Prediction of egg freshness during storage using electronic nose

Effect of l-cysteine on functional properties and fibrous structure formation of 3D-printed meat analogs from plant-based proteins

The development of three-dimensional (3D) printed meat analogs with fiber, texture, and sensory resembling meat remains challenging. This study investigated the effect of l-cysteine on functionality enhancement and fibrous structure formation in mixtures of mung bean protein isolate (MBPI) and wheat gluten (WG) for meat analog production. 3D printing was used to construct fibrous filaments. Raw MBPI-WG mixtures decreased rheological properties when increasing l-cysteine contents (0.0%-0.6%), promoting ink extrudability. The cys-0.4% ink exhibited the highest printing resolution and structural stability, correlated with its higher mechanical strength and increased disulfide cross-links. After cooking, the cys-0.4% sample showed a pronounced fibrousness in agreement with its microstructure image. This meat analog displayed a muscle-meat-like structure, improved texture, and reduced beany odor and bitter taste. Excessive cysteine contents (0.5%-0.6%) negatively affected the functionality of meat analogs. This study provides guidance for optimizing the amount of l-cysteine in meat analogs to improve product quality.

Correlations between flavor and fermentation days and changes in quality-related physiochemical characteristics of fermented Aurantii Fructus

The effects of different fermentation times (0, 1, 2, 3, 4, and 5 days) on the physicochemical properties and flavor components of fermented Aurantii Fructus (FAF) were evaluated. Component analysis identified 66 compounds in positive ion mode and 32 compounds in negative ion mode. Flash GC e-nose results showed that propanal, (+)-limonene and n-nonanal may be the flavor characteristic components that distinguish FAF with different fermentation days. Furthermore, we found that the change of total flavonoid content was closely related to colony growth vitality. The total flavonoid content of FAF gradually decreased from 3rd day and then increased from 5th day (3rd day: 0.766 ± 0.123 mg/100 g; 4th day: 0.464 ± 0.001 mg/100 g; 5th day: 0.850 ± 0.192 mg/100 g). Finally, according to antioxidant activity correlation analysis, meranzin, (+)-limonene and total flavonoids were found to be the key substances affecting the fermentation days of FAF. Overall, the optimal fermentation time for FAF was 4 days.

Applications of Gas Sensing in Food Quality Detection: A Review

Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them valuable tools for food quality evaluation. The various gas sensor types, such as metal oxide (MOX), metal oxide semiconductor (MOS) gas sensors, surface acoustic wave (SAW) sensors, colorimetric sensors, and electrochemical sensors, each offer distinct advantages. They hold significant potential for practical applications in food quality monitoring. This review comprehensively covers the progress in gas sensor technology for food quality assessment, outlining their advantages, features, and principles. It also summarizes their applications in detecting volatile gases during the deterioration of aquatic products, meat products, fruit, and vegetables over the past decade. Furthermore, the integration of data analytics and artificial intelligence into gas sensor arrays is discussed, enhancing their adaptability and reliability in diverse food environments and improving food quality assessment efficiency. In conclusion, this paper addresses the multifaceted challenges faced by rapid gas sensor-based food quality detection technologies and suggests potential interdisciplinary solutions and directions.

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.

Combination of sensory evaluation with conventional physiochemical analyses to evaluate quality changes during long-term storage and estimate the shelf life of chicken eggs

