Classification of the green tea varieties based on Support Vector Machines using Terahertz spectroscopy

Chen, Xiai; Zhang, Guangxin; Huang, Pingjie; Hou, Dibo; Kang, Xusheng; Ze-kui, Zhou

doi:10.1109/imtc.2011.5944018

Cited by 10 publications

(3 citation statements)

References 15 publications

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“…The quality of Chinese green tea was measured using THz‐TDS by acquiring the refractive index and absorption spectrum of 80 samples over the range of 0.2 to 1.5 THz (Xi‐Ai et al., ). Back propagation artificial neural network (BPANN), naive Bayes, and least squares support vector machines (LS‐SVM) were used to perform a multi‐class classification on the results, and LS‐SVM showed the best classification results.…”

Section: Food Applications Of Thz Spectroscopy and Imagingmentioning

confidence: 99%

A Comprehensive Review on Food Applications of Terahertz Spectroscopy and Imaging

Afsah‐Hejri

Hajeb

Ara

et al. 2019

Comp Rev Food Sci Food Safe

224

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Food product safety is a public health concern. Most of the food safety analytical and detection methods are expensive, labor intensive, and time consuming. A safe, rapid, reliable, and nondestructive detection method is needed to assure consumers that food products are safe to consume. Terahertz (THz) radiation, which has properties of both microwave and infrared, can penetrate and interact with many commonly used materials. Owing to the technological developments in sources and detectors, THz spectroscopic imaging has transitioned from a laboratory‐scale technique into a versatile imaging tool with many practical applications. In recent years, THz imaging has been shown to have great potential as an emerging nondestructive tool for food inspection. THz spectroscopy provides qualitative and quantitative information about food samples. The main applications of THz in food industries include detection of moisture, foreign bodies, inspection, and quality control. Other applications of THz technology in the food industry include detection of harmful compounds, antibiotics, and microorganisms. THz spectroscopy is a great tool for characterization of carbohydrates, amino acids, fatty acids, and vitamins. Despite its potential applications, THz technology has some limitations, such as limited penetration, scattering effect, limited sensitivity, and low limit of detection. THz technology is still expensive, and there is no available THz database library for food compounds. The scanning speed needs to be improved in the future generations of THz systems. Although many technological aspects need to be improved, THz technology has already been established in the food industry as a powerful tool with great detection and quantification ability. This paper reviews various applications of THz spectroscopy and imaging in the food industry.

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Section: Food Applications Of Thz Spectroscopy and Imagingmentioning

confidence: 99%

A Comprehensive Review on Food Applications of Terahertz Spectroscopy and Imaging

Afsah‐Hejri

Hajeb

Ara

et al. 2019

Comp Rev Food Sci Food Safe

224

View full text Add to dashboard Cite

show abstract

“…Most of the approaches perform an extraction of features either with statistical methods [5], [6] or linear feature mapping [7], [8]. Rather uncommon is the use of raw data [9], time-domain features [10], and filtered data. Support vector machines (SVM) and neural networks are widely used for automatic learning of relationships in measurement data [5]- [11].…”

Section: Introductionmentioning

confidence: 99%

Material Classification for Terahertz Images Based on Neural Networks

Kubiczek

Balzer

2022

IEEE Access

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Terahertz time-domain spectroscopy enables the extraction of electrical properties from materials. An extraction of the complex permittivity can be carried out with measurements in transmission or reflection geometry enabling the identification of materials. To perform an exact identification, the sample thickness and the angle of incidence of the terahertz radiation must be known. However, when those parameters are unknown and additionally the materials show strong absorbances, a precise differentiation between materials is challenging. A promising approach is the use of a neural network for automated material classification of terahertz images from different materials. Here, we show that a trained neural network can differentiate between 16 3D printed dielectric materials with a high accuracy of 98 % from measurements taken in transmission mode. As the constitution of the dataset has a big impact on the accuracy, various data preparations were investigated as well as the number of traces needed for achieving a well-trained network was determined. Finally, the trained neural network was evaluated with different sample thicknesses, revealing the huge impact of the materials absorbance on the extrapolation ability. This approach can be used in security application to classify harmful substances as well as for the automated generation of material maps.

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“…However, in the majority of applications, powerful but expensive and bulky desktop equipment, e.g., an HP4195A network analyzer with an impedance measurement extension, Agilent 4294, LCZ meter model 4277A, or Xiton Hydra 4200, etc., are used. Applications are in the field of bio-impedance spectroscopy (Spiller et al, 2006) and electrochemical-impedance spectroscopy (Yang and Bashir, 2008), and medical tasks like skin cancer or wound healing monitoring (Schröter et al, 2013) or fish, liver, or meat freshness determination (Guermazi and Kanoun, 2013), tea quality (Xi-Ai et al, 2011) or general food monitoring in the food industry (Ghosh and Jayas, 2009), as well as water monitoring and detergent concentration determination (Gruden et al, 2013) in, e.g., dishwashers. The size of common instrumentation equipment hampers the system realization beyond discrete proof-ofprinciple prototypes.…”

Section: Introductionmentioning

confidence: 99%

Lab-on-Spoon – a 3-D integrated hand-held multi-sensor system for low-cost food quality, safety, and processing monitoring in assisted-living systems

König¹,

Thongpull

2015

J. Sens. Sens. Syst.

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Abstract. Distributed integrated sensory systems enjoy increasing impact leveraged by the surging advance of sensor, communication, and integration technology in, e.g., the Internet of Things, cyber-physical systems, Industry 4.0, and ambient intelligence/assisted-living applications. Smart kitchens and "white goods" in general have become an active field of R&D. The goal of our research is to provide assistance for unskilled or challenged consumers by efficient sensory feedback or context on ingredient quality and cooking step results, which explicitly includes decay and contamination detection. As one front end of such a culinary-assistance system, an integrated, multi-sensor, low-cost, autonomous, smart spoon device, denoted as Lab-on-Spoon (LoS), has been conceived. The first realized instance presented here features temperature, color, and impedance spectroscopy sensing in a 3-D-printed spoon package. Acquired LoS data are subject to sensor fusion and decision making on the host system. LoS was successfully applied to liquid ingredient recognition and quality assessment, including contamination detection, in several applications, e.g., for glycerol detection in wine. In future work, improvement to sensors, electronics, and algorithms will be pursued to achieve an even more robust, dependable and self-sufficient LoS system.

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Classification of the green tea varieties based on Support Vector Machines using Terahertz spectroscopy

Cited by 10 publications

References 15 publications

A Comprehensive Review on Food Applications of Terahertz Spectroscopy and Imaging

A Comprehensive Review on Food Applications of Terahertz Spectroscopy and Imaging

Material Classification for Terahertz Images Based on Neural Networks

Lab-on-Spoon – a 3-D integrated hand-held multi-sensor system for low-cost food quality, safety, and processing monitoring in assisted-living systems

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