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Non-destructive evaluation is advancing in examining the properties of fruits. Kiwi fruit stands out as one of the popular fruits globally. Due to the influence of various environmental factors and storage conditions, diligent checking and storage of this fruit are essential. Therefore, monitoring changes in its properties during storage in cold storage facilities is crucial. One nondestructive method utilised in recent years to investigate changes in fruit texture is the hyperspectral method. This study uses the support vector machine (SVM) method to assess hyperspectral method‘s effectiveness in examining property changes in four kiwi varieties during storage in addition to predicting the properties such as acidity and soluble solid content. The evaluation of the predictive machine learning model revealed an accuracy of 95% in predicting acidity and soluble solid content (SSC) changes in kiwi fruit during storage. Further, investigations found that the support vector machine method provided relatively lower accuracy and sensitivity in identifying product variety during storage, with an average accuracy ranging from about 91% to 94%. These findings suggest that integrating machine learning methods with outputs from techniques like hyperspectral imaging enhances the non-destructive detection capability of fruits. This integration transforms obtained results into practical outcomes, serving as an interface between software and hardware.

The present study measured physical properties of lentil and wild oat weed; mass of 1000 seeds, true density, porosity and coefficient of static friction. A gravity table separator was used to separate wild oat weeds from lentil seeds. The gravity table separator had five adjustable parameters; longitudinal slope, latitudinal slope, amplitude of oscillation, frequency of oscillation and air velocity. The effect of these parameters was investigated in order to maximize the separation of wild oat weed from lentil. Results of tests indicated that an increase in latitudinal slope of the table from 0.5° to 1° and longitudinal slope from 1.5° to 2° resulted in increased separation of wild oat weed from lentil. At longitudinal slope of 2°, latitudinal slope of 1°, amplitude of oscillation of 5 mm, frequency of oscillation of 400 cycles min-1, and air velocity of 5.7 m s-1, the separation percentage was 37%. Finally, after determination of the most suitable settings for amplitude of oscillation and air velocity; using the information on longitudinal slope, latitudinal slope and frequency of oscillation of the table was used to calculate mathematical relations of separation percentage of wild oat weed from lentil clumps using Datafit Software. [ABSTRACT FROM AUTHOR]