Fusion of Spectra and Texture Data of Hyperspectral Imaging for the Prediction of the Water-Holding Capacity of Fresh Chicken Breast Filets

Yi, Yang; Wang, Wei; Zhuang, Hong; Yoon, Seung-Chul; Jiang, Hongzhe

doi:10.3390/app8040640

Cited by 30 publications

(16 citation statements)

References 44 publications

(61 reference statements)

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“…The final study to quantify sensory attributes of poultry products also fused textural and spectral data, but this time to predict water-holding capacity of whole chicken breast fillets using NIR HSI ( Yang et al., 2018b ). Water-holding capacity is directly related to sensory attributes such as tenderness and juiciness.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

Literature review: spectral imaging applied to poultry products

Falkovskaya

Gowen

2020

Poultry Science

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“…An imaging algorithm was developed to transfer the wavelengths selected based on a successive projection algorithm and partial least squares regression (SPA– PLSR) model to each pixel in the image, with the goal of developing distribution maps. Yang (2018) integrated spectra and texture features via hyperspectral imaging at 1000–2500 nm range and adopted the PLSR model to predict the water-holding capacity of chicken breast fillets.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

“…157 Xiong et al. (2015) Chicken Grading PLSR RMSEp, multiple results Yang et al. (2018) Egg Grading SPA-SVR, SVC 96.3% for scattered yolk Zhang et al.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Deep learning and machine vision for food processing: A survey

Zhu

Spachos

Pensini

et al. 2021

Current Research in Food Science

160

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The quality and safety of food is an important issue to the whole society, since it is at the basis of human health, social development and stability. Ensuring food quality and safety is a complex process, and all stages of food processing must be considered, from cultivating, harvesting and storage to preparation and consumption. However, these processes are often labour-intensive. Nowadays, the development of machine vision can greatly assist researchers and industries in improving the efficiency of food processing. As a result, machine vision has been widely used in all aspects of food processing. At the same time, image processing is an important component of machine vision. Image processing can take advantage of machine learning and deep learning models to effectively identify the type and quality of food. Subsequently, follow-up design in the machine vision system can address tasks such as food grading, detecting locations of defective spots or foreign objects, and removing impurities. In this paper, we provide an overview on the traditional machine learning and deep learning methods, as well as the machine vision techniques that can be applied to the field of food processing. We present the current approaches and challenges, and the future trends.

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“…However, due to hyperspectral data having a high dimension, classification can be challenging, especially if there is a lack of training data available. Thus, texture can provide additional information for classification of agricultural land using three-dimensional wavelet texture features (Qian et al, 2012), or with a Gray-Level Co-Occurrence Matrix to assess either building materials (Lerma et al, 2000) and meat quality (Yang et al, 2018). The texture can be described using different approaches and the algorithm is chosen to suit the needs.…”

Section: Urban Materials Datasets and Classification Approachesmentioning

confidence: 99%

Urban Material Classification Using Spectral and Textural Features Retrieved From Autoencoders

Ilehag

Leitloff

Weinmann

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Classification of urban materials using remote sensing data, in particular hyperspectral data, is common practice. Spectral libraries can be utilized to train a classifier since they provide spectral features about selected urban materials. However, urban materials can have similar spectral characteristic features due to high inter-class correlation which can lead to misclassification. Spectral libraries rarely provide imagery of their samples, which disables the possibility of classifying urban materials with additional textural information. Thus, this paper conducts material classification comparing the benefits of using close-range acquired spectral and textural features. The spectral features consist of either the original spectra, a PCA-based encoding or the compressed spectral representation of the original spectra retrieved using a deep autoencoder. The textural features are generated using a deep denoising convolutional autoencoder. The spectral and textural features are gathered from the recently published spectral library KLUM. Three classifiers are used, the two well-established Random Forest and Support Vector Machine classifiers in addition to a Histogram-based Gradient Boosting Classification Tree. The achieved overall accuracy was within the range of 70–80% with a standard deviation between 2–10% across all classification approaches. This indicates that the amount of samples still is insufficient for some of the material classes for this classification task. Nonetheless, the classification results indicate that the spectral features are more important for assigning material labels than the textural features.

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Fusion of Spectra and Texture Data of Hyperspectral Imaging for the Prediction of the Water-Holding Capacity of Fresh Chicken Breast Filets

Cited by 30 publications

References 44 publications

Literature review: spectral imaging applied to poultry products

Literature review: spectral imaging applied to poultry products

Deep learning and machine vision for food processing: A survey

Urban Material Classification Using Spectral and Textural Features Retrieved From Autoencoders

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