The Application of Molecular Spectroscopy in Combination with Chemometrics for Halal Authentication Analysis: A Review
Abstract
:1. Introduction
2. Molecular Spectroscopy
3. Chemometrics
3.1. Chemometrics of Classification
3.2. Chemometrics of Quantification
4. Application of Molecular Spectroscopy Combined with Chemometrics for Analysis of Non-Halal Components
4.1. UV–vis Spectroscopy
4.2. Infrared Spectroscopy
4.3. Raman Spectroscopy
4.4. Nuclear Magnetic Resonance (NMR) Spectroscopy
5. Methods
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Non-Halal Components | Issue | Infrared (NIR/MIR) Spectroscopy Condition | Chemometrics Techniques | Results | References |
---|---|---|---|---|---|
Lard (pork fat) | Adulteration of lard in palm oil | NIR at wavelength 950–1650 nm using transflectance and transmission sampling techniques | Classification using SIMCA, quantification using PLS | SIMCA can classify palm oil and palm oil adulterated with lard with model accuracy of 0.93 (transflectance) and 0.95 (transmission). NIR can predict lard content described by an equation relating between actual value of lard (x) and NIR-PLS predicted value (y) as: Y = 0.9987x + 0.02032 (transflectance)Y = 0.9994x + 0.01024 (transmission) | [52] |
Lard | Adulteration of beef with pork through analysis of lard | The extraction was performed using Soxhlet apparatus at 70 °C for 6 h with n-hexane. FTIR normal spectra at 1/λ 1200–1000 cm−1 using ATR technique | Classification using PCA and quantification with PLS regression | FTIR spectra combined with PCA could classify sausages with pork and beef. PLS gave an equation of predicted value = 0.921 x (actual value) + 4.623 R2 = 0.985 and RMSEC = 2.094%; RMSEP = 4.77% RMSECV = 5.12%. | [53] |
Lard | Analysis of lard in crackers “rambak” (Foods consumed among Indonesian people made from various kinds of animal skin) | The “rambak” crackers containing pigskin and cow skin was subjected to Soxhlet extraction using hexane. ATR-FTIR spectra at 1/λ 1200–1000 cm−1 | PLS regression | PLS regression could predict lard extracted from “rambak” crackers with R2 (calibration) of 0.946 with RMSEC of 2.77%. For validation models, R2 of 0.997 and RMSEP of 2.77. | [54] |
Lard | Analysis of lard in “rambak” crackers containing buffalo skin | The “rambak” crackers was extracted using Soxhlet procedure with hexane as extracting solvent. ATR-normal FTIR spectra at 1/λ 1200–1000 cm−1 | Classification using PCA and quantification with PLS regression | PCA could classify “rambak” crackers according to animal skin (pigskin and buffalo skin). PLS regression could predict pigskin in rambak with R2 of 0.96, RMSEC of 2.56%, and RMSEP of 1.10%. | [55] |
Lard | Analysis of lard in bread formulation | Lard in bread was extracted using Bligh and Dyer method by extensive vortexing at each step. Second derivative spectra at 1/λ 1190–900 cm−1 using ATR technique | PLS regression for quantification | PLS using the selected FTIR spectra region could quantify lard in bread successfully with detection limit of 1% wt/wt | [56] |
Lard (pork fat) | Adulteration of chicken fat with pork fat in food products | Normal spectra at 1/λ 1236 and 3007 cm−1 using ATR technique | Classification using PCA | Combination of FTIR spectra and chemometrics could classify lard in chicken fat, pure lard, food containing lard, palm oil, and chicken fat | [57] |
Lard | Differentiation of lard chicken, mutton, tallow- and palm-based shortening | Samples are heated at different temperatures (120, 180 and 240 °C) and time (30, 60, 120 and 180 min) and normal FTIR spectra at 4000–650 cm−1 were evaluated for differentiation | Classification using PCA, k-mean cluster analysis and LDA | The combination of PCA with k-mean CA was capable of differentiating the heated lard and other samples according to its origin. LDA method was successfully used to classify 80.5 % of samples according to its group | [58] |
Lard | Analysis of lard in crude palm oil (CPO) for authenticity issue | Lard in the mixture with CPO using ATR at the combined wavenumbers of 1481–999 and 1793–1650 cm−1 | PLS regression | PLS could predict the levels of lard in CPO with R2 value of 0.998 and RMSEC of 1.291% (v/v) and RMSECV value of 0.838% (v/v). | [59] |
Lard | Analysis of lard in palm oil | The samples were directly subjected to short wave near-infrared spectroscopy (NIR) at wavelength 800–1600 nm and measured with transflectance and transmission modes | Spectra were subjected to variable selection. Classification using SIMCA, quantification with PLS | SIMCA algorithm could classify lard and palm oil mixed with lard with accuracy level of >0.95 for both transflectance and transmission modes. PLS regression could predict the levels of lard in palm oil with R2 of 0.9987 (transflectance) and 0.9994 (transmission) with RMSEC of 0.5931 (transflectance) and 0.6703 (transmission). | [52] |
