Detection of Ripeness Grades of Strawberry Using an Electronic Nose

The estimation of ripeness is a significant section of quality estimation since maturity at harvest can affect on sensory and storage properties of fruits. A possible tactic for defining the grade of ipeness, sensing the aromatic volatiles released by fruit using e-nose. For detection the five ripeness grades of strawberry, the e-nose machine was designed and fabricated. Organic healthy samples were collected and divided into five ripeness grades (RG1 = totally ripe, RG2 = close to ripeness, RG3 = intermediate to ripeness, RG4 = close to unripe and RG5= unripe), according to the criteria used by expert growers (based on physical size and appearance as well as estimated maturity stages) during June2019. organic and inorganic sour cherries have different response patterns. This indicates that their aromatic compounds are different. It is clear that different degrees of maturity have different response patterns. This indicates that their aromatic compounds are different. Generally, in organic strawberry MQ3, TGS2602 and TGS822 sensors had the highest response and role in detecting of Ripeness Grades of Strawberry. Artificial neural networks (ANN), principal components analysis (PCA) and linear discriminant analysis (LDA) were applied for pattern recognition of array sensors. The best structure (10–11-5) can classify the samples in five classes in ANN analysis with precision of 98.3 for strawberry. Also, PCA analysis characterized 99% variance in the strawberry. The LDA distinguished the strawberry ripening grades well with just a little overlap between the RG4 and RG5. The accuracy of the analysis was 93.3%. Each three methods can be detected RGs, but PCA with correct classification percentage 99%, is the best method. According to the study, it can be expressed that the e-nose is a suitable instrument for detecting RGs of strawberry and can be used with less time and cost to determine the appropriate harvest time and RGs of strawberry.