Monitoring storage time and quality attribute of egg based on electronic nose

Non-destructive egg breed separation using advanced VOC analytical techniques HSSE-GC-MS, PTR-TOF-MS, and SIFT-MS: Assessment of performance and systems' complementarity

Over the past decade, advanced analytical techniques have been utilized to examine volatile organic compounds (VOCs) in eggs. These VOCs offer valuable insights into factors such as freshness, fertility, the presence of cracks, embryo sex, and breed. In our study, we assessed three mass spectrometry-based systems (headspace sorptive extraction gas chromatography-mass spectrometry; HSSE-GC-MS, proton transfer reaction time-of-flight-mass spectrometry; PTR-TOF-MS; and selected ion flow tube mass spectrometry; SIFT-MS) to analyze and identify VOCs present in intact hatching eggs from three distinct breeds (Dekalb white layer, Shaver brown layer, and Ross 308 broiler). The eggs were sampled on incubation days 2 and 8, to identify VOCs that distinguish breeds irrespective of incubation day. VOC measurements were conducted on 15 eggs per breed by placing them together with PDMS-coated stir bars inside inert Teflon® air sampling bags. After an accumulation period of 2 h, the headspace was analyzed using PTR-TOF-MS and SIFT-MS, while the VOCs adsorbed onto the stir bars were analyzed using GC-MS for additional compound identification. Partial least squares discriminant analysis (PLS-DA) models were constructed for breed differentiation, and variable selection was performed. As a result, 111 VOCs were identified using HSSE-GC-MS, with alcohols and esters being the most abundant. The PLS-DA models demonstrated the efficacy of breed discrimination, with the HSSE-GC-MS and the PTR-TOF-MS exhibiting the highest balanced accuracy of 95.5 % using a reduced set of 11 VOCs and 5 product ions, respectively. The SIFT-MS model had a balanced accuracy of 92.8 % with a reduced set of 11 product ions. Furthermore, complementarity was observed between HSSE-GC-MS, which primarily selected higher molecular weight VOCs, and PTR-TOF-MS and SIFT-MS. A higher correlation was found for compound abundances between the HSSE-GC-MS and the PTR-TOF-MS relative to the SIFT-MS, indicating that the PTR-TOF-MS was better suited to quantify specific compounds identified by the HSSE-GC-MS. Finally, the findings support the presence of VOCs originating from both synthetic and natural sources, highlighting the ability of the VOC analysis systems to non-destructively perform quality control and reveal differences in management practices or biological information encoded in eggs.

Construction of a simple dual-mode ATP-sensing system for reliable fish freshness evaluation

Adenosine triphosphate (ATP), the main carrier of chemical energy, plays a key role in various biochemical reactions such as cellular metabolism. Currently, ATP levels are considered important indicators of microbial content in food safety, and food freshness can be determined by detecting ATP content. Some ATP sensing strategies have been applied to evaluate food freshness. However, cumbersome nanomaterial preparation, low sensitivity, and low reliability hamper their widespread application. Herein, a simple, high-performance, and reliable dual-mode sensing system based on hemin-G-quadruplex (G4) DNAzyme was established to detect ATP and assess fish freshness. Two nucleic acid probes, including subunits of the hemin-G4 DNAzyme in inactive structures and anti-ATP aptamer, self-assemble upon the input of ATP into the active hemin-G4 DNAzyme unit. The generated DNAzyme acts as a biocatalyst for colorimetric or fluorescent readout of the sensing process. The colorimetric and fluorescent dual-mode sensing system enables highly sensitive and reliable analysis of target ATP with detection limits of 71 nM and 73 nM, respectively. Moreover, the biosensor exhibited good selectivity for differentiating ATP from other interfering analytes. The proposed system was used to detect ATP in perch samples, and a linear correlation between ATP level and microbial content was confirmed. The established ATP-sensing system reliably evaluated fish freshness. Notably, in comparison with microbiological counts, the proposed DNAzyme-based dual-mode strategy for freshness evaluation is facile, highly efficient, and cost-effective, thus providing a promising method for food safety and quality monitoring.

