Cell-based biosensors: Recent trends, challenges and future perspectives.

Recently, there is an increasing demand for food safety and quality monitoring in the agrifood sector, needing active and smart packaging. Radio Frequency Identification (RFID) has the potential to become one of the most promising technology for the food industry since it can satisfy all the requirements of a sensor and identification in food safety monitoring, packages tracking, inventory control, early warning, and easy check-out. The purpose of this paper is to introduce the recent advancement of the chipless RFID humidity, temperature, gas, pH, and bio-sensors, as these types of sensors are required for evaluating the quality of the food in the food industry. Additionally, the paper explains the requirements of the food industry for quality monitoring of the food products, covers the existing challenges of chipless RFID sensor technology, and highlights the future direction of the research.

Potential Chipless RFID Sensors for Food Packaging Applications: A Review

Background Sensory analysis is the evaluation of the signals received through senses of sight, sound, taste, smell, and touch. The traditional methods for the analysis of sensory properties including trained sensory panel, colourimeters and texture analysers are invasive, laborious, and small-scale procedures. Hyperspectral imaging systems (HSI) has emerged as a less time-consuming and non-destructive method to determine the sensory properties of a diverse range of food products. Scope and approach This review provides a comprehensive overview of recent application developments since 2010 for identifying sensory properties including colour, defects, texture, flavour, freshness, and maturity in various food products by HSI. Key findings and conclusions The Vis-NIR (400–1000 nm) hyperspectral imaging is most used for the assessment of sensory properties. Moreover, the commonly applied multivariate analysis in the sensory evaluation by HSI is linear regression algorithms (PLSR and MLR), but non-linear analysis achieves better performance for the prediction of sensory features. Regression models are applied for determining the texture, colour, flavour, and freshness of food products while classification models are used to detect the defects and maturity by which the most accurate results are obtained. Furthermore, the combination of algorithms and the integration of spectral and spatial information yield more accurate results in the assessment of sensory features. Although there are still some limitations to overcome for the improvement of the HSI system, studies on the application of HSI in the real-time monitoring of sensory properties have proved a great potential for industrial applications.

Rapid and noninvasive sensory analyses of food products by hyperspectral imaging: Recent application developments

A deep feature mining method of electronic nose sensor data for identifying beer olfactory information

With the rapid development of high technology, chemical science is not as it used to be a century ago. Many chemists acquire and utilize skills that are well beyond the traditional definition of chemistry. The digital age has transformed chemistry laboratories. One aspect of this transformation is the progressing implementation of electronics and computer science in chemistry research. In the past decade, numerous chemistry-oriented studies have benefited from the implementation of electronic modules, including microcontroller boards (MCBs), single-board computers (SBCs), professional grade control and data acquisition systems, as well as field-programmable gate arrays (FPGAs). In particular, MCBs and SBCs provide good value for money. The application areas for electronic modules in chemistry research include construction of simple detection systems based on spectrophotometry and spectrofluorometry principles, customizing laboratory devices for automation of common laboratory practices, control of reaction systems (batch- and flow-based), extraction systems, chromatographic and electrophoretic systems, microfluidic systems (classical and nonclassical), custom-built polymerase chain reaction devices, gas-phase analyte detection systems, chemical robots and drones, construction of FPGA-based imaging systems, and the Internet-of-Chemical-Things. The technology is easy to handle, and many chemists have managed to train themselves in its implementation. The only major obstacle in its implementation is probably one's imagination.

Elevating Chemistry Research with a Modern Electronics Toolkit.

Food freshness has been paid great attention due to its direct relationship with human health and safety, and approaches for food freshness evaluation have attracted much interest from researchers In this paper, we developed a miniaturized electronic nose system for convenient, direct and real-time food freshness evaluation by analyzing gases in a refrigerator (4 °C) The proposed system consists of a gas sampling module and MOS gas sensor array The gas sampling module was used to extract gases from the refrigerator and clean the gas path by controlling a pump and a three-way valve The gas sensor array is composed of three MOS sensors to monitor odor changes in the refrigerator Meanwhile, a food freshness assessment model was established based on the sensor array results and a comparison with human sensory evaluation results In order to confirm the effectiveness of the system, we performed experiments on meat, vegetables and fruits with three freshness levels including fresh, semi-fresh and spoiled The accuracy of the system to identify the three freshness levels is 848%, 680% and 962% respectively The experimental results demonstrated that the developed electronic nose can effectively evaluate the food freshness level Therefore, the proposed electronic nose provides a non-destructive, low cost and convenient platform for fast and real-time evaluation of food freshness in refrigerators

Real-time assessment of food freshness in refrigerators based on a miniaturized electronic nose

Integrated olfaction, gustation and toxicity detection by a versatile bioengineered cell-based biomimetic sensor.

The hydrogen and methane breath test has been widely used in the diagnosis of SIBO (small intestinal bacterial overgrowth), which is noninvasive and convenient with good accuracy. However, most studies use commercial GC-MS to analyze breath components which requires complex operation and has a high cost, while the most common simplified devices only have a single function with some drawbacks. This study proposes a convenient, small-sized and multi-functional fast gas chromatograph (Fast GC) with a low detection limit and sufficient resolution to simultaneously determine hydrogen and methane concentrations, corrected using the carbon dioxide concentration, in breath. The system can eliminate the interference caused by dilution with outside gases, the breathing mode of the patient and other factors of alveolar gas. The detection range of the Fast GC system was 2–200 ppm, the resolution was 1 ppm, the precision was less than 10%, and the preheating time was significantly shortened to 2 hours, as well as the size being reduced to 40 cm × 30 cm × 15 cm. Two SIBO diagnostic models were established and samples from forty-seven healthy people and patients were collected and analyzed by our system. This method has the advantages of high specificity, and non-invasive and simple operation, so that it can be used for the diagnosis of increasing volumes of clinical and domestic patients.

Fan Gao

Papers

Real-time assessment of food freshness in refrigerators based on a miniaturized electronic nose

Integrated olfaction, gustation and toxicity detection by a versatile bioengineered cell-based biomimetic sensor.

Simultaneous detection of hydrogen and methane in breath for the diagnosis of small intestinal bacterial overgrowth by fast gas chromatography