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Effect of dielectric barrier discharge (DBD) cold plasma on decontamination of strawberry and its quality characteristics was considered. Weight loss, color change, and firmness of the samples were investigated under low‐temperature storage condition. Plasma levels of 7% and 14% duty cycle were examined at exposure times of 5, 10, and 20 min. Destruction of microorganisms on treated samples surface was clearly observed by scanning electron microscopy images. Treatment duration of 20 min reduced total aerobic mesophilic bacteria and yeasts/molds 1.46 and 2.75 log CFU/g, respectively. Texture and color of the treated samples showed no significant difference compared to the control and their quality attributes indicated their superiority over the untreated one. The treated samples with 14% duty cycle DBD plasma for 20 min were even marketable at the end of the storage period. The cold plasma generated by DBD could, therefore, be utilized to prolong fruits shelf life. Practical applications Cold plasma cleaning is a novel dry and nonthermal technology without the use of chemicals. The present study would be practically advantageous as it explains decontamination of strawberry as a part of many people's diet all over the world via dielectric barrier discharge (DBD) plasma. The plasma‐treated samples for 20 min were marketable after 12 days of storage. Hence, the nonthermal DBD plasma treatment is an effective way to prolong the shelf life of strawberry.

In this paper, an efficient procedure for ripeness detection of watermelon was presented. A nondestructive method was used based on vibration response to determine the internal quality of watermelon. The responses of samples to vibration excitation were optically recorded by a Laser Doppler (LD) vibrometer. Vibration data was collected from watermelons of two qualities, namely, ripe and unripe. Vibration signals were transformed from time-domain to frequency-domain by fast Fourier transform (FFT). Twenty nine features were extracted from the FFT amplitude and phase angle of the vibration signals. K-nearest neighbor (KNN) analysis was applied as a classifier in decision-making stage. The experimental results showed that the usage of the FFT amplitude of the vibration signals gave the maximum classification accuracy. This method allowed identification at a 95.0 % level of efficiency. Hence, the proposed method can reliably detect watermelon ripeness.

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