Advantages Of Principal Component Analysis Research Articles

Experimental and Statistical Analysis of Iron Powder for Green Heat Production

In the current investigation, a novel methodology was employed to assess iron powder as a recyclable and sustainable energy carrier. Concurrently, an examination of the modeling of iron powder ignition and the ensuing heat output from the burner was undertaken. The flame temperature was determined by examining the light intensity emitted by the particles as they melted, which is directly related to the particle’s cross-sectional area. An account of the characterization of the experimental procedure, validation, and calibration is presented. Through measurements, distinct one-to-one correlations have been established between the scales of flame combustion and the temperatures of particles of varying sizes of iron. Additionally, a theoretical model for the combustion of expanding particles, particularly iron, within the diffusion-limited regime has been rigorously developed. This model delves into the spectra acquired from particle flames within the burner, utilizing Partial Least Squares Regression (PLSR) and Principal Component Analysis (PCA). This study investigates the use of optical fiber spectroscopy to predict flame temperature and assess iron powder size. The aim was to investigate how different sizes of iron powder affect flame temperature and to create calibration models for non-destructive prediction. The study shows that smaller particles had an average temperature of 1381 °C while larger particles reach up to 1842 °C, demonstrating the significant impact of particle size on combustion efficiency. The results were confirmed using advanced statistical methods, including PLSR and PCA, with PCA effectively differentiating between particle sizes and PLSR achieving an R2 value of 0.90 for the 30 µm particles.

An Approach for Evaluation and Recognition of Facial Emotions Using EMG Signal

Background: Facial electromyography (fEMG) records muscular activities from the facial muscles, which provides details regarding facial muscle stimulation patterns in experimentation. Objectives: The Principal Component Analysis (PCA) is mostly implemented, whereas the actual or unprocessed initial fEMG data are rendered into low-spatial units with minimizing the level of data repetition. Methods: Facial EMG signal was acquired by using the instrument BIOPAC MP150. Four electrodes were fixed on the face of each participant for capturing the four different emotions like happiness, anger, sad and fear. Two electrodes were placed on arm for grounding purposes. Results: The aim of this research paper is to propagate the functioning of PCA in synchrony with the subjective fEMG analysis and to give a thorough apprehension of the advanced PCA in the areas of machine learning. It describes its arithmetical characteristics, while PCA is estimated by implying the covariance matrix. Datasets which are larger in size are progressively universal and their interpretation often becomes complex or tough. So, it is necessary to minimize the number of variables and elucidate linear compositions of the data to explicate it on a huge number of variables with a relevant approach. Therefore, Principal Component Analysis (PCA) is applied because it is an unsupervised training method that utilizes advanced statistical concept to minimize the dimensionality of huge datasets. Conclusion: This work is furthermore inclined toward the analysis of fEMG signals acquired for four different facial expressions using Analysis of Variance (ANOVA) to provide clarity on the variation of features.

Research of Light Pollution Levels Based on Principal Component Analysis and Topsis

With the development of society, a large amount of light has a bad impact on people’s lives. The assessment and governance of light pollution levels also brought difficulties to government management in various regions. An evaluation model of light pollution level under the influence of main factors is constructed based on EWM-TOPSIS scoring method, combined with the advantages of principal component analysis in dealing with complex factors. This paper divides the cities into four areas based on hierarchical cluster analysis according to population density: protected land location, rural community, suburban community and urban community. Each region takes a city as an example group to calculate in our evaluation model, and the light pollution level score is as follows: Greater Khingan Mountains region: 3.7, Panzhihua City: 4.09, Suzhou City: 6.2, New York: 9.97. The results indicate that the areas with high population density have serious light pollution, which is consistent with the actual situation and verifies the rationality of the model.

