Food Quality Control Research Articles

Methods and Applications of Raman Spectroscopy: A Powerful Technique in Modern Research, Diagnosis, and Food Quality Control

Background:Raman spectroscopy has evolved into an important fast, rapid, direct, and non-destructive technique that has recently been applied in different fields.Objective:The present work aims to study the theoretical bases and the experimental techniques relate to Raman spectroscopy and highlight the performance as well as the different applications of the technique.Methods:Spectroscopy, in general, is the study of the interaction between electromagnetic radiation and matter, which corresponds to the emission or transmission of energy in the form of a wave at a given frequency. Raman spectroscopy is based on the inelastic diffusion of photons on electrons. The change in electron energy level leads to different modes of vibration of a molecule. These different vibration modes occur at specific frequencies for each molecule.Results:Raman spectroscopy is used in chemistry as a tool to identify molecules in a sample. Indeed, each Raman peak is associated with a vibration mode of a molecule; it is considered as a more useful approach to monitor the chemical parameters of samples tested in several fields, especially in food safety.Conclusion:This review covers the current research status and prospects of Raman spectroscopy. The Raman effect is considered from the time of its discovery as a great gift for chemists because it contributes to a better characterization of the structure of matter.

High-accuracy classification and origin traceability of peanut kernels based on near-infrared (NIR) spectroscopy using Adaboost - Maximum uncertainty linear discriminant analysis

Peanut kernels, known for their high nutritional value and palatability, are classified as nut food. In this study, peanut kernel samples from six distinct cities in Shandong Province, China, were examined to categorize and trace their origins. Near-infrared (NIR) spectra of samples were captured using a portable NIR-M-R2 spectrometer. After the application of Savitzky-Golay (SG) filtering, the classification was attempted using principal component analysis (PCA) plus linear discrimination analysis (LDA). Additionally, maximum uncertainty linear discriminant analysis (MLDA) was applied for comparison. A specific number of eigenvectors could respectively maximize the classification accuracies, 81.48% for PCA + LDA and 76.54% for MLDA. In order to further improve the classification accuracies, Adaboost-MLDA was proposed to develop a stronger classifier. This method, after 18 iterations, achieved remarkable effects, achieving a high accuracy of 95.06%. In a similar vein, the enhancement with preprocessing techniques multiplicative scatter correction (MSC) + SG and standard normal variate (SNV) + SG raised accuracies to 98.77% and 97.53%, respectively. The results of classifying first-order and second-order derivative spectra using Adaboost-MLDA were also described, achieving accuracies near 100%. The experiment demonstrates that integrating Adaboost with NIR spectroscopy offers a highly accurate method for peanut kernel classification, promising for practical applications in food quality control.

Hyperspectral identification of oil adulteration using machine learning techniques

Food adulteration is a global concern, drawing attention from safety authorities due to its potential health risks. Detecting and categorizing oil adulteration is crucial for consumer safety and food industry integrity. This research explores hyperspectral imaging (HSI) analysis to identify substandard oil adulteration at different stages. Using the non-destructive HSI Specim Fx 10 system, a method for precise and easy imaging-based fraud detection and classification was proposed. The 670 oil samples, including pure (Almond, Mustard, Coconut, Olive) and adulterated (Sunflower, Castor, Liquid Paraffin), were analyzed. The Savitzky-Golay filter preprocessed the images to remove noise and smooth spectral signatures. The oils were identified using various machine learning approaches, including Support Vector Machines, Logistic Regression, Linear Discriminant Analysis, Random Forests, Decision Trees, K-Nearest Neighbors, and Naïve Bayes with Linear Discriminant Analysis excelling in identification. Performance parameters, including precision, recall, F1-score, and overall accuracy, were calculated. The proposed method achieved a validation accuracy of 100%, outperforming numerous state-of-the-art approaches. This study introduces a robust pipeline for effective oil adulteration detection, offering a significant advancement in food safety and quality control.

An Overview of Modern Biotechnological Tools in Aquatic Food Production – A Review

Abstract Aquatic food production system raises aquatic organisms including fish, shellfish and seaweeds for human consumption and associated value chains. Moreover, as the global human population continues to expand at a high rate and is expected to reach over 9 billion by 2030, developing a cost-efficient production method is a significant challenge in the future development of the aquatic food production industry to provide food and nutritional security with high-quality animal protein. Recent advancements in biotechnological tools and approaches provided a new toolset that can be used to design and optimize the existing processes such as food preservation, fermentation, packaging, quality control and setting proper guidelines to manufacture and process genetically modified fish. At the same time, with the refinement of technology, these are becoming easier applicable and transferable to several other aquatic species production systems. These trends have resulted in exploiting new and unconventional microbial systems with sophisticated properties, which render promising results in the production industry. Here, we highlight the recent advances in the newly emerging biotechnological technology in the production of fish and fish products and discuss the potential of these tools as a sustainable platform for centuries to come with a significant impact on the aquatic food production industry.