1. This study developed a comprehensive sensory evaluation system that consisted of descriptions corresponding to United States Department of Agriculture photos to evaluate overall acceptability, albumen and yolk appearances and odours. It determined physiochemical parameters of eggs stored at 7°C (7W12 and 7U12 for washed and unwashed, respectively) for 12 weeks and stored at 25°C (25W4 and 25U4 for washed and unwashed, respectively) for four weeks.2. Throughout storage, there was a general downward trend in Haugh units (HU) and yolk index and an upward trend in air cell size, weight loss and S-ovalbumin content were observed (P < 0.05). The 25W4 and 25U4 egg quality rapidly deteriorated from grade AA (HU 81.7) to grade B after two weeks (HU 46.5 and 49.6), whereas 7W12 and 7U12 eggs remained grade A after 12 weeks (HU 67.3 and 66.9). High correlations were observed between the sensory and physiochemical parameters (i.e., R2 = 0.93, 0.93, 0.88 and 0.94 for albumen appearance, yolk appearance, sensorial odour and overall acceptability, respectively, with HU in 25W4 eggs).3. Eggs stored at 25°C and classified into 'premium', 'class I', and 'class II' on the basis of their HU had estimated shelf life of 0.5, 1.5 and 2.5 weeks, while shelf lives of 4, 9 and 15 weeks were estimated for 7°C-stored premium, class I and II eggs, respectively.4. In conclusion, distinct HU requirements for eggs of different quality classes under two storage temperatures need to be established. Incorporating sensory evaluation with conventional physiochemical analyses is promising to assess and estimate egg quality changes. Further research work about the influences of different storage temperatures and possible temperature fluctuations during storage on egg quality changes is needed.

Nondestructive Detection of Egg Freshness Based on Infrared Thermal Imaging

In this paper, we proposed a nondestructive detection method for egg freshness based on infrared thermal imaging technology. We studied the relationship between egg thermal infrared images (different shell colors and cleanliness levels) and egg freshness under heating conditions. Firstly, we established a finite element model of egg heat conduction to study the optimal heat excitation temperature and time. The relationship between the thermal infrared images of eggs after thermal excitation and egg freshness was further studied. Eight values of the center coordinates and radius of the egg circular edge as well as the long axis, short axis, and eccentric angle of the egg air cell were used as the characteristic parameters for egg freshness detection. After that, four egg freshness detection models, including decision tree, naive Bayes, k-nearest neighbors, and random forest, were constructed, with detection accuracies of 81.82%, 86.03%, 87.16%, and 92.32%, respectively. Finally, we introduced SegNet neural network image segmentation technology to segment the egg thermal infrared images. The SVM egg freshness detection model was established based on the eigenvalues extracted after segmentation. The test results showed that the accuracy of SegNet image segmentation was 98.87%, and the accuracy of egg freshness detection was 94.52%. The results also showed that infrared thermography combined with deep learning algorithms could detect egg freshness with an accuracy of over 94%, providing a new method and technical basis for online detection of egg freshness on industrial assembly lines.

Effects of Germination, Fermentation and Extrusion on the Nutritional, Cooking and Sensory Properties of Brown Rice Products: A Comparative Study

In this study, cooked brown rice (BR), germinated brown rice (GBR), fermented brown rice (FBR) and white rice (WR) were prepared by traditional cooking techniques, and extruded brown rice (EBR) was obtained by extrusion processing technology. The nutritional, cooking and sensory properties of different BR products were investigated. The results indicated that the soluble dietary fiber (SDF) content, free total phenolic content (TPC), total flavonoid content (TFC) and antioxidant capacity (DPPH, ABTS, T-AOC) in processed BR products were significantly higher than those in cooked BR and WR. The values of SDF, free TPC, TFC and T-AOC in EBR increased by 38.78%, 232.36%, 102.01% and 153.92%, respectively, compared with cooked BR. Cooked FBR and EBR had more nutrients, required less cooking time, had a softer texture and were whiter than cooked GBR and BR, especially EBR. In addition, the water absorption rate of EBR was 14.29% and 25.41% higher than that of cooked FBR and GBR. The hardness of EBR was significantly lower than that of cooked FBR and BR, even lower than that of cooked WR. However, there was no significant difference between the hardness of cooked GBR and that of cooked BR. The flavor compounds in EBR were similar to that of cooked WR, while those in cooked GBR and FBR did not differ greatly compared to cooked BR. Collectively, cooked FBR and EBR had better nutritional value, cooking and sensory properties than cooked BR, and the comprehensive value of EBR was higher.

