Use of electronic nose technology for identifying rice infestation by Nilaparvata lugens

Zhang, Bo; Wang, Jun

doi:10.1016/j.snb.2011.07.002

Cited by 45 publications

(30 citation statements)

References 18 publications

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“…Each sensor has a certain degree of affi nity towards specifi c chemical or volatile Acta Alimentaria 45, 2016 compounds. The nomenclature and characteristics of the sensors have been reported (ZHOU & WANG, 2011).…”

Section: The E-nose and Detection Proceduresmentioning

confidence: 99%

Fast Determination of Lycopene Content and Soluble Solid Content of Cherry Tomatoes Using Metal Oxide Sensors Based Electronic Nose

Xiao¹,

Wang

Hong³

et al. 2016

Acta Alimentaria

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Lycopene content (LC) and soluble solid content (SSC) are important quality indicators for cherry tomatoes. This study attempted simultaneous analysis of inner quality of cherry tomato by Electronic nose (E-nose) using multivariate analysis. E-nose was used for data acquisition, the response signals were regressed by multiple linear regression (MLR) and partial least square regression (PLS) to build predictive models. The performances of the predictive models were tested according to root mean square and correlation coeffi cient (R 2 ) in the training set and prediction set. The results showed that MLR models were superior to PLS model, with higher value of R 2 and lower values of for RMSE fi rmness, pH, SSC, and LC. Together with MLR, E-nose could be used to obtain fi rmness, pH, soluble solid and lycopene contents in cherry tomatoes.

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Section: The E-nose and Detection Proceduresmentioning

confidence: 99%

Fast Determination of Lycopene Content and Soluble Solid Content of Cherry Tomatoes Using Metal Oxide Sensors Based Electronic Nose

Xiao¹,

Wang

Hong³

et al. 2016

Acta Alimentaria

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“…However, although the levels of mineral elements of same producing areas are quite stable, the rice samples from the same areas could have different compositions of the volatile substances (Feng et al ., ; Promchan et al ., ). The published studies showed that the origin discrimination of rice based on one factor (mineral elements or volatile components) do not comprehensively reflect the attribute of the products (Gonzálvez et al ., ; Zhou & Wang, ,b; Promchan et al ., ). Furthermore, IRMS analysis for food discrimination is significantly influenced by the environmental conditions, which might not be suitable for mineral elements analysis of rice in this case, as Wuchang and Panjin are both located in the north‐eastern part of China, having similar environmental and geographical factors (Luo et al ., ,b).…”

Section: Introductionmentioning

confidence: 97%

“…The E‐nose response combined with PCA enabled a rapid differentiation of the rice extrudates with various mesona blumes gum contents based on ketones, aldehydes, pyrrole, piperazine, furan and some herbal flavour compounds (Feng et al ., ). The E‐nose and chemometrics containing PCA and LDA were able to distinguish rice by different pest treatments (Zhou & Wang, ,b).…”

Section: Introductionmentioning

confidence: 99%

“…The four rice samples of long grain type could be accurately classified using an E‐nose (Zheng et al ., ). The rice plants with different types of damage were discriminated by E‐nose combined with chemometrics (Zhou & Wang, ,b). The good discrimination between scented and nonscented rice samples was obtained based on the analysis of rice volatile substances (Peris & Escuder‐Gilabert, ).…”

Section: Introductionmentioning

confidence: 99%

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Multiangle discrimination of geographical origin of rice based on analysis of mineral elements and characteristic volatile components

Fang

Shen

et al. 2018

Int J of Food Sci Tech

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Summary The aim of the study was to develop a multiangle discrimination method which combines mineral elements with volatile compounds to identify the geographical origin of rice. The levels of twenty‐four elements and characteristic volatile compositions of rice samples from different growing area (Panjin, Wuchang and Sheyang city of China) were measured by inductively coupled plasma mass spectrometer (ICP‐MS) and electronic nose (E‐nose), respectively. The obtained data were subject to the analysis of variance (anova), principal component analysis (PCA) and linear discriminant analysis (LDA). The twenty elements including B, Mg, Al, Ti, V, Cr, Mn, Fe, Ni, Zn, Ga, As, Sr, Cd, Sn, Sb, Ba, Pb, Bi and Tl and eleven sensors including LY2/LG,LY2/G, LY2/AA, LY2/GH, LY2/gCTL, LY2/gCT, P10/1, T30/1,T70/2, P10/2 and PA/2 were mainly contributed to discriminate the origin of the rice, and the average classification accuracy was 93.5%. Compared with one‐dimensional discrimination method, the multiangle analytical method of combining mineral elements with volatile compounds significantly improved the discrimination accuracy of origin of rice.

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“…The E-nose does not resolve the sample's volatiles into its individual components but responds to the whole set of volatiles in a unique digital pattern. E-nose technology has been successfully used in a variety of applications, including food quality and control [5,6,14], environmental monitoring [7,17,18], micro-organism identification (Magan et al, 2000), medical and health detection [15,16] and plant monitoring [9][10][11][12][13]. Recently, more research has been directed towards early detection of insect and disease attack.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Different Types Damage of Tomato Seedling by Electronic Nose

Cheng

Wang

et al. 2017

ITM Web Conf.

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Abstract:The profiles of volatile compounds emitted by plants varies in response to different damage.The potential of electronic nose technology to monitor such changes,with the aim of diagnosing plant health was investingted.An electronic nose(E-nose)was used to analyse tomato seedlings that were subjected to differnent types of damage(infection by Early blight disease, infection by Gray mold disease, mechanically damage, and undamaged). Principal component analysis(PCA),linear discrimination analysis(LDA),back-propagation neural network(BPNN),and support vector machine (SVM) network were used to evaluate the E-nose data.The results indicated that the E-nose can successfully discriminate between tomato seedling with different types of damage. The results of PCA and LDA showed that clusters of data were divided into 3 groups (ZP, HP, and CP/MP). Samples from groups CP and MP overlapped partially. Back-propagation neural network (BPNN) and support vector machine (SVM) network were used to evaluate the E-nose data. Good discrimination results were obtained using SVM and BPNN. The results demonstrate that it is plausible to use E-nose technology as a method for monitoring damage in tomato seedling.

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Use of electronic nose technology for identifying rice infestation by Nilaparvata lugens

Cited by 45 publications

References 18 publications

Fast Determination of Lycopene Content and Soluble Solid Content of Cherry Tomatoes Using Metal Oxide Sensors Based Electronic Nose

Fast Determination of Lycopene Content and Soluble Solid Content of Cherry Tomatoes Using Metal Oxide Sensors Based Electronic Nose

Multiangle discrimination of geographical origin of rice based on analysis of mineral elements and characteristic volatile components

Discrimination of Different Types Damage of Tomato Seedling by Electronic Nose

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