Effect of Substrate Temperature and Target-Substrate Distance on Growth of TiO2 Thin Films by Using DC- Reactive Sputtering Technique

Titanium oxide (TiO2) thin films have been deposited by a DC sputtering technique onto microscope glass slides. The effect of substrate temperature (Ts) and target-substrate distance (Dts) on some optical and electrical properties have been studied each individually. The structure of TiO2 thin films has been improved and became more crystalline when Ts has been increased (from 150 ºC to 250 ºC). The conductivity (ϭ), deposition rate (DR) and average values of grain size (G.S) have been increased with increasing Ts while the values of band gap (Eg) and weight percentage of the anatase phase (WA) have been decreased. The thickness of TiO2 film has been increased from 920 nm to 960 nm with increase Ts while it has been decreased from 960 nm to 680 nm with increase Dts (from 25mm to 35mm). As Dts has been increased, the conductivity ϭ, thickness (d) and average values of grain size have been decreased. The decreasing of conductivity at Dts=35 maybe attributes to increase the weight percentage of the rutile phase (WR). The XRD results show that the TiO2 structure phase has been varied. The results show that the optical and electrical properties of TiO2 film affected by changes the condition parameters especially Ts and Dts as well as the density and energy of the impinging atoms. The surface morphology and component of TiO2 thin films, resistance, optical transmittance and structure of film were characterized by SEM (EDX), I-V meter, UV-VIS spectrophotometer and XRD respectively

Prototype of Electronic Nose Based on Gas Sensors Array and Back Propagation Neural Network for Tea Classification

We have developed an electronic nose based on metal oxide gas sensor array and back-propagation neural network for tea classification. The sensor array consists of six Tagushi Gas Sensor (TGS) type devices. To recognize the pattern formed by the six sensors we used six neurons in the input layer. Since we only want to classify four tea samples, we used two neurons in the output layer. The four tea samples (different tea flavors) were purchased from local super store in Yogyakarta, namely, black tea, green tea, vanilla tea and jasmine tea. Under the relatively similar conditions, we measured each sample of tea as a function of time. Prior to the exposure of tea samples, the sensor array was tested with air ambient. Then the electronic nose was trained by using one set of four tea samples without pre-processing step. By using all data sets, the electronic nose is able to recognize the pattern for almost 80%. This result prove that our electronic nose is capable of discriminating between the flavors of tea samples. For further investigation, the performance of this system should be compared with the data sets with pre-processing. Keywords : Odor, Tea flavor, Metal oxide gas sensor, Sensor array, Back Propagation Neural networ

Aplikasi Jaringan Syaraf Tiruan Propagasi Balik Pada System Olfaktori Elektronik Larik Sensor Gas Untuk Deteksi Jenis Bahan Herbal

Penggunaan sistem jaringan syaraf tiruan propagasi Balik untuk mengenali pola keluaran larik sensor gas dalam sistem olfaktori elektronik atau electronic nose (yang selanjutnya disebut enose) telah terapkan terhadap empat macam sampel bahan herbal. Keempat bahan herbal tersebut meliputi: jahe (zingiber officinale), kencur (Kaempferia galanga) , kunyit (curcuma domestica val) dan lengkuas (languas galanga). Enose yang digunakan terdiri atas empat sensor gas berbahan logam oksida seri TGS 822, TGS 825, TGS 826, dan TGS 880. Sepertidalam sistem olfaktori pada manusia (hidung) maka untuk dapat mengidentifikasi pola berdasarkan aroma khas yang ada pada setiap sampel bahan herbal, enose harus melalui serangkaian proses pelatihan dan pengujian dengan model tertentu, salah satunya dengan menggunakan jaringan syaraf tiruan (JST). Sebelum diproses lebih lanjut, sinyal keluaran (berupa tegangan) dari masing-masing sensor yang membentuk suatu pola perlu diekstraksi untuk memperoleh karakteristik ciri masing-masing sampel sekaligus mereduksi himpunan datanya.Penelitian ini menerapkan dekomposisi wavelet daubechies 4 tingkat 8, sehingga sinyal asli keluaran yang membentuk sinyal kontinyu tak periodik dengan himpunan data sangat besar berbentuk matriks 400×4 tereduksi menjadi himpunan data yang tersusun atas matriks berukuran 16×4. Dalam hal ini, setiap sensor diwakili dengan himpunan data berdimensi 16 yang merupakan nilai koefisien aproksimasi cA8, dan himpunan data koefisien detail cD8. Matriks berukuran 16×4 inilah yang merupakan hasil ekstraksi ciri masing-masing sinyal keluaran sistem larik empat sensor sekaligus menjadi input data pada sistem jaringan syaraf tiruan. Selanjutnya dengan melakukan serangkaian pelatihan dan pengujian empat jenis bahan herbal, sistem jaringan syaraf tiruan propagasi Balik mampu untuk mengenali jenis bahan herbal dengan ketelitian mencapai 93 %

