http://irgu.unigoa.ac.in/drs/handle/unigoa/26 2026-05-12T15:34:30Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7739 Stimulus-Evoked Brain Signals for Parkinson's Detection: A Comprehensive Benchmark Performance Analysis on Cross-Stimulation and Channel-Wise Experiments Patel, K.; Gad, R.S.; de Ataide, D.; Vetrekar, N.T.; Ferreira, T.; Ramachandra, R. Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects both motor and cognitive functions, often resulting in misdiagnosis during its early stages. The condition severely impacts daily living, diminishing an individual's ability to work and carry out routine tasks independently. Consequently, the development of automated methods for reliable PD detection has gained growing research interest. Among the available approaches, Electroencephalography (EEG) has emerged as a promising non-invasive and cost-effective tool. Nevertheless, most existing studies have predominantly focused on resting-state EEG, which constrains the generalizability and robustness of the proposed detection models. This study introduces a cross-stimulation evaluation framework to assess its impact on Parkinson's disease detection algorithms and conducts channel-wise analysis to identify the most discriminative brain regions for accurate diagnosis. To support this research, we present the newly introduced Parkinson's disease EEG (ParEEG) database, comprising 203,520 EEG samples from 60 subjects recorded based on Resting-State Visual Evoked Potential (RSVEP) and Steady-State Visually Evoked Potential (SSVEP) stimuli. In this study, we evaluate the performance of individual EEG channels using two handcrafted and two deep learning-based methods, employing a 10-fold cross-validation strategy, to ensure statistical reliability and establish benchmark results. Experimental results show that CRC and LSTM consistently achieved high accuracies (95-100 percent) with low variability (standard deviation less than 2 percent). The analysis indicates that EEG channels in the frontal, fronto-central, and central-parietal regions consistently yield higher classification accuracy in Parkinson's disease detection. Our findings offer valuable insights into channel-specific neural alterations for better interpretability in PD, and the cross-stimulation evaluation enhances the generalizability of EEG-based PD detection for practical diagnostic purposes. 2025-01-01T00:00:00Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7675 Detecting starch-adulterated turmeric using Vis-NIR spectroscopy and multispectral imaging with machine learning Lanjewar, M.; Asolkar. S.; Parab, J.S.; Morajkar, P.P. Chemicals are often added to turmeric to increase profits, posing significant health risks to consumers. At the same time, traditional methods for detecting contaminants in turmeric are complicated and time-consuming. This study aimed to develop a more practical approach using visible-near infrared (Vis-NIR) and multispectral imaging (MSI) techniques to detect starch adulteration in turmeric. The turmeric powder was mixed with starch (0.1, 0.5, 1, 2.5, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, and 100 percent (w/w)) to create spectral and MSI datasets within the 400-1050 nm wavelength range. Spectra were corrected using spectral preprocessing techniques such as Savitzky-Golay (SG), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV). Principal Component Analysis (PCA) applied to reduce the dimensions and various machine learning (ML) models for prediction. The Random Forest Regressor (RFR) achieved a coefficient of determination (R sup(2)) of 0.999, a root mean squared error (RMSE) of 0.391 mg (w/w), and a residual predictive deviation (RPD) of 92.3 percent in regression analysis. For classification, the Random Forest Classifiers (RFC) achieved an F1 score of 96.0 percent and a Matthews Correlation Coefficient (MCC) of 94.6 percent. In MSI analysis, the DenseNet201 model obtained an F1 score of 92.9 percent and an MCC of 91.9 percent. Moreover, the robustness of these models was cross-validated using leave-one-out cross-validation (LOOCV) and K-fold methods. The significance of the study lies in several critical areas, such as public health, advancement in technology, etc. The study's findings reveal that Vis-NIR and MSI approaches are excellent in detecting starch adulteration in turmeric with reliability. It has important implications for public health and food safety by offering a reliable tool for verifying the purity of turmeric and other food items. 2024-01-01T00:00:00Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7677 Detection and quantification of formaldehyde adulteration in cow and buffalo milk using UV-Vis-NIR spectroscopy with machine learning Rosa, D.G.; Malik, V.V.; Patle, L.B.; Parab, J.S.; Lanjewar, M. This work uses UV-Vis-NIR spectroscopy (200-1700 nm), spectral preprocessing, principal component analysis (PCA), and machine learning (ML) to identify and quantify formalin adulteration in cow and buffalo milk. Formalin was added to milk at various concentrations with increments of 0.5 percent, 1.0 percent, and up to 50.0 percent. The analysis was carried out under three scenarios: cow, buffalo, and a combination. The spectral datasets were separated into training, validation, and test sets. Regression modeling yielded coefficients of determination (R sup(2)) of 0.998-0.999, root mean squared errors (RMSE) of 0.16-0.80, and RPD values of 80.13-182.79. Leave-one-out cross-validation (LOOCV) was obtained (R sup(2) = 0.999, RMSE ranged from 0.24 to 0.476). The classification accuracy varied from 73.0 percent to 100.0 percent, with 5-fold cross-validation average accuracies of 92.9 percent, 90.0 percent, and 82.2 percent for Scenarios I, II, and III, respectively. This outcome indicates the possibility of a practical, real-time, and non-destructive milk quality assessment system. 2025-01-01T00:00:00Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7674 Robust method for detecting metanil yellow in turmeric: Integrating Vis-NIR spectroscopy and machine learning Lanjewar, M.; Morajkar, P.P.; Parab, J.S. Food color significantly influences the quality and marketability of food, but harmful adulterants like metanil yellow (MY) are sometimes added to turmeric to improve its appearance, posing severe health risks. In this study, two datasets were created by mixing turmeric with different concentrations of MY (0, 0.1, 0.5, and 1-50 percent). The reflectance spectra were recorded in the 400-2050 nm range and pre-processed using Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Savitzky-Golay (SG) filtering. Principal Component Analysis (PCA) was then applied to reduce dimensionality. For dataset-1, the K-Nearest Neighbors Regressor (KNR) combined with SNV-PCA achieved an R sup(2) of 0.998, an RMSE of 0.664 mg (w/w), and leave-one-out cross-validation (LOO-CV) results of R sup(2) = 0.997 and RMSE = 0.849 mg (w/w). For dataset-2, KNR with SNV-PCA showed R sup(2) = 0.998, RMSE = 0.833 mg (w/w), and LOO-CV R sup(2) = 0.999 with RMSE = 0.645 mg (w/w). When the datasets were combined, KNR with MSC-PCA recorded R sup(2) = 0.992, RMSE = 1.479 mg (w/w), LOO-CV R sup(2) = 0.998, and RMSE = 0.741 mg (w/w). The classification model achieved 97.0 percent accuracy, an F1 score of 97.0 percent, and a 10-fold cross-validation F1 score of 80.8 percent. 2025-01-01T00:00:00Z