Simulation analysis and freshness prediction of eggs laid at room temperature

BACKGROUND

Respiration is an important physiological activity of eggs and is closely related to their freshness. To further observe the diffusion of carbon dioxide released by egg respiration, we used a respirometer to measure the respiration parameters of eggs stored at room temperature and performed a respiration simulation using Fluent software. This paper also explores the relationship between respiratory intensity, freshness, and storage period.

RESULTS

The results demonstrate that the diffusion of carbon dioxide released from the respiration of eggs is related to the characteristics of heavy gas diffusion. By comparison, the simulated value (0.0199 m s^-1 ) is close to the experimental value (0.0208 m s^-1 ), which indicates that the simulation and analysis results are valid. In addition, the logarithmic model was used to assess the relationship between respiration intensity, Haugh unit, and the yolk index (R² values 0.89 and 0.87). The R² of the relationship between the real and the predicted Haugh unit value and the yolk index are 0.9 and 0.84 respectively, indicating that the model is a good fit. The equivalent egg age model was established using nonlinear regression, where the correlation coefficient R was 0.888 and P < 0.01, indicating it was both stable and reliable.

CONCLUSION

The standard k-ε model is suitable for egg respiration simulation analysis. Respiratory intensity can be used as a potential index for nondestructive testing of egg freshness, which is a new method for nondestructive testing of egg freshness and storage period. © 2022 Society of Chemical Industry.

Characterisation of Flavour Attributes in Egg White Protein Using HS-GC-IMS Combined with E-Nose and E-Tongue: Effect of High-Voltage Cold Plasma Treatment Time

Egg white protein (EWP) is susceptible to denaturation and coagulation when exposed to high temperatures, adversely affecting its flavour, thereby influencing consumers' decisions. Here, we employ high-voltage cold plasma (HVCP) as a novel nonthermal technique to investigate its influence on the EWP's flavour attributes using E-nose, E-tongue, and headspace gas-chromatography-ion-mobilisation spectrometry (HS-GC-IMS) due to their rapidness and high sensitivity in identifying flavour fingerprints in foods. The EWP was investigated at 0, 60, 120, 180, 240, and 300 s of HVCP treatment time. The results revealed that HVCP significantly influences the odour and taste attributes of the EWP across all treatments, with a more significant influence at 60 and 120 s of HVCP treatment. Principal component analyses of the E-nose and E-tongue clearly distinguish the odour and taste sensors' responses. The HS-GC-IMS analysis identified 65 volatile compounds across the treatments. The volatile compounds' concentrations increased as the HVCP treatment time was increased from 0 to 300 s. The significant compounds contributing to EWP characterisation include heptanal, ethylbenzene, ethanol, acetic acid, nonanal, heptacosane, 5-octadecanal, decanal, p-xylene, and octanal. Thus, this study shows that HVCP could be utilised to modify and improve the EWP flavour attributes.

The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor

The water displacement and flotation are two of the most accurate and rapid methods for grading and assessing freshness of agricultural products based on density determination. However, these techniques are still not suitable for use in agricultural inspections of products such as eggs that absorb water which can be considered intrusive or destructive and can affect the result of measurements. Here we present a novel proposal for a method of non-destructive, non-invasive, low cost, simple and real-time monitoring of the grading and freshness assessment of eggs based on density detection using machine vision and a weighing sensor. This is the first proposal that divides egg freshness into intervals through density measurements. The machine vision system was developed for the measurement of external physical characteristics (length and breadth) of eggs for evaluating their volume. The weighing system was developed for the measurement of the weight of the egg. Egg weight and volume were used to calculate density for grading and egg freshness assessment. The proposed system could measure the weight, volume and density with an accuracy of 99.88%, 98.26% and 99.02%, respectively. The results showed that the weight and freshness of eggs stored at room temperature decreased with storage time. The relationship between density and percentage of freshness was linear for the all sizes of eggs, the coefficient of determination (R2) of 0.9982, 0.9999, 0.9996, 0.9996 and 0.9994 for classified egg size classified 0, 1, 2, 3 and 4, respectively. This study shows that egg freshness can be determined through density without using water to test for water displacement or egg flotation which has future potential as a measuring system important for the poultry industry.

