Quantification of Visual Characteristics of Whipped Cream by Image Analysis and Machine Vision: Method Development

Abstract

The appearance (color, shine, surface roughness, bumpiness, view area, height) of whipped cream samples change with time. Quantitative methods based on image analysis were developed to measure these parameters in cream samples stored at 3 temperatures (room temperature, 10 °C and 2 °C) for up to 24 h. There was a small decrease in L* and a* values over time, and a significant increase in b* values. ΔE values suggest that these color changes are significant and observable (2.96 for room temperature, 9.46 for 10 °C, 12.6 for 2 °C). Difference between polarized and nonpolarized images was used to quantify shine. The length of a laser line over the cream sample was measured to quantify the change in bumpiness of the surface (33.8%, 41.78%, and 33.27% reduction in laser line length for room temperature, 10 °C, and 2 °C, respectively). For room temperature stored samples, most changes occurred during the first 5 min. For samples stored at 10 °C and 2 °C, most changes occurred during the first 30 min. Turn angles data did not provide useful information. The changes in view area depended on temperature: at room temperature the area increased over time, at 10 °C it increased first then decreased; at 2 °C the view area decreased over time. Correlation of the results of these methods with sensory evaluation can make the evaluation of the appearance of whipped cream more objective, repeatable, and quantitative.

Evaluation of browning ratio in an image analysis of apple slices at different stages of instant controlled pressure drop‐assisted hot‐air drying (AD‐DIC)

“…Colorimeters and spectrophotometers only show the average value of the testing region, and obtain quite unrepresentative results and restrict global analysis . Recently, computer vision‐based image analysis systems and machine vision have been used to objectively measure the surface colour of various products, since they can provide some obvious advantages because of their objectivity and quantitative capabilities . The visual characteristics obtained by computer vision‐based image analysis systems include colour, shine, surface roughness, bumpiness, view area and height .…”

“…Recently, computer vision‐based image analysis systems and machine vision have been used to objectively measure the surface colour of various products, since they can provide some obvious advantages because of their objectivity and quantitative capabilities . The visual characteristics obtained by computer vision‐based image analysis systems include colour, shine, surface roughness, bumpiness, view area and height . The system commonly is mainly composed of the illuminant, a digital camera, digitizer and software to analysis the pictures, and two processes – image acquisition and image processing – are usually carried out.…”

“…18 -20 The visual characteristics obtained by computer vision-based image analysis systems include colour, shine, surface roughness, bumpiness, view area and height. 20,21 The system commonly is mainly composed of the illuminant, a digital camera, digitizer and software to analysis the pictures, 22 and two processes -image acquisition and image processing -are usually carried out. Image processing is considered the most important part of a computer vision-based image analysis system with data analysis, 23,24 and it consists of three steps: (i) pre-processing (pixel pre-processing or local pre-processing) improves the quality of the image by removing noise; (ii) when performing image intermediate processing, an image is partitioned into several parts by segmentation techniques (thresholding-based segmentation, region-based segmentation, gradient-based segmentation, classification-based segmentation and so on), to acquire precise data; (iii) high-level processing builds a predictive model from those features to describe the region of interest.…”

Potential of Incorporating a Functional Probiotic Encapsulant in Whipped Cream

“…The results revealed that the 1% whipped cream variant showed significantly higher values of a* and b* compared to the 0.1% whipped cream variant and control (with no encapsulant addition). The increase in b* values indicated the yellowish color of the samples [28]. This could be due to the golden-brown color of the spray-dried encapsulant spiked at 1% in the whipped cream.…”