Food Freshness Measurements and Product Distinguishing by a Portable Electronic Nose Based on Organic Field-Effect Transistors

Three Isomeric Dioctyl Derivatives of 2,7-Dithienyl[1]benzo-thieno[3,2-b][1]benzothiophene: Synthesis, Optical, Thermal, and Semiconductor Properties

Organic semiconductor materials are interesting due to their application in various organic electronics devices. [1]benzothieno[3,2-b][1]benzothiophene (BTBT) is a widely used building block for the creation of such materials. In this work, three novel solution-processable regioisomeric derivatives of BTBT-2,7-bis(3-octylthiophene-2-yl)BTBT (1), 2,7-bis(4-octylthiophene-2-yl)BTBT (2), and 2,7-bis(5-octylthiophene-2-yl)BTBT (3)-were synthesized and investigated. Their optoelectronic properties were characterized experimentally by ultraviolet-visible and fluorescence spectroscopy, time-resolved fluorimetry, and cyclic voltammetry and studied theoretically by Time-Dependent Density Functional Theory calculations. Their thermal properties were investigated by a thermogravimetric analysis, differential scanning calorimetry, polarizing optical microscopy, and in situ small-/wide-angle X-ray scattering measurements. It was shown that the introduction of alkyl substituents at different positions (3, 4, or 5) of thiophene moieties attached to a BTBT fragment significantly influences the optoelectronic properties, thermal stability, and phase behavior of the materials. Thin films of each compound were obtained by drop-casting, spin-coating and doctor blade techniques and used as active layers for organic field-effect transistors. All the OFETs exhibited p-channel characteristics under ambient conditions, while compound 3 showed the best electrical performance with a charge carrier mobility up to 1.1 cm2·V-1s-1 and current on/off ratio above 107.

Machine Vision with a CMOS-Based Hyperspectral Imaging Sensor Enables Sensing Meat Freshness

Imaging spectral information of materials and analysis of its properties have become an intriguing tool for consumer electronics used for food inspection, beauty care, etc. Those sensory physical quantities are difficult to quantify. Hyperspectral imaging cameras, which capture the figure and spectral information simultaneously, can be a good candidate for nondestructive remote sensing. In this study, with the aid of a hyperspectral imaging system (HIS) and machine learning (ML) techniques, meat freshness is converted into a measurable physical quantity, i.e., the freshness index (FI). Herein, the FI is defined as meat fluorescence, which has a strong correlation with the bacterial density. Combined with ML techniques, hyperspectral data are processed more efficiently. By employing linear discriminant and quadratic component analyses, the FI can be estimated from its decision boundary after hyperspectral data are obtained in an unknown freshness state. We demonstrate that the HIS integrated with ML performs as the artificial eye and brain, which is advanced machine vision for consumer electronics, including refrigerators and smartphones. Advanced sensing versatility utilized by computational sensing systems allows hyper-personalization and hyper-customization of human life.

Prediction of total volatile basic nitrogen (TVB-N) in fish meal using a metal-oxide semiconductor electronic nose based on the VMD-SSA-LSTM algorithm

BACKGROUND

The total volatile basic nitrogen (TVB-N) is the main indicator for evaluating the freshness of fish meal, and accurate detection and monitoring of TVB-N is of great significance for the health of animals and humans. Here, to realize fast and accurate identification of TVB-N, in this article, a self-developed electronic nose (e-nose) was used, and the mapping relationship between the gas sensor response characteristic information and TVB-N value was established to complete the freshness detection.

RESULTS

The TVB-N variation curve was decomposed into seven subsequences with different frequency scales by means of variational mode decomposition (VMD). Each subsequence was modelled using different long short-term memory (LSTM) models, and finally, the final TVB-N prediction result was obtained by adding the prediction results based on different frequency components. To improve the performance of the LSTM, the sparrow search algorithm (SSA) was used to optimize the number of hidden units, learning rate and regularization coefficient of LSTM. The prediction results indicated that the high accuracy was obtained by the VMD-LSTM model optimized by SSA in predicting TVB-N. The coefficient of determination (R2), the root-mean-squared error (RMSE) and relative standard deviation (RSD) between the predicted value and the actual value of TVBN were 0.91, 0.115 and 6.39%.

CONCLUSIONS

This method improves the performance of e-nose in detecting the freshness of fish meal and provides a reference for the quality detection of e-nose in other materials. © 2024 Society of Chemical Industry.

A Review of Advanced Sensor Technologies for Aquatic Products Freshness Assessment in Cold Chain Logistics

The evaluation of the upkeep and freshness of aquatic products within the cold chain is crucial due to their perishable nature, which can significantly impact both quality and safety. Conventional methods for assessing freshness in the cold chain have inherent limitations regarding specificity and accuracy, often requiring substantial time and effort. Recently, advanced sensor technologies have been developed for freshness assessment, enabling real-time and non-invasive monitoring via the detection of volatile organic compounds, biochemical markers, and physical properties. The integration of sensor technologies into cold chain logistics enhances the ability to maintain the quality and safety of aquatic products. This review examines the advancements made in multifunctional sensor devices for the freshness assessment of aquatic products in cold chain logistics, as well as the application of pattern recognition algorithms for identification and classification. It begins by outlining the categories of freshness criteria, followed by an exploration of the development of four key sensor devices: electronic noses, electronic tongues, biosensors, and flexible sensors. Furthermore, the review discusses the implementation of advanced pattern recognition algorithms in sensor devices for freshness detection and evaluation. It highlights the current status and future potential of sensor technologies for aquatic products within the cold chain, while also addressing the significant challenges that remain to be overcome.