Monitoring aging of hen egg by integrated quantitative peptidomic procedures

Environmental conditions and timing of egg storage highly affect raw material quality. Aging and endogenous processing of constituent proteins can determine important changes in specific functions and technological properties of inner egg compartments. We here used integrated peptidomic procedures to identify peptide markers of egg freshness. At first, peptides extracted from egg white and yolk plasma taken from eggs stored for different times were subjected to a label-free untargeted quantitation procedure based on nanoLC-ESI-Q-Orbitrap-MS/MS, which identified 836 and 1974 unique variable molecules, respectively. By applying stringent criteria for filtering data, 30 and 66 putative egg aging markers were selected for egg white and yolk plasma, respectively. Proposed molecules were then validated through a targeted label-free parallel reaction monitoring procedure based on nanoLC-ESI-Q-Orbitrap-MS/MS, confirming quantitative trends for 19 and 25 peptides in egg white and yolk plasma, respectively, and generating a robust panel of egg storage markers. Quantitative results reflected physico-chemical phenomena occurring in egg compartments during storage and offered essential information for the development of novel control procedures to assess quality features of fresh/stored raw material.

SPME-GC-MS & metal oxide E-Nose 18 sensors to validate the possible interactions between bio-active terpenes and egg yolk volatiles

This study was focused on the differences between three concentrations of four safe bio-active volatile terpenes and natural compounds (trans-cinnamaldehyde, thymol, menthol, and vanillin) on the egg yolk volatile components. In which, Headspace Solid phase Micro Extraction (HS-SPME) followed by GC/MS were used for volatiles analysis, and electronic nose (E-Nose) with 18 sensors was used for the aroma pattern. In the results, a total of 111 different volatile compounds were identified by using GC/MS. Some ketones, amines, nitro and organic acid compounds such as hexanone were significantly reduced after using these compounds. On the other hand, first principal components (PC1) of trans-cinnamaldehyde (0.1%) was decreased significantly (p < .05) to -4.8 ± 0.8, compared to the control with 4.3 ± 2.1. And, it increased benzaldehyde and nonanal peaks area significantly to 46.56 ± 14.46 and 9.22 ± 4.81 MPA. In menthol (0.1%), the content of d-limonene was decreased to 4.65 ± 0.49 MPA, compared to the control with 14.16 ± 4.95 MPA. On the other hand, by E-Nose, vanillin (0.1%) PC1 was decreased significantly to -4.48 ± 0.43. Moreover, thymol (0.1%) decreased hydrocarbon sensation to 0.448 ± 0.005. And, vanillin (0.1%) decreased hydrogen sulfide and ketones sensation to 0.169 ± 0.01 and 0.344 ± 0.01, respectively. In conclusion, this study is providing novel information to understand the effects of bio-active molecules on the biological fatty media volatility. Also, it explains how terpenes can protect egg yolk media from the unacceptable odor formation which results from the degradation of its lipids and proteins.

Kinetic modeling impacts of relative humidity, storage temperature, and air flow velocity on various indices of hen egg freshness

