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Hindawi Journal of Analytical Methods in Chemistry Volume 2023, Article ID 1271409, 8 pages https://doi.org/10.1155/2023/1271409 Review Article Discrimination of Free-Range and Caged Eggs by Chemometrics Analysis of the Elemental Profiles of Eggshell 1 2 2 2 3 Shunping Xie , Chengying Hai , Song He , Huanhuan Lu , Lu Xu , and Haiyan Fu Technology Center, China Tobacco Guizhou Industrial Co., Ltd., Guiyang 550009, Guizhou, China Te Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, College of Pharmacy, South-Central Minzu University, Wuhan 430074, China College of Material and Chemical Engineering, Tongren University, Tongren 554300, Guizhou, China Correspondence should be addressed to Lu Xu; email@example.com and Haiyan Fu; firstname.lastname@example.org Received 6 July 2022; Revised 22 August 2022; Accepted 16 September 2022; Published 28 February 2023 Academic Editor: Alessandro Buccolieri Copyright © 2023 Shunping Xie et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. As one of the foods commonly eaten all over the world, eggs have attracted more and more attention for their quality and price. A method based on elemental profles and chemometrics to discriminate between free-range and caged eggs was established. Free- range (n1 � 127) and caged (n2 � 122) eggs were collected from diferent producing areas in China. Te content of 16 elements (Zn, Pb, Cd, Co, Ni, Fe, Mn, Cr, Mg, Cu, Se, Ca, Al, Sr, Na, and K) in the eggshell was determined using a inductively coupled plasma atomic emission spectrometer (ICP-AES). Outlier diagnosis is performed by robust Stahel–Donoho estimation (SDE) and the Kennard and Stone (K-S) algorithm for training and test set partitioning. Partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) were used for classifcation of the two types of eggs. As a result, Cd, Mn, Mg, Se, and K make an important contribution to the classifcation of free-range and caged eggs. By combining column-wise and row-wise rescaling of the elemental data, the sensitivity, specifcity, and accuracy were 91.9%, 91.1%, and 92.7% for PLS-DA, while the results of LS-SVM were 95.3%, 95.6%, and 95.1%, respectively. Te result indicates that chemometrics analysis of the elemental profles of eggshells could provide a useful and efective method to discriminate between free-range and caged eggs. cage) and feeds (plant or animal sources) varies signifcantly 1. Introduction [12, 13]. Free-range eggs contain about one-third and one- Chicken eggs are one of the main foodstufs consumed quarter less cholesterol and saturated fat than regular caged worldwide, mainly consisting of eggshell, shell membrane, eggs, respectively, and there are signifcant diferences in vi- egg white, and yolk [1, 2]. It contains a wide range of nu- tamin A, vitamin E, and beta-carotene content between free- trients and health-promoting components, such as lecithin, range and caged eggs [14–16]. In addition, free-range eggs are calcium ions, iron ions, and vitamin A [3–5]. Eggs have considered to have higher nutritional value, favor, and safety a high digestibility and absorption rate and are an in- than caged eggs, so people are willing to pay a higher price for expensive and abundant source of high-quality animal them. In recent years, egg adulteration and fraud have become protein (about 13 grams of protein per 100 grams of egg) more frequent, with traders selling caged eggs as free-range [6–8]. eggs for proft, which seriously undermines the lives, health, At present, various types of eggs exist in the Chinese and legal rights of consumers. Terefore, it is important to market, such as organic and ordinary eggs, which have diferent develop a quick and reliable method to identify free-range or nutritional composition and commercial value [9–11]. It has caged eggs on the market. been demonstrated that the nutritional composition of eggs Presently, the methods applied to egg identifcation produced by hens fed diferent