Application of Principal Component Analysis to advancing digital phenotyping of plant disease in the context of limited memory for training data storage

Despite its widespread employment as a highly efficient dimensionality reduction technique, limited research has been carried out on the advantage of Principal Component Analysis (PCA)–based compression/reconstruction of image data to machine learning-based image classification performance and storage space optimization. To address this limitation, we designed a study in which we compared the performances of two Convolutional Neural Network-Random Forest Algorithm (CNN-RF) guava leaf image classification models developed using training data from a number of original guava leaf images contained in a predefined amount of storage space (on the one hand), and a number of PCA compressed/reconstructed guava leaf images contained in the same amount of storage space (on the other hand), on the basis of four criteria – Accuracy, F1-Score, Phi Coefficient and the Fowlkes–Mallows index. Our approach achieved a 1:100 image compression ratio (99.00% image compression) which was comparatively much better than previous results achieved using other algorithms like arithmetic coding (1:1.50), wavelet transform (90.00% image compression), and a combination of three transform-based techniques – Discrete Fourier (DFT), Discrete Wavelet (DWT) and Discrete Cosine (DCT) (1:22.50). From a subjective visual quality perspective, the PCA compressed/reconstructed guava leaf images presented almost no loss of image detail. Finally, the CNN-RF model developed using PCA compressed/reconstructed guava leaf images outperformed the CNN-RF model developed using original guava leaf images by 0.10% accuracy increase, 0.10 F1-Score increase, 0.18 Phi Coefficient increase and 0.09 Fowlkes–Mallows increase.

Short-term Power Load Forecasting Based on Orthogonal PCA-LPP Dimension Reduction and IGWO-BiLSTM

Background: Accurate power load forecasting is of great significance in ensuring power load planning, reliability and economic operation. The traditional power load is easy to be affected by climate, environment, date type and other factors, resulting in the problem of poor forecasting accuracy. Therefore, it is necessary to study power load forecasting. Objective: Through machine learning, dimension reduction method and intelligent optimization algorithm, the accuracy of load forecasting is improved Methods: In order to fully extract load information and improve the accuracy of short-term load forecasting for campus electricity, an improved combination of orthogonal dimensionality reduction and Bilstm is proposed to optimize the hyperparameters in BiLSTM using an improved gray wolf algorithm. Firstly, using the advantages of principal component analysis (PCA) and Locality Preserving Projection (LPP) to maintain the global and local structure of the data, respectively, the Orthogonal PCA-LPP（OPCA-LPP） dimensionality reduction method is proposed to reduce the dimensionality of the original multidimensional data. Finally, the dimensionality-reduced data is used as the input of BiLSTM and optimized by the improved Gray Wolf algorithm, which can enhance the prediction capability of the model and thus achieve accurate prediction of short-term electric load. Results: The Mae and RMSE of this paper are 1.6585 and 1.7602 respectively. The results show that the method proposed in this paper is reasonable Conclusion: This method is applied to power load forecasting. The comparative experimental results show that this method reduces the dimension of data input, simplifies the complexity of network input data, and improves the accuracy of load forecasting. Compared with other methods, it can effectively improve the accuracy of load forecasting, and provide a basis for formulating reasonable power grid operation mode and balanced dispatching of power grid.

Spatial-temporal flood inundation nowcasts by fusing machine learning methods and principal component analysis

