 95%. Our findings suggest that a near real-time intraoperative histology method can be developed using these three optical modalities together that can aid pathologists and surgeons in rapid, accurate identification of ganglion cells to guide surgical decisions with minimal human intervention.

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Multiscale Supervised Classification of Point Clouds with Urban and Forest Applications

Sensors ◽

10.3390/s19204523 ◽

2019 ◽

Vol 19 (20) ◽

pp. 4523 ◽

Cited By ~ 1

Author(s):

Carlos Cabo ◽

Celestino Ordóñez ◽

Fernando Sáchez-Lasheras ◽

Javier Roca-Pardiñas ◽

and Javier de Cos-Juez

Keyword(s):

Random Forest ◽

Laser Scanning ◽

Supervised Classification ◽

Computing Time ◽

Principal Component ◽

Point Clouds ◽

Support Vector ◽

Linear Discriminant ◽

Vector Machines ◽

Input Variables

We analyze the utility of multiscale supervised classification algorithms for object detection and extraction from laser scanning or photogrammetric point clouds. Only the geometric information (the point coordinates) was considered, thus making the method independent of the systems used to collect the data. A maximum of five features (input variables) was used, four of them related to the eigenvalues obtained from a principal component analysis (PCA). PCA was carried out at six scales, defined by the diameter of a sphere around each observation. Four multiclass supervised classification models were tested (linear discriminant analysis, logistic regression, support vector machines, and random forest) in two different scenarios, urban and forest, formed by artificial and natural objects, respectively. The results obtained were accurate (overall accuracy over 80% for the urban dataset, and over 93% for the forest dataset), in the range of the best results found in the literature, regardless of the classification method. For both datasets, the random forest algorithm provided the best solution/results when discrimination capacity, computing time, and the ability to estimate the relative importance of each variable are considered together.

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Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose

Sensors ◽

10.3390/s18092936 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2936 ◽

Cited By ~ 6

Author(s):

Xianghao Zhan ◽

Xiaoqing Guan ◽

Rumeng Wu ◽

Zhan Wang ◽

You Wang ◽

...

Keyword(s):

Herbal Medicine ◽

Electronic Nose ◽

Nearest Neighbor ◽

Herbal Medicines ◽

Principal Component ◽

Support Vector ◽

Linear Discriminant ◽

Convenient Means ◽

Electronic Nose System ◽

Insight Into

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

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Traceability of the geographical origin of Siraitia grosvenorii based on multielement contents coupled with chemometric techniques

Scientific Reports ◽

10.1038/s41598-021-00664-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xiao-Ping Huang ◽

Lei Lei ◽

Shun-Xin Lei ◽

Wei-Wei Zhu ◽

Jun Yan

Keyword(s):

Inductively Coupled Plasma ◽

Geographical Origin ◽

Principal Component ◽

Emission Spectrometry ◽

Support Vector ◽

Linear Discriminant ◽

Protected Designation Of Origin ◽

Siraitia Grosvenorii ◽

Discrimination Rate ◽

Chemometric Techniques

AbstractSiraitia grosvenorii (LHG) is widely used as a medicinal and edible material around the world. The objective of this study was to develop an effective method for the authentication of the geographical origin of LHG in its main producing area Guangxi, China, which is identified as Chinese Protected Designation of Origin product, against other producing regions in China. The content of 14 elements (K, Na, Ca, P, Mg, Al, B, Ba, Cu, Fe, Mn, Ni, Zn, and Sr) of 114 LHG samples was determined by inductively coupled plasma optical emission spectrometry. Multivariate analysis was then performed to classify the geographical origin of LHG samples. The contents of multielement display an obvious trend of clustering according to the geographical origin of LHG samples based on radar plot and principal component analysis. Finally, three supervised statistical techniques, including linear discriminant analysis (LDA), k-nearest neighbours (k-NN), and support vector machine (SVM), were applied to develop classification models. Finally, 40 unknown LHG samples were used to evaluate the predictive ability of model and discrimination rate of 100%, 97.5% and 100% were obtained for LDA, k-NN, and SVM, respectively. This study indicated that it is feasible to attribute unknown LHG samples to its geographical origin based on its multielement content coupled with chemometric techniques.