feeding methods (free-range or include high performance liquid chromatography [17, 18], 2 Journal of Analytical Methods in Chemistry Table 1: Detailed information of the free-range and caged eggs Table 2: Analysis of wavelengths of 16 mineral elements in analysed. free-range and caged eggshells. Producing area Batch size Type Element Wavelength (nm) Element Wavelength (nm) Guizhou 10 F Zn 213.85 Pb 220.35 Guizhou 11 C Cd 226.50 Co 228.61 Henan 22 F Ni 231.60 Fe 238.20 Henan 18 C Mn 257.61 Cr 267.71 Anhui 20 F Mg 280.26 Cu 324.75 Anhui 16 C Se 361.38 Ca 393.36 Jiangsu 15 F Al 396.15 Sr 407.77 Jiangsu 20 C Na 589.60 K 766.49 Hubei 26 F Hubei 19 C Guangxi 16 F the manufacturer after confrming the type in 2019. Five eggs Guangxi 17 C will be taken from each batch of samples for parallel analysis, Hunan 18 F and the remaining two eggs will be used as spares. Te detailed Hunan 21 C information concerning the samples is shown in Table 1. −1 F � “free-range” and C � “caged”. Standard reserve solutions (1000 μg·mL ) of Zn, Cd, Co, Cr, Cu, Ca, Mg, Mn, Mo, Ni, Pb, Sr, Fe, Na, and K were obtained from the National Standard Material Center of gas chromatography-mass spectrometry [19, 20], hyper- China. HNO and H O were purchased from Sinopharm 3 2 2 spectral imaging , elemental analysis , and near Chemical Reagent Co., Ltd. infrared spectroscopy [23, 24]. For example, Mi et al.  investigated the diferentiation of Deqingyuan, Taihe, and crossbred eggs based on multielemental and lipidomic data 2.2. Digestion of Eggshells. Te eggshell was rinsed with tap combined with chemometric analysis and obtained a panel water after removing the internal membrane. Ten, the of 22 potential lipid markers for diferentiating Deqingyuan, eggshell was washed with deionized water and dried at 120 C Taihe, and crossbred egg yolks. Rogers et al.  successfully using electric sleeve heating. About 1 gram of dried eggshell used stable isotopes to analyse and discriminate between was weighed accurately on the electro-optic balance, then eggs produced under cage, barn, free-range, and organic smashed into small pieces, and put into a 50-mL conical farming systems in the Netherlands and New Zealand. fask. For digestion, 8 mL HNO (65%, w/w %) and However, fewer studies have been performed to discriminate 2 mL H O (30%, w/w %) were added. Te conical fask was 2 2 between caged and free-range eggs in China. So, it is nec- heated and kept at 60 C until a colourless solution was essary to analyse and discriminate between caged and free- obtained. Te solution was cooled naturally and transferred range eggs in China. to a 50 mL volumetric fask, where deionized water was In China, a signifcant diference between caged and free- added to a constant volume. Te blank was prepared using range eggs is the use of diferent feeds. Free-range eggs are 4 mL HNO (65%, w/w %) and 1 mL H O (30%, w/w %). 3 2 2 produced in a small scale by individual farmers using grains as the main feed, while caged eggs are produced in a large scale 2.3. Elemental Analysis by ICP-AES. Te concentration of the using commercial feeds [26, 27]. Te diferences in feeding 16 mineral elements in the eggshells was determined using styles can cause the diferences in elemental contents, which can a Shimadzu ICPS-7510 sequential plasma emission spectrom- be used to identify diferent types of eggs [28, 29]. Terefore, the eter (Shimadzu, Kyoto, Japan). Te spectrometer parameters aim of this work is to develop an egg classifcation method to −1 were as follows: power: 1300 W; plasma fow rate: 15 L min ; discriminate between free-range and cage eggs using element −1 −1 carrier gas fow rate: 0.8 L min ; auxiliary fow rate: 0.2 L min ; analysis combined with chemometrics. In this work, using egg −1 atomization fow rate: 0.8 L min ; pump fow rate: shells as an analytical object to distinguish between free-range −1 1.5 mL min ; axial observation distance: 15 mm; and the in- and caged eggs, an inductively coupled plasma-atomic emission