Real‐time prediction of pre‐cooked Japanese sausage color with different storage days using hyperspectral imaging

Feng, Chao‐Hui; Makino, Yoshio; Yoshimura, Masashi; Rodríguez-Pulido, Francisco J.

doi:10.1002/jsfa.8746

Cited by 27 publications

(24 citation statements)

References 47 publications

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“…In order to simplify the models and speed up the data analysis, the wavelengths which provide the most important useful information were selected. In this way, the uninformative wavelengths were eliminated (Xu and others ; Feng and others ), computing speed enhanced (Cheng and others , ) and the spectral dimension was reduced to a large extent (Wu and others ; Jia and others ). Furthermore, the feature wavelengths selection will facilitate the design an optimized online multispectral imaging system (Kamruzzaman and others , ; Feng and others ).…”

Section: Resultsmentioning

confidence: 99%

“…It includes different combinations of preprocessing transformations and regression algorithms (Qi and others ). The preprocessing transfromations generally used in hyperspectral imaging are normalization (N), multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative (1st D), and second derivative (2nd D) (Feng and others ). Normalization can enhance the spectral features (Pan and others ) and render the sepctra to have an equal area under the curve to easily compare the features of the spectra in the same plot (Skjelvareid and others ).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Hyperspectral Imaging in Tandem with R Statistics and Image Processing for Detection and Visualization of pH in Japanese Big Sausages Under Different Storage Conditions

Feng

Makino

Yoshimura

et al. 2017

Journal of Food Science

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In this paper, hyperspectral imaging is for the first time used to detect pH in cooked sausages using R statistics, which provides another useful information for the researchers who do not have the access to Matlab. Eleven optimal wavelengths were successfully selected, which were used for simplifying the PLSR model established based on the full wavelengths. This simplified model achieved a high R (0.909) and a low root mean square error of prediction (0.035), which can be useful for the design of multispectral imaging systems.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Hyperspectral Imaging in Tandem with R Statistics and Image Processing for Detection and Visualization of pH in Japanese Big Sausages Under Different Storage Conditions

Feng

Makino

Yoshimura

et al. 2017

Journal of Food Science

Self Cite

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show abstract

“…Consequently, HSI should be implemented further down the production line in future work, once the products are completely processed and packaged, to ensure safe products for consumers. Although not the same as imaging through packaging, real-time HSI has been successfully applied to imaging meat through sausage casing ( Feng et al., 2018 ). Future work is required to assess HSI utility for successfully imaging poultry products through packaging.…”

Section: Discussionmentioning

confidence: 99%

Literature review: spectral imaging applied to poultry products

Falkovskaya

Gowen

2020

Poultry Science

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Consumption of poultry products is increasing worldwide, leading to an increased demand for safe, fresh, high-quality products. To ensure consumer safety and meet quality standards, poultry products must be routinely checked for fecal matter, food fraud, microbiological contamination, physical defects, and product quality. However, traditional screening methods are insufficient in providing real-time, nondestructive, chemical and spatial information about poultry products. Novel techniques, such as hyperspectral imaging ( HSI ), are being developed to acquire real-time chemical and spatial information about products without destruction of samples to ensure safety of products and prevent economic losses. This literature review provides a comprehensive overview of HSI applications to poultry products. The studies used for this review were found using the Google Scholar database by searching the following terms and their synonyms: “poultry” and “hyperspectral imaging”. A total of 67 studies were found to meet the criteria. After all relevant literature was compiled, studies were grouped into categories based on the specific material or characteristic of interest to be detected, identified, predicted, or quantified by HSI. Studies were found for each of the following categories: food fraud, fecal matter detection, microbiological contamination, physical defects, and product quality. Key findings and technological advancements were briefly summarized and presented for each category. Since the first application to poultry products 20 yr ago, HSI has been shown to be a successful alternative to traditional screening methods.

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“…[21,22] A typical hyperspectral image consists of a series of images of different wavelengths, and each pixel of image is a spectrum on this position, which covers the Vis/NIR range. HSI has been used to detect many important quality attributes of agricultural materials, such as color of sausage, [23] defect on jujube, [24] allicin, and soluble solid content of garlic, [25] chilling injury of peaches, [26,27] SSC and firmness of pear, [28] contaminants on wheat [29] and internal qualities of apples. [30] Therefore, the use of hyperspectral imaging has great potential for quality assessments of agricultural materials.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive detection of storage time of strawberries using visible/near-infrared hyperspectral imaging

Weng

Dong

et al. 2020

International Journal of Food Properties

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In this study, visible/near-infrared (Vis/NIR) hyperspectral imaging was used for the nondestructive detection of storage time of strawberries. Storage time was calculated immediately after freshly picking. Support vector machine (SVM) with multiplicative scatter correction can differentiate strawberries of different storage time with an accuracy of 100%. Then, the model developed by partial least square regression with full-range spectra was used to predict the storage time of strawberries with a determination coefficient of prediction (R p 2 ) of 0.9999 and root-mean-square error of prediction (RMSEP) of 0.0721, and deviation was small at different periods. With the spectra of 10 important wavelengths obtained by uninformative variable elimination, the SVM model obtained relatively acceptable results with R p 2 of 0.9943 and RMSEP of 1.3213. The prediction experiments for the separately picked strawberry samples also got the similar results. Finally, distribution maps of storage time generated based on the pixel-wise spectra and established model clearly show the quality transformation of the strawberries. ARTICLE HISTORY

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Real‐time prediction of pre‐cooked Japanese sausage color with different storage days using hyperspectral imaging

Abstract: HSI combined with PLSR and FSMR can be used to quantify and visualize evolution of sausage redness under different storage days. © 2017 Society of Chemical Industry.

Cited by 27 publications

References 47 publications

Hyperspectral Imaging in Tandem with R Statistics and Image Processing for Detection and Visualization of pH in Japanese Big Sausages Under Different Storage Conditions

Hyperspectral Imaging in Tandem with R Statistics and Image Processing for Detection and Visualization of pH in Japanese Big Sausages Under Different Storage Conditions

Literature review: spectral imaging applied to poultry products

Nondestructive detection of storage time of strawberries using visible/near-infrared hyperspectral imaging

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