• Build a novel forecast model (PCA-SOM-NARX) by statistical & machine learning tools. • 2-dimensional SOBEK simulates the full-scale flooding processes of rainfall patterns. • PCA extracts principal components to present spatial distributions of inundation. • SOM clusters inundation data to form a spatial–temporal topological feature map. • PCA-SOM-NARX makes reliable and accurate multi-step-ahead regional flood nowcasts. The frequency and severity of floods have noticeably increased worldwide in the last decades due to climate change and urbanization. This study aims to build an urban flood warning system for reducing the impact of flood disasters. A great number of storm-induced rainfall data were collected in Taipei, Taiwan, and the corresponding 2-D inundation maps were simulated for illustrating urban rainfall-flood inundation processes. We proposed a novel urban flood forecast methodology framed by machine learning and statistical techniques to mine the spatial–temporal features between rainfall patterns and inundation maps for making multi-step-ahead regional flood inundation forecasts. The proposed methodology (PCA-SOM-NARX) integrated the advantages of Principal Component Analysis (PCA), Self-Organizing Map (SOM), and Nonlinear Autoregressive with Exogenous Inputs (NARX). PCA was used to extract principal components representing the different spatial distributions of urban inundation. SOM was used to cluster high dimensional inundation datasets to form a two-dimensional topological feature map. NARX was used to establish multi-step-ahead flood forecast models for the next hour at a 10-minute scale. The results show that the PCA-SOM-NARX approach not only produced more stable and accurate multi-step-ahead forecasts on flood inundation depth but was also more indicative of the spatial distribution of inundation caused by torrential rain events, compared to the SOM-NARX approach (the benchmark). The results demonstrate the proposed methodology can adequately grasp the inundation status associated with different rainfall distributions to reliably and accurately forecast regional flood inundation depths, which can help decision makers respond to flooding earlier and mitigate flood disasters.

Comprehensive Monitoring of Complex Industrial Processes with Multiple Characteristics

Traditional onefold data-driven methods for fault detection in complex process industrial systems with high-dimensional, linear, nonlinear, Gaussian, and non-Gaussian coexistence often have less than satisfactory monitoring performance because only a single distribution of process variables is considered. To address this problem, a hybrid fault detection model based on PCA-KPCA-ICA-KICA-BI (Bayesian inference) is proposed, taking into account the advantages of principal component analysis (PCA), kernel principal component analysis (KPCA), independent component analysis (ICA), and kernel independent component analysis (KICA) in terms of dimensionality reduction and feature extraction. Foremost, this paper proposed a nonlinear evaluation method and divided the feature variables into Gaussian linear blocks, Gaussian nonlinear blocks, non-Gaussian linear blocks, and non-Gaussian nonlinear blocks by using the Jarque–Bera (JB) test and nonlinear discrimination method. Each division was monitored by the PCA-KPCA-ICA-KICA model, and finally the Bayesian fusion strategy proposed in this study is used to synthesize the detection results for each block. The hybrid model helps in evaluating variable features and bettering detection performance. Ultimately, the superiority of this hybrid model was verified through the Tennessee Eastman (TE) process and the Continuous Stirred Tank Reactor (CSTR) process, and the fault monitoring results showed an average accuracy of 85.91% for this hybrid model.

Research on Micro-Fault Detection and Multiple-Fault Isolation for Gas Sensor Arrays Based on Serial Principal Component Analysis

Machine learning algorithms play an important role in fault detection and fault diagnosis of gas sensor arrays. Because the gas sensor array will see stability degradation and a shift in output signal amplitude under long-term operation, it is very important to detect the abnormal output signal of the gas sensor array in time and achieve accurate fault location. In order to solve the problem of low detection accuracy of micro-faults in gas sensor arrays, this paper adopts the serial principal component analysis (SPCA) method, which combines the advantages of principal component analysis (PCA) in the linear part and the advantages of kernel principal component analysis (KPCA) in the nonlinear part. The experimental results show that this method is more sensitive to micro-faults and has better fault detection accuracy than the fault detection methods of PCA and KPCA. In addition, in order to solve the current problem of low accuracy of multiple-fault isolation, a SPCA-based reconstruction contribution fault isolation method is proposed in this paper. The experimental results show that this method has higher fault isolation accuracy than the method based on contribution graph.