Monitoring of meat quality and change-point detection by a sensor array and profiling of bacterial communities

BACKGROUND

Real-time monitoring of food consumer quality remains challenging due to diverse bio-chemical processes taking place in the food matrices, and hence it requires accurate analytical methods. Thresholds to determine spoiled food are often difficult to set. The existing analytical methods are too complicated for rapid in situ screening of foodstuff.

RESULTS

We have studied the dynamics of meat spoilage by electronic nose (e-nose) for digitizing the smell associated with volatile spoilage markers of meat, comparing the results with changes in the microbiome composition of the spoiling meat samples. We apply the time series analysis to follow dynamic changes in the gas profile extracted from the e-nose responses and to identify the change-point window of the meat state. The obtained e-nose features correlate with changes in the microbiome composition such as increase in the proportion of Brochothrix and Pseudomonas spp. and disappearance of Mycoplasma spp., and with representative gas sensors towards hydrogen, ammonia, and alcohol vapors with R2 values of 0.98, 0.93, and 0.91, respectively. Integration of e-nose and computer vision into a single analytical panel improved the meat state identification accuracy up to 0.85, allowing for more reliable meat state assessment.

SIGNIFICANCE

Accurate identification of the change-point in the meat state achieved by digitalizing volatile spoilage markers from the e-nose unit holds promises for application of smart miniaturized devices in food industry.

System Design Based on Biological Olfaction for Meat Analysis Using E-Nose Sensors

The deterioration of food, especially in meat products, can lead to serious health problems. Even with modern preservation technologies, a significant amount of food is lost due to microbial deterioration. As the very first step of the preservation process, the microflora that grows during the storage time and in spoiling foods should be well-known to identify critical levels. Electronic nose and gas chromatography analysis systems can provide sensitive and promising results. Similarly, bacterial analysis is an important process for determining bacterial groups that result in the emergence of such gases in gas chromatography-mass spectrometry (GC-MS) analysis during the degradation time. This study aims to determine the degradation levels for some meat types under different environmental conditions, such as temperature and duration, to compare with other measurement techniques for evaluating the verification of data. E-nose device, developed in this study, can detect carbon monoxide (CO), methane (CH4), ethanol (C2H5OH), and ammonia (NH3) using metal oxide semiconductor (MOS) sensors. In order to test sensory measurements during this period, GC-MS and microbial measurements were used. E-nose measurements show that the results are in accord with each other. This system can easily be made portable, occupying very little space.

Acetone Detection and Classification as Biomarker of Diabetes Mellitus Using a Quartz Crystal Microbalance Gas Sensor Array

A gas sensor array was developed and evaluated using four high-frequency quartz crystal microbalance devices (with a 30 MHz resonant frequency in fundamental mode). The QCM devices were coated with ethyl cellulose (EC), polymethylmethacrylate (PMMA), Apiezon L (ApL), and Apiezon T (ApT) sensing films, and deposited by the ultrasonic atomization method. The objective of this research was to propose a non-invasive technique for acetone biomarker detection, which is associated with diabetes mellitus disease. The gas sensor array was exposed to methanol, ethanol, isopropanol, and acetone biomarkers in four different concentrations, corresponding to 1, 5, 10, and 15 µL, at temperature of 22 °C and relative humidity of 20%. These samples were used because human breath contains them and they are used for disease detection. Moreover, the gas sensor responses were analyzed using principal component analysis and discriminant analysis, achieving the classification of the acetone biomarker with a 100% membership percentage when its concentration varies from 327 to 4908 ppm, and its identification from methanol, ethanol, and isopropanol.

Review on Sensor Array-Based Analytical Technologies for Quality Control of Food and Beverages

Food quality control is an important area to address, as it directly impacts the health of the whole population. To evaluate the food authenticity and quality, the organoleptic feature of the food aroma is very important, such that the composition of volatile organic compounds (VOC) is unique in each aroma, providing a basis to predict the food quality. Different types of analytical approaches have been used to assess the VOC biomarkers and other parameters in the food. The conventional approaches are based on targeted analyses using chromatography and spectroscopies coupled with chemometrics, which are highly sensitive, selective, and accurate to predict food authenticity, ageing, and geographical origin. However, these methods require passive sampling, are expensive, time-consuming, and lack real-time measurements. Alternately, gas sensor-based devices, such as the electronic nose (e-nose), bring a potential solution for the existing limitations of conventional methods, offering a real-time and cheaper point-of-care analysis of food quality assessment. Currently, research advancement in this field involves mainly metal oxide semiconductor-based chemiresistive gas sensors, which are highly sensitive, partially selective, have a short response time, and utilize diverse pattern recognition methods for the classification and identification of biomarkers. Further research interests are emerging in the use of organic nanomaterials in e-noses, which are cheaper and operable at room temperature.