Fruit Inspection: Characterization and classification of rotten blueberries

Quality inspection in the food industry traditionally has been based on direct visual inspection. This type of assessment may be subjective and significantly dependent on the expertise of the inspector. The process is in most cases labor- and time-intensive, and difficult to implement accurately on the whole product volume in a cost-effective manner. In addition, there might be quality attributes that are not visible to the human eye or that require years of training to identify reliably. While machine vision techniques are being rapidly adopted across the food industry to address these issues, they are mostly limited to RGB imaging which also limits its applicability.

Hyperspectral imaging (HSI) offers an alternative non-invasive solution to food inspection that provides objective information in real-time from large production volumes. It can be used for automated quality assessment, improving sorting accuracy and speed. In addition, the spectroscopic data from HSI can provide information about chemical content typically only available from destructive wet lab analysis (WLA).

Blue berries and other small fruits are particularly sensitive to handling and storage, and the quality parameters tend to deteriorate rapidly if the product is not managed carefully. HSI can be used for evaluating external attributes such as color, texture, firmness, surface damage, bruise, defects and contaminants in berries and other small fruits. The HSI data can also be correlated to the WLA results to determine the internal nutritional parameters without damaging the product. Typical parameters suited for HSI analysis include taste, flavor, moisture content, dry matter, total soluble solid content, anthocyanin, acidity, pH, sugar content and vitamin C.