Artificial scent screening systems (known as electronic noses, E-noses) have been researched extensively. A portable, automatic, and accurate, real-time E-nose requires both robust cross-reactive sensing and fingerprint pattern recognition. Few E-noses have been commercialized because they suffer from either sensing or pattern-recognition issues. Here, cross-reactive colorimetric barcode combinatorics and deep convolutional neural networks (DCNNs) are combined to form a system for monitoring meat freshness that concurrently provides scent fingerprint and fingerprint recognition. The barcodes-comprising 20 different types of porous nanocomposites of chitosan, dye, and cellulose acetate-form scent fingerprints that are identifiable by DCNN. A fully supervised DCNN trained using 3475 labeled barcode images predicts meat freshness with an overall accuracy of 98.5%. Incorporating DCNN into a smartphone application forms a simple platform for rapid barcode scanning and identification of food freshness in real time. The system is fast, accurate, and non-destructive, enabling consumers and all stakeholders in the food supply chain to monitor food freshness.

Portable Food-Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks

Fast-scan cyclic voltammetry (FSCV) at carbon-fiber microelectrodes (CFMEs) is a versatile electrochemical technique to probe neurochemical dynamics in vivo. Progress in FSCV methodology continues to address analytical challenges arising from biological needs to measure low concentrations of neurotransmitters at specific sites. This review summarizes recent advances in FSCV method development in three areas: (1) waveform optimization, (2) electrode development, and (3) data analysis. First, FSCV waveform parameters such as holding potential, switching potential, and scan rate have been optimized to monitor new neurochemicals. The new waveform shapes introduce better selectivity toward specific molecules such as serotonin, histamine, hydrogen peroxide, octopamine, adenosine, guanosine, and neuropeptides. Second, CFMEs have been modified with nanomaterials such as carbon nanotubes or replaced with conducting polymers to enhance sensitivity, selectivity, and antifouling properties. Different geometries can be obtained by 3D-printing, manufacturing arrays, or fabricating carbon nanopipettes. Third, data analysis is important to sort through the thousands of CVs obtained. Recent developments in data analysis include preprocessing by digital filtering, principal components analysis for distinguishing analytes, and developing automated algorithms to detect peaks. Future challenges include multisite measurements, machine learning, and integration with other techniques. Advances in FSCV will accelerate research in neurochemistry to answer new biological questions about dynamics of signaling in the brain.

Recent advances in fast-scan cyclic voltammetry

Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play an important role in warning people about and controlling air pollution. We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NOX) concentration in an Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), and coefficient of determination (R2) were used to evaluate the performance of the regression models. Experimental results showed that the SVR-based model performed better in the prediction of the AQI (RMSE = 7.666, R2 = 0.9776, and r = 0.9887), and the RFR-based model performed better in the prediction of the NOX concentration (RMSE = 83.6716, R2 = 0.8401, and r = 0.9180). This work also illustrates that combining machine learning with air quality prediction is an efficient and convenient way to solve some related environment problems.

Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms

Recent advancements and major breakthroughs in machine learning (ML) technologies in the past decade have made it possible to collect, analyze, and interpret an unprecedented amount of sensory information. A new era for “smart” sensor systems is emerging that changes the way that conventional sensor systems are used to understand the world. Smart sensor systems have taken advantage of classic and emerging ML algorithms and modern computer hardware to create sophisticated “smart” models that are tailored specifically for sensing applications and fusing diverse sensing modalities to gain a more holistic appreciation of the system being monitored. Herein, a review of the recent sensing applications, which harness ML enabled smart sensor systems, is presented. First well‐known ML algorithms implemented in smart sensor systems for practical sensing applications are discussed. Subsequent sections summarize the practical sensing applications under two major categories: physical and chemical sensing and visual imaging sensing describing how the sensor technologies are coupled with ML “smart” models and how these systems achieve practical benefits. Finally, an outlook on the current trajectory and challenges that will be faced by future smart sensing systems and the opportunities that may be unlocked is provided.

Machine Learning‐Enabled Smart Sensor Systems

An electronic nose (e-nose) is a measuring instrument that mimics human olfaction and outputs ‘fingerprint’ information of mixed gases or odors. Generally speaking, an e-nose is mainly composed of two parts: a gas sensing system (gas sensor arrays, gas transmission paths) and an information processing system (microprocessor and related hardware, pattern recognition algorithms). It has been more than 30 years since the e-nose concept was introduced in the 1980s. Since then, e-noses have evolved from being large in size, expensive, and power-hungry instruments to portable, low cost devices with low power consumption. This paper reviews the development of compact e-nose design and calculation over the last few decades, and discusses possible future trends. Regarding the compact e-nose design, which is related to its size and weight, this paper mainly summarizes the development of sensor array design, hardware circuit design, gas path (i.e. the path through which the mixed gases to be measured flow inside the e-nose system) and sampling design, as well as portable design. For the compact e-nose calculation, which is directly related to its rapidity of detection, this review focuses on the development of on-chip calculation and wireless computing. The future trends of compact e-noses include the integration with the internet of things, wearable e-noses, and mobile e-nose systems.

https://iopscience.iop.org/article/10.1088/1361-6501/abef3b/pdf

Development of compact electronic noses: a review

In this study, a portable electronic nose (E-nose) prototype is developed using metal oxide semiconductor (MOS) sensors to detect odors of different wines. Odor detection facilitates the distinction of wines with different properties, including areas of production, vintage years, fermentation processes, and varietals. Four popular machine learning algorithms-extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and backpropagation neural network (BPNN)-were used to build identification models for different classification tasks. Experimental results show that BPNN achieved the best performance, with accuracies of 94% and 92.5% in identifying production areas and varietals, respectively; and SVM achieved the best performance in identifying vintages and fermentation processes, with accuracies of 67.3% and 60.5%, respectively. Results demonstrate the effectiveness of the developed E-nose, which could be used to distinguish different wines based on their properties following selection of an optimal algorithm.

/pdf/bionic-electronic-nose-based-on-mos-sensors-array-and-gsnshitrh5.pdf

Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection.

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Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection.