Pengembangan Metode Profil Sidik Jari Metabolit Susu Sapi MenggunakanLC-MS/MS

Abstract

Susu sapi mengandung berbagai metabolit penting, termasuk asam amino, karbohidrat, asam lemak, dan vitamin. Profil metabolit menggunakan liquid chromatography–tandem mass spectrometry/mass spectrometry (LC-MS/MS) (LC-MS/MS) memungkinkan deteksi metabolit yang komprehensif; namun, keberhasilan pemisahan sangat bergantung pada kondisi instrumen yang optimum. Penelitian ini bertujuan untuk mengembangkan metode profil metabolit untuk komponen susu sapi menggunakan LC-MS/MS. Pengoptimuman dilakukan terhadap parameter LC-MS/MS (fase gerak, elusi gradien, dan rentang pemindaian) dalam enam variasi, serta teknik pemisahan (jenis pelarut, waktu ekstraksi, dan kecepatan sentrifugasi) dalam dua belas variasi. Evaluasi didasarkan pada jumlah total metabolit yang terdeteksi dan teridentifikasi, kualitas kromatogram, dan nilai hierarchical chromatographic response function (HCRF). Kinerja analitik metode optimum dievaluasi sesuai dengan pedoman Association of Official Analytical Chemists (AOAC) yang berfokus pada parameter presisi untuk memastikan keandalan metode. Metode optimum diterapkan untuk membedakan profil metabolit tiga jenis susu sapi (segar, UHT, dan pasteurisasi) menggunakan Principal Component Analysis (PCA).

Hasil pengoptimuman menunjukkan bahwa variasi LC_2 (asetonitril–air, 25 menit, 100–1500 m/z) dan TP_10 (3% asam format dalam asetonitril, sentrifugasi pada 3420×g selama 30 menit) dipilih sebagai kondisi optimum, karena memungkinkan deteksi dan identifikasi metabolit yang komprehensif, menghasilkan bentuk puncak kromatografi yang optimum, dan mencapai nilai HCRF tertinggi. Sebanyak 56 metabolit berhasil diidentifikasi, termasuk metabolit primer dalam susu sapi, seperti asam amino, karbohidrat, asam lemak, dan vitamin. Evaluasi kinerja analitik menggunakan sampel susu segar dan UHT menunjukkan nilai %RSD <2% untuk waktu retensi relatif (RRT) dan <20% untuk luas puncak relatif (RPA), yang menunjukkan bahwa metode ini sensitif, presisi, dan andal untuk analisis metabolit susu sapi. Berdasarkan hasil analisis komponen utama (PCA), kondisi optimum dari kombinasi LC_2 dan TP_10 menghasilkan data metabolit yang akurat dan secara signifikan membedakan ketiga jenis susu sapi.

Cow's milk contains various essential metabolites, including amino acids, carbohydrates, fatty acids, and vitamins. Metabolomics profiling using liquid chromatography–tandem mass spectrometry/mass spectrometry(LC-MS/MS), enables comprehensive metabolite detection; however, successful separation strongly depends on optimized instrument conditions. This study aimed to develop a metabolite profiling method for cow’s milk components using LC-MS/MS. Optimization was performed on LC-MS/MS parameters (mobile phase, gradient elution, and scan range) in six variations, as well as separation techniques (solvent type, extraction time, and centrifugation speed) in twelve variations. Evaluation was based on the total number of detected and identified metabolites, chromatogram quality, and hierarchical chromatographic response function (HCRF) values. Analytical performance of the optimum method was evaluated according to the Association of Official Analytical Chemists (AOAC) guidelines, focusing on precision parameters to ensure method reliability. The optimum method was applied to distinguish the metabolite profiles of cow's milk with three different types of cow's milk (fresh, UHT, and pasteurized) using Principal Component Analysis (PCA).

The optimization results showed that the variations LC_2 (acetonitrile–water, 25 min, 100–1500 m/z) and TP_10 (3% formic acid in acetonitrile, centrifugation at 3420 × g for 30 min) were selected as optimal conditions, as they enable comprehensive metabolite detection and identification, produce optimal chromatographic peak shapes, and achieve the highest HCRF values. A total of 56 metabolites were successfully identified, including primary metabolites in cow's milk, such as amino acids, carbohydrates, fatty acids, and vitamins. Evaluation of analytical performance using fresh and UHT milk samples showed %RSD values of <2% for relative retention time (RRT) and <20% for relative peak area (RPA), demonstrating that the method is sensitive, precise, and reliable for cow's milk metabolite analysis. Based on principal component analysis (PCA) results, the optimal conditions from the combination of LC_2 and TP_10 provided accurate metabolite data and significantly distinguished the three types of cow's milk.