Fourier transform infrared spectrum pre-processing technique selection for detecting PYLCV-infected chilli plants

Pre-processing is a crucial step in analyzing spectra from Fourier transform infrared (FTIR) spectroscopy because it can reduce unwanted noise and enhance system performance. Here, we present the results of pre-processing technique optimization to facilitate the detection of pepper yellow leaf curl virus (PYLCV)-infected chilli plants using FTIR spectroscopy. Optimization of a range of pre-processing techniques was undertaken, namely baseline correction, normalization (standard normal variate, vector, and min–max), and de-noising (Savitzky-Golay (SG) smoothing, 1st and 2 derivatives). The pre-processing was applied to the mid-infrared spectral range (4000 – 400 cm−1) and the biofingerprint region (1800 – 900 cm−1) then the discrete wavelet transform (DWT) was used for dimension reduction. The pre-processed data were then used as an input for classification using a multilayer perceptron neural network, a support vector machine, and linear discriminant analysis. The pre-processing method with the highest classification model accuracy was selected for the further use in the processing. It was seen that only the SG 1st derivative method applied to both wavenumber ranges could produce 100% accuracy. This result was supported by principal component analysis clustering. Thus, we have demonstrated that by using the right pre-processing technique, classification success can be increased, and the process simplified by optimization and minimization of the technique used

PENGEMBANGAN METODE UJI BARU UNTUK PENENTUAN MUTU MINYAK GORENG BERDASARKAN SIFAT ELEKTROOPTIS

A  Determination  of  fraction  of  transmitted  light  intensity, ζ,  as  an  alternative  parameter test of frying oil quality has been conducted based on electrooptics  parameter.  The  samples  used  here  were  several  palm  oils,  coconuts  oil,  refine  olive oil, and corn oil. The ζ�s value was obtained through measurement of the  change  polarisation  of  ligth  against  the  magnitude  of  applied  external  electric  field on the samples. The electric field was produced by high DC voltage power  supply on two parallel plates in a separated distance of 1,2 cm and in area of 3  cm�5 cm. The sources of ligth used in the experiment were red diode laser 5 mW  (λ=650 nm), green laser diode 5 mW (λ=532 nm), He�Ne laser 1 mW (λ=633 nm),  dan ligth bulp 100 W (λ�580 nm). The experimental results show that ζ increases  polynomial  in  3rd  or  4th  order  against  the  increasing  of  electric  potential  difference between plates. The average value of �ζ at 26 0 C in the range of 0�9 kV  was  slightly  higher  than  �ζ  at  22 0 C,  and  also  apparently  decreased  with  increasing the wavelength of ligth. For palm oils, �ζ was approximately between  3�10 �4  and 8�10 �4  kV �1 . For coconuts oil and corn oil, �ζ was in the range value of  palm oil, however for refine olive oil, �ζ was the smallest and out of the range of  palm  oil.  The  increasing  of  transmitted  intensity  or  free  radicals�  number  was  proportional to the oil concentration between 40% and 100%. According to the  hipothese, �ζ was proportional to the number of saturated fatty acid of oil with  the best corelation coefficient of 0,8 until 0,9. This method has some advantages,  i.e. it is an alternative quality test of frying oil without previous treatment and is  relative  simply.  However,  the  method  is  still  in  the  previous  phase  for  an  alternative  test  of  frying  oil  quality  based  on  electrooptics  and  still  to  be  evaluated and optimized using various variables, in order to obtain an alternative  complete  test  for  quality.  In  future,  it  is  needed  to  design  a  simple  portable  equipment for fast determination of �ζ. It opens also possibilities to study and to  develope applications in many purposes of various fields.

Fourier transform infrared spectrum pre-processing technique selection for detecting PYLCV-infected chilli plants

Pre-processing is a crucial step in analyzing spectra from Fourier transform infrared (FTIR) spectroscopy because it can reduce unwanted noise and enhance system performance. Here, we present the results of pre-processing technique optimization to facilitate the detection of pepper yellow leaf curl virus (PYLCV)-infected chilli plants using FTIR spectroscopy. Optimization of a range of pre-processing techniques was undertaken, namely baseline correction, normalization (standard normal variate, vector, and min–max), and de-noising (Savitzky-Golay (SG) smoothing, 1st and 2 derivatives). The pre-processing was applied to the mid-infrared spectral range (4000 – 400 cm−1) and the biofingerprint region (1800 – 900 cm−1) then the discrete wavelet transform (DWT) was used for dimension reduction. The pre-processed data were then used as an input for classification using a multilayer perceptron neural network, a support vector machine, and linear discriminant analysis. The pre-processing method with the highest classification model accuracy was selected for the further use in the processing. It was seen that only the SG 1st derivative method applied to both wavenumber ranges could produce 100% accuracy. This result was supported by principal component analysis clustering. Thus, we have demonstrated that by using the right pre-processing technique, classification success can be increased, and the process simplified by optimization and minimization of the technique used