Evaluation the effects of temperature and packaging conditions on the quality of button mushroom during storage using e-nose system

In this study, the effects of different packaging conditions on the quality of button mushrooms and some its chemical properties (pH and TSS) using an e-nose system equipped with ten sensors have been investigated. The button mushrooms were packaged using two types of films in two atmospheric modes. They were stored at 25 and 4 °C for ten days. During the storage, they were tested every other day. The results showed a mild increase in pH levels in all treatments during the ten days. Changes in TSS in ordinary polyethylene film-packed samples and ambient atmosphere at room temperature showed the highest value. Moreover, investigating the sensor response during the storage period showed that the most significant changes in the response of all sensors occurred in samples packed with polyethylene film and ambient atmosphere at 25 °C. Also, the scoring diagram of principal component analysis (PCA) showed that the completely distinct groups were detectable at two temperatures, two packaging films, and two different packaging atmosphere. At the same time, there was an overlap between the groups in six storage times. The support vector machine (SVM-C) and artificial neural network (ANN)classified the samples with 81 and 66% accuracy in six different storage times. The values of R 2 for predicting TSS and pH using PLS (partial least squares regression), MLR (multiple linear regression) and PCR (principal component regression) ranged between 51 and 68 and 54-59%, respectively, however prediction of TSS had a higher accuracy.

Graphical abstract

E-Nose Technology for Mycotoxin Detection in Feed: Ready for a Real Context in Field Application or Still an Emerging Technology?

Mycotoxin risk in the feed supply chain poses a concern to animal and human health, economy, and international trade of agri-food commodities. Mycotoxin contamination in feed and food is unavoidable and unpredictable. Therefore, monitoring and control are the critical points. Effective and rapid methods for mycotoxin detection, at the levels set by the regulations, are needed for an efficient mycotoxin management. This review provides an overview of the use of the electronic nose (e-nose) as an effective tool for rapid mycotoxin detection and management of the mycotoxin risk at feed business level. E-nose has a high discrimination accuracy between non-contaminated and single-mycotoxin-contaminated grain. However, the predictive accuracy of e-nose is still limited and unsuitable for in-field application, where mycotoxin co-contamination occurs. Further research needs to be focused on the sensor materials, data analysis, pattern recognition systems, and a better understanding of the needs of the feed industry for a safety and quality management of the feed supply chain. A universal e-nose for mycotoxin detection is not realistic; a unique e-nose must be designed for each specific application. Robust and suitable e-nose method and advancements in signal processing algorithms must be validated for specific needs.

A Comparison between the Egg Yolk Flavor of Indigenous 2 Breeds and Commercial Laying Hens Based on Sensory Evaluation, Artificial Sensors, and GC-MS

The focus of this study was to compare the yolk flavor of eggs from laying hens of Chinese indigenous and commercial, based on detection of volatile compounds, fatty acids, and texture characteristics determination, using sensory evaluation, artificial sensors (electronic nose (E-nose), electronic tongue (E-tongue)), and gas chromatography-mass spectrometry (GC-MS). A total of 405 laying hens (Hy-Line Brown (n = 135), Xueyu White (n = 135), and Xinyang Blue (n = 135)) were used for the study, and 540 eggs (180 per breed) were collected within 48 h of being laid and used for sensory evaluation and the instrument detection of yolk flavor. Our research findings demonstrated significant breed differences for sensory attributes of egg yolk, based on sensory evaluation and instrument detection. The milky flavor, moisture, and compactness scores (p < 0.05) of egg yolk from Xueyu White and Xinyang Blue were significantly higher than that of Hy-Line Brown. The aroma preference scores of Xinyang Blue (p < 0.05) were significantly higher, compared to Hy-Line Brown and Xueyu White. The sensor responses of WIW and W2W from E-nose and STS from E-tongue analysis were significantly higher foe egg yolks of Hy-Line Brown (p < 0.05), compared to that of Xueyu White and Xinyang Blue. Additionally, the sensor responses of umami from E-tongue analysis, was significantly higher for egg yolks of Xueyu White (p < 0.05), compared to that of Hy-Line Brown and Xinyang Blue. Besides, the contents of alcohol and fatty acids, such as palmitic acid, oleic acid, and arachidonic acid, in egg yolk were positively correlated with egg flavor. The texture analyzer showed that springiness, gumminess, and hardness of Hy-Line Brown and Xueyu White (p < 0.05) were significantly higher, compared to Xinyang Blue. The above findings demonstrate that the egg yolk from Chinese indigenous strain had better milky flavor, moisture, and compactness, as well as better texture. The egg yolk flavors were mainly due to presence of alcohol and fatty acids, such as palmitic acid, oleic acid, and arachidonic acid, which would provide research direction on improvement in egg yolk flavor by nutrition. The current findings validate the strong correlation between the results of egg yolk flavor and texture, based on sensory evaluation, artificial sensors, and GC-MS. All these indicators would be beneficial for increased preference for egg yolk flavor by consumers and utilization by food processing industry, as well as a basis for the discrimination of eggs from different breeds of laying hens.

