Dr. Honglu Yu Profile

Dr. Honglu Yu

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Proceedings Article | 19 November 2004 Paper

Online prediction of organileptic data for snack food using color images

Honglu Yu, John MacGregor

Proceedings Volume 5587, (2004) https://doi.org/10.1117/12.579428

KEYWORDS: Coating, Principal component analysis, Wavelets, Data modeling, Error analysis, Sensors, Wavelet transforms, Image filtering, RGB color model, Image segmentation

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In this paper, a study for the prediction of organileptic properties of snack food in real-time using RGB color images is presented. The so-called organileptic properties, which are properties based on texture, taste and sight, are generally measured either by human sensory response or by mechanical devices. Neither of these two methods can be used for on-line feedback control in high-speed production. In this situation, a vision-based soft sensor is very attractive. By taking images of the products, the samples remain untouched and the product properties can be predicted in real time from image data. Four types of organileptic properties are considered in this study: blister level, toast points, taste and peak break force. Wavelet transform are applied on the color images and the averaged absolute value for each filtered image is used as texture feature variable. In order to handle the high correlation among the feature variables, Partial Least Squares (PLS) is used to regress the extracted feature variables against the four response variables.

Proceedings Article | 2 February 2001 Paper

Multivariate image analysis for process monitoring and control

John MacGregor, Manish Bharati, Honglu Yu

Proceedings Volume 4188, (2001) https://doi.org/10.1117/12.417168

KEYWORDS: Image processing, Image analysis, Principal component analysis, Process control, Satellites, Satellite imaging, Landsat, Earth observing sensors, Feedback control, Optical sensors

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Information from on-line imaging sensors has great potential for the monitoring and control of quality in spatially distributed systems. The major difficulty lies in the efficient extraction of information from the images, information such as the frequencies of occurrence of specific and often subtle features, and their locations in the product or process space. This paper presents an overview of multivariate image analysis methods based on Principal Component Analysis and Partial Least Squares for decomposing the highly correlated data present in multi-spectral images. The frequencies of occurrence of certain features in the image, regardless of their spatial locations, can be easily monitored in the space of the principal components. The spatial locations of these features can then be obtained by transposing highlighted pixels from the PC score space into the original image space. In this manner it is possible to easily detect and locate even very subtle features from online imaging sensors for the purpose of statistical process control or feedback control of spatial processes. The concepts and potential of the approach are illustrated using a sequence of LANDSAT satellite multispectral images, depicting a pass over a certain region of the earth’s surface. Potential applications in industrial process monitoring using these methods will be discussed from a variety of areas such as pulp and paper sheet products, lumber and polymer films.

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