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Harnessing intrinsic fluorescence for typing of secondary structures of DNA

10.1101/2020.01.15.907501 ◽

2020 ◽

Cited By ~ 1

Author(s):

Michela Zuffo ◽

Aurélie Gandolfini ◽

Brahim Heddi ◽

Anton Granzhan

Keyword(s):

High Throughput ◽

Conformational Changes ◽

Emission Spectra ◽

Principal Component ◽

Secondary Structures ◽

Intrinsic Fluorescence ◽

Label Free ◽

Linear Discriminant ◽

Effective Manner

ABSTRACTDNA is polymorphic since, despite its ubiquitous presence as a double-stranded helix, it is able to fold into a plethora of other secondary structures both in vitro and in cells. Despite the considerable advances that have been made in understanding this structural diversity, its high-throughput investigation still faces severe limitations. This mainly stems from the lack of suitable label-free methods, combining a fast and cheap experimental workflow with high information content. Here, we explore the use of intrinsic fluorescence emitted by nucleic acids for this scope. After a preliminary assessment of the suitability of this phenomenon for tracking the conformational changes of DNA, we examined the intrinsic steady-state emission spectra of an 89-membered set of synthetic oligonucleotides with reported conformation (G-quadruplexes, i-motifs, single- and double stranded DNA) by means of multivariate analysis. Specifically, principal component analysis of emission spectra resulted in successful clustering of oligonucleotides into three corresponding conformational groups, albeit without discrimination between single- and double-stranded structures. Linear discriminant analysis of the same training set was exploited for the assessment of new sequences, allowing the evaluation of their G4-forming propensity. Our method does not require any labelling agent or dye, avoiding the related intrinsic bias, and can be utilized to screen novel sequences of interest in a high-throughput and cost-effective manner. In addition, we observed that left-handed (Z-) G4 structures were systematically more fluorescent than most other G4 structures, almost reaching the quantum yield of 5′-d[(G3T)3G3]-3′ (G3T), the most fluorescent G4 structure reported to date. This property is likely to arise from the similar base-stacking geometry in both types of structures.

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Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9040252 ◽

2020 ◽

Vol 9 (4) ◽

pp. 252 ◽

Cited By ~ 4

Author(s):

Kwanele Phinzi ◽

Dávid Abriha ◽

László Bertalan ◽

Imre Holb ◽

Szilárd Szabó

Keyword(s):

Machine Learning ◽

Strategic Decision ◽

Support Vector ◽

Accurate Identification ◽

Study Region ◽

Linear Discriminant ◽

Binary Approach ◽

Class Level ◽

Productive Land ◽

Number Of Classes

Gullies reduce both the quality and quantity of productive land, posing a serious threat to sustainable agriculture, hence, food security. Machine Learning (ML) algorithms are essential tools in the identification of gullies and can assist in strategic decision-making relevant to soil conservation. Nevertheless, accurate identification of gullies is a function of the selected ML algorithms, the image and number of classes used, i.e., binary (two classes) and multiclass. We applied Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Random Forest (RF) on a Systeme Pour l’Observation de la Terre (SPOT-7) image to extract gullies and investigated whether the multiclass (m) approach can offer better classification accuracy than the binary (b) approach. Using repeated k-fold cross-validation, we generated 36 models. Our findings revealed that, of these models, both RFb (98.70%) and SVMm (98.01%) outperformed the LDA in terms of overall accuracy (OA). However, the LDAb (99.51%) recorded the highest producer’s accuracy (PA) but had low corresponding user’s accuracy (UA) with 18.5%. The binary approach was generally better than the multiclass approach; however, on class level, the multiclass approach outperformed the binary approach in gully identification. Despite low spectral resolution, the pan-sharpened SPOT-7 product successfully identified gullies. The proposed methodology is relatively simple, but practically sound, and can be used to monitor gullies within and beyond the study region.

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Application of Visible-Near Infrared Spectra Technique Based on to the Non-Destructive Measurement of the Salmon Quality

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.961 ◽

2014 ◽

Vol 926-930 ◽

pp. 961-964

Author(s):

Jiao Jiao Yin

Keyword(s):

Infrared Spectra ◽

Near Infrared ◽

Principal Component ◽

Support Vector ◽

Analysis Method ◽

Linear Discriminant ◽

Near Infrared Spectra ◽

Destructive Measurement ◽

Non Destructive

Because the reflectivity of astaxanthin vary in different bands (mainly 400nm-600nm), so we use the visible-near infrared spectra technique to irradiate the salmon. Because in daily life, people grade the salmon flesh with a color card. In this paper, we first use principal component analysis to reduce the dimensionality of the spectral data of salmon, then use linear discriminant analysis method, least squares support vector machine classification method to distinguish the flesh quality. The correct classification rates are 60%and73.3%. The results show that we can use visible – near infrared spectra to distinguish the quality of the salmon which doesn’t be dissected.