Use of hyperspectral imaging devices for the measurement of small granular samples: Evaluation of grape seed protein concentrates

Hyperspectral imaging is a well-known technique for quality control of food and agricultural products. This technique is often applied to the measurement of large and heterogeneous samples, where chemical imaging is extremely useful. In addition, when the amount of sample is limited by different factors (price, other analyses, sample production, etc.) hyperspectral imaging is an alternative to traditional spectroscopes for acquiring its spectral information.In this study, a new and specific methodology to acquire hyperspectral information from small amounts of granular samples has been developed. For this purpose, two different hyperspectral devices (400–1100 nm, 900–1700 nm) have been used. A statistical procedure has been followed to test the proposed method. In grape seed protein concentrates, NIR radiation (900–1700 nm) penetrates deeper into the sample than VisNIR radiation (400–1100 nm). Therefore, the minimum amount of sample needed to measure in the NIR range is larger than that needed to measure in the VisNIR range. Finally, different calibration models have been developed for the control of protein content in the tested samples. Standard errors of prediction obtained in external validation are similar to those reported in the literature when sample amount is not an issue (9–10 %).

Toxoplasmosis outbreak in São Paulo, Brazil: Epidemiology and visual outcome.

To describe a 2019 acute toxoplasmosis outbreak in the city of São Paulo, Brazil, and to evaluate the laboratory serological profile for toxoplasmosis for three consecutive years. The ophthalmological manifestations of the patients involved in the outbreak were also studied. A cross-sectional descriptive study of a toxoplasmosis outbreak in São Paulo, Brazil, between February and May 2019. Epidemiological data were described, as were the observed ocular manifestations. As part of this study the number of patients with positive IgM toxoplasmosis serology was obtained from a large laboratory network (DASA) for three consecutive years, including the year of the outbreak (2018, 2019, 2020). Eighty-three individuals were identified in the outbreak and two clusters were studied. The clinical picture of at least 77% of the patients, the epidemiological analysis, and the short incubation period (5-8 days) suggested contamination by oocysts. Serological laboratory data analysis revealed an increase of positive toxoplasmosis IgM in 2019 of 73% compared to the previous year. Ophthalmological examination revealed that at least 4.8% of the patients developed toxoplasmic retinochoroiditis, none of whom had been treated during the acute systemic disease. Our findings indicate vegetable contamination as the possible source of this outbreak, a high prevalence of toxoplasmosis in São Paulo during the outbreak period, and a drop in the number of tests during the COVID-19 pandemic. Retinochoroiditis was observed in at least 4.8% of the cases. We confirm the need to implement effective means for the prevention, diagnosis, and treatment of the disease. This may involve raising awareness among the population of the importance of vegetable hygiene, and improved quality control of food and water.

Acrylamide in food products and the role of electrochemical biosensors in its detection: a comprehensive review.

In this review, the mechanisms of acrylamide formation in food, along with aspects related to its toxicity and associated consumption risks, are investigated, highlighting the potential impact on human health. European regulations regarding acrylamide content in food products are also addressed, emphasizing the importance of monitoring and detecting this substance in nutrition, by public health protection measures. The primary objective of the research is to explore and analyze innovative methods for detecting acrylamide in food, with a particular focus on electrochemical biosensors. This research direction is motivated by the need to develop rapid, sensitive, and efficient monitoring techniques for this toxic compound in food products, considering the associated consumption risks. The research has revealed several significant results. Studies have shown that electrochemical biosensors based on hemoglobin exhibited increased sensitivity and low detection limits, capable of detecting very low concentrations of acrylamide in processed foods. Additionally, it has been found that the use of functionalized nanomaterials, such as carbon nanotubes and gold nanoparticles, has led to the improvement of electrochemical biosensor performance in acrylamide detection. The integration of these technological innovations and functionalization strategies has enhanced the sensitivity, specificity, and stability of biosensors in measuring acrylamides. Thus, the results of this research offer promising perspectives for the development of precise and efficient methods for monitoring acrylamides in food, contributing to the improvement of food quality control and the protection of consumer health.