Electronic nose and wireless sensor network for environmental monitoring application in pulp and paper industry: a review

Pulp and paper industries emit various odorous gases during the pulp production and paper-making phase, which are unpleasant and have harmful effects on the human body. The working staffs are continuously exposed to these gases and develop various health issues. Hence, regular monitoring and analysis of such gases are necessary to avoid any sudden high concentration exposure and to prevent adverse health effects on the staff. An electronic nose (EN) has an array of gas sensors with an alert system for early detection of gases. Various ENs have been developed for varying applications till date. The detailed knowledge of the sensors used, their sensitivity and technology is helpful in development of any EN. The objective of this study is to comprehensively review various developed ENs with respect to their gas sensing and pattern recognition (PR) technologies. The information on gases released from pulp and paper industries is also compiled. The evolution of EN technology, its various applications, challenges in developing EN and its utility in safeguarding the industrial workers' life have been described. Further, gap analysis among previously developed EN, contemporary EN and wireless sensor network (WSN) is elaborated. It will facilitate future researchers for better selection of sensors and PR technologies while developing EN. The commonly used sensing technologies are described with their advantages, disadvantages and working principles. Metal oxide semiconductor (MOS) gas sensor and ANN algorithm show better result and hence recommended in the development of EN, whereas ZigBee protocol has been widely used for WSN.

Data mining-based discriminant analysis as a tool for the study of egg quality in native hen breeds

Despite the wide biodiversity of avian species of zootechnical interest in Spain, projects aimed at characterizing these genotypes and their products are necessary. External and internal egg quality traits were measured in 819 eggs laid by hens of 10 different genotypes: White, Franciscan, Black and Partridge varieties of Utrerana, Blue Andalusian, Spanish White-Faced, Andalusian Tufted White and Black varieties, Araucana; and Leghorn Lohmann LSL-Classic lineage (commercial hybrid line) hen breeds. After multicollinearity analysis of egg quality-related traits was performed (VIF ≤ 4), major diameter, minor diameter, egg weight, and albumen height were deemed redundant explanatory variables and discarded. A stepwise discriminant canonical analysis was developed to cluster eggs across hen genotypes considering egg quality attributes. Shell a* and b* variables reported the highest discriminant power (Wilks' lambda: 0.699 and 0.729, respectively). The first two discriminant functions captured 60.48% of the variance across groups (F1: 39.36%; F2: 21.12%). Clear quality differentiation signs are evidenced for Mediterranean native breeds' eggs when compared to Leghorn's eggs. Consequently, this evidence of egg quality differentiation may favor the standardization of breed- and variety-linked distinctive products, which may open new market opportunities based on the existence of a wide spectrum of diet or culinary applications.

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.

Detection of soybean oil adulteration in cow ghee (clarified milk fat): an ultrafast study using flash gas chromatography electronic nose coupled with multivariate chemometrics

BACKGROUND

Cow ghee is one of the expensive edible fats in the dairy sector. Ghee is often adulterated with low-priced edible oils, like soybean oil, owing to its high market demand. The existing adulteration detection methods are time-consuming, requiring sample preparation and expertise in these fields. The possibility of detecting soybean oil adulteration (from 10% to 100%) in pure cow ghee was investigated in this study. The fingerprint information of volatile compounds was collected using a flash gas chromatography electronic nose (FGCEN) instrument. The classification results were studied using the pattern recognition chemometric models principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), and discriminant function analysis (DFA).

