Discrimination of producing areas of Astragalus membranaceus using electronic nose and UHPLC-PDA combined with chemometrics

Wen-Long, Wei; Li, Jinhua; Huang, Linfang

doi:10.17221/126/2016-cjfs

Cited by 4 publications

(2 citation statements)

References 16 publications

(17 reference statements)

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“…These experience-based methods require greater experience by experts, and their accuracy cannot be controlled. The chemical analysis methods, such as high-performance liquid chromatography (HPLC), 9,10 thin layer chromatography (TLC), 11 and other methods are practical tools to identify the geographical origin of Radix Glycyrrhizae. Although these methods can successfully identify the geographical origins of Glycyrrhiza Glycyrrhizae, they are limited to destructive sampling, complex processing, and high technical requirements, which lead to a low identication efficiency.…”

Section: Introductionmentioning

confidence: 99%

Application and interpretation of deep learning methods for the geographical origin identification of Radix Glycyrrhizae using hyperspectral imaging

et al. 2020

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Section: Introductionmentioning

confidence: 99%

Application and interpretation of deep learning methods for the geographical origin identification of Radix Glycyrrhizae using hyperspectral imaging

et al. 2020

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“…A radar chart is a graphical representation that effectively illustrates multidimensional data by expressing the values of each TABLE 1 The sensors installed in the E-Nose are (Wei et al, 2017).…”

Section: Calculation Used On the Response Of The Sensorsmentioning

confidence: 99%

Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime

Herrmann,

Santos Luccas,

Ferreira

et al. 2024

Front. Plant Sci.

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The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean. This experiment was conducted for 22 days, observing the stages of plant growth during this period. This chamber is embedded with relative humidity [RH (%)], temperature (°C), and CO2 concentration (ppm) sensors, as well as the natural light intensity, which was monitored. These systems allowed intermittent monitoring of each parameter to create a database. The soil used was the red-yellow dystrophic type and was covered to avoid evapotranspiration effects. The measurement with the electronic nose was done daily, during the morning and afternoon, and in two phenological situations of the plant (with the healthful soy irrigated with deionized water and underwater stress) until the growth V5 stage to obtain the plant gases emissions. Data mining techniques were used, through the software “Weka™” and the decision tree strategy. From the evaluation of the sensors database, a dynamic variation of plant respiration pattern was observed, with the two distinct behaviors observed in the morning (~9:30 am) and afternoon (3:30 pm). With the initial results obtained with the E-Nose signals and ML, it was possible to distinguish the two situations, i.e., the irrigated plant standard and underwater stress, the influence of the two periods of daylight, and influence of temporal variability of the weather. As a result of this investigation, a classifier was developed that, through a non-invasive analysis of gas samples, can accurately determine the absence of water in soybean plants with a rate of 94.4% accuracy. Future investigations should be carried out under controlled conditions that enable early detection of the stress level.

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Identification of biphenyls - contaminants responsible for off-flavour in soft drinks

Šístková¹,

Kružík²,

Rajchl³

et al. 2018

Czech J. Food Sci.

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Off-flavour in soft drinks is one of the main threats to manufacturers, which can result in expensive recalls, and discredit the brand. Off-flavours can occur for a variety of reasons (e.g . chemical contamination of raw material). The presented case study has proved there is a relationship between the identification of biphenyl and its derivatives in the used preser vative (benzoate) and off-flavours in the drink . The project consisted of three phases: (1) the assessment of the probable cause of off-f lavours based on sensor y evaluation and GC-MS -Olfactometr y profiling of volatiles; (2) the quantification of biphenyls and the characterisation of their sensor y properties; (3) the screening of commercially available benzoates for the presence of biphenyls. Based on the odour threshold obtained by GC-O (0.03 mg/l for 4-methyl-1,1'-biphenyl and 0.02 mg/l for biphenyl in an aqueous solution) and the common benzoate content in soft drinks (0.14 g/l), the ‘non-obser vable sensory’ levels of contamination were determined to be maximally 0.143 mg/g of benzoate for biphenyl and 0.214 mg/g of benzoate for 4-methyl-1,1 '-biphenyl .

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Discrimination of producing areas of Astragalus membranaceus using electronic nose and UHPLC-PDA combined with chemometrics

Cited by 4 publications

References 16 publications

Application and interpretation of deep learning methods for the geographical origin identification of Radix Glycyrrhizae using hyperspectral imaging

Application and interpretation of deep learning methods for the geographical origin identification of Radix Glycyrrhizae using hyperspectral imaging

Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime

Identification of biphenyls - contaminants responsible for off-flavour in soft drinks

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