Early discrimination and growth tracking of Aspergillus spp. contamination in rice kernels using electronic nose

Gu, Shuang; Wang, Jun; Wang, Yongwei

doi:10.1016/j.foodchem.2019.04.054

Cited by 67 publications

(35 citation statements)

References 33 publications

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“…Kinetic models can be used to predict the formation of compounds. Knol et al used a logistic model to predict the formation and degradation of acrylamide in potato chips [21,22]. The formation kinetics of furfural in the coconut jams during the concentration process is consistent with the four-parameter logistic equation:…”

Section: Kinetics Of Furfural Formationmentioning

confidence: 88%

Characterization of Volatile Profiles and Marker Substances by HS-SPME/GC-MS during the Concentration of Coconut Jam

Zhang

Chen

Wang

et al. 2020

Foods

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Characteristic aromas are usually key labels for food products. In this study, the volatile profiles and marker substances of coconut jam during concentration were characterized via sensory evaluation combined with headspace solid phase microextraction-gas chromatography-tandem mass spectrometry (HSPME/GC-MS). A total of 33 aroma compounds were detected by HSPME/GC-MS. Principal component analysis revealed the concentration process of coconut jam can be divided into three stages. In the first stage, esters and alcohols were the two main contributors to the aroma of the coconut jam. Next, a caramel smell was gradually formed during the second stage, which was mainly derived from aldehydes, ketones and alcohols. The concentration of aldehydes increased gradually at this stage, which may be the result of a combination of the Maillard reaction and the caramelization reaction. In the final sterilization stage, the 'odor intensity' of caramel reached the maximum level and a variety of aroma compounds were produced, thereby forming a unique flavor for the coconut jam. Finally, furfural fit a logistic model with a regression coefficient (r 2 ) of 0.97034. Therefore, furfural can be used as a marker substance for monitoring the concentration of coconut jam.

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Section: Kinetics Of Furfural Formationmentioning

confidence: 88%

Characterization of Volatile Profiles and Marker Substances by HS-SPME/GC-MS during the Concentration of Coconut Jam

Zhang

Chen

Wang

et al. 2020

Foods

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“…Detection by the electronic nose of fungal contamination of wood has been reported [24,25]. Other authors reported studies of detection of fungal infection of various grains [26][27][28][29][30][31][32]. Gębicki and Szulczyński [33] presented discrimination of several fungi species based on their odor profile.…”

Section: Introductionmentioning

confidence: 99%

Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic Oomycetes Pythium intermedium and Phytophthora plurivora

Borowik

Adamowicz

Tarakowski

et al. 2021

Sensors

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Compared with traditional gas chromatography–mass spectrometry techniques, electronic noses are non-invasive and can be a rapid, cost-effective option for several applications. This paper presents comparative studies of differentiation between odors emitted by two forest pathogens: Pythium and Phytophthora, measured by a low-cost electronic nose. The electronic nose applies six non-specific Figaro Inc. metal oxide sensors. Various features describing shapes of the measurement curves of sensors’ response to the odors’ exposure were extracted and used for building the classification models. As a machine learning algorithm for classification, we use the Support Vector Machine (SVM) method and various measures to assess classification models’ performance. Differentiation between Phytophthora and Pythium species has an important practical aspect allowing forest practitioners to take appropriate plant protection. We demonstrate the possibility to recognize and differentiate between the two mentioned species with acceptable accuracy by our low-cost electronic nose.

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“…The 2100-dimensional original cyclic voltammetry curve dataset is represented as a linearly independent vector among various dimensions by linear transformation 28 and replaced with fewer comprehensive variables under the principle of minimum original data loss. 29 The original cyclic voltammetry curve data with dimension P can be expressed as X ¼…”

Section: Modeling Proceduresmentioning

confidence: 99%

A novel method for total chlorine detection using machine learning with electrode arrays

Huang

Chen

2019

RSC Adv.

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Early discrimination and growth tracking of Aspergillus spp. contamination in rice kernels using electronic nose

Cited by 67 publications

References 33 publications

Characterization of Volatile Profiles and Marker Substances by HS-SPME/GC-MS during the Concentration of Coconut Jam

Characterization of Volatile Profiles and Marker Substances by HS-SPME/GC-MS during the Concentration of Coconut Jam

Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic Oomycetes Pythium intermedium and Phytophthora plurivora

A novel method for total chlorine detection using machine learning with electrode arrays

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