RESULTS

The most powerful fingerprint odor of all the samples identified from FGCEN analysis was acetaldehyde (Z)-4-heptenal, 2-propanol, ethyl propanoate, and pentan-2-one. The odor analysis investigation was accomplished with an average analysis time of 90 s. A clear differentiation of all the samples with an excellent classification accuracy of more than 99% was achieved with the PCA and DFA chemometric methods. However, the results of the SIMCA model showed that SIMCA could only be used to detect ghee adulteration at higher concentration levels (30% to 100%). The validation study shows good agreement between FGCEN and gas chromatography-mass spectrometry methods.

CONCLUSION

The methodology demonstrated coupled with PCA and DFA methods for adulteration detection in ghee using FGCEN apparatus has been an efficient and convenient technique. This study explored the capability of the FGCEN instrument to tackle the adulteration problems in ghee. © 2022 Society of Chemical Industry.

Real-Time Monitoring the Effects of Storage Conditions on Volatile Compounds and Quality Indexes of Halal-Certified Kimchi during Distribution Using Electronic Nose

The food logistics system is an essential sector for maintaining and monitoring the safety and quality of food products and becoming more crucial, especially during and after the pandemic of COVID-19. Kimchi is a popular traditional fermented food originally from Korea and easily changes because of the storage conditions. This study aims to evaluate the effects and the contributions of temperature to volatile compounds, quality indexes, and the shelf life of Halal-certified Kimchi, and to identify alcohol and find the correlation between the identified variables using an electronic nose and conventional method with the integration of multivariate analysis. Thirty-two volatile compounds (VOCs) were detected and correlated with pH, titratable acidity (TA), and lactic acid bacteria (LAB) counts during storage time. Ethanol was also found in the ripened Kimchi and possibly became the critical point of halal Kimchi products besides total acidity, pH, and LAB. Furthermore, the correlation between pH and benzaldehyde, titratable acidity and 3-methylbutanoic acid, and among lactic acid bacteria with ethanol, acetic acid, ethyl acetate, and 3-methylbutanoic acid properly can be used as a given set of variables in the prediction of food quality during storage and distribution.

Overview of advanced technologies for volatile organic compounds measurement in food quality and safety

Food quality and nutrition have received much attention in recent decades, thanks to changes in consumer behavior and gradual increases in food consumption. The demand for high-quality food necessitates stringent quality assurance and process control measures. As a result, appropriate analytical tools are required to assess the quality of food and food products. VOCs analysis techniques may meet these needs because they are nondestructive, convenient to use, require little or no sample preparation, and are environmentally friendly. In this article, the main VOCs released from various foods during transportation, storage, and processing were reviewed. The principles of the most common VOCs analysis techniques, such as electronic nose, colorimetric sensor array, migration spectrum, infrared and laser spectroscopy, were discussed, as well as the most recent research in the field of food quality and safety evaluation. In particular, we described data processing algorithms and data analysis captured by these techniques in detail. Finally, the challenges and opportunities of these VOCs analysis techniques in food quality analysis were discussed, as well as future development trends and prospects of this field.

Direct authentication and composition quantitation of red wines based on Tri-step infrared spectroscopy and multivariate data fusion

A robust data fusion strategy integrating Tri-step infrared spectroscopy (IR) with electronic nose (E-nose) was established for rapid qualitative authentication and quantitative evaluation of red wines using Cabernet Sauvignon as an example. The chemical fingerprints of four types of wines were thoroughly interpreted by Tri-step IR, and the defined spectral fingerprint region of alcohol and sugar was 1200-950 cm^-1. The wine types were authenticated by IR-based principal component analysis (PCA). Furthermore, ten quantitative models by partial least squares (PLS) were built to evaluate alcohol and total sugar contents. In particular, the model based on the fusion datasets of spectral fingerprint region and E-nose was superior to the others, in which RMSEP reduced by 47.95% (alcohol) and 79.90% (total sugar), r_p increased by 11.95% and 43.47%, and RPD >3.0. The developed methodology would be applicable for mass screening and rapid multi-chemical-component quantification of wines in a more comprehensive and efficient manner.

