Amino acids (AAs) are fundamental units of proteins, synthesised from translated DNA sequences, thus forming the building blocks of proteins. Given their significant role in protein composition, AAs are pivotal in authenticating halal sources, particularly as target analytes for profiling. The wide variety of AAs, with 17 distinct types identified in certain studies, supports a profiling approach for authentication rather than a targeted method. Researchers have utilised high-performance liquid chromatography with fluorescence detection (HPLC-FLD) to identify and quantify these AAs, following a protocol that includes freeze-drying the samples to achieve moisture levels below 10% to mitigate interference from the sample matrix. Subsequent acid hydrolysis and incubation at 110°C for 24 hours prepare the samples for analysis.

Prior to amino acid quantification, it is essential to establish calibration curves for each AA using a minimum of seven working standards spiked with an internal standard. An internal standard, such as L-aminobutyric acid (AABA), is incorporated in ultra-high-performance liquid chromatography with a diode-array detector (UHPLC-DAD) to minimise matrix effects. This protocol includes AABA spiking in the acid-hydrolysed samples. It is also advisable to avoid serial dilution to prevent systematic errors in preparing working standards. The derivatisation of AAs depends on the instrumental analysis requirements, with fluorescence derivatisation often employed for HPLC-FLD analysis. For instance, ortho-phthalaldehyde (OPA) and 9-fluorenyl-methyl chloroformate (FMOC) have been used to derivatise primary and secondary AAs, respectively. Similarly, fluorescence derivatisation agents may be applied before UHPLC-DAD injection, demonstrating the versatility of fluorescence derivatisation for both FLD and DAD.

 The UHPLC or HPLC analysis of AAs typically involves eluting two mobile phases through a membrane filter to a C18 column. A common setup employs an aqueous solution of AccQ.Tag concentrate as eluent A, alongside an acetonitrile-water mixture as eluent B. This gradient setup allows optimal AA separation, achieving effective peak resolution for quantification, typically within a 10-minute elution window for satisfactory AA separation. Each AA’s peak emerges at a specific retention time, and as working standard concentrations increase, peak areas expand, creating a calibration curve with robust linearity. This calibration curve can be expressed as:

As/Ais = m Cs/Cis​ + c

Where As and Ais represent the peak areas of the working standard and internal standard, respectively; m denotes the calibration curve’s slope; Cs and Cis are the working and internal standard concentrations, and c is the y-axis intercept. The linearity of this calibration curve is confirmed by criteria such as a high correlation coefficient near 1, insignificant variance differences between experimental and theoretical F-values, bounded confidence intervals, and a randomly distributed residual plot.

Following AA identification and quantification, multivariate data analysis (MDA), including principal component analysis (PCA), discriminant analysis (DA), and partial least square-discriminant analysis (PLS-DA), is recommended to authenticate protein-based products. In the next post, we'll delve into targeted polypeptide analysis as part of a protein-based approach. Further reading: https://www.sciencedirect.com/science/article/abs/pii/B9780323916622000156 and https://journals.iium.edu.my/inst/index.php/hs/article/view/83/84