Optimasi Konsentrasi Bakteri Escherichia coli Berdasarkan Data Gabungan Spektrofotometri dan Spektroskopi Impedansi Listrik (SIL) Menggunakan Regresi Linier
DOI:
https://doi.org/10.59632/magnetic.v6i2.864Keywords:
Bakteri; Spektroskopi impedansi listrik; Spektrofotometer; Regresi linier; Optimasi prediksiAbstract
Pengukuran konsentrasi bakteri yang cepat dan akurat adalah aspek penting dalam berbagai aplikasi bioteknologi. Penelitian ini bertujuan untuk mengintegrasikan parameter listrik dalam bentuk Spektroskopi Impedansi Listrik (SIL) dan parameter optik dalam bentuk kerapatan optik (OD600) untuk meningkatkan akurasi prediksi konsentrasi bakteri. Sampel E. coli ATCC 25922 disiapkan dalam tujuh tingkat konsentrasi (5%, 10%, 20%, 40%, 60%, 80%, dan 100%), dan setiap konsentrasi diukur sebanyak tiga kali pengulangan menggunakan spektrofotometer UV–Vis pada 600 nm dan sistem SIL pada rentang frekuensi 1 Hz–100 kHz, dengan Total Plate Count (TPC) sebagai data acuan. Metode yang digunakan adalah fusi data melalui model Regresi Linier Berganda (MLR) untuk model gabungan. Hasil penelitian menunjukkan bahwa frekuensi 60 kHz adalah titik optimal karena berada dalam zona relaksasi beta (?-dispers), yang memberikan linearitas terbaik untuk populasi bakteri. Secara individu, OD600 memiliki nilai R2 sebesar 0,9452 dan impedansi memiliki nilai R2 sebesar 0,8327. Integrasi kedua parameter meningkatkan nilai R2 menjadi 0.9457, terdapat pengurangan signifikan dalam tingkat kesalahan prediksi. Model OD600 memberikan R² = 0,9452 dan RMSE = 0,0982 Log CFU/mL, sedangkan model impedansi memberikan R² = 0,8327 dan RMSE = 1762,84 ?. Integrasi kedua parameter menghasilkan R² = 0,9457 dan RMSE = 0,3094 Log CFU/mL, menunjukkan peningkatan stabilitas model dan penurunan kesalahan estimasi dibandingkan penggunaan impedansi tunggal.Penurunan RMSE membuktikan bahwa kombinasi data bio-optik (massa sel) dan data bio-elektrik (integritas membran) dapat mengurangi noise dan menutupi keterbatasan masing-masing sensor. Penelitian ini berhasil merumuskan persamaan regresi gabungan y = (-2.21 x 10-5 x Imp) + (3.29 x OD) + 8.28, yang menghasilkan model estimasi konsentrasi bakteri yang lebih stabil dan menunjukkan kesesuaian yang baik terhadap nilai TPC sebagai data acuan.
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