Investigation of Urinary Exosome Metabolic Patterns in Membranous Nephropathy by Titania‐Assisted Intact Exosome Mass Spectrometry

Exosomes are regarded as the emerging potential targets for liquid biopsy and bioprocess study owing to their abundant inclusive cargos that carry significant disease information. In addition, metabolites have been promising biomarkers for diagnosis. However, little metabolic research on exosomes is carried out by now. Herein, the mix‐crystal titania‐assisted laser desorption/ionization mass spectrometry (LDI‐MS) method is established, which features fast speed, high throughput, and efficiency, to directly extract urinary exosome metabolic patterns of healthy controls (HC) and membrane nephropathy (MN) patients. Besides, this method is also adopted to acquire the primitive urinary metabolic patterns from the same samples for comparison. By taking advantage of principal component analysis, unpaired parametric t‐test, and orthogonal partial least squares discriminant analysis on the exosome metabolic patterns, 27 significant m/z signals are filtrated, which possess more prominent differentiation capacity toward HC and MN patients (AUC = 0.942), and hold greater potential in MN diagnosis, compared to primitive urine (AUC = 0.801). The work reveals the important clinical value of exosome metabolic analysis, and paves a way to exosome‐based diagnosis at metabolomic level toward large‐scale clinical use.

Research on Classification of Remote Sensing Images Based on Artificial Intelligence

With the rapid development of image processing technology, remote sensing technology has received increasing attention. Relying on artificial intelligence technology and using the advantages of principal component analysis (PCA) to reduce the dimensionality of features, this paper proposes a remote sensing image classification method based on SVM. First, LBP operator is used to extract remote sensing image features, and then PCA is used to perform remote sensing image features. The dimensionality reduction process reduces the feature dimensionality and eliminates feature redundant information, and obtains features that have a large contribution to the classification result. Finally, SVM is used for remote sensing image classification. The results show that PCA-SVM improves the efficiency and accuracy of remote sensing image classification.

Dual-emission samarium macrocycle as a lab-on-a-molecule enables high-throughput discrimination of anionic sulfonate surfactants

Nowadays, it is still a huge challenge to efficiently discriminate of analogous anionic sulfonate surfactants, which are noxious xenobiotics related to many safety concerns of environment and life. Recently, great advances in orthogonal detection have shown the great advantages of principal component analysis (PCA) to construct a comprehensive statistical method for sensing signals in recognizing more analytes with similar structures. Inspired by sensor arrays, we propose a new ‘one-element-two-channel’ protocol upon a dual-emission samarium macrocycle Sm-2o as the lab-on-a-molecule, containing two isolated emissions of macrocycle (443 nm) and Sm(III) (643 nm) emitters as two fluorescence channels. Herein, these targeted surfactants instruct original fluorescences of Sm-2o to generate diverse sensing signals which can finally convert into unique fingerprint patterns and distinguish four anionic surfactants (SDS, SDSO, SCI and AOS) from other ten ones, even their proportional concentrations. Significantly, this high-throughput discrimination is realized by different receptor-analyte interactions and the structure transformation of Sm-2o in diverse microenvironments formed in various types of surfactants. Furthermore, these sensing systems also show satisfactory discrimination and semiquantitation performances for mixtures of anionic surfactants, especially the SDS and SDBS, in real water sample, revealing the practicability of our lab-on-a-molecule probe. On the basis of the PCA method, this work provides a vital reference for the design and application of multianalyte chemosensors.

A novel framework for carbon price prediction using comprehensive feature screening, bidirectional gate recurrent unit and Gaussian process regression

Effective analysis and prediction of carbon price can not only promote the development and maturity of carbon trading market, but also contribute to the rational allocation of carbon resources. In order to improve the prediction accuracy of carbon prices and provide more reference information for related researchers and market practitioners, this study proposes a novel carbon price forecasting model, which innovatively combines the comprehensive feature screening technology (CFS) and the method of probability estimation. First of all, the carbon price data is decomposed by the improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the wavelet transform (WT) algorithm is used to denoise after obtaining the combination of intrinsic mode function (IMF) with different complexity. Then, a feature screening technology that combines the advantages of principal component analysis (PCA), random forest (RF) and gradient boosted decision tree (GBDT) methods is design to deeply extract the influencing factors of predictive variables. Finally, as an improved predictor, bidirectional gated recurrent unit (BIGRU) is used to establish a point prediction model, and on this basis, Gaussian process regression (GPR) is used to measure the probability interval of carbon price change. In the empirical analysis of the three markets, the hybrid model proposed in this study is better than other comparison models. Taking the Shanghai market as an example, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the model are 1.554, 1.081 and 0.028, respectively. In the other two markets, the hybrid model proposed in this study also performs best.

