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Prototype instruments for determination of aroma and flavor quality of brewed black tea
Blit I.W.a, Juwita A.B.b, Suprijantoc, Nugrahad
a Master Program of Instrumentation and Control, Indonesia
b Undergraduate Program of Engineering Physics, Indonesia
c Instrumentation and Control Research Group, Indonesia
d Engineering Physics Research Group, Faculty of Industrial Technology, Institut Teknologi Bandung, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2015 IEEE.Black tea is one of Indonesia’s major leading export products. In terms of trading, quality of black tea, especially flavor and aroma, will determine its selling price. The purpose of this research is to design a prototype of electronic nose, which integrated gas sensors, data acquisition, and computer, to detect the aroma of brewed black tea. This electronic nose consists of four gas sensors (TGS 822, TGS 825, TGS 826, and TGS 2620) MOS type with SnO2 as sensing element. The response of sensor is processed by using power spectral estimation method and the result is being used as an input for Artificial Neural Network (ANN) to classify the quality of brewed black tea. ANN is trained by varying 4 sets of data input for each quality classes: 31, 35, 37, and 39, which are being used as an ANN inputs. The performance of ANN that has been developed is tested by using 9 sets of brewed black tea aroma data which are different from trained data. The results of the test show that the system is able to recognize the aroma with 11,11% errors.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Black tea,Data input,Export products,Integrated gas sensors,Power spectral estimation,Prototype instrument,Selling prices,Sensing elements[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]aroma and flavor,artificial neural network,brewed black tea,electronic nose,gas sensor,power spectral estimation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICICI-BME.2015.7401351[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]