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Multivariate characterisation of Italian monovarietal red wines using MIR spectroscopy

OENO One ◽

10.20870/oeno-one.2019.53.4.2558 ◽

2019 ◽

Vol 53 (4) ◽

Cited By ~ 5

Author(s):

Giuseppina P. Parpinello ◽

Arianna Ricci ◽

Panagiotis Arapitsas ◽

Andrea Curioni ◽

Luigi Moio ◽

...

Keyword(s):

Principal Component ◽

Rapid Screening ◽

Support Vector ◽

Red Wines ◽

Grape Variety ◽

Absorption Bands ◽

Linear Discriminant ◽

Mir Spectroscopy ◽

Grape Varieties

Aim: The aim of this study was to investigate the application of mid-infrared (MIR) spectroscopy combined with multivariate analysis, to provide a rapid screening tool for discriminating among different Italian monovarietal red wines based on the relationship between grape variety and wine composition in particular phenolic compounds.Methods and results: The MIR spectra (from 4000 to 700 cm‒1) of 110 monovarietal Italian red wines, vintage 2016, were collected and evaluated by selected multivariate data analyses, including principal component analysis (PCA), linear discriminant analysis (DA), support vector machine (SVM), and soft intelligent modelling of class analogy (SIMCA). Samples were collected directly from companies across different regions of Italy and included 11 grape varieties: Sangiovese, Nebbiolo, Aglianico, Nerello Mascalese, Primitivo, Raboso, Cannonau, Teroldego, Sagrantino, Montepulciano and Corvina. PCA showed five wavelengths that mainly contributed to the PC1, including a much-closed peak at 1043 cm‒1, which correspond to the C–O stretch absorption bands that are important regions for glycerol, whereas the ethanol peaks at around 1085 cm‒1. The band at 877 cm‒1 are related to the C–C stretching vibration of organic molecules, whereas the asymmetric stretching for C–O in the aromatic –OH group of polyphenols is within spectral regions from 1050 to 1165 cm‒1. In particular, the (1175)–1100–1060 cm‒1 vibrational bands are combination bands, involving C–O stretching and O–H deformation of phenolic rings. The 1166–1168 cm‒1 peak is attributable to in-plane bending deformations of C–H and C–O groups of polyphenols, respectively, for which polymerisation may cause a slight peak shift due to the formation of H-bridges.The best result was obtained with the SVM, which achieved an overall correct classification for up to 72.2% of the training set, and 44.4% for the validation set of wines, respectively. The Sangiovese wines (n=19) were split into two sub-groups (Sang-Romagna, n=12 and Sang-Tuscany, n=7) considering the indeterminacy of its origins, which is disputed between Romagna and Tuscany. Although the classification of three grape varieties was problematic (Nerello Mascalese, Raboso and Primitivo), the remaining wines were almost correctly assigned to their actual classes.Conclusions: MIR spectroscopy coupled with chemometrics represents an interesting approach for the classification of monovarietal Italian red wines, which is important in quality control and authenticity monitoring.Significance and impact of the study: Authenticity is a main issue in winemaking in terms of quality evaluation and adulteration, in particular for origin certified/protected wines, for which the added marketing value is related to the link of grape variety with the area of origin. This study is part of the D-wine project “The diversity of tannins in Italian red wines”.

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Raman Microspectral Study and Classification of the Pathological Evolution of Breast Cancer Using Both Principal Component Analysis-Linear Discriminant Analysis and Principal Component Analysis-Support Vector Machine

Journal of Spectroscopy ◽

10.1155/2021/5572782 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Heping Li ◽

Yu Ren ◽

Fan Yu ◽

Dongliang Song ◽

Lizhe Zhu ◽

...

Keyword(s):

Breast Cancer ◽

Principal Component Analysis ◽

Support Vector Machine ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Ductal Carcinoma ◽

Principal Component ◽

Component Analysis ◽

Support Vector ◽

Linear Discriminant

To facilitate the enhanced reliability of Raman-based tumor detection and analytical methodologies, an ex vivo Raman spectral investigation was conducted to identify distinct compositional information of healthy (H), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). Then, principal component analysis-linear discriminant analysis (PCA-LDA) and principal component analysis-support vector machine (PCA-SVM) models were constructed for distinguishing spectral features among different tissue groups. Spectral analysis highlighted differences in levels of unsaturated and saturated lipids, carotenoids, protein, and nucleic acid between healthy and cancerous tissue and variations in the levels of nucleic acid, protein, and phenylalanine between DCIS and IDC. Both classification models were principal component analysis-linear discriminant analysis to be extremely efficient on discriminating tissue pathological types with 99% accuracy for PCA-LDA and 100%, 100%, and 96.7% for PCA-SVM analysis based on linear kernel, polynomial kernel, and radial basis function (RBF), respectively, while PCA-SVM algorithm greatly simplified the complexity of calculation without sacrificing performance. The present study demonstrates that Raman spectroscopy combined with multivariate analysis technology has considerable potential for improving the efficiency and performance of breast cancer diagnosis.