Discriminative feature analysis of dairy products based on machine learning algorithms and Raman spectroscopy

Discriminant analysis of similar food samples is an important aspect of achieving food quality control. The effective combination of Raman spectroscopy and machine learning algorithms has become an extremely attractive approach to develop intelligent discrimination techniques. Feature spectral analysis can help researchers gain a deeper understanding of the data patterns in food quality discrimination. Herein, this work takes the discrimination of three brands of dairy products as an example to investigate the Raman spectral feature based on the support vector machines (SVM), extreme learning machines (ELM) and convolutional neural network (CNN) algorithms. The results show that there are certain differences in the optimal spectral feature interval corresponding to different machine learning algorithms. Selecting the appropriate spectral feature interval can maintain high recognition accuracy and improve the computational efficiency of the algorithm. For example, the SVM algorithm has a recognition accuracy of 100% in the 890-980 cm−1, 1410-1500 cm−1 fusion spectral range, which takes about 200 s. The ELM algorithm also has a recognition accuracy of 100% in the 890-980 cm−1, 1410-1500 cm−1 fusion spectral range, which takes less than 0.3 s. The CNN algorithm has a recognition accuracy of 100% in the 890-980 cm−1, 1050-1180 cm−1, 1410-1500 cm−1 fusion spectral range, which takes about 80 s. In addition, by analyzing the distribution of spectral feature intervals based on Euclidean distance, the distribution of experimental samples based on feature spectra is visually displayed. Through the spectral feature analysis process of similar samples, a set of analysis strategies is provided to deeply reveal the data foundation of classification algorithms, which can provide reference for the analysis of relevant discriminative research patterns.

USO DE FILMES DE QUITOSANA PARA EXTRAÇÃO DO CORANTE ALIMENTAR BORDEAUX S COM QUANTIFICAÇÃO POR ANÁLISE DE IMAGENS DIGITAIS

USE OF CHITOSAN FILMS TO REMOVE BORDEAUX S FOOD COLORING WITH QUANTIFICATION BY DIGITAL IMAGE ANALYSIS. Bordeaux S is a food dye still allowed in Brazil, being present in soft drinks, gelatins, chewing gums, candies and many other industrialized foodstuffs. Because of its carcinogenic potential, it is forbidden to be added to food in Japan, in the USA and in most of the European Union. Many studies related to the employment of digital images analysis to food quality control have been published in recent years mainly due to its low costs of implementation. Nevertheless, one of the few drawbacks of applying this technique is its poor sensitivity, which can be significantly improved by employing solid substrates, such as chitosan films. In this work, chitosan sorbent films were synthesized, characterized and used to retain Bordeaux S present in gelatin samples, followed by recording the films’ images and quantifying the analyte by digital images analysis. The proposed method was able to extract the analyte and eliminate interferences present in the matrixes in a single step. LOD of 20 µg L-1 and LOQ of 74 µg L-1 were obtained, allowing adequate quantification when considering the recommendations of the Brazilian Health Regulatory Agency (ANVISA).

Structure-Function Relationship of Highly Reactive CuOx Clusters on Co3 O4 for Selective Formaldehyde Sensing at Low Temperatures.

Designing reactive surface clusters at the nanoscale on metal-oxide supports enables selective molecular interactions in low-temperature catalysis and chemical sensing. Yet, finding effective material combinations and identifying the reactive site remains challenging and an obstacle for rational catalyst/sensor design. Here, the low-temperature oxidation of formaldehyde with CuOx clusters on Co3 O4 nanoparticles is demonstrated yielding an excellent sensor for this critical air pollutant. When fabricated by flame-aerosol technology, such CuOx clusters are finely dispersed, while some Cu ions are incorporated into the Co3 O4 lattice enhancing thermal stability. Importantly, infrared spectroscopy of adsorbed CO, near edge X-ray absorption fine structure spectroscopy and temperature-programmed reduction in H2 identified Cu+ and Cu2+ species in these clusters as active sites. Remarkably, the Cu+ surface concentration correlated with the apparent activation energy of formaldehyde oxidation (Spearman's coefficient ρ = 0.89) and sensor response (0.96), rendering it a performance descriptor. At optimal composition, such sensors detected even the lowest formaldehyde levels of 3 parts-per-billion (ppb) at 75°C, superior to state-of-the-art sensors. Also, selectivity to other aldehydes, ketones, alcohols, and inorganic compounds, robustness to humidity and stable performance over 4 weeks are achieved, rendering such sensors promising as gas detectors in health monitoring, air and food quality control.

