Optimization of Electronic Nose Sensor Array for Tea Aroma Detecting Based on Correlation Coefficient and Cluster Analysis

The electronic nose system is widely used in tea aroma detecting, and the sensor array plays a fundamental role for obtaining good results. Here, a sensor array optimization (SAO) method based on correlation coefficient and cluster analysis (CA) is proposed. First, correlation coefficient and distinguishing performance value (DPV) are calculated to eliminate redundant sensors. Then, the sensor independence is obtained through cluster analysis and the number of sensors is confirmed. Finally, the optimized sensor array is constructed. According to the results of the proposed method, sensor array for green tea (LG), fried green tea (LF) and baked green tea (LB) are constructed, and validation experiments are carried out. The classification accuracy using methods of linear discriminant analysis (LDA) based on the average value (LDA-ave) combined with nearest-neighbor classifier (NNC) can almost reach 94.44~100%. When the proposed method is used to discriminate between various grades of West Lake Longjing tea, LF can show comparable performance to that of the German PEN2 electronic nose. The electronic nose SAO method proposed in this paper can effectively eliminate redundant sensors and improve the quality of original tea aroma data. With fewer sensors, the optimized sensor array contributes to the miniaturization and cost reduction of the electronic nose system.

Predicting the colour associated with odours using an electronic nose

Predicting olfactory perception with an electronic nose can aid in the design and evaluation of olfactory-based experiences. We investigate whether the human perception of odours can be predicted outside the bounds of perceived pleasantness and semantic descriptors. We tuned an electronic nose to predict an odour's colour in the CIELAB colour space using human judgements. This revealed that the crossmodal associations people have towards colours could be predicted. Our electronic nose system can predict an odour's colour with a 70 – 81% machine-human similarity rating. These findings suggest a systematic and predictable link exists between the chemical features of odours and the colour associated to them. These findings highlight the possibilities of predicting human olfactory perception using an electronic nose.

HIDUNG ELEKTRONIK BERBASIS SENSOR GAS MOS UNTUK KARAKTERISASI KEMATANGAN BUAH KELAPA SAWIT

The ripeness of oil palm fruits is one of the key factors for crude palm oil qualities. Recently, electronic nose systems have been developed intensively for fruit quality assessment which relates odors to ripeness levels. This study developed an electronic nose system to characterize the ripeness levels of oil palm fruits using output voltage of each sensor and fruit hardness. The system consisted of a sensor chamber and a sample chamber. The sensor chamber consisted of eight MOS gas sensor modules of MQ series. Samples were oil palm fruits taken from oil palm fresh fruit bunches (FFB) which were previously categorized traditionally into unripe, ripe, over ripe, peeled and put into the sample chamber. Some of the fruits were also used for hardness measurement. To quantify the output voltages for each sensor, integrated trapezoid areas were calculated and related to the fruit hardness values. The results showed a significant voltage difference of each sensor for the three ripeness levels. Only four out of eight sensors showed significantly higher voltages. Three sensors which can significantly differentiate the ripeness levels are MQ3, MQ5, and MQ135 which MQ135 is the best. This shows that the electronic nose is potential for oil palm fruits. Keywords: electronic nose, fruit hardness, MOS gas sensor, oil palm fruit, ripeness