Background The quality of products depends on their processing. Effective way of monitoring and controlling these processes will ensure the quality and safety of products. Since traditional measurement methods cannot achieve on-line monitoring, imaging spectroscopy, as a fast, accurate and non-destructive detection tool, has been widely used to evaluate quality and safety attributes of foods undergoing various processes. Scope and Approach In the current review, detailed applications of hyperspectral imaging (HSI) system in various food processes are outlined, including cooking, drying, chilling, freezing and storage, and salt curing. The study emphasized the ability of HSI technique to detect internal and external quality parameters in different food processes. Also, the advantages and disadvantages of HSI applications on these food processes are discussed. Key Findings and Conclusions The literature presented in this review clearly demonstrate that HSI has the ability to inspect and monitor different food manufacturing processes and has the potential to control the quality and safety of the processed foods. Although still with some barriers, it can be expected the HSI systems will find more useful and valuable applications in the future evaluation of food processes.

Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications

Objective quality assessment and efficacious safety surveillance for agricultural and food products are inseparable from innovative techniques. Hyperspectral imaging (HSI), a rapid, nondestructive, and chemical-free method, is now emerging as a powerful analytical tool for product inspection by simultaneously offering spatial information and spectral signals from one object. This paper focuses on recent advances and applications of HSI in detecting, classifying, and visualizing quality and safety attributes of fruits and vegetables. First, the basic principles and major instrumental components of HSI are presented. Commonly used methods for image processing, spectral pretreatment, and modeling are summarized. More importantly, morphological calibrations that are essential for nonflat objects as well as feature wavebands extraction for model simplification are provided. Second, in spite of the physical and visual attributes (size, shape, weight, color, and surface defects), applications from the last decade are reviewed specifically categorized into textural characteristics inspection, biochemical components detection, and safety features assessment. Finally, technical challenges and future trends of HSI are discussed.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1541-4337.12123

Recent Progress of Hyperspectral Imaging on Quality and Safety Inspection of Fruits and Vegetables: A Review

Vis-NIR hyperspectral imaging in visualizing moisture distribution of mango slices during microwave-vacuum drying.

Combined hot-air and microwave-vacuum drying for improving drying uniformity of mango slices based on hyperspectral imaging visualisation of moisture content distribution

Hyperspectral imaging sensors are promising tools for monitoring crop plants or vegetation in different environments. Information on physiology, architecture or biochemistry of plants can be assessed non-invasively and on different scales. For instance, hyperspectral sensors are implemented for stress detection in plant phenotyping processes or in precision agriculture. Up to date, a variety of non-imaging and imaging hyperspectral sensors is available. The measuring process and the handling of most of these sensors is rather complex. Thus, during the last years the demand for sensors with easy user operability arose. The present study introduces the novel hyperspectral camera Specim IQ from Specim (Oulu, Finland). The Specim IQ is a handheld push broom system with integrated operating system and controls. Basic data handling and data analysis processes, such as pre-processing and classification routines are implemented within the camera software. This study provides an introduction into the measurement pipeline of the Specim IQ as well as a radiometric performance comparison with a well-established hyperspectral imager. Case studies for the detection of powdery mildew on barley at the canopy scale and the spectral characterization of Arabidopsis thaliana mutants grown under stressed and non-stressed conditions are presented.

Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection.

Prediction of color and moisture content for vegetable soybean during drying using hyperspectral imaging technology

Review of seed quality and safety tests using optical sensing technologies

A b s t r a c t. Moisture content uniformity is one of critical parameters to evaluate the quality of dried products and the drying technique. The potential of the hyperspectral imaging technique for evaluating the moisture content uniformity of maize kernels during the drying process was investigated. Predicting models were established using the partial least squares regression method. Two methods, using the prediction value of moisture content to calculate the uniformity (indirect) and predicting the moisture content uniformity directly, were investigated. Better prediction results were achieved using the direct method (with correlation coefficients R P = 0.848 and root-mean-square error of prediction RMSEP = 2.73) than the indirect method (R P = 0.521 and RMSEP = 10.96). The hyperspectral imaging technique showed significant potential in evaluating moisture content uniformity of maize kernels during the drying process. K e y w o r d s: maize kernels, hyperspectral imaging, moisture content uniformity, drying

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Prediction of moisture content uniformity using hyperspectral imaging technology during the drying of maize kernel

The invention relates to a hyperspectral-image-technology-based multi-quality nondestructive testing method for dried green soybeans. According to the technical scheme, the method comprises the steps of: a. selecting dried green soybeans; b. collecting hyperspectral images of the dried green soybeans by utilizing a hyperspectral image collecting system; c. extracting contour information of the dried green soybeans by utilizing a threshold segmenting method; d. extracting image entropy characteristic parameters by utilizing the obtained contour information; e. collecting the color, moisture rate, hardness and shrinkage rate indexes of the dried green soybeans by utilizing a destructive instrument; f. constructing an evaluation prediction model of the dried green soybeans by utilizing a partial least-squares regression algorithm; g. collecting the hyperspectral images of the dried green soybeans and inputting the hyperspectral images of the dried green soybeans to the evaluation prediction model to obtain the evaluation results on the color, moisture rate, hardness and shrinkage rate indexes of the dried green soybeans. Through the evaluation prediction model and the hyperspectral image collecting system, a multi-quality evaluation result can be obtained under the situation of being nondestructive for the majority dried green soybeans; the hyperspectral-image-technology-based multi-quality nondestructive testing method is easy to operate, good in real-time property and high in reliability.

Hyperspectral-image-technology-based multi-quality nondestructive testing method for dried green soybeans

The invention discloses a fuzzy-energy active contour model based hyperspectral image segmentation method which is applicable to multi-target rapid contour extraction of hyperspectral images. The method includes: dividing a to-be-segmented image into two parts with different membership values by means of defining an initial contour, recalculating membership of each pixel according to average strength of the two parts, if energy of the image decreases, updating the membership of each pixel, keeping initial membership unchanged otherwise, and finishing segmentation till total energy of the image reaches minimum. Application of the fuzzy-energy active contour model based hyperspectral image segmentation method is in rapid nondestructive testing of producing areas and purities of agricultural products. On the basis of accurate contour extraction of multiple corn seed targets in hyperspectral images of the corn seeds, nondestructive testing of producing areas and purities of the corn seeds is realized, and the method has the advantages of simplicity, rapidness, high accuracy and good reliability.

Qingguo Wang

Papers

Prediction of color and moisture content for vegetable soybean during drying using hyperspectral imaging technology

Review of seed quality and safety tests using optical sensing technologies

Prediction of moisture content uniformity using hyperspectral imaging technology during the drying of maize kernel

Hyperspectral-image-technology-based multi-quality nondestructive testing method for dried green soybeans

Fuzzy-energy active contour model based hyperspectral image segmentation method