A novel method for detection of internal quality of walnut kernels using low-field magnetic resonance imaging

This study presents a novel non-destructive method for assessing walnut internal quality using Low-Field Magnetic Resonance Imaging (LF-MRI) and radiomics technology. Due to the hard shell of walnuts, determining their interior quality is challenging. By analyzing walnut kernel Low-Field Nuclear Magnetic Resonance (LF-NMR) relaxation curve characteristics and LF-MRI imaging, radiomics techniques were employed to extract, select, and reduce the dimensionality of features from MRI images. Ten significant features strongly correlated with walnut kernel rancidity were identified, and machine learning models were built using six optimized classification algorithms. The Random Forest (RF) models achieved impressive performance with a test accuracy of 93.52%, test recall scores of 92.78%, and test F1 scores of 96.81%. Additionally, the RF model demonstrated a higher net benefit within the threshold probability range of 0.02 to 0.98, as indicated by the DCA curve. The study's findings have significant implications for the walnut industry and food quality control, providing a reliable and efficient means of detecting and identifying walnut kernel rancidity.

Rapid and high accurate identification of Escherichia coli active and inactivated state by hyperspectral microscope imaging combing with machine learning algorithm

Rapid identification of the active state of foodborne bacteria is crucial for ensuring the safety and quality control of food or pharmaceutical products. In this study, a combination of hyperspectral microscope imaging (HMI) and machine learning algorithm is employed for the identification of active state of Escherichia coli (E. coli). Hyperspectral microscope images of live, 100 ℃ heat inactivation and 121 ℃ high-pressure inactivation of E. coli are collected in wavelength range of 370–1060 nm. Savitzky-Golay (SG) smoothing combing with normalization is used for spectra preprocessing. And principal component analysis (PCA) is employed for spectral dimension reduction. Four different regions of interest (ROIs), including the entire bacterial cell ROI (cell), the outer cell wall ROI (cell_r), the membrane structure ROI (cell_w) formed by the cell wall and cell membrane, and the central of the cell ROI (cell_cy), are extracted and used as model input variables to investigate the influence on the modeling results. Five model algorithms, support vector machines (SVM), random forests (RF), k-nearest neighbors (KNN) algorithms, discriminant analysis (DA) classifiers, and long short-term memory (LSTM) neural networks are used and compared. Modeling results with spectral data of cell_r perform better than those with other ROIs. Accuracy of the models with data of the cell_r ROI are as follows: 79.78% for SVM, 95.11% for RF, 91.33% for KNN, 98.22% for DA, and 93.78% for LSTM. DA achieves the highest classification accuracy. The results show that high-temperature inactivation induces changes in bacterial tissue and morphology, resulting in certain spectral differences among bacteria in three different states. The combination of hyperspectral microscope imaging and machine learning algorithm can provide an effective method for identification of active and inactive states of E. coli. Furthermore, the model, constructed with the data of cell_r ROI, exhibits the best performance in identification.

Development of Electrochemical Paper-Based Devices for Field Assays

The birth of the modern field of paper-based analytical devices (PADs) was marked by the pioneering work by the Whitesides’ group which demonstrated that it was possible to perform complex manipulation of liquids within hydrophilic paper channels delimited by hydrophobic barriers [1]. The key features of paper as a platform are: a) flexibility; b) low thickness and lightness; c) absorbency; d) high surface-to-volume ratio; e) hydrophilicity and capillary action; f) chemical and biological inertness; g) disposability and biodegradability, and; h) low cost and wide availability. Among the various detection modes that are compatible with PADs, electrochemical detection is very attractive since electrochemical transducers can be readily and rapidly patterned on paper using various approaches. Combined with low-cost and portable instrumentation, electrochemical PADs (ePADs) are well suited to on-site assays and point-of-care testing and relevant applications have been developed in various fields such as clinical diagnostics, environmental monitoring and food quality control [2-4].The methods of patterning of the hydrophobic barriers and of depositing the electrodes are critical in the fabrication of ePADs and several approaches have been proposed in the literature [2-4]. Τhe aim of this work was the development of different approaches for the fabrication of ePADs. For this purpose, hydrophobic barriers were delimited using: x-y plotting with commercial hydrophobic marker pens, wax printing and thermal printing. Electrodes were deposited on the PADs using x-y plotting with commercial writing pencils and conductive inks formulated in-house and screen-printing. Different fabrication parameters were optimized including the selection of the paper substrate, the conditions for the formation of the hydrophobic barriers and the electrode deposition parameters. Applicability of the ePADs was demonstrated for different applications for on-site assays including the enzymatic amperometric detection of glucose, the determination of trace metals by stripping analysis and the voltammetric detection of organic compounds.

