Table 4.
Summary of lipidomic application in chicken authentication. Type of samples and performance (limit of detection and discriminating accuracy) are included when available.
| Purpose of Analysis | Main Instrument | Statistical Analysis | Markers/Differentiation Features | References | Highlight |
|---|---|---|---|---|---|
| Analysis of tallow, lard, and chicken fat adulterations in canola oil. | DSC, HPLC, GC-FID | SMLR | Thermogram profile. | [114] | Chicken fat adulteration is impossible to be determined under DSC thermoprofiling. |
| Analysis of lard, body fats of lamb, cow, and chicken. | FTIR | PLS-DA | FTIR spectrum at fingerprint region (1500–900 cm−1) of lipid components. | [115] | The equation obtained from the calibration model can predict lard mixed with cow and chicken fat percentage at 1500–900 cm−1. |
| Analysis of cod liver oil, mutton fat, chicken fat, and beef fat. | FTIR | PLS-DA | FTIR mid-region (4000–650 cm−1). | [116] | PLS model can be used for the quantification of chickenfat in CLO with 100% accuracy. |
| Analysis of lard, chicken fat, beef fat, and mutton fat. | GC-MS, EA-IRMS | PCA | Stearic, oleic, and linoleic acids; carbon isotope ratios (δ 13C). | [117] | PCA of stearic, oleic, and linoleic acids data and significant differences in the values of carbon isotope ratios (δ 13C) of all animal fats can potentially discriminate meat species. |
| Analysis of chicken fat adulteration in butter | FTIR, GC-FID | PLS | FTIR spectrum at fingerprint region of (1200–1000 cm−1). | [118] | PLS can be successfully used to quantify the level of chicken fat adulterant with R2 of 0.981 at the selected fingerprint region of 1200–1000 cm−1. |
| Acylglycerols analysis of lard, chicken fat, beef fat, and mutton fat. | GC-MS, EA-IRMS | PCA | MAG and DAG profiles; carbon isotope ratios (δ 13C). | [119] | The presence of small amounts of arachidic acid and differences in the proportions of several fatty acids in the chicken diacylglycerols can differentiate chicken from lard. Variation in δ 13C values can also discriminate MAG and DAG in different species. |
| To authenticate fats originated from beef, chicken, and lard. | NIR | SVM | Wavelength region from 1300 to 2200 nm. | [120] | Using the developed SVM model, lard can be classified 100% correctly from chicken and beef fat, but only 86.67% accuracy was obtained when the three fats were classified together. |
| Lipid composition characterization of Taihe black-boned silky fowls and comparison to crossbred black-boned silky fowls. | UPLC/MS/MS, Q-TOF/MS | OPLS-DA | 47 lipid molecules as markers to distinguish Taihe and crossbred black-boned silky fowls. | [121] | OPLS-DA analysis reveals 47 lipid compounds were statistically significant and can be used as potentialmarkers for the authentication of Taihe black-boned silky fowl. |
| Post-heat treated lard differentiation from chicken fats, mutton, tallow, and palm-based shortening. | FTIR | PCA, k-mean CA, LDA | Wavenumbers at region 3488–3980, 2160–2300, and 1200–1900 cm−1. | [122] | The combination of PCA with k-mean CA was able to differentiate heated fats according to their origin. LDA only possesses 80.5% classification accuracy where mutton and tallow cannot be classified correctly. |
| Wavelength profiling in a different mixture of fat samples containing chicken, lamb, beef, and palm oil. | FTIR | PCA | Wavelength at 1236 and 3007 cm−1. | [123] | The biomarker wavelengths identified from the spectra of the studied samples at positions 1236 and 3007 cm−1 separated at notable distances can be used to discriminate the fat from different species. |
| Triacylglycerols (TAGs) fingerprinting on beef, pork, chicken in meat products | DART–HRMS | PCA, PLS-DA | 3 TAGs ion m/z. | [124] | DART–HRMS could be used primarily as a screening method, and suspected samples are required to be confirmed by PCR. |
| Profiling of lard with beef tallow, mutton tallow, and chicken fat. | GC-FID, HPLC, DSC | ANOVA, PCA | Score plot of 7 fatty acid composition, OOL/SPO ratio, and thermogram profile. | [125] | Score plot of PCA model, a significant difference in OOL/SPO ratio and thermal profile can provide a basis for differentiating chicken fat from lard. |
SMLR, stepwise multiple linear regression analysis; DSC, differential scanning calorimetry; GC-FID, gas chromatography with flame ionization detector; FTIR, Fourier transform infrared spectroscopy; EA-IRMS, elemental analyzer–isotope ratio mass spectrometry; NIR, near-infrared spectroscopy; SVM, support vector machine; MAG, Monoacylglycerols; DAG, diacylglycerols; PLSR, partial least square regression; OPLS-DA, orthogonal partial least squares-discriminant analysis; UPLC/MS/MS, ultra-performance liquid chromatography-tandem mass spectrometry; k-mean CA, k-mean cluster analysis; LDA, linear discriminant analysis; DART–HRMS, direct analysis in real-time coupled with high-resolution mass spectrometry; PLS-DA, partial least squares discriminant analysis; OOL/SPO, oleic oleic linoleic/stearic palmitic oleic.