strumentation stabilization time of 30 s. Analytical lines (Ta- spectrometer (ICP-AES) was used to analyse the content of ble 2) were selected by considering the overlapping and intensity 16 mineral elements in eggshells. Various classifcation models of signals. A standard curve was developed for each element. For such as PLS-DA and LS-SVM were established to discriminate each batch, elemental contents were reported as the average of between free-range and caged eggs, and the performance of eggshell samples analysed in triple. diferent methods was compared to obtain the best classifcation model. 2.4. Data Preprocessing, Outlier Diagnosis, and Data Splitting. All data preprocessing and further analysis were performed 2. Materials and Methods using Matlab 7.0.1 (Mathworks, Sherborn, MA). When the 2.1. Experimental Materials and Reagents. Representative measured data are infuenced by signifcant bias and other caged and free-range egg samples were collected from diferent undesirable factors, the performance and reliability of classif- producing areas in China. 127 free-range samples and 122 caged cation modeling would be degraded; therefore, the potential eggs were analysed. All egg samples are purchased directly from outliers should be detected and removed. In order to solve the Journal of Analytical Methods in Chemistry 3 Table 3: Analytical results of 16 elements in free-range and caged masking efect of multiple outliers, the Stahel–Donoho estimate eggshells. (SDE) of outlyingness was used for outlier diagnosis of ele- mental data, which is a robust statistical method with dimension Average levels (SD) (μg/g) Elements DL (μg/g) reduction techniques . Te SDE calculates a large number of Free-range Caged projections of randomly selected objects in each direction, and Zn 1.72 (0.39) 2.33 (0.51) 0.05 through the robust positioning and scatter estimators of the Pb 1.04 (0.27) 2.16 (0.19) 0.09 projection, the SDE outlier of each sample is obtained. In this Cd 0 0.34 (0.08) 0.03 work, the SDE was used for outlier diagnosis in free-range and Co 0.22 (0.04) 0.32 (0.09) 0.03 caged eggs separately. Ni 0.44 (0.12) 0.59 (0.10) 0.05 Subsequently, the measured data are divided into Fe 2.1 (0.6) 3.2 (1.0) 0.04 a training set and a prediction set by the Kennard and Stone Mn 0.67 (0.13) 1.01 (0.31) 0.07 Cr 0.71 (0.28) 2.32 (1.03) 0.02 (K-S) algorithm . Te K-S algorithm will select a rep- Mg 6042 (2106) 5363 (1849) 0.08 resentative training set to make the objects as scattered in the Cu 1.56 (0.31) 2.13 (0.55) 0.07 data space as possible. Because the distributions of two Se 4.22 (1.68) 2.18 (1.01) 0.04 classes of eggs were not the same, the K-S method was Ca 366310 (46778) 342560 (35458) 0.08 performed separately for the free-range and caged eggs. Al 71.9 (6.5) 88.5 (11.7) 0.10 Sr 120.4 (24.8) 171.3 (44.6) 0.02 2.5. Multivariate Discriminate Analysis. For pattern recog- Na 2156 (388) 3016 (612) 0.33 K 1850 (302) 2305 (226) 0.76 nition, linear partial least squares discriminant analysis (PLS- DA)  and nonlinear least squares support vector machine Te detection limit was related to the 3σ signal, where σ was estimated from 11 repeated measurements of the blank. Nondetected. (LS-SVM)  are performed to distinguish free-range and caged eggs. Monte Carlo Cross Validation (MCCV)  is used to evaluate the number of PLS-DA latent variables, and the “negatives.” Sensitivity (Sens), specifcity (Spec), and overall parameters of LS-SVM are optimized to obtain the lowest accuracy (Accu) can be computed as follows: MCCV error rate (MCCVER) and reduce the risk of model TP overftting. Sens � TP + FN Principal component analysis (PCA) is an unsupervised data dimensionality reduction method, which converts a set of TN potentially correlated variable data into a set of linearly un- Spec � , (1) TN + FP correlated variables through orthogonal transformation, and the converted variables are called principal components. In recent TN + TP Accu � . years, PCA has been widely