A novel strategy for discriminating different cultivation and screening odor and taste flavor compounds in Xinhui tangerine peel using E-nose, E-tongue, and chemometrics

A rapid strategy for discriminating Quanzhi (QZ) and Bozhi (BZ) of different cultivation of Xinhui tangerine peel was established by combining electronic nose, electronic tongue and chemometrics, which can be used as tool in the market to identify food fraud. 30 volatiles and 34 low molecular weight compounds of characteristic fingerprints of Xinhui tangerine peel of 108 samples were identified using GC-MS and UHPLC-Q-TOF-MS. Key compounds of BZ and QZ were screened and further compared by chemometrics. We discriminated odor and taste of BZ and QZ using electronic nose and electronic tongue, respectively. Our studies showed that β-myrcene, limonene, β-trans-Ocimene, γ-terpinene and terpinolene, etc, were screened the chief volatile flavor compounds by Spearman's rank correlation. Hydroxymethyl furfural, hesperitin, nobiletin and tangeretin, etc, were screened the key taste flavor compounds based gray relational analysis and partial least squares regression. Our study provides further insight for quality evaluation of Xinhui tangerine peel.

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.

Discriminating Eggs from Two Local Breeds Based on Fatty Acid Profile and Flavor Characteristics Combined with Classification Algorithms

This study discriminated fatty acid profile and flavor characteristics of Beijing You Chicken (BYC) as a precious local breed and Dwarf Beijing You Chicken (DBYC) eggs. Fatty acid profile and flavor characteristics were analyzed to identify differences between BYC and DBYC eggs. Four classification algorithms were used to build classification models. Arachidic acid, oleic acid (OA), eicosatrienoic acid, docosapentaenoic acid (DPA), hexadecenoic acid, monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), unsaturated fatty acids (UFA) and 35 volatile compounds had significant differences in fatty acids and volatile compounds by gas chromatography-mass spectrometry (GC-MS) (p<0.05). For fatty acid data, k-nearest neighbor (KNN) and support vector machine (SVM) got 91.7% classification accuracy. SPME-GC-MS data failed in classification models. For electronic nose data, classification accuracy of KNN, linear discriminant analysis (LDA), SVM and decision tree was all 100%. The overall results indicated that BYC and DBYC eggs could be discriminated based on electronic nose with suitable classification algorithms. This research compared the differentiation of the fatty acid profile and volatile compounds of various egg yolks. The results could be applied to evaluate egg nutrition and distinguish avian eggs.

Comparison of Sensory Qualities in Eggs from Three Breeds Based on Electronic Sensory Evaluations

The present study was conducted on three commercial laying breeder strains to evaluate differences of sensory qualities, including texture, smell, and taste parameters. A total of 140 eggs for each breed were acquired from Beinong No.2 (B) laying hens, Hy-Line Brown (H) laying hens, and Wuhei (W) laying hens. Sensory qualities of egg yolks and albumen from three breeds were detected and discriminated based on different algorithms. Texture profile analysis (TPA) showed that the eggs from three breeds had no differences in hardness, adhesiveness, springiness, and chewiness other than cohesiveness. The smell profiles measured by electronic nose illustrated that differences existed in all 10 sensors for albumen and 8 sensors for yolks. The taste profiles measured by electronic tongue found that the main difference of egg yolks and albumen existed in bitterness and astringency. Principal component analysis (PCA) successfully showed grouping of three breeds based on electronic nose data and failed in grouping based on electronic tongue data. Based on electronic nose data, linear discriminant analysis (LDA), fine k-nearest neighbor (KNN) and linear support vector machine (SVM) were performed to discriminate yolks, albumen, and the whole eggs with 100% classification accuracy. While based on electronic tongue data, the best classification accuracy was 96.7% for yolks by LDA and fine tree, 88.9% for albumen by LDA, and 87.5% for the whole eggs by fine KNN. The experiment results showed that three breeds’ eggs had main differences in smells and could be successfully discriminated by LDA, fine KNN, and linear SVM algorithms based on electronic nose.

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.

