Determination of Avocado Fruit Ripening Stage Using an Electronic Nose with Fuzzy Logic Algorithm

Academic Paper from the year 2019 in the subject Electrotechnology, , language: English, abstract: This paper examines and measures the concentration of the metal oxide gas sensors in determining the ripeness using the ethylene gas in fruits. The preliminary performance of the electronic nose has been demonstrated at the ripening stage of the avocado fruit and is compared to the separation machine. Confusion matrix was used to show the accuracy of the system in detecting ripening stage. This study used the fuzzy logic algorithm to classify and achieve an accuracy rate of 82,5 percent of classifying unripe, ripe, and overripe of the fruits. Ethylene is a gaseous plant hormone that naturally occurs in fruits and helps speed up the ripening process. Persea Americana, or Avocado, is a climacteric fruit that does not produce large amounts of ethylene while still attached to the tree. Therefore, it does not ripen until harvested, and thus, its ripeness cannot be determined by the naked eye. Since fruit quality is judged by consumers primarily from their perception of the acceptability of fruits based on characteristics including visual appeal (lack of blemishes, color, size, and texture), relying on such methods is not applicable for Avocado in general. The use of an electronic nose - an intelligent sensing device that can sense aroma more effectively than the human sense of smell - would prove to be useful. It also possesses a non-destructive property, therefore resulting into it being selected to be the ideal digital, electronic device for identifying, characterizing, and grading fruits ripeness. The main objective of this study is to measure ethylene and other metal oxide gases present in determining the ripeness stage of persea americana samples through electronic nose using fuzzy based classification algorithms and to verify the accuracy of results by comparing it to data from human expert graders of fruits. This study categorizes the metal oxide gas present in fruit aroma of the sample and utilizes the use of the fuzzy logic algorithm for the software. The response level of the metal oxide concentration in the fruits sample are classified as unripe, ripe and overripe, which is displayed in GUI as the output.