used for classifcation and iden- TN + TP + FN + FP tifcation of varieties, origins, and adulteration of food and Among them, TP represents true positive, FN represents agricultural products . Partial least squares discriminant false negative, TN represents true negative, and FP repre- analysis (PLS-DA) is a supervised discriminant analysis sta- sents false positive. tistical method which is often used to deal with classifcation and discriminant problems. It can well solve those classifcation problems in which the diferences between groups are small and 3. Results and Discussion the sample sizes of the groups vary widely . LS-SVM (least squares support vector machines) is mainly used to solve 3.1. Elemental Data of Eggshells. Table 3 showed the pattern classifcation and function estimation problems. Te ICP-AES analysis results of 16 elements in free-range and optimization of the model parameters such as the kernel caged eggs. Te elemental contents of Ca, Mg, Na, and K function parameter (σ) and the regularization parameter (c) is were the highest in free-range and caged eggs. Among required when using it. Te kernel parameter has a direct them, free-range eggs have higher content of Ca, Mg, and impact on the complexity of the distribution of low- Se compared to caged eggs, while caged eggs have higher dimensional sample data in the mapping space, while the content of Na, K, Al, Sr, Fe, and Mn, which is consistent regularization parameter is related to the ft of the model to the with previous studies . It is noteworthy that caged training samples and the generalization ability of the eggs have higher content of heavy metals such as Pb, Cd, model . Cr, and Cu, and there is no detected Cd element in the Sensitivity and specifcity were used to estimate and free-range eggs. It is known that elements Ca, Mg, Na, compare the performance of classifcation models. Free-range and K are involved in various metabolisms in the human eggs are denoted as “positives,” and caged eggs are denoted as body and are essential elements required by the human body, and Se is an important nutrient for the prevention 4 Journal of Analytical Methods in Chemistry Loadings pf PC1 1.5 0.5 –0.5 –1 –1.5 –2 –10 –6 –4 –2 0 2 46 Zn Pb Cd Co Ni Fe Mn Cr Mg Cu Se Ca Al Sr Na K PC1 free–range caged (a) (b) Figure 1: (a) Principal component 1 (PC1) and principal component 2 (PC2) score plot in free-range and caged eggshells. (b) PC1 loadings of the elemental profles of free-range and caged eggshells. of tumors and liver diseases as well as the improvement of 127 free-range eggshells and 122 caged eggshells, according to the 3-σ rule. A critical value of 3 was adopted, and an immunity. To illustrate the data distribution, principal component object with an outlyingness value above 3 was considered an analysis (PCA) was used on the column-wise and row-wise outlier. 2 and 1 objects for free-range and caged eggs were rescaled data without outlier diagnosis (Figure 1). Principal detected as outliers, respectively (Figure 2). Further tracing component 1 and principal component 2 explained 90.06% of the samples indicates that the labels of these eggs were of all data variation, and projection of the raw data onto PC1 suspicious. Terefore, these objects were excluded from and PC2 to obtain score plots showed that free-range eggs discriminant analysis. and caged eggs basically achieved a better separation, which After eliminating outliers, the remaining 125 free-range were clustered into two groups, respectively, where some eggs and 121 caged eggs were used to develop and test samples overlapped due to the small diferences in trace classifcation models. Te K-S algorithm was performed element contents in these samples (Figure 1(a)). Te loading separately for the two groups, dividing the free-range eggs plot of principal component 1 is shown in Figure 1(b), which into 80 training subjects and 45 test subjects and then di- shows that the contents of Cd, Mn, Mg, Se, and K contribute viding