Assignment Matrix Free Algorithms for On-line Estimation of Dynamic Origin-Destination Matrices

Dynamic Traffic Assignment (DTA) models represent fundamental tools to forecast traffic flows on road networks, assessing the effects of traffic management and transport policies. As biased models lead to incorrect predictions, which can cause inaccurate evaluations and huge social costs, the calibration of DTA models is an established and active research field. When it comes to estimating Origin-Destination (OD) demand flows, perhaps the most important input for DTA models, one algorithm suggested to outperform all the others for real-time applications: the Kalman Filter (KF). This paper introduces a non-linear Kalman Filter framework for online dynamic OD estimation that reduces the number of variables and can easily incorporate heterogeneous data sources to better explain the non-linear relationship between traffic data and time-dependent OD-flows. Specifically, we propose a model that takes advantage of Principal Component Analysis (PCA) to capture spatial correlations between variables and better exploit the local nature of a specific KF recently proposed in literature, the Local Ensemble Transformed Kalman filter (LETKF). The main advantage of the LETKF is that the Kalman gain is not explicitly formulated which means that, differently from other approaches proposed in the literature, there is no need to compute the assignment matrix or its approximation. The paper shows that the LETKF can easily incorporate different data sources, such as traffic counts and link speeds. Additionally, thanks to the PCA, the model can identify local patterns within the data and better explain the correlation between variables and data. The effectiveness of the proposed methodology is demonstrated first through synthetic experiments where non-linear functions are used to benchmark the model in different conditions and then on the real-world network of Vitoria, Spain (2,884 nodes, 5,799 links) using the mesoscopic simulator Aimsun. Results show that the proposed method leads to better state estimation performances with respect to other Ensemble-based Kalman filters, providing improvements as high as 64% in terms of traffic data reproduction with a 17-fold problem dimensionality reduction.

Application of Partial Least Squares-Discriminate Analysis Model Based on Water Chemical Compositions in Identifying Water Inrush Sources from Multiple Aquifers in Mines

Mine water inrush seriously threatens the safety of coal mine production. Quick and accurate identification of mine water inrush sources is of great significance to preventing mine water hazards. This paper combined partial least squares-discriminate analysis (PLS-DA) with inrush water chemical composition to identify the source of water inrush from multiple aquifers in mines. The Renlou Coal Mine in the Linhuan mining area was selected for this study, and seven conventional water chemical compositions from 54 water samples in three aquifers were collected and tested, of which 45 water samples were used to establish the PLS-DA discriminant model, and nine were used to test the prediction effect. To improve model accuracy and predictive ability, hierarchical clustering analysis method was used to eliminate seven unqualified water samples to reduce the errors caused by improper data. PCA and PLS-DA methods were used to analyze and process the remaining water sample data, and on the basis of PCA analysis, the remaining 38 water samples were used to establish the PLS-DA discriminant model. The model was validated using permutation and external prediction tests. The research shows the following results: (1) Both PCA and PLS-DA methods can distinguish water samples from three different water sources, but the classification effect of PLS-DA was better than PCA because it can strengthen the difference of water chemical composition between different water sources. (2) The correct discrimination rate of the PLS-DA discriminant model was as high as 100%, and permutation tests showed that the model was not overfit. External validation found that the model had good stability and discrimination. (3) HCO3- and total dissolved solids (TDS) were the most important differential marker compositions that affected the discrimination results based on Variable Importance for the Projection (VIP) scores. The discriminant model established in this study combined the advantages of principal component analysis and multiple regression analysis, providing a new method for accurately identifying the sources of water inrush in mines.

