Applications, challenges and prospects of bionic nose in rapid perception of volatile organic compounds of food

Zheng, Xing; Zogona, Daniel; Wu, Ting; Pan, Siyi; Xu, Xiaoyun

doi:10.1016/j.foodchem.2023.135650

Cited by 12 publications

(5 citation statements)

References 124 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Schematic diagram of electronic nose device. ( a ) A comparison between electronic nose and human olfactory processes [ 73 ]. ( b ) The composition and working principle of electronic nose system [ 74 ].…”

Section: Figurementioning

confidence: 99%

Intelligent System/Equipment for Quality Deterioration Detection of Fresh Food: Recent Advances and Application

Wang,

Zhang,

Jiang

et al. 2024

Foods

View full text Add to dashboard Cite

The quality of fresh foods tends to deteriorate rapidly during harvesting, storage, and transportation. Intelligent detection equipment is designed to monitor and ensure product quality in the supply chain, measure appropriate food quality parameters in real time, and thus minimize quality degradation and potential financial losses. Through various available tracking devices, consumers can obtain actionable information about fresh food products. This paper reviews the recent progress in intelligent detection equipment for sensing the quality deterioration of fresh foods, including computer vision equipment, electronic nose, smart colorimetric films, hyperspectral imaging (HSI), near-infrared spectroscopy (NIR), nuclear magnetic resonance (NMR), ultrasonic non-destructive testing, and intelligent tracing equipment. These devices offer the advantages of high speed, non-destructive operation, precision, and high sensitivity.

show abstract

Section: Figurementioning

confidence: 99%

Intelligent System/Equipment for Quality Deterioration Detection of Fresh Food: Recent Advances and Application

Wang,

Zhang,

Jiang

et al. 2024

Foods

View full text Add to dashboard Cite

show abstract

“…The performance of conventional techniques is based on the measurement of specific volatiles. In contrast, E‐Nose is mapped out with several non‐selective sensors that link to odor molecules (Xing et al., 2023 ). The result of such connections is a diverse signal's classes which are sent to a computer to recognize their patterns by multivariate statistics (Aghili et al., 2023 ).…”

Section: Introductionmentioning

confidence: 99%

“…The performance of conventional techniques is based on the measurement of specific volatiles. In contrast, E-Nose is mapped out with several non-selective sensors that link to odor molecules (Xing et al, 2023).…”

mentioning

confidence: 99%

Complementary assessment of nano‐packaged garlic properties by electronic nose

Makarichian,

Ahmadi,

Amiri Chayjan

et al. 2024

Food Science & Nutrition

View full text Add to dashboard Cite

It is crucial to initiate appropriate storage conditions for garlic depending on its properties. Fungal contamination can reduce the quality of garlic through changes in its properties which result in its aroma alteration. This study aimed to evaluate the effects of treatments such as fungal infection (FI), material of packaging (MP), and storage duration (SD) on various characteristics of garlic. An electronic nose was used complementarily to trace the aroma changes as a non‐destructive indicator. The Fusarium oxysporum (FS), Alternaria embellisia (AL), and Botrytis allii (BT) fungi were adopted for inoculation. The low‐density polyethylene (LDPE) and silicone nano‐emulsions (SNE) were used for packing samples. The data were analyzed by diverse approaches such as ANOVA, PLS, PCA, LDA, and BPNN. The results revealed that the evaluated properties changed during the storage. The implementation of treatments altered the intensity of these changes. The highest values of weight loss (21.14%), color changes (50.21), and acidity (7.48) were observed in the FS‐infected samples kept in LDPE for 28 days. The accuracy of PCA, LDA, and BPNN in the multivariate analysis of aroma had an increasing‐decreasing trend. The best accuracy of PCA in categorizing the FI and MP treatments achieved in the twelfth day of storage (96%). The optimal accuracy of classifications based on FI and MP treatments was obtained at d#12 (100%) and d#24 (100%), respectively. The PLS exposed that the aroma changes in garlic had a high correlation with the changes of studied properties (R2 ≥ .7), except for the mechanical properties.

show abstract

“…5,6 Rapid assessment of the volatile profile is critical to improvising and standardising cooking methods, preferably during and/or immediately after cooking. Many methods for the measurement of volatile organic compounds (VOCs) exist, for example, electronic and bionic sensors, [7][8][9] including the conventional solid-phase microextraction (SPME)-based head-space analysis using gas chromatography-mass spectrometry (GC-MS), 4,[10][11][12] and contemporary ambient atmospheric MS techniques. [13][14][15] An appraisal of the pros and cons of these techniques is not within the ambit of this study, although in essence, ambient atmospheric techniques offer greater convenience (nil/minimal sample pre-treatment) and speed at (near) real time compared to conventional techniques.…”

Section: Introductionmentioning

confidence: 99%

Volatile fingerprints of beef cooking methods using sol–gel‐based solid‐phase microextraction (SPME) and direct analysis in real‐time mass spectrometry (DART‐MS)

Subbaraj,

Deb‐choudhury,

Pavan

et al. 2023

Rapid Comm Mass Spectrometry

View full text Add to dashboard Cite

RationaleThe aroma profile of food is a complex mixture of volatile compounds that constitutes a major component of the overall eating experience. The food service industry and chefs therefore constantly seek ways to investigate and thereby enhance the aroma profile. Oven cooking, sous vide and pan fry are three cooking methods of beef commonly practised by chefs. Near real‐time analysis of volatile compounds from these three cooking methods will provide insight into respective volatile fingerprints and help improve cooking techniques.MethodsVolatile compounds from three beef cooking methods were captured using an in‐house sol–gel based solid phase microextraction (SPME) method and analysed using direct analysis in real‐time mass spectrometry (DART‐MS). A volatile organic compound (VOC) standard was used to demonstrate successful implementation of the sol–gel coating technique. Volatile features discriminating the three cooking methods were shortlisted and statistically assessed by univariate and multivariate analyses.ResultsThe VOC standard was successfully adsorbed by the sol–gel method and detected by DART‐MS. Hierarchical cluster analysis clearly demarcated three beef cooking methods based on their volatile fingerprints. Out of 65 significant features differentiating the cooking methods, 50 were at highest concentrations from pan‐fry cooking only, followed by 14 with highest concentrations from oven cooking followed by pan frying. Sous vide followed by pan frying showed lowest concentrations of almost all volatile features.ConclusionsThe sol–gel‐based solid‐phase microextraction technique combined with DART‐MS was successful in differentiating beef cooking methods based on their volatile fingerprints. A workflow for rapid assessment of the volatile profile from beef cooking methods was established, providing a baseline to further explore volatile profiles from other key ingredients.

show abstract

Applications, challenges and prospects of bionic nose in rapid perception of volatile organic compounds of food

Cited by 12 publications

References 124 publications

Intelligent System/Equipment for Quality Deterioration Detection of Fresh Food: Recent Advances and Application

Intelligent System/Equipment for Quality Deterioration Detection of Fresh Food: Recent Advances and Application

Complementary assessment of nano‐packaged garlic properties by electronic nose

Volatile fingerprints of beef cooking methods using sol–gel‐based solid‐phase microextraction (SPME) and direct analysis in real‐time mass spectrometry (DART‐MS)

Contact Info

Product

Resources

About