Volatile organic compounds gas sensor based on quartz crystal microbalance for fruit freshness detection: A review

Liu, Kewei; Zhang, Chao

doi:10.1016/j.foodchem.2020.127615

Cited by 75 publications

(29 citation statements)

References 84 publications

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“…Brezolin et al fabricated gravimetric sensors utilizing silicon cantilevers coated with polyaniline (PANI) or alternating bilayers of multi-wall carbon nanotubes and PANI for the detection of the brown stinkbug ( Euschistus heros ) pheromone, methyl 2,6,10-trimethyltridecanoate, with a LOD lower than 1 ppt. , Similarly, Steffens et al used PANI-functionalized cantilevers as sensing elements for the detection and quantification of the alarm pheromone 2-heptanone, with a LOD of 31 ppm . QCM-based sensors have also shown remarkable efficiency in the detection of plant-produced ICs. , Iqbal et al used polystyrene-coated QCM to detect ppm levels (<20 ppm) of limonene, eucalyptol, and α-pinene, which have been applied for the push–pull management of tomato whitefly, Bemisia tabaci , cabbage fly, Delia radicum , and red palm weevil, Rhynchophorus ferrugineus , respectively. − QCM sensors coated with cyclodextrins, cyclic oligosaccharides able to host volatiles in their cavity, were used for the detection of the floral ICs geraniol, ethyl butyrate, and limonene . Additional enhancement of selectivity and sensitivity can be achieved by coating the QCM with MIPs which exhibit tailored recognition sites complementary to targeted volatiles, in terms of shape, size, and functional groups .…”

Section: Chemical Sensorsmentioning

confidence: 99%

“…129 QCM-based sensors have also shown remarkable efficiency in the detection of plant-produced ICs. 72,130 Iqbal et al used polystyrene-coated QCM to detect ppm levels (<20 ppm) of limonene, eucalyptol, and α-pinene, which have been applied for the push−pull management of tomato whitefly, Bemisia tabaci, cabbage fly, Delia radicum, and red palm weevil, Rhynchophorus ferrugineus, respectively. 131−134 QCM sensors coated with cyclodextrins, cyclic oligosaccharides able to host volatiles in their cavity, were used for the detection of the floral ICs geraniol, 135 ethyl butyrate, and limonene.…”

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confidence: 99%

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Sensing of Airborne Infochemicals for Green Pest Management: What Is the Challenge?

et al. 2021

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One of the biggest global challenges for our societies is to provide natural resources to the rapidly expanding population while maintaining sustainable and ecologically friendly products. The increasing public concern about toxic insecticides has resulted in the rapid development of alternative techniques based on natural infochemicals (ICs). ICs (e.g., pheromones, allelochemicals, volatile organic compounds) are secondary metabolites produced by plants and animals and used as information vectors governing their interactions. Such chemical language is the primary focus of chemical ecology, where behavior-modifying chemicals are used as tools for green pest management. The success of ecological programs highly depends on several factors, including the amount of ICs that enclose the crop, the range of their diffusion, and the uniformity of their application, which makes precise detection and quantification of ICs essential for efficient and profitable pest control. However, the sensing of such molecules remains challenging, and the number of devices able to detect ICs in air is so far limited. In this review, we will present the advances in sensing of ICs including biochemical sensors mimicking the olfactory system, chemical sensors, and sensor arrays (e-noses). We will also present several mathematical models used in integrated pest management to describe how ICs diffuse in the ambient air and how the structure of the odor plume affects the pest dynamics.

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Section: Chemical Sensorsmentioning

confidence: 99%

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confidence: 99%

Sensing of Airborne Infochemicals for Green Pest Management: What Is the Challenge?

et al. 2021

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“…Many plant-based fragrant components, such as secondary metabolites, essential oils, polyphenols, terpenes, and other compounds, are volatile compounds that may be used as early onset indicators for age, health, and methods of plant production [245][246][247][248][249]. In food products, they are often present as odor indicators for food freshness, shelf life, and/or pathogen contamination [250][251][252]. Mangos, for example, are the fifth most commercialized fruit around the world [253].…”

Section: Gas-phase Qcm Advancesmentioning

confidence: 99%

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Bwambok

Siraj

Macchi

et al. 2020

Sensors

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Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

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“…Although there have been a few previous reviews on the E-Nose, they focus either on specific applications [37][38][39] or only a portion of the entire E-Nose data processing pipeline [22,40]. In comparison, this review aims to provide a comprehensive study of machine learning techniques for general E-Nose applications.…”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Liu

2021

Sensors

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Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

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Volatile organic compounds gas sensor based on quartz crystal microbalance for fruit freshness detection: A review

Cited by 75 publications

References 84 publications

Sensing of Airborne Infochemicals for Green Pest Management: What Is the Challenge?

Sensing of Airborne Infochemicals for Green Pest Management: What Is the Challenge?

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

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