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

A novel fault diagnosis method is proposed, depending on a cloud service, for the typical faults in the hydraulic directional valve. The method, based on the Machine Learning Service (MLS) HUAWEI CLOUD, achieves accurate diagnosis of hydraulic valve faults by combining both the advantages of Principal Component Analysis (PCA) in dimensionality reduction and the eXtreme Gradient Boosting (XGBoost) algorithm. First, to obtain the principal component feature set of the pressure signal, PCA was utilized to reduce the dimension of the measured inlet and outlet pressure signals of the hydraulic directional valve. Second, a machine learning sample was constructed by replacing the original fault set with the principal component feature set. Third, the MLS was employed to create an XGBoost model to diagnose valve faults. Lastly, based on model evaluation indicators such as precision, the recall rate, and the F1 score, a test set was used to compare the XGBoost model with the Classification And Regression Trees (CART) model and the Random Forests (RFs) model, respectively. The research results indicate that the proposed method can effectively identify valve faults in the hydraulic directional valve and have higher fault diagnosis accuracy.

Efficient Dimensionality Reduction using Principal Component Analysis for Image Change Detection

Change detection in image processing is the process of identifying differences by comparing images taken at different times. There are several digital change detection techniques; nevertheless, there is no universally optimal change detection methodology: the choice is dependent upon the application. Change detection methods based on multispectral space transformations like Principal Component Analysis (PCA) show good solutions for remote sensing applications. One advantage of PCA is in reducing data redundancy between bands and emphasizing different information in derived components. This work focus on the PCA exploitation for the SPOT multispectral image change detection. Thresholds are applied to the transformed image (PC2) to isolate the pixels that have changed. Thresholding methods require a decision as to where to place threshold boundaries in order to separate areas of change from those of no change. The accuracy of change detection maps that are derived with SPOT data is represented in terms of producer’s accuracy, user’s accuracy, and overall accuracy, which are calculated from an error matrix (or confusion matrix). The obtained results have demonstrated solving efficiently the change detection problem.

An intelligent interconnected network with multiple reservoir computing

In this paper, considering the decomposition and composition mechanism of the traditional reservoir, an intelligent interconnected network with multiple reservoir computing in series–parallel configuration, called MRC-IIN, is proposed for multivariate time series classification and prediction. Firstly, according to the unsupervised learning of restricted Boltzmann machine and the Pearson correlation of multivariate inputs, the number of 1st column reservoirs of MRC-IIN can be determined, and each row interconnected reservoirs of MRC-IIN can also be determined. Secondly, according to the input autocorrelation of 1st column reservoirs, the number of each row reservoirs of MRC-IIN can be determined, such that the dynamic characteristics of the corresponding input can be fully reflected. Thirdly, through using the advantages of principal component analysis, each reservoir size of MRC-IIN can be determined, such that the feature information of each row reservoir states can be reduced layer by layer, and the most essential feature information can be retained. Fourthly, in order to ensure the stable application of MRC-IIN, a sufficient condition for global echo state property of MRC-IIN is given. Finally, the simulation results show that the MRC-IIN can improve classification and prediction performance.