Storage experiments were conducted to study the impacts of the environmental factors (temperature (T) (°C), relative humidity (RH) (%), and air flow velocity (VEL) (m/s)) on the hen egg quality indices and to develop kinetic model(s) for freshness prediction. VEL had negligible effect on relative weight loss (RWL). All factors had significant effect on Haugh unit (HU) but only T impacted S-ovalbumin content (SO). Fitted regression lines for the RWL and the HU had determination coefficient (R²) of 0.996 and 0.95, respectively. The HU equation reflected impacts of all factors, and the impact of temperature shift-up increases the HU decrease, where the impact decreases with RH and increases with flow velocity. Kinetic model for SO was developed using isothermal (5, 10, 20, 25, and 28.5°C) conditions and validated under dynamic (10 to 20 and 10 to 28.5°C) conditions. The accuracy and bias factor values were 1.091 and 0.917 at 10 to 20°C and 1.206 and 1.204 at 10 to 28.5°C, respectively, which indicates that the SO model performed well. The SO model can be used along with the HU model (as the HU model can reflect the combined effect of temperature, humidity, and air flow velocity) to predict hen egg freshness at 5 to 28.5°C storage condition.

Prediction of egg freshness during storage using electronic nose

The aim of the present study was to investigate the potential of a fast gas chromatography (GC) e-nose for freshness discrimination and for prediction of storage time as well as sensory and internal quality changes during storage of hen eggs. All samples were obtained from the same egg production farm and stored at 20 °C for 20 d. Egg sampling was conducted every 0, 3, 6, 9, 12, 16, and 20 d. During each sampling time, 4 egg cartons (each containing 10 eggs) were randomly selected: one carton for Haugh units, one carton for sensory evaluation and 2 cartons for the e-nose experiment. The e-nose study included 2 independent test sets; calibration (35 samples) and validation (28 samples). Every sampling time, 5 replicates were prepared from one egg carton for calibration samples and 4 replicates were prepared from the remaining egg carton for validation samples. Sensors (peaks) were selected prior to multivariate chemometric analysis; qualitative sensors for principal component analysis (PCA) and discriminant factor analysis (DFA) and quantitative sensors for partial least square (PLS) modeling. PCA and DFA confirmed the difference in volatile profiles of egg samples from 7 different storage times accounting for a total variance of 95.7% and 93.71%, respectively. Models for predicting storage time, Haugh units, odor score, and overall acceptability score from e-nose data were developed using calibration samples by PLS regression. The results showed that these quality indices were well predicted from the e- nose signals, with correlation coefficients of R² = 0.9441, R² = 0.9511, R² = 0.9725, and R² = 0.9530 and with training errors of 0.887, 1.24, 0.626, and 0.629, respectively. As a result of ANOVA, most of the PLS model results were not significantly (P > 0.05) different from the corresponding reference values. These results proved that the fast GC electronic nose has the potential to assess egg freshness and feasibility to predict multiple egg freshness indices during its circulation in the supply chain.

Predictive modeling for monitoring egg freshness during variable temperature storage conditions

The overall aim of this research was to develop egg freshness prediction models in terms of selected quality indices. Six experiments (4 constant temperatures and 2 variable temperatures) were carried out on hen eggs for a total period of 10, 21, 26, 13, and 105 d at storage temperatures of 30, 20, 20 to 10, 30 to 10, and 5 and 10°C, to observe trends in the relative weight loss (RWL), Haugh unit (HU), yolk index (YI), albumin index (AI), yolk pH, and albumin pH. The results showed that there was an increasing trend in the RWL and a decreasing trend in the YI, AI, and HU for all temperature conditions. The changes in the yolk and albumin pH were not uniform. The data from the constant temperature conditions were used to determine the coefficients of the egg quality prediction models, which consisted of the primary model controlling the change rate of the quality indicator at a temperature condition in differential equation form, and the secondary model controlling the change rate with temperature, which was in quadratic polynomial form. The models were applied to the data from the fluctuating temperature conditions, and the zeroth, third, and eighth order kinetic models described the stepwise change in the RWL, HU, and YI, respectively. The accuracy and bias factor values for the RWL, HU, and YI were 1.116 and 0.940, 1.028 and 1.001, and 1.038 and 0.966, respectively. It can be concluded that the models can be used to predict egg freshness in terms of the RWL, HU, and YI at any temperature condition with in the range of 5 to 30°C during storage.