Applications of Artificial Intelligence and Machine Learning in Food Quality Control and Safety Assessment

Applications of Artificial Intelligence and Machine Learning in Food Quality Control and Safety Assessment

Detection and quantification of corn starch and wheat flour as adulterants in milk powder by near- and mid-infrared spectroscopy coupled with chemometric routines

Adulterating foods, such as milk powder (MP), is a common practice in countries with no rigid policy on food quality control. This study employs Fourier transform near-infrared and mid-infrared (FT-NIR, FT-MIR) spectroscopy and chemometric analysis to detect milk powder adulteration with corn starch (CS) and wheat flour (WF) from 0.00 to 30.00% w/w concentrations. Partial least square regression (PLSR) models were developed, optimized, and compared to quantify corn starch and wheat flour adulterations. According to the results, the root mean square error prediction (RMSEP) for FT-NIR and FT-MIR in corn starch was 0.74 and 1.69% w/w and 0.82 and 2.63% w/w for wheat flour, respectively. FT-NIR spectroscopy, rather than FT-MIR coupled with the appropriate chemometrics models represents a more valuable tool for simple, rapid, and nondestructive detection of adulterants in milk powder. The recent availability of portable instruments, combined with suitable chemometric tools, makes it possible to discriminate adulterated food samples in situ.

Structural switching aptamer-based electrochemical sensor for mycotoxin patulin detection

In this study, an electrochemical and aptamer-based aptasensor was developed for the sensitive detection of patulin, a mycotoxin commonly found in fruits and fruit-based products. The aptasensor used an innovative structural switching signal-off platform for detecting patulin. The aptamer immobilization on screen-printed carbon electrodes was achieved through Au electrodeposition and thiol group (-SH) route. Response surface methodology was used to determine the optimal incubation times for the aptamer, blocking agent, and target molecule, which were found to be 180 min, 40 min, and 89 min, respectively. The response of the aptamer to different concentrations of patulin was measured using square wave voltammetry by exploiting the structural switching mechanism. The sensor response was determined by quantifying differences in the aptasensor's background current. The aptasensor exhibited a linear working range of 1–25 μM and a low detection limit of 3.56 ng/mL for patulin. The aptasensor's relative standard deviation and accuracy were determined to be 0.067 and 94.4%, respectively. A non-specific interaction was observed at low concentrations of two other mycotoxins, ochratoxin A and zearalenone. The interference from ochratoxin A in the measurements was below 10%. In real sample tests using apple juice, interference, particularly at low concentrations, had changed the recovery of patulin negatively with a significant effect on the structural switching behavior. Nevertheless, at a concentration of 25 ng/mL, the interference effect was eliminated, and the recovery standard deviation improved to 6.6%. The aptasensor's stability was evaluated over 10 days, and it demonstrated good performance, retaining 13.12% of its initial response. These findings demonstrate the potential of the developed electrochemical aptasensor for the sensitive detection of patulin in fruit-based products, with prospects for application in food safety and quality control.

Microwave-assisted UV digestion of starch and skimmed milk powder: Environmentally friendly protocol for essential and toxic elements determination

The present work evaluated a microwave-assisted wet digestion method using diluted HNO3 with in situ UV radiation for the digestion of starch and skimmed milk powder for further metals determination by spectrometric plasma-based techniques. The sample digestion was conducted using an in situ UV lamp (electrodeless discharge lamp), and the digestion efficiency was improved by employing O2 (20 bar) and 2 mL 30 % H2O2 as auxiliary reagents. The accuracy of the proposed digestion method was evaluated by metals determination (Ca, Cd, Cu, Fe, K, Mg, Mo, Mn, Na, Pb, and Zn) in certificated reference material, which agreed with certified values (Student t-test <0,05). With the use of a UV lamp an environmentally friendly protocol was developed for starch and skimmed milk powder digestion using 0.1 mol L−1 HNO3 with auxiliary reagents (H2O2 or O2). The RCC value ranged from 0.9 to 1.2 % (starch and skimmed milk powder, respectively). The simultaneous cooling approach further improved the digestion efficiency (RCC <0,3 % for both samples), allowing to use milder digestion conditions, or even just water, being environmentally friendly, reducing the waste generation and reagents consumption, allowing food quality control through a greener approach.

