Pattern recognition is a multidisciplinary area that received more scientific attraction during this period of rapid technological innovation. Today, many real issues and scenarios require pattern recognition to aid in the faster resolution of complicated problems, particularly those that cannot be solved using traditional human heuristics. One common problem in pattern recognition is dealing with multidimensional data, which is prominent in studies involving spectral data such as ultraviolet-visible (UV/Vis), infrared (IR), and Raman spectroscopy data. UV/Vis, IR, and Raman spectroscopy are well-known spectroscopic methods that are used to determine the atomic or molecular structure of a sample in various fields. Typically, pattern recognition consists of two components: exploratory data analysis and classification method. Exploratory data analysis is an approach that involves detecting anomalies in data, extracting essential variables, and revealing the data’s underlying structure. On the other hand, classification methods are techniques or algorithms used to group samples into a predetermined category. This article discusses the fundamental assumptions, benefits, and limitations of some well-known pattern recognition algorithms including Principal Component Analysis (PCA), Kernel PCA, Successive Projection Algorithm (SPA), Genetic Algorithm (GA), Partial Least Square Regression (PLS-R), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Partial Least Square-Discriminant Analysis (PLS-DA) and Artificial Neural Network (ANN). The use of UV/Vis, IR, and Raman spectroscopy for disease classification is also highlighted. To conclude, many pattern recognition algorithms have the potential to overcome each of their distinct limits, and there is also the option of combining all of these algorithms to create an ensemble of methods.

Missing data in large data analysis has affected further analysis conducted on dataset. To fill in missing data, Nearest Neighbour Method (NNM) and Expectation Maximization (EM) algorithm are the two most widely used methods. Thus, this research aims to compare both methods by imputing missing data of air quality in five monitoring stations (CA0030, CA0039, CA0042, CA0049, CA0050) in Sabah, Malaysia. PM10 (particulate matter with aerodynamic size below 10 microns) dataset in the range from 2003–2007 (Part A) and 2008–2012 (Part B) are used in this research. To make performance evaluation possible, missing data is introduced in the datasets at 5 different levels (5%, 10%, 15%, 25% and 40%). The missing data is imputed by using both NNM and EM algorithm. The performance of both data imputation methods is evaluated using performance indicators (RMSE, MAE, IOA, COD) and regression analysis. Based on performance indicators and regression analysis, NNM performs better compared to EM in imputing data for stations CA0039, CA0042 and CA0049. This may be due to air quality data missing at random (MAR). However, this is not the case for CA0050 and part B of CA0030. This may be due to fluctuation that could not be detected by NNM. Accuracy evaluation using Mean Absolute Percentage Error (MAPE) shows that NNM is more accurate imputation method for most of the cases.

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Coronavirus disease-19 (COVID-19) is caused by SARS-CoV-2, a highly contagious respiratory virus that has caused a global pandemic. Despite the urgent need for effective diagnostic screening technologies, ideal methods for COVID-19 detection have not yet been developed. To address this issue, we developed a Raman spectroscopy technique for rapid and sensitive on-site detection of SARS-CoV-2, utilizing the unique spectral fingerprint of molecular vibrations. The proposed technique is non-invasive and label-free that enables the detection of molecular vibrations, providing a unique spectral fingerprint for different molecules. Raman spectra from 75 positive and 75 negative swab samples were analyzed, processed by smoothening and baseline correction of spectral data. The peaks in the processed data were detected and assigned based on literature peak, with peaks specific to positive samples used for detection with minimal false positives. These peaks were attributed to various molecules, including amino acids in proteins, glycoproteins, lipids, and protein structures. Our Raman spectroscopy technique provides a reliable and non-invasive approach for the detection of SARS-CoV-2, with potential to expand to other infectious agents. This method has significant implications for global health, aiding in effective control measures against COVID-19.

This study investigates the effects of Gamma-irradiation on the structural, morphological and optical properties of 3,16-bis(tri isopropyl silylethynyl)pentacene (TIPS Pentacene) organic semiconductor films. The TIPS Pentacene thin films were irradiated at 10 to 300 kGy at a dose rate of 1.58 kGy/hr. The films were characterized using X-Ray Diffractometer (XRD), Atomic Force Microscopy (AFM) and Ultraviolet-Visible Spectroscopy (UV-Vis). The XRD analysis showed that the pre-irradiated thin films were of crystalline structure, indicating a broad wave diagram. The XRD and AFM results show that these variations can be attributed to the radiation-induced local heating and microscopic atomic mobility. Based on the UV-Vis results, the thin films exhibit approximately 70% optical transmittance in the visible region at pre-irradiation. At post-irradiation, optical transmittance decreased to 55% at the maximum absorbed dose. The corresponding optical bandgap decreased from 1.87 to 1.50 eV after a total ionizing dose of 300 kGy. The findings showed that TIPS Pentacene thin film has good mitigation towards gamma irradiation and can withstand harsh radiation while retaining its semiconductor properties. It is a potential candidate for flexible electronics for space applications.