the caged eggs into 80 training subjects and 41 test signifcantly to the separation between groups achieved by subjects. Terefore, a training set of 160 (80 + 80) objects and PC1, while the elements Zn, Co, Ni, Gr, Cu, and Al have a test set of 86 (45 + 41) objects were obtained to develop and negative efects in the classifcation. Te combined content evaluate the classifcation model. analysis showed that Cd, Mn, Mg, Se, and K had important Te PLS-DA model and the LS-SVM model based on the contributions in the classifcation of free-range eggs and eggshell element data were established. Te two parameters caged eggs and could be used as efective elements to dis- c and σ are optimized in the LS-SVM model. Te kernel tinguish free-range eggs from caged eggs. Although the PCA width parameter σ is related to the data confdence and the model achieved the distinction between free-range eggs and nonlinear nature of the model, and the smaller σ means the caged eggs, the classifcation accuracy did not reach 100%. narrower the kernel width, which may force the model to So, supervised chemometric models are still needed to shift to more complex nonlinear solutions. Another pa- achieve accurate classifcation of the two classes. rameter c is a regularization parameter, which involves the trade-of between learning accuracy and structural risk. To simultaneously optimize (σ, c), a grid search method was 3.2. Development of Classifcation Models. Considering the performed by MCCV. In addition, MCCV is to estimate the relative contents of diferent elements and the diference in number of meaningful PLS-DA latent variables (LV). All each sample weight, rescaling of the data was necessary to parameters of PLS-DA and LS-SVM are optimized by analyse the elemental data. In this work, the data for an minimizing the MCCV error rate. For MCCV, 70% of the object was divided by its sample weight followed by a col- samples were used for the training set and 30% for the test umn-wise transformation into unit variance for each ele- set. Te random data split number of MCCV is 100, and the ment. Te SDE outlyingness analysis was performed optimization of model parameters is shown in Figure 3. separately on each of the two classes using the rescaled data. Te optimization parameters and classifcation results of Outlying values were estimated by 1,000 random pro- PLS-DA and LS-SVM models are shown in Table 4. For PLS- jections. Figure 2 shows the SDE outlier diagnostic curve for PC2 Journal of Analytical Methods in Chemistry 5 4 4 3.5 3.5 3 3 2.5 2.5 2 2 1.5 1.5 0.5 0.5 0 0 0 204060 80 100 120 140 020 40 60 80 100 120 140 Free–range eggs Caged eggs (a) (b) Figure 2: SDE outlier diagnosis of 127 free-range eggshells (a) and 122 caged eggshells (b) based on rescaled element data. Table 4: Classifcation of free-range and caged eggs using rescaled elemental data of eggshells by PLS-DA and LS-SVM. Models Parameters ERMCCV (%) Accuracy (%) Sensitivity (%) Specifcity (%) a b c PLS-DA 4 8.36 91.9 91.1 (41/45) 92.7 (38/41) LS-SVM (700, 5) 2.47 95.3 95.6 (43/45) 95.1 (39/41) a b c d 2 Number of PLS-DA latent variables. TP/TP + FN. TN/TN + FP. Values of (σ , c). 0.24 11 0.22 0.2 0.18 0.16 0.14 0.12 0.1 0 1000 0.08 600 200 400 12345 6 71 8 9 0 Number of PLSDA latent variables (a) (b) Figure 3: MCCV optimization of PLS-DA (a) and LS-SVM (b) model parameters. DA, the model has the lowest MCCVER (8.36%) when 2 caged eggs were misclassifed as free-range eggs, with the LV � 4 (Figure 3(a)), which indicates that better classifca- models’ accuracy, sensitivity, and specifcity of 95.3%, 95.6%, and 95.1%, respectively. Te LS-SVM model has higher tion of free-range eggs and caged eggs can be achieved with lower model complexity. For LS-SVM, the lowest value of classifcation accuracy compared to PLS-DA, demonstrating MCCVER (2.47%) was obtained when the values of σ and c that LS-SVM is more suitable for the classifcation of free- were 700 and 5, respectively; so, this parameter was chosen range eggs