Lard | Detection of the presence of lard in pure ghee (heat clarified milk fat) | Normal FTIR spectra at combined 1/λ region of 3030–2785, 1786–1680, 1490–919 cm−1 | Classification using SIMCA, quantification using PLS | Pure ghee and the one adulterated with lard could be classified using PCA. Using SIMCA, 90% of the samples were classified into their respective class. PLSR could quantify lard with R2 >0.99 in calibration and prediction models. Detection limit reported was 3% wt/wt | [60] |
Lard | Analysis of lard in cheese samples | FTIR normal spectra at wavenumbers of 700, 1140–1070, 756 and 720 cm−1 | Quantification of lard using PLS regression | PLS could quantify the level of lard in cheese samples successfully | [61] |
Lard | Analysis of lard in lipstick | Lard was extracted from lipstick using saponification method followed by liquid/liquid extraction with hexane/dichlorometane (DCM)/ethanol/water, saponification method followed by liquid/liquid extraction with DCM/ethanol/water, and Bligh and Dyer method. ATR-FTIR spectra were measured at 1200–800 cm−1 | Classification of lipstick with and without lard was performed using PCA, while quantification of lard was performed using PLS regression | PCA could classify lipstick with lard and without lard in its formulation with PC1 accounted for 63.7%, and PC2 accounted for 26.4% (90.1% of the variance is described by PC1 and PC2). PLS is capable of predicting the amount of lard in lipstick formulation with the equation y (predicted value) = 1.0070 x (actual value) − 4563 (R2 = 0.9956) in calibration model and y = 0.9811x + 0.3381 (R2 of 0.9970) in validation equation. | [62] |
Lard, lard olein (LO) and lard stearin (LS) | Differentiation of LO and LS from other common animal fats | Normal spectra at wavenumbers region (4000–650 cm−1) | PCA | Due to its fingerprint nature, FTIR spectra combined with PCA could differentiate and could classify LO and LS from chicken fat, lard, beef fat, and mutton fat | [63] |
Pork | Adulteration of beef meatball with pork | The extraction of lard is performed using concentrated hydrochloric acid as a hydrolytic agent and petroleum benzene as solvent extraction. Normal FTIR spectra 1000–1200 cm−1 using ATR technique | Classification using PCA and quantification using PLS regression | PLS regression offered good relationship between actual value and predicted value of lard with FTIR predicted value with R2 0.997 and standard error of calibration of 0.04%. PCA could classify beef meatball and beef meatball mixed with pork | [64] |
Dog meat | Adulteration of dog meat in beef meatball | The lipid fraction of meatball was obtained using Bligh-Dyer and Folch extraction methods. ATR-Normal FTIR spectra at 1700–700 cm−1. | Classification using PCA and quantification using PLS regression | FTIR spectroscopy, coupled with chemometrics at 1700–700 cm−1, is capable of classifying dog meatballs and beef meatballs. PLS offered reliable quantitative analysis of dog meat in beef meatballs with acceptable statistical results | [65,66] |
Porcine gelatin | Analysis of porcine gelatin in candies and its classification from other gelatin types | Direct analysis using ATR technique. FTIR spectra were analyzed at 1734–1528 cm−1 | Classification between halal gelatin and non-halal gelatin using HCA, PCA, and PLS-DA | Gummy candy samples could be classified accurately according to its sources with accuracy levels of 100% using Ward’s algorithm (HCA), PLS-DA, and PCA. The results were confirmed by real-time polymerase chain reaction | [67] |
Porcine gelatin | Differentiation between porcine gelatin and bovine gelatin | Direct analysis using ATR at combined region of 3290–3280 and 1660–1200 cm−1 | PCA and DA | DA based on the Cooman’s plot obtained using the software TQ Analyst could classify and discriminate gelatines without any misclassification exploiting the same peaks used in PCA analysis | [68] |
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Rohman, A.; Windarsih, A. The Application of Molecular Spectroscopy in Combination with Chemometrics for Halal Authentication Analysis: A Review. Int. J. Mol. Sci. 2020, 21, 5155. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21145155
Rohman A, Windarsih A. The Application of Molecular Spectroscopy in Combination with Chemometrics for Halal Authentication Analysis: A Review. International Journal of Molecular Sciences. 2020; 21(14):5155. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21145155
Chicago/Turabian StyleRohman, Abdul, and Anjar Windarsih. 2020. "The Application of Molecular Spectroscopy in Combination with Chemometrics for Halal Authentication Analysis: A Review" International Journal of Molecular Sciences 21, no. 14: 5155. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21145155