Evaluating the Effect of a Brewery By-Product as Feed Supplementation on the Quality of Eggs by Means of a Human Panel and E-Tongue and E-Nose Analysis

The objective of our research was to evaluate the possible alteration of the organoleptic properties of eggs produced by hens (Lohmann Brown-Classic) fed with diets containing different doses of an industrial by-product enriched with organic zinc (Zincoppyeast, ZP): Control 0%, ZP 2.5%, and ZP 5.0%. Eggs were collected after 30 days (batch 1) and 60 days (batch 2) of feeding with the experimental diets and subjected to chemical, microbiological, human sensory, e-nose, and e-tongue analyses. There was no significant difference among the microbiological status of eggs of the three groups, but there were significant differences (p < 0.05) in the fat (9.5% vs. 9.3%) and protein contents (12.7% vs. 13.4%) of the Control and ZP 5.0% groups, respectively. Human sensory analysis showed no clear change in the organoleptic characteristics of the eggs. Using linear discriminant analysis (LDA), the e-tongue could recognize the three groups of eggs in batch 1 and batch 2 with 95.9% and 100% accuracy and had a prediction accuracy of 64.8% and 56.2%, respectively. When the eggs were incubating at 50 °C or 80 °C before the e-nose analysis, the groups of eggs could be recognized with 98.0% and 82.7% accuracy, and predicted with 68.5% and 62.2% accuracy, respectively, using principal component analysis-based discriminant analysis (PCA–DA). The aroma compounds and respective sensory descriptors showing changes among the different groups of eggs (batch, storage, and feeding) were identified based on the e-nose analysis. The supplementation of laying hens’ feed with the investigated industrial by-product can be applied without any substantial effect on egg quality, which can, however, be detected with advanced analytical methods.

A novel electronic nose for the detection and classification of pesticide residue on apples

Excessive pesticide residues are a serious problem faced by food regulatory authorities, suppliers, and consumers. To assist with this challenge, this work aimed to develop a method of detecting and classifying pesticide residue on fruit samples using an electronic nose, through the application of three different data-recognition algorithms. The apple samples carried various concentrations of two known pesticides, namely cypermethrin and chlorpyrifos. Data collection was performed using a PEN3 electronic nose equipped with 10 metal oxide semiconductor (MOS) sensors. In order to classify and analyze these pesticide residues on the apple samples, principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) results were combined with sensor output responses to realize MOS sensor array data visualization. The results indicated that all three data-recognition algorithms accurately identified the pesticide residues in the apple samples, with the PCA algorithm exhibiting the best classification and discrimination ability. Consequently, this work has shown that the MOS electronic nose, in combination with data-recognition algorithms, can provide support for the rapid and non-destructive identification of pesticide residues in fruits and can provide an effective tool for the detection of pesticide residues in agricultural products.

The MOS electronic nose in combination with data-recognition algorithms can provide an effective tool for the detection of pesticide residues in agricultural products.

Characterization of chilled chicken spoilage using an integrated microbiome and metabolomics analysis

Spoilage of chilled chicken can occur as a result of microbial development and consumption of meat nutrients by spoilage bacteria, ultimately resulting in the release of undesired metabolites. Characterizing the profiles of the microbiota and metabolites and clarifying their relationships will contribute to an improved understanding of the mechanism underlying chilled chicken spoilage. In the present study, 16S rRNA gene sequencing and ultra-high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS)-based untargeted metabolomics analyses were applied to determine the microbial and metabolic profiles in chicken during chilled storage. The microbial and metabolic datasets were subjected to combined analysis using weighted gene co-expression network analysis (WGCNA) and Spearman's correlation analysis. Brochothrix, Carnobacterium, Photobacterium, Pseudomonas, Acinetobacter, Serratia, Kurthia, Shewanella, and Obesumbacterium genera were identified as the dominant spoilage bacteria in chilled chicken. Ten metabolic pathways, including histidine metabolism and purine metabolism, were identified as potential mechanisms underlying chilled chicken spoilage. Correlation analysis demonstrated that spoilage bacterial genera were highly correlated with spoilage-related metabolites. Taken together, the present study proposed an integrated microbiome and metabolomics approach to investigate the mechanism of chilled chicken spoilage caused by microbial activity. The results obtained by this approach provide a comprehensive insight into changes in the microbial and metabolic profiles of chilled chicken during spoilage.

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.

