A data fusion approach on confocal Raman microspectroscopy and electronic nose for quantitative evaluation of pesticide residue in tea

Sanaeifar, Alireza; Liu, Yuanyuan; He, Yong; Huang, Zhifu; Zhan, Zhihao

doi:10.1016/j.biosystemseng.2021.08.016

Cited by 54 publications

(20 citation statements)

References 65 publications

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“…For example, E-nose has achieved excellent results in detecting citrus fruit Bactrocera dorsalis infection [32], garlic Alternariaembellisia infection [33], and pine mushroom Aspergillus niger infection [34]. In the quantitation of toxins, E-nose has been used to quantify aflatoxin B 1 and fumonisin in corn [35] and has been combined with Raman microspectroscopy to quantify pesticide residues in tea [36].…”

Section: Introductionmentioning

confidence: 99%

Early Discrimination and Prediction of C. fimbriata-Infected Sweetpotatoes during the Asymptomatic Period Using Electronic Nose

Pang

Zhang

et al. 2022

Foods

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Sweetpotato is prone to disease caused by C. fimbriata without obvious lesions on the surface in the early period of infection. Therefore, it is necessary to explore the possibility of developing an efficient early disease detection method for sweetpotatoes that can be used before symptoms are observed. In this study, sweetpotatoes were inoculated with C. fimbriata and stored for different lengths of time. The total colony count was detected every 8 h; HS-SPME/GC–MS and E-nose were used simultaneously to detect volatile compounds. The results indicated that the growth of C. fimbriata entered the exponential phase at 48 h, resulting in significant differences in concentrations of volatile compounds in infected sweetpotatoes at different times, especially toxic ipomeamarone in ketones. The contents of volatile compounds were related to the responses of the sensors. E-nose was combined with multiple chemometrics methods to discriminate and predict infected sweetpotatoes at 0 h, 48 h, 64 h, and 72 h. Among the methods used, linear discriminant analysis (LDA) had the best discriminant effect, with sensitivity, specificity, precision, and accuracy scores of 100%. E-nose combined with K-nearest neighbours (KNN) achieved the best predictions for ipomeamarone contents and total colony counts. This study illustrates that E-nose is a feasible and promising technology for the early detection of C. fimbriata infection in sweetpotatoes during the asymptomatic period.

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Section: Introductionmentioning

confidence: 99%

Early Discrimination and Prediction of C. fimbriata-Infected Sweetpotatoes during the Asymptomatic Period Using Electronic Nose

Pang

Zhang

et al. 2022

Foods

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“… [ 215 ] Quantitative evaluation of pesiticide residue in tea Confocal Raman microspectroscopy; E-nose MLDF* PLS; SVM; ANN VIP; iPLS; rPLS; GA; CARS; SPA DF-individual model comparison MLDF > individual model; ANN> PLS/SVM e.d. [ 216 ] Quantify the composition of roasted and ground coffee NIR; TXRF LLDF; MLDF PLS SVPII -> PLS; GA -> PLS; OPS -> PLS DF-individual model comparison LLDF > MLDF; SVPII > GA/OPS e.d. + trueness, precision, linearity, working range [ 77 ] Predict total volatile basic nitrogen content in chicken meat Colorimetric sensor; optical sensor LLDF; MLDF PCA-BPANN ILA; LLA-(hyperspectral data); Pearson’s correlation coefficient based variable selection; Pearson correlation analysis MLDF > LLDF; removing uncorrelated data improved results e.d.…”

Section: Table A1mentioning

confidence: 99%

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

et al. 2022

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The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

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“…To explore the best method for the quantitative detection of pesticide residues in tea, Alireza Sanaeifar et al [ 41 ] applied electronic smelling and confocal Raman microspectroscopy for the detection of chlorpyrifos concentrations, with complementary data obtained using electronic smelling and confocal Raman microspectroscopy (CRM) sensing techniques. Based on the fact that tea leaves with different pesticide concentrations have different volatile compounds and that the responses generated by nanoelectronic smelling vary, various features of the nanoelectronic smelling sensor array were extracted and combined with partial least-squares (PLS), artificial neural network (ANN), and support vector machine (SVM) methods to build a suitable model system, with a total of 108 variables selected to construct the electronic smelling data vector.…”

Section: Analysis Of Plant-based Foods By Nanoelectronic Smellingmentioning

confidence: 99%

Progress of Research on the Application of Nanoelectronic Smelling in the Field of Food

Sha

2022

Micromachines

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In the past 20 years, the development of an artificial olfactory system has made great progress and improvements. In recent years, as a new type of sensor, nanoelectronic smelling has been widely used in the food and drug industry because of its advantages of accurate sensitivity and good selectivity. This paper reviews the latest applications and progress of nanoelectronic smelling in animal-, plant-, and microbial-based foods. This includes an analysis of the status of nanoelectronic smelling in animal-based foods, an analysis of its harmful composition in plant-based foods, and an analysis of the microorganism quantity in microbial-based foods. We also conduct a flavor component analysis and an assessment of the advantages of nanoelectronic smelling. On this basis, the principles and structures of nanoelectronic smelling are also analyzed. Finally, the limitations and challenges of nanoelectronic smelling are summarized, and the future development of nanoelectronic smelling is proposed.

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A data fusion approach on confocal Raman microspectroscopy and electronic nose for quantitative evaluation of pesticide residue in tea

Cited by 54 publications

References 65 publications

Early Discrimination and Prediction of C. fimbriata-Infected Sweetpotatoes during the Asymptomatic Period Using Electronic Nose

Early Discrimination and Prediction of C. fimbriata-Infected Sweetpotatoes during the Asymptomatic Period Using Electronic Nose

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

Progress of Research on the Application of Nanoelectronic Smelling in the Field of Food

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