Application of Electronic Nose Systems on Animal-Source Food

Electronic nose, which is designed to perceive artificially the odour-active molecules in a sample headspace, has seen an increased use in the food industry as a rapid and reliable tool for quality assessment, classification, and authentication of several food items. The use of chemometrics and pattern recognition methods, together with gas sensors, emerged to be a very powerful analytical approach. In this chapter, an overview of the recent achievements in the field of electronic nose applications on animal-source food is given. Moreover, the authors deal with the recent research trends to overcome the actual sensor shortcomings, including sensor fusion techniques and their applications to evaluate animal-source foods and novel electronic nose systems.

Qualitative and quantitative analysis of fatty acid profiles of Chinese pecans (Carya cathayensis) during storage using an electronic nose combined with chemometric methods

Chinese pecans (Carya cathayensis) continuously deteriorate during storage because of their high fatty acid contents.

Using multilayer perceptron computation to discover ideal insect olfactory receptor combinations in the mosquito and fruit fly for an efficient electronic nose

The model organism, Drosophila melanogaster, and the mosquito Anopheles gambiae use 60 and 79 odorant receptors, respectively, to sense their olfactory world. However, a commercial "electronic nose" in the form of an insect olfactory biosensor demands very low numbers of receptors at its front end of detection due to the difficulties of receptor/sensor integration and functionalization. In this letter, we demonstrate how computation via artificial neural networks (ANNs), in the form of multilayer perceptrons (MLPs), can be successfully incorporated as the signal processing back end of the biosensor to drastically reduce the number of receptors to three while still retaining 100% performance of odorant detection to that of a full complement of receptors. In addition, we provide a detailed performance comparison between D. melanogaster and A. gambiae odorant receptors and demonstrate that A. gambiae receptors provide superior olfaction detection performance over D. melanogaster for very low receptor numbers. The results from this study present the possibility of using the computation of MLPs to discover ideal biological olfactory receptors for an olfactory biosensor device to provide maximum classification performance of unknown odorants.

Quality control of Lonicera japonica stored for different months by electronic nose

The objective of this study was to investigate the potential of the electronic nose (E-nose) technique for monitoring the storage time and quality of L. japonica. An E-nose was used to detect odors of L. japonica samples during a storage period of 16 months. Linear discriminant analysis (LDA) and radial basis function (RBF) neural network were performed to differentiate L. japonica samples stored for different months. The content of chlorogenic acid of L. japonica was determined to confirm the quality changes and investigate its correlation with the odor response values. Results showed that L. japonica samples of different storage months could be classified correctly by LDA and RBF neural network. The change trends of the odor response and the content of chlorogenic acid had both declined along with the storage time. Also, there was a significant correlation (p=0.000) between the odor index and the content of chlorogenic acid. In conclusion, the odor intensity could reflect the quality of L. japonica to a certain degree. The E-nose technique could be used as a rapid, simple, sensitive and effective method for the quality control of L. japonica.

Diverse applications of electronic-nose technologies in agriculture and forestry

Electronic-nose (e-nose) instruments, derived from numerous types of aroma-sensor technologies, have been developed for a diversity of applications in the broad fields of agriculture and forestry. Recent advances in e-nose technologies within the plant sciences, including improvements in gas-sensor designs, innovations in data analysis and pattern-recognition algorithms, and progress in material science and systems integration methods, have led to significant benefits to both industries. Electronic noses have been used in a variety of commercial agricultural-related industries, including the agricultural sectors of agronomy, biochemical processing, botany, cell culture, plant cultivar selections, environmental monitoring, horticulture, pesticide detection, plant physiology and pathology. Applications in forestry include uses in chemotaxonomy, log tracking, wood and paper processing, forest management, forest health protection, and waste management. These aroma-detection applications have improved plant-based product attributes, quality, uniformity, and consistency in ways that have increased the efficiency and effectiveness of production and manufacturing processes. This paper provides a comprehensive review and summary of a broad range of electronic-nose technologies and applications, developed specifically for the agriculture and forestry industries over the past thirty years, which have offered solutions that have greatly improved worldwide agricultural and agroforestry production systems.

