Prediction of egg freshness during storage using electronic nose

Abstract

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.

Keywords: chemometric method; egg freshness; fast GC e-nose; prediction model; quality discrimination.

Figure 1.

Fast GC based HERACLES Electronic Nose with HS100 autosampler.

Figure 2.

Changes in the Haugh units of eggs during storage at 20°C (mean ± SD, n = 10).

Figure 3.

Changes in the sensory quality of eggs during storage at 20°C (mean ± SD, n = 15).

Figure 4.

Mean bar graphs of selected peak areas used as raw data in chemometrics representing key chemical compounds, which are important for discrimination of egg freshness (where C00 = d 0, C03 = d 3, C06 = d 6, C09 = d 9, C13 = d 13, C16 = d 16, and C20 = d 20). Along the x-axis are variables (sensors) and values along the y-axis represent abundance.

Figure 5.

PCA score plots of eggs stored for 0 to 20 d. Individual symbols indicate replicate samples taken at different storage time. Where C00 = d 0, C03 = d 3, C06 = d 6, C09 = d 9, C13 = d 13, C16 = d 16, and C20 = d 20.

Figure 6.

DFA model analysis results based on electronic nose data obtained from egg yolk samples ((A), calibration plot; (B), validation plot) for the discrimination of eggs stored for 0 to 20 d. Individual symbols indicate replicate samples taken at different storage time. C00 = d 0, C03 = d 3, C06 = d 6, C09 = d 9, C13 = d 13, C16 = d 16, and C20 = d 20.

Figure 7.

PLS model results for the prediction of Haugh units (HU) based on electronic nose data obtained from (A) calibration and (B) validation egg yolk samples. The prediction is shown as measured (predicted by e-nose) vs. reference (measured by conventional method) HU values. Individual symbols indicate replicate samples taken at different storage time. C00 = d 0, C03 = d 3, C06 = d 6, C09 = d 9, C13 = d 13, C16 = d 16, and C20 = d 20.

Figure 8.

PLS model results for the prediction of storage time based on electronic nose data obtained from (A) calibration and (B) validation egg yolk samples. The prediction is shown as measured (predicted by e-nose) vs. reference (measured by conventional method) d of storage. Individual symbols indicate replicate samples taken at different storage time. C00 = d 0, C03 = d 3, C06 = d 6, C09 = d 9, C13 = d 13, C16 = d 16, and C20 = d 20.

Figure 9.

PLS model results for the prediction of odor scores based on electronic nose data obtained from (A) calibration and (B) validation egg yolk samples. The prediction is shown as measured (predicted by e-nose) vs. reference (measured by conventional method) odor scores. Individual symbols indicate replicate samples taken at different storage time. C00 = d 0, C03 = d 3, C06 = d 6, C09 = d 9, C13 = d 13, C16 = d 16, and C20 = d 20.

Figure 10.

PLS model results for the prediction of overall acceptability scores based on electronic nose data obtained from (A) calibration and (B) validation egg yolk samples. The prediction is shown as measured (predicted by e-nose) vs. reference (measured by conventional method) overall acceptability scores. Individual symbols indicate replicate samples taken at different storage time. C00 = d 0, C03 = d 3, C06 = d 6, C09 = d 9, C13 = d 13, C16 = d 16, and C20 = d 20.