Computerized recognition of pineapple grades using physicochemical properties and flicking sounds

• Published in: International journal of agricultural and biological engineering 2014-06, Vol.7 (3), p.93
• Main Authors: Phoophuangpairoj, Rong, Srikun, Niyomsri
• Format: Article
• Language: English
• Subjects: Automation; Consumers; Food quality; Fruits; Quality standards; Signal processing; Voice recognition
• ISSN: 1934-6344; 1934-6352
• DOI: 10.3965/j.ijabe.20140703.011

Fruit is one of the essential sources of human nutrition. Consumers around the world need to be able to purchase fruit of reliable flavor and nutritional quality. Physical appearance and physicochemical properties play a key role in determining desirable quality and flavor. However, for some fruits such as watermelon, durian, pineapple, it is very hard to determine quality and flavor by external appearance. Therefore, a practical method to predict physical and physicochemical properties of fruit needs to be developed. In this study, a computerized technique is investigated to determine pineapple grades and their physical and physicochemical properties, including ripeness, total soluble solids, pH value and water content. The results reveal that by grading using pulp characteristics it is possible to classify pineapples into three distinct groups, which are significantly different in TSS, pH value and water content. In addition, predicting pineapple grades using flicking sounds and signal processing demonstrates that pineapples classified as grade 1 and grade 3 are significantly different in TSS, pH value and water content. This suggests that the estimation of the texture of pineapple pulp and its physicochemical properties can be performed prior to cutting. Therefore, it is feasible to develop an automated grading technique that can be used to determine pineapple quality as accurately as destructive grading to predict pineapple grades, texture and physicochemical properties.