Rapid discrimination of Chinese red ginseng and Korean ginseng using an electronic nose coupled with chemometrics

Red ginseng is a precious and widely used traditional Chinese medicine. At present, Chinese red ginseng and Korean ginseng are both commonly found on the market. To rapidly and nondestructively discriminate between Chinese red ginseng and Korean ginseng, an electronic nose coupled with chemometrics was developed. Different red ginseng samples, including Chinese red ginseng (n=30) and Korean ginseng (South Korean red ginseng and North Korean red ginseng n=26), were collected. The metal oxide sensors on an electronic nose were used to measure the red ginseng samples. Multivariate statistical analyses, including principal component analysis (PCA), discriminant factorial analysis (DFA) and soft independent modeling of class analogy (SIMCA), were employed. All of the samples were analyzed by PCA. Most of the samples were used to set up DFA and SIMCA models, and then the remaining samples (Nos. 9, 10, 17, 18, 29, 30, 34, 43, 44, 50, and 51) were projected onto the DFA and SIMCA models in the form of black dots to validate the models. The results indicated that Chinese red ginseng and Korean ginseng were successfully discriminated using the electronic nose coupled with PCA, DFA and SIMCA. The checking scores of the DFA and SIMCA models were 100. The samples projected onto the DFA and SIMCA models were all correctly discriminated. The DFA and SIMCA models were robust. Electronic nose technology is a rapid, accurate, sensitive and nondestructive method to discriminate between Chinese red ginseng and Korean ginseng.

Electronic noses and tongues: applications for the food and pharmaceutical industries

The electronic nose (e-nose) is designed to crudely mimic the mammalian nose in that most contain sensors that non-selectively interact with odor molecules to produce some sort of signal that is then sent to a computer that uses multivariate statistics to determine patterns in the data. This pattern recognition is used to determine that one sample is similar or different from another based on headspace volatiles. There are different types of e-nose sensors including organic polymers, metal oxides, quartz crystal microbalance and even gas-chromatography (GC) or combined with mass spectroscopy (MS) can be used in a non-selective manner using chemical mass or patterns from a short GC column as an e-nose or "Z" nose. The electronic tongue reacts similarly to non-volatile compounds in a liquid. This review will concentrate on applications of e-nose and e-tongue technology for edible products and pharmaceutical uses.

Can odors of TCM be captured by electronic nose? The novel quality control method for musk by electronic nose coupled with chemometrics

Musk is a precious and wide applied material in traditional Chinese medicine, also, an important material for the perfume industry all over the world. To establish a rapid, cost-effective and relatively objective assessment for the quality of musk, different musk samples, including authentic, fake and adulterate, were collected. A oxide sensor based electronic nose (E-nose) was employed to measure the musk samples, the E-nose generated data were analyzed by principal component analysis (PCA), the responses of 18 sensors of E-nose were evaluated by loading analysis. Results showed that a rapid evaluation of complex response of the samples could be obtained, in combination with PCA and the perception level of the E-nose was given better results in the recognition values of the musk aroma. The authentic, fake and adulterate musk could be distinguished by E-nose coupled with PCA, sensor 2, 3, 5, 12, 15 and 17 were found to be able to better discriminate between musk samples, confirming the potential application of an electronic instrument coupled with chemometrics for a rapid and on-line quality control for the traditional medicines.

Metal oxide sensors for electronic noses and their application to food analysis

Electronic noses (E-noses) use various types of electronic gas sensors that have partial specificity. This review focuses on commercial and experimental E-noses that use metal oxide semi-conductors. The review covers quality control applications to food and beverages, including determination of freshness and identification of contaminants or adulteration. Applications of E-noses to a wide range of foods and beverages are considered, including: meat, fish, grains, alcoholic drinks, non-alcoholic drinks, fruits, milk and dairy products, olive oils, nuts, fresh vegetables and eggs.