Feasibility of imaging under structured illumination for evaluation of white striping in broiler breast fillets

Olaniyi, Ebenezer O.; Lu, Yutong; Cai, Jiaxu; Sukumaran, Anuraj T.; Jarvis, Tessa; Rowe, Clinton

doi:10.1016/j.jfoodeng.2022.111359

Cited by 8 publications

(5 citation statements)

References 44 publications

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“…4 shows examples of demodulated DC and AC images of beef samples of different marbling categories at spatial frequencies of 0.05-0.40 cycles/mm. As in our prior investigations [33,35,46], AC images provide enhanced image contrast and resolution varying with the spatial frequency of illumination patterns. AC images resolve better marbling characteristics than DC images, with better clarity at higher spatial frequencies, while the overall intensity of AC images diminishes as the spatial frequency increases because of more strong light attenuation by biological tissues at higher spatial frequencies.…”

Section: Resultssupporting

confidence: 59%

“…Textural analysis is important to extracting discriminant features for pattern classification tasks, which was applied for food quality evaluation by structured illumination imaging [36,46]. For marbling assessment in this study, the segmented LD regions were subjected to features extraction by a pretrained deep learning model, i.e., RexNext-101 [53] with 101 convolutional layers followed by a fully connected layer (https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/).…”

Section: Marbling Classificationmentioning

confidence: 99%

“…Despite still limited imaging depth (within millimeters), structured illumination imaging was proven effective in detecting structural features from near-surface tissues, e.g., bruises in apples [36][37][38][39][40], chilling injury/bruises in cucumbers [41][42][43], and early decay in peaches [44] and oranges [45]. Very recently, it was used for assessing white striping defects in broiler breast meat [46]. Textural features were extracted from demodulated DC and AC images of broiler meat and then fed into a linear discriminant model for classifying poultry samples into 2-4 grades, and the overall accuracy improvement of AC over DC ranged from 7.93% to 12.30%.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced segmentation of beef longissimus dorsi muscle using structured illumination reflectance imaging with deep learning

Cai

Olaniyi

et al. 2023

Sensing for Agriculture and Food Quality and Safety XV

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Marbling is one of the most important determinants for beef quality and assessed in terms of the abundance and spatial distribution of visible fat flecks in the longissimus dorsi (LD) muscle. Visual appraisal by trained professionals is the currently prevailing practice in beef marbling assessment, but it suffers from the drawbacks of human subjective, laborintensive, and time-consuming. Computer vision technology offers a promising alternative for objective and automated beef quality assessment. Recently, imaging technology under structured illumination has emerged for the detection of meat quality characteristics as opposed to existing imaging modalities using uniform illumination. By modulating light at certain spatial frequencies, structured illumination reflectance imaging has the potential to resolve subtle texture features of meat surface/subsurface for enhanced quality assessment. This study represents the proof-of-concept evaluation of the applicability of an inhouse-assembled structured illumination imaging system, combined with deep learning, for beef marbling assessment. Beef samples of varying marbling degrees were imaged under sinusoidal illumination at spatial frequencies of 0.05-0.40 cycles/mm. The acquired images were demodulated into direct component (DC) and amplitude component (AC) images at each spatial frequency. A deep learning segmentation model, SegFormer, was built using DC and AC (0.05-0.40 cycles/mm) images for segmenting the LD muscle. Texture features were extracted by a pretrained ResNext model from the LD muscle segments, and then used for building discriminative models to classify samples of three marbling categories. The DC images yielded the overall classification accuracy of 76.32%, while the AC images resulted in improved accuracies of 76.84%-81.05%, which the best accuracy attained at the spatial frequency of 0.40 cycles/mm. This study shows the effectiveness of imaging under sinusoidal illumination in place of uniform illumination for enhanced assessment of beef marbling.

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Section: Resultssupporting

confidence: 59%

Section: Marbling Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhanced segmentation of beef longissimus dorsi muscle using structured illumination reflectance imaging with deep learning

Cai

Olaniyi

et al. 2023

Sensing for Agriculture and Food Quality and Safety XV

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“…Without cameras involved, these detection systems [12][13][14][15] cannot obtain spatial information that may be useful for WB assessment. Imaging technology offers a promising alternative for rapid, non-destructive, and automated food quality assessment [16,17]. Caldas-Cueva et al [18] developed an image analysis method for WB assessment by extracting geometric features (e.g., length, width, area, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…Other optical sensing technologies have also been investigated for poultry quality assessment [21][22][23][24]. Wold et al [25] applied near-infrared spectroscopy (NIRS) to detect WB conditions in chicken fillets and reported on using an online NIR scanning system for rapid WB detection [26].…”

Section: Introductionmentioning

confidence: 99%

Detection of woody breast condition in broiler breast fillets using multispectral scattering imaging

Cai,

2024

Sensing for Agriculture and Food Quality and Safety XVI

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Muscular myopathies such as woody or wooden breast (WB), which impair the eating quality and marketability of poultry products, are threatening the profitability of poultry industries worldwide, with an estimated annual loss exceeding $500 million for the United States (U.S.) poultry industry. WB-affected fillets are characterized by abnormal tissue hardness and muscle rigidity with varying degrees of severity. The assessment of WB conditions at processing facilities currently relies on tactile palpation combined with a visual examination by trained personnel. This approach is subjective, labor-intensive, costly, and may induce contamination due to physical contact. Optical imaging technology offers a promising alternative for objective and non-invasive quality assessment of broiler meat. This study presents a proof-of-concept evaluation of a new scattering imaging technique that captures light-scattering characteristics of meat tissues for the detection of WB conditions in broiler breast fillets. Broadband scattering images, generated under the illumination of a highly focused broadband beam, were acquired from broiler meat samples. Two types of image features, i.e., 1) deep-learning-based and 2) hand-crafted scattering features, were extracted for building classification models using regularized linear discriminant analysis to differentiate meat samples into two categories, i.e., "Normal (no WB)" and "Defective", according to WB conditions. Deep-learning-based features yielded an overall classification accuracy of 80.9%, while an improved accuracy of 88.7% was obtained by hand-crafted scattering features, representing a significant improvement of 7.8% (P < 0.01). Furthermore, feature selection based on minimum redundancy maximum relevance (MRMR) was conducted to select a subset of scattering image features for discriminant modeling, leading to a further accuracy improvement to 90.5% with top-ranked 65 features. This study has demonstrated the promise of the light scattering imaging technique for WB detection in broiler breast meats.

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Non-Destructive Assessment of Microbial Spoilage of Broiler Breast Meat Using Structured Illumination Reflectance Imaging with Machine Learning

Olaniyi,

Lu,

Zhang

et al. 2024

Food Anal. Methods

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Feasibility of imaging under structured illumination for evaluation of white striping in broiler breast fillets

Cited by 8 publications

References 44 publications

Enhanced segmentation of beef longissimus dorsi muscle using structured illumination reflectance imaging with deep learning

Enhanced segmentation of beef longissimus dorsi muscle using structured illumination reflectance imaging with deep learning

Detection of woody breast condition in broiler breast fillets using multispectral scattering imaging

Non-Destructive Assessment of Microbial Spoilage of Broiler Breast Meat Using Structured Illumination Reflectance Imaging with Machine Learning

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