Suhu Pemanas Sampel Optimal Untuk Klasifikasi Teh Hitam Menggunakan Electronic Nose

Abstract

The optimization of heating temperature of black tea samples for the measurement of aroma with electronic nose (e-nose) has been successfully performed. Sample heating is done because black tea has a low aroma intensity and easily lost. However, the selection of such temperature should be selective because it can result in damage to the aroma of the sample. Therefore, temperature optimization needs to be done so that the resulting sensor response comes from the transformation of the undamaged aroma.The method used to obtain the optimum heating temperature by analyzing the sensor response of the aroma transformation that is captured by e-nose. Consistency and pattern changes formed from the sensor response are used as a comparison of optimal heating temperature selection. The measured sample varied in temperature (30 - 60 °C) so that the resulting sensor response was observed. Change in patterns indicate the aroma has been burning. After optimal temperature is obtained then black tea (50 gr) Broken Orange Pokoe, Broken Pokoe II and Bohea with a total sample of 300 bags were measured with e-nose. For further analysis, the result of classification by method of Principal Component Analysis (PCA) as proof of sample heating temperature optimization successfully done.The experimental results show optimal sample heating for black tea 3 quality 40 - 45 °C. After then with the third PCA the sample can be classified up to 92.5% of the total data variant. This indicates the aroma of tea is relatively constant and there is no pattern change.

Komparasi Metode Manipulasi Baseline untuk Uji Diskriminasi Keberadaan Residu Pestisida pada Sayuran Sawi Hijau Berbasis Electronic Nose

“…Setiap aroma E-ISSN: 2746-2536 memiliki pola unik dan pola inilah yang menjadi kunci untuk algoritma machine learning dapat mengenali aroma tersebut [5]. Instrumen ini telah berhasil mengidentifikasi berbagai objek seperti bahan dan minuman herbal, infeksi luka, teh hitam, dan lain-lain [1], [6]- [8].…”

“…Nilai persen variansi menunjukkan variasi data yang ada, Nilai persen variansi pada PC ke-n yang semakin tinggi menunjukkan diskriminasi antara kedua sampel. Perbedaan nilai persen variansi inilah yang menjadi tolak ukur karakteristik dari setiap data yang ada [8]. Nilai persen variansi PC 1 mengindikasikan tingkat diskriminasi data sehingga menunjukkan metode manipulasi baseline yang paling optimal untuk diterapkan dalam pengolahan data selanjutnya [6].…”