Application of volatile and spectral profiling together with multimode data fusion strategy for the discrimination of preserved eggs

The maturity level of eggs during pickling is conventionally assessed by choosing few eggs from each curing batch to crack open. Yet, this method is destructive, creates waste and has consequences for financial losses. In this work, the feasibility of integrating electronic nose (EN) with reflectance hyperspectral (RH) and transmittance hyperspectral (TH) data for accurate classification of preserved eggs (PEs) at different maturation periods was investigated. Classifier models based solely on RH and TH with EN achieved a training accuracy (93.33%, 97.78%) and prediction accuracy (88.89%; 93.33%) respectively. The fusion of the three datasets, (EN + RH + TH) as a single classifier model yielded an overall training accuracy of 98.89% and prediction accuracy of 95.56%. Also, 52 volatile compounds were obtained from the PE headspace, of which 32 belonged to seven functional groups. This study demonstrates the ability to integrate EN with RH and TH data to effectively identify PEs during processing.

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Scattering hyperspectral technology is a nondestructive testing method with many advantages. Here, we propose a method to improve the accuracy of egg freshness, research the influence of incident angles of light source on the accuracy, and explain its mechanism. A variety of weak classifiers classify eggs based on the spectra after preprocessing and feature wavelength extraction to obtain three classifiers with the highest accuracy. The three classifiers are used as metamodels of stacking ensemble learning to improve the highest accuracy from 96.25% to 100%. Moreover, the highest accuracy of scattering, reflection, transmission, and mixed hyperspectral of eggs are 100.00%, 88.75%, 95.00%, and 96.25%, respectively, indicating that the scattering hyperspectral for egg freshness detection is better than that of the others. In addition, the accuracy is inversely proportional to the angle of incidence, i.e., the smaller the incident angle, the camera collects a larger proportion of scattering light, which contains more biochemical parameters of an egg than that of reflection and transmission. These results are very important for improving the accuracy of non-destructive testing and for selecting the incident angle of a light source, and they have potential applications for online non-destructive testing.

Assessment of poultry egg liking scores using sighted and blind people

BACKGROUND

Of the 18 043 bird species, the eggs of only hen and quail are generally available to consumers. Thus people are deprived of the opportunity to benefit from the huge diversity of eggs offered by nature. Poultry eggs can vary in their color of albumen and yolk, smell, taste and texture. In this study, sighted and blind people were employed for sensory evaluation with the aim of determining the preferences of consumers toward hard-boiled and scrambled eggs of different species of birds, and whether the appearance of these eggs has an effect on the perception of other sensory impressions.

RESULTS

Sighted people differently evaluated the texture of both boiled and scrambled eggs as compared with blind people. This was mainly because blind people largely used their sense of touch for evaluation. All other attributes of boiled eggs were evaluated similarly by both groups of panelists, whereas those of scrambled eggs were evaluated differently.

CONCLUSION

The obtained results unequivocally demonstrated that differences in taste of scrambled eggs when served hot are easier to evaluate than those of boiled eggs. On the basis of ranking by the sensory panel, it was established that eggs of birds belonging to the order Galliformes are more preferred by consumers than those of duck and goose. By contrast, eggs of ostrich and emu are characterized by unfavorable sensory profiles; moreover, the albumen of boiled ostrich eggs has an unsightly appearance. © 2019 Society of Chemical Industry.

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.

Egg product freshness evaluation: A metabolomic approach

Egg products' freshness is a crucial issue for the production of safe and high-quality commodities. Up to now, this parameter is assessed with the quantification of few compounds, but the possibility to evaluate more molecules simultaneously could help to provide robust results. In this study, 31 compounds responsible of freshness and not freshness of egg products were selected with a metabolomic approach. After an ultrahigh-pressure liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) analysis, different chemometric models were created to select gradually the most significant features that were finally extracted and identified through HRMS data. Sample lots were collected directly from their arrival at the production plant sites, extracted immediately after, then left at room temperature, and extracted again after 24 and 48 hours (first day and second day, respectively). A total amount of 79 samples was used for the model creation. Furthermore, the same compounds were detected in seven new egg products sample lots not used for the model creation and treated with the same experimental design (total amount of samples, 21). The results obtained clearly demonstrate that these 31 molecules can be considered real freshness or not freshness chemical markers. Furthermore, this UHPLC-HRMS metabolomic approach allows for the detection of a larger set of metabolites clearly related to possible microbial growth over time, which is a relevant point for also ensuring food safety.