and caged eggs. According to previous studies, for classifcation. Figure 4 shows the score plot of the the discrimination of free-range, caged, organic, and ordi- prediction set of the PLS-DA model (Figure 4(a)), which nary eggs is mainly based on the analysis of chemical shows that four free-range eggs were misclassifed as caged components such as carotenoids , lipid extracts , eggs and three caged eggs were misclassifed as free-range proteins, and moisture in eggs , which enable an accurate eggs, and the models’ accuracy, sensitivity, and specifcity identifcation of diferent varieties of eggs, but the pre- were 91.9%, 91.1%, and 92.7%, respectively. In the LS-SVM treatment of these methods is more complicated. In addi- model, 2 free-range eggs were misclassifed as caged eggs and tion, mineral element-based methods combined with MCCVER (%) SDE outlyingness MCCVER (%) SDE outlyingness 6 Journal of Analytical Methods in Chemistry PLSDA LS–SVM 1.5 1.5 0.5 0.5 –0.5 –0.5 –1 –1 –1.5 –2 –1.5 0 10203040 50 60 70 80 90 0 10203040 50 60 70 80 90 Test objects Test objects (a) (b) Figure 4: Classifcation results by (a) PLS-DA and (b) LS-SVM. Objects 1–45, free-range eggs; objects 46–86, caged eggs. chemometrics have been successfully applied to identify PCA: Principal component analysis free-range and caged eggs. In Dao’s study, signifcantly SDE: Stahel–Donoho estimate higher levels of the mineral elements P, Mg, and Na and MCCV: Monte Carlo cross validation lower levels of the trace elements Cu, Fe, K, S, and Mn were ERMCCV: Misclassifcation rate of MCCV found in Australian free-range eggs, and a good classifcation LVs: Latent variables of free-range and caged eggs from Australia and Syria was SD: Standard deviations. achieved . Te above studies show that mineral element- based methods combined with chemometrics can achieve Data Availability accurate identifcation of free-range eggs and caged eggs in China. Te data supporting the fndings of the current study are available from the corresponding author upon request. 4. Conclusions As a result, 16 mineral elements (Zn, Pb, Cd, Co, Ni, Fe, Mn, Disclosure Cr, Mg, Cu, Se, Ca, Al, Sr, Na, and K) in eggshells combined with chemometrics can distinguish between free-range and Shunping Xie and Chengying Hai are the co-frst authors. caged egg samples, and Cd, Mn, Mg, Se, and K have a sig- nifcant infuence on the classifcation as potential factors for Conflicts of Interest free-range and caged eggs. PCA, PLS-DA, and LS-SVM are Te authors declare that there are no conficts of interest. applied to the classifcation of free-range and cage-reared eggs. Both PLS-DA and LS-SVM could obtain good dis- crimination results. Especially, LS-SVM can obtain better Authors’ Contributions classifcation performance with an overall accuracy of 95.3%, a sensitivity of 95.6%, and a specifcity of 95.1%. So elemental Shunping Xie was involved in methodology, writing, and analysis combined with chemometrics can be used as editing. Chengying Hai investigated the study and wrote the a simple and efective method to identify free-range and original draft. Song He and Huanhuan Lu performed formal caged egg samples. analysis. Lu Xu conceptualized and supervised the study and was involved in funding acquisition. Haiyan Fu conceptu- Abbreviations alized the study and was involved in funding acquisition. ICP-AES: Inductively coupled plasma atomic emission spectrometer Acknowledgments PLS-DA: Partial least squares discriminant analysis LS-SVM: Least squares support vector machines Te authors are grateful to the fnancial support from the SDE: Stahel–Donoho estimation National Key R&D Program of China (no. Predicted responses Predicted responses Journal of Analytical Methods in Chemistry 7  S. Rakonjac, S. Bogosavljevic-Bo ´ ˇskovic, ´ Z. 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Journal of Analytical Methods in Chemistry – Hindawi Publishing Corporation
Published: Feb 28, 2023
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