M20 - COMBINING POLYGENIC RISK SCORES ACROSS SEVERAL TRAITS CAN IMPROVE SCHIZOPHRENIA RISK PREDICTION

Background Schizophrenia is a highly heritable disorder and is known to have a substantial common polygenetic component [1] . The polygenic risk score approach [1] allows common genetic liability to the disorder to be directly estimated in individuals regardless of their affected status. In addition to weakly predicting schizophrenia affected status in independent case-control datasets, polygenic risk score approaches have revealed significant shared SNP heritability between schizophrenia and multiple psychiatric and behavioural traits [2 , 3] . We therefore postulated that combining risk score profiles derived from several traits might improve the prediction of schizophrenia risk. Methods Polygenic risk score profiles were calculated for the CLOZUK schizophrenia GWAS study [4] (11260 cases, 24542 controls) by training on six recent GWAS datasets: schizophrenia (independent of the CLOZUK set), bipolar disorder, bipolar vs schizophrenia [5] , major depression disorder, educational attainment [6] and neuroticism [7] . Principal component analysis (PCA) was employed to generate factor loadings for the six polygenic risk score profiles. The advantage of PCA is that it converts a set of possibly correlated variables (e.g. due to shared genetic liability) into a set of uncorrelated variables, with the first principal component accounting for the greatest degree of variance in the dataset outcome. We investigated the prediction accuracy of the first principal component compared to the schizophrenia polygenic risk score by means of area under the curve (AUC) analysis. Results Our analysis indicates that schizophrenia case prediction accuracy of the combined schizophrenia and neuropsychiatric-related first principal component is increased as compared to the AUC of schizophrenia polygenic risk scores alone. The first principal component also visually separates cases and controls in the CLOZUK individuals based upon their combined (schizophrenia and neuropsychiatric-related traits) profiles. Discussion We have proposed an approach to combine genetic evidence for schizophrenia and neuropsychiatric-related traits into one score and tested its prediction accuracy. This approach takes advantage of the shared genetic risk between schizophrenia, psychiatric disorders and behavioural traits, and at present, can improve the prediction of schizophrenia risk.

3,3′,5,5′-tetramethylbenzidine-based quadruple-channel visual colorimetric sensor array for highly sensitive discrimination of serum antioxidants

A quadruple-channel visual colorimetric sensor array has been developed to discriminate serum antioxidants. The redox reaction between 3,3′,5,5′-tetramethylbenzidine (TMB) and sodium hypochlorite (NaClO) can successively generate colorful products with strong absorption at different wavelengths. The presence of five antioxidants containing glutathione, cysteine, lipoic acid, melatonin, and uric acid disrupts the redox reaction between TMB and NaClO, giving birth to distinct colorimetric response patterns. By taking advantage of principal component analysis, the five antioxidants are identified with an accuracy of 100%. The sensor array is capable of visually discriminating different concentrations of the same antioxidant and binary mixtures of two antioxidants as well as working well in real fetal bovine serum. The distinguishable lowest concentration of antioxidants using this sensor array is 1μM, which is about one or three orders of magnitude lower than that of the recently reported colorimetric sensor array.

Raman-microscopy investigation of vitrification-induced structural damages in mature bovine oocytes.

Although oocyte cryopreservation has great potentials in the field of reproductive technologies, it still is an open challenge in the majority of domestic animals and little is known on the biochemical transformation induced by this process in the different cellular compartments. Raman micro-spectroscopy allows the non-invasive evaluation of the molecular composition of cells, based on the inelastic scattering of laser photons by vibrating molecules. The aim of this work was to assess the biochemical modifications of both the zona pellucida and cytoplasm of vitrified/warmed in vitro matured bovine oocytes at different post-warming times. By taking advantage of Principal Component Analysis, we were able to shed light on the biochemical transformation induced by the cryogenic treatment, also pointing out the specific role of cryoprotective agents (CPs). Our results suggest that vitrification induces a transformation of the protein secondary structure from the α-helices to the β-sheet form, while lipids tend to assume a more packed configuration in the zona pellucida. Both modifications result in a mechanical hardening of this cellular compartment, which could account for the reduced fertility rates of vitrified oocytes. Furthermore, biochemical modifications were observed at the cytoplasmic level in the protein secondary structure, with α-helices loss, suggesting cold protein denaturation. In addition, a decrease of lipid unsaturation was found in vitrified oocytes, suggesting oxidative damages. Interestingly, most modifications were not observed in oocytes exposed to CPs, suggesting that they do not severely affect the biochemical architecture of the oocyte. Nevertheless, in oocytes exposed to CPs decreased developmental competence and increased reactive oxygen species production were observed compared to the control. A more severe reduction of cleavage and blastocyst rates after in vitro fertilization was obtained from vitrified oocytes. Our experimental outcomes also suggest a certain degree of reversibility of the induced transformations, which renders vitrified oocytes more similar to untreated cells after 2 h warming.