Laser light backscattering to monitor moisture content, soluble solid content and hardness of apple tissue during drying

Postharvest monitoring of organic potato (cv. Anuschka) during hot-air drying using visible-NIR hyperspectral imaging

BACKGROUND

The potential of hyperspectral imaging (500-1010 nm) was evaluated for monitoring of the quality of potato slices (var. Anuschka) of 5, 7 and 9 mm thickness subjected to air drying at 50 °C. The study investigated three different feature selection methods for the prediction of dry basis moisture content and colour of potato slices using partial least squares regression (PLS).

RESULTS

The feature selection strategies tested include interval PLS regression (iPLS), and differences and ratios between raw reflectance values for each possible pair of wavelengths (R[λ₁ ]-R[λ₂ ] and R[λ₁ ]:R[λ₂ ], respectively). Moreover, the combination of spectral and spatial domains was tested. Excellent results were obtained using the iPLS algorithm. However, features from both datasets of raw reflectance differences and ratios represent suitable alternatives for development of low-complex prediction models. Finally, the dry basis moisture content was high accurately predicted by combining spectral data (i.e. R[511 nm]-R[994 nm]) and spatial domain (i.e. relative area shrinkage of slice).

CONCLUSIONS

Modelling the data acquired during drying through hyperspectral imaging can provide useful information concerning the chemical and physicochemical changes of the product. With all this information, the proposed approach lays the foundations for a more efficient smart dryer that can be designed and its process optimized for drying of potato slices. © 2017 Society of Chemical Industry.

The potential of computer vision, optical backscattering parameters and artificial neural network modelling in monitoring the shrinkage of sweet potato (Ipomoea batatas L.) during drying

BACKGROUND

Drying is a method used to preserve agricultural crops. During the drying of products with high moisture content, structural changes in shape, volume, area, density and porosity occur. These changes could affect the final quality of dried product and also the effective design of drying equipment. Therefore, this study investigated a novel approach in monitoring and predicting the shrinkage of sweet potato during drying. Drying experiments were conducted at temperatures of 50-70 °C and samples thicknesses of 2-6 mm. The volume and surface area obtained from camera vision, and the perimeter and illuminated area from backscattered optical images were analysed and used to evaluate the shrinkage of sweet potato during drying.

RESULTS

The relationship between dimensionless moisture content and shrinkage of sweet potato in terms of volume, surface area, perimeter and illuminated area was found to be linearly correlated. The results also demonstrated that the shrinkage of sweet potato based on computer vision and backscattered optical parameters is affected by the product thickness, drying temperature and drying time. A multilayer perceptron (MLP) artificial neural network with input layer containing three cells, two hidden layers (18 neurons), and five cells for output layer, was used to develop a model that can monitor, control and predict the shrinkage parameters and moisture content of sweet potato slices under different drying conditions. The developed ANN model satisfactorily predicted the shrinkage and dimensionless moisture content of sweet potato with correlation coefficient greater than 0.95.

CONCLUSION

Combined computer vision, laser light backscattering imaging and artificial neural network can be used as a non-destructive, rapid and easily adaptable technique for in-line monitoring, predicting and controlling the shrinkage and moisture changes of food and agricultural crops during drying. © 2017 Society of Chemical Industry.

Effect of Hot-Water Blanching Pretreatment on Drying Characteristics and Product Qualities for the Novel Integrated Freeze-Drying of Apple Slices

The effect of hot-water blanching (HWB) on drying characteristics and product qualities of dried apple slices with the novel integrated freeze-drying (NIFD) process was investigated by comparing with 3 different FD methods. Compared with the NIFD process without HWB pretreatment (VF-FD), the NIFD process with HWB pretreatment (HWB-VF-FD) resulted in a significantly higher mass loss and more sufficient freezing in vacuum-frozen samples, significantly higher rehydration ratio (RR), higher shrinkage ratio (SR), smaller Vitamin C (VC) content and lower hardness and better apparent shape in freeze-dried samples, and fewer change to the color of the dried or rehydrated samples (p<0.05). Compared with the conventional FD process with HWB pretreatment (HWB-PF-FD), HWB-VF-FD cost significantly less processing time and FD time and obtained significantly higher RR (p<0.05), almost the equivalent SR, VC content, and hardness, and similar appearance in dried samples. The microstructure of apple cell tissues was analyzed by transmission electron microscopy and scanning electron microscopy to interpret the above differences in drying characteristics and product qualities. The results suggested that the NIFD process of apple slices with HWB pretreatment was a promising alternative method to decrease drying time, achieve similar product quality, and simplify the process steps of the conventional FD technology.