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ASSESSMENT OF FEATURE SELECTION AND CLASSIFICATION APPROACHES TO ENHANCE INFORMATION FROM OVERNIGHT OXIMETRY IN THE CONTEXT OF APNEA DIAGNOSIS

International Journal of Neural Systems ◽

10.1142/s0129065713500202 ◽

2013 ◽

Vol 23 (05) ◽

pp. 1350020 ◽

Cited By ~ 35

Author(s):

DANIEL ÁLVAREZ ◽

ROBERTO HORNERO ◽

J. VÍCTOR MARCOS ◽

NIELS WESSEL ◽

THOMAS PENZEL ◽

...

Keyword(s):

Feature Selection ◽

High Performance ◽

Principal Component ◽

Screening Tools ◽

Support Vector ◽

Feature Subset ◽

Blood Oxygen Saturation ◽

Test Set ◽

Linear Discriminant ◽

Validation Set

This study is aimed at assessing the usefulness of different feature selection and classification methodologies in the context of sleep apnea hypopnea syndrome (SAHS) detection. Feature extraction, selection and classification stages were applied to analyze blood oxygen saturation (SaO2) recordings in order to simplify polysomnography (PSG), the gold standard diagnostic methodology for SAHS. Statistical, spectral and nonlinear measures were computed to compose the initial feature set. Principal component analysis (PCA), forward stepwise feature selection (FSFS) and genetic algorithms (GAs) were applied to select feature subsets. Fisher's linear discriminant (FLD), logistic regression (LR) and support vector machines (SVMs) were applied in the classification stage. Optimum classification algorithms from each combination of these feature selection and classification approaches were prospectively validated on datasets from two independent sleep units. FSFS + LR achieved the highest diagnostic performance using a small feature subset (4 features), reaching 83.2% accuracy in the validation set and 88.7% accuracy in the test set. Similarly, GAs + SVM also achieved high generalization capability using a small number of input features (7 features), with 84.2% accuracy on the validation set and 84.5% accuracy in the test set. Our results suggest that reduced subsets of complementary features (25% to 50% of total features) and classifiers with high generalization ability could provide high-performance screening tools in the context of SAHS.

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Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA and SVM)

Czech Journal of Food Sciences ◽

10.17221/113/2014-cjfs ◽

2014 ◽

Vol 32 (No. 6) ◽

pp. 538-548 ◽

Cited By ~ 21

Author(s):

A. Sanaeifar ◽

S.S. Mohtasebi ◽

M. Ghasemi-Varnamkhasti ◽

H. Ahmadi ◽

J. Lozano

Keyword(s):

Electronic Nose ◽

Low Cost ◽

Principal Component ◽

Oxide Semiconductor ◽

Support Vector ◽

Computational Techniques ◽

Laboratory Equipment ◽

Linear Discriminant ◽

Ripening Stages ◽

Non Destructive

Potential application of a metal oxide semiconductor based electronic nose (e-nose) as a non-destructive instrument for monitoring the change in volatile production of banana during the ripening process was studied. The proposed e-nose does not need any advanced or expensive laboratory equipment and proved to be reliable in recording meaningful differences between ripening stages. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Soft Independent Modelling of Class Analogy (SIMCA) and Support Vector Machines (SVM) techniques were used for this purpose. Results showed that the proposed e-nose can distinguish between different ripening stages. The e-nose was able to detect a clear difference in the aroma fingerprint of banana when using SVM analysis compared with PCA and LDA, SIMCA analysis. Using SVM analysis, it was possible to differentiate and to classify the different banana ripening stages, and this method was able to classify 98.66% of the total samples in each respective group. Sensor array capabilities in the classification of ripening stages using loading analysis and SVM and SIMCA were also investigated, which leads to develop the application of a specific e-nose system by applying the most effective sensors or ignoring the redundant sensors.  

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