Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2026Scopus SJRQ1Non-Destructive Intelligent Microwave Sensing of Water Content in Sand–Water Mixtures Using S-Parameter Features and PLS Regression
Çakır, Mehmet
The accurate and rapid estimation of water content in granular materials is important for geotechnical engineering, construction-material evaluation, agricultural monitoring, and non-destructive material assessment. This study presents a controlled laboratory feasibility framework for estimating water content in sand–water mixtures using microwave S-parameter measurements and physically interpretable features. Sixty measurements were acquired from six nominal mixture levels containing 0%, 5%, 10%, 15%, 20%, and 25% water by mass using a WR-229 waveguide-based fixture over 3.30–4.90 GHz. Descriptors were extracted from the raw and empty-reference-normalized S11 and S21 responses, including extrema, slopes, area-based indicators, band-averaged values, selected-frequency responses, and phase statistics. Ridge regression, partial least squares regression, support vector regression, Gaussian process regression, random forest, and gradient boosting regression were evaluated. The selected PLS model achieved RMSE = 3.56%, MAE = 2.92%, and R2 = 0.826 under leave-one-mixture-level-out group-wise cross-validation, which was used as the primary indicator of generalization to an unseen mixture level. The substantially lower error obtained under repeated-measurement leave-one-out cross-validation primarily reflects within-level repeatability under controlled conditions and should not be interpreted as evidence of universal calibration performance. The results demonstrate that magnitude- and phase-derived microwave descriptors, particularly transmission-based features, provide an interpretable and repeatable framework for water-content estimation under the specific sand type, sample geometry, water source, and laboratory conditions investigated. Further validation using independent preparation batches, different granular materials, measured water conductivity, temperature variation, and field-like conditions is required before practical deployment.
- MakaleAçık Erişim2026Scopus SJRQ1A Single-Source Pilot Study of Machine Learning-Assisted Microwave S-Parameter Screening for Glucose Syrup and Water Adulteration in Grape Molasses
Engin, Mustafa Alptekin; Cakir, Mehmet; Cakil, Turan
Grape molasses, known as pekmez in Turkish, is a traditional concentrated fruit product that may be adulterated with cheaper sweeteners or water. This study evaluated broadband microwave S-parameter measurements as a rapid, non-destructive screening approach for detecting glucose syrup substitution and water dilution in grape molasses. Nine physical mixture groups were prepared, including pure grape molasses, glucose syrup–substituted mixtures at 5–30%, pure glucose syrup as an endpoint reference, and water-diluted mixtures at 10–30%. For each group, five consecutive technical measurements were recorded using a Libre vector network analyzer connected to a WR-229 waveguide-based two-port setup over 3.30–4.90 GHz. All mixtures were prepared from a single commercial grape molasses source and a single glucose syrup source; the results should therefore be interpreted as pilot-scale, single-source evidence rather than a generalizable screening method. Glucose syrup substitution produced a systematic upward shift in S11 resonance frequency. In the practical 0–30% range, S11 resonance frequency showed a strong linear relationship with glucose syrup content on the calibration data (R2 = 0.9955; inverse prediction error = 0.72 percentage points), though this reflects calibration fit rather than independently validated prediction accuracy. Water dilution produced stronger S21 attenuation. The detection principle is based on the complementary use of S11 resonance behavior for glucose syrup substitution and S21 transmission loss for water dilution. In group-blocked point-wise classification, the Ensemble Bagged Trees classifier achieved 88.79% accuracy and a macro-F1 score of 0.874. These results indicate that S11 and S21 provide complementary proof-of-concept indicators for controlled-mixture screening of grape molasses adulteration.
- MakaleAçık Erişim2026Scopus SJRQ1Waveguide-Based Microwave S-Parameter Measurements for Machine Learning-Assisted Classification of Paprika Powder Adulterants
Alptekin Engin, Mustafa
Detecting food adulteration in powdered spices through visual inspection is difficult, and paprika powder is particularly susceptible to adulteration due to its fine particulate structure and intense color. This study proposes a waveguide-based microwave S-parameter measurement approach combined with machine learning for the classification of organic and mineral adulterants in paprika powder. The experimental dataset was created by intentionally adulterating a commercial paprika brand with five organic and five mineral materials in varying proportions. The S11 and S21 scattering parameters, measured in the 3.3-4.9 GHz frequency range, of these samples, comprising a total of 11 different classes including pure paprika, were used as input in the machine learning-based classification process. The proposed framework directly uses frequency and S11 and S21 responses to distinguish pure paprika from ten adulterant types without complex feature extraction. S-parameter measurements were performed iteratively for each condition using a vector network analyzer, and the average values were used in the analyses. The frequency-point-level performance of the model was examined with different training/test data splitting ratios during the classification process. Furthermore, 10-fold cross-validation was performed during the training phase, and the performance of different classifiers was evaluated comparatively using overall and balanced validation and test accuracies. Under random point-wise partitioning, Subspace KNN achieved the highest numerical performance, with a mean frequency-point-level test accuracy of 99.86±0.06% across 10 independent runs. The findings demonstrate the frequency-point-level separability of microwave S-parameter responses under the investigated experimental conditions.