Graphene Pseudoreference Electrode for the Development of a Practical Paper-Based Electrochemical Heavy Metal Sensor.

Paper-based electrochemical devices (PEDs) have emerged as versatile platforms that bridge analytical chemistry and materials science, demonstrating advantages of portability, cost-effectiveness, and environmental sustainability. This study investigates the integration of a graphene pseudoreference electrode (GPRE) into a PED, and it exhibits potential advantages over the traditional Ag/AgCl pseudoreference electrode (PRE). In addition, the electrochemical properties and stability of GPRE are compared with those of the traditional Ag/AgCl PRE. The results demonstrate that GPRE exhibits a stable and reproducible potential during electrochemical measurement throughout 180 days, demonstrating its suitability as an alternative to an expensive metal PRE. Furthermore, a GPRE-incorporated paper-based device is designed and evaluated for use in the electrochemical detection of cadmium (Cd) and lead (Pb) using an in situ bismuth-modified electrode. The GPRE-incorporated PED exhibited good analytical performance, with a low limit of detection of 0.69 and 5.77 ng mL-1 and electrochemical sensitivities of 70.16 and 38.34 μA·mL·μg-1·cm-2 for Cd(II) and Pb(II), respectively. More than 99.9% accuracy of the sensor was obtained for both ions with respect to conventional inductively coupled plasma-mass spectrometry. The results highlight the effectiveness and suitability of the GPRE-incorporated PED as a sensor for various applications, such as environmental monitoring, food quality control, and medical diagnostics.

A commentary on the development and use of smartphone imaging devices.

Current-age smartphones are known for their wide array of functionality and are now being utilized in the field of healthcare and medicine due to their proven capabilities as smartphone imaging devices (SIDs). Recent technical advancements enabled the integration of special add-on lenses with smartphones to transform them into SIDs. With the rising demand for efficient point-of-care (PoC) devices for better diagnostic applications, SIDs will be a one-stop solution. Additionally, portability, user-friendliness and low-cost make it accessible for all even at remote locations. Furthermore, improvements in resolution, magnification and field-of-view (FOV) have attracted the scientific community to use SIDs in various biomedical applications such as disease diagnosis, food quality control and pathogen detection. SIDs can be arranged in various combinational setups by using different illumination sources and optics to achieve suitable contrast and visibility of the specimen under study. This Commentary illustrates the various illumination sources used in SID and also spotlights their design and applications.

A tunable star-shaped highly sensitive microwave sensor for solid and liquid sensing

This paper presents the development of an innovative, tunable star-shaped microwave sensor operating at two distinct frequency bands C and X band, optimized for high-sensitivity sensing of both solid and liquid mediums. The sensor's innovative star-shaped geometry augments the surface interaction with the target substances, significantly enhancing the sensor's response and sensitivity. Furthermore, tunability allows the sensor to adapt to different sensing environments, maximizing the detection potential at both frequencies. This research delves into the sensor's performance when using four different substrates for solid sensing containing FR-4, RO-3035, RO-5880, and RO-6202. Each substrate, characterized by unique electromagnetic properties, contributes differently to the sensor's sensitivity and resolution. The extensive experimental results highlight the sensor's proficiency in detecting and differentiating between these substrates, revealing the impact of material selection on sensor performance. The sensor's ability to distinguish between various liquids, including water, milk, oil, and energy drinks, is evaluated in the liquid sensing scenario. The sensor successfully detects the subtle variances in the electromagnetic properties of these liquids, suggesting its capability for high-resolution liquid sensing. The proposed MTM-based sensor resonates at 4.46 GHz and 11.48 GHz, respectively. Numerical software CST was used to analyze all the parameters. A 3 × 3 array was used in the prototype design, and it was used to measure the C-band resonance frequency. On the other hand, a compact unit cell was used to measure the X-band resonance frequency. The outcomes suggest a substantial potential for this sensor design in various industries and scientific fields, including food quality control, the oil industry, biomedical applications, and environmental monitoring. The unique combination of star-shaped design, tunable frequencies, and various substrates offers unprecedented flexibility and sensitivity in solid and liquid sensing on microwave frequency ranges.