Loquat Bruise Detection Using Optical Coherence Tomography Based on Microstructural Parameters

Wang

J. Food Process. Preserv.

et al. 2021

Self Cite

With a distinct flavor, plum fruit is rich in proteins, potassium, calcium, phosphorus, and nitrogen and has high nutritional and medical value (Crisosto & Kader, 2000;Ertekin et al., 2006). During harvesting, transportation and storage, however, mechanical injury is frequently caused by external vibrations and bumps (Lien et al., 2009). Consequently, browning gradually occurs in the injured tissues and spreads around afterwards, resulting in a massive decay (Ceponis et al., 1987), which shortens the plum's shelf life, weakens its sales value, and causes huge economic losses (Martinez-Romero et al., 2004). A fast non-destructive discriminate method, which can help to detect the mechanical injury in the epidermal tissues, turns to be particularly significant. Refrigeration is usually applied in normal storage to decelerate the decay, which, on the other hand, restrains the obvious browning in plum's injured tissues that have intact epidermis. Hence, detecting plum's early mechanical injury, which has undamaged epidermis, is always a challenge (Li et al., 2017).

Section: Discussionmentioning

confidence: 95%

mentioning

confidence: 99%

Feasibility study of detecting plum’s early mechanical injury based on optical coherence tomography and cell morphological parameters

Wang

J. Food Process. Preserv.

et al. 2021

Self Cite

Critical Reviews in Food Science and Nutrition

“…OCT is a non-invasive and contactless optical imaging method that can acquire three-dimensional (3D) resolved images of plant tissues with a depth of up to 2 mm and a resolution of 5-20 μm (Li et al, 2019a). For the detection of mechanical damage in fruit, Zhou et al (2018) measured the cellular morphology changes of loquat using OCT and found that the total cell surface area and cell amount were good indicators for bruise identification in loquat fruit. In another study, they extracted attenuation coefficients (μt) from the regions of interest in the OCT images and found that the μt values of intact and bruised tissues of 16 loquat fruit were different (Zhou et al, 2017).…”

Section: Direct Methods For Damage Detectionmentioning

confidence: 99%

Mechanical damages and packaging methods along the fresh fruit supply chain: A review

Lin

Fawole

Saeys

et al. 2022

Self Cite

“…In recent years, several studies have explored the application of OCT in agriculture and food-related elds, such as detecting disease in seeds and kernels (Lee et al, 2011), analyzing dried fruit tissue (Rizzolo et al, 2014), investigating the structural and tissue properties of fruits and vegetables (Li et al, 2015;Verboven et al, 2013), and sub-skin bruise detection in fruits (Zhou et al, 2018(Zhou et al, , 2021. Zhou et al (2022) used OCT for automatic non-invasive early detection of mold on maize kernels based on locating the mold attached to the upper pericarp layer.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Assessment of Woody Breast Myopathy in Chicken Fillets Using Optical Coherence Tomography Imaging with Machine Learning

Ekramirad

Yoon

Bowker

et al. 2023

Preprint

Woody breast (WB) myopathy is a major muscle abnormality in chicken fillets, causing excessive hardness and chewiness. The WB condition can potentially cause big economical losses in the poultry industry by decreasing meat quality, increasing waste, degrading nutritional content, and reducing customer satisfaction. A histological technique using a light microscope has been the gold standard to characterize the sub-surface properties of the muscle with the WB condition, which is destructive, costly, time-consuming, and limited to analyzing only small sample areas. It is currently very challenging to assess the degree of WB myopathy objectively and rapidly in individual fillets. There is a need to develop an effective sensing technology for rapidly characterizing the WB condition by measuring the sub-surface cross-sections of the entire fillet at a high resolution. In this study, we utilized optical coherence tomography (OCT) to image the sub-surface microstructure of chicken muscle tissue along the entire fillet with a micrometer resolution. The OCT images provided valuable microstructural features, which were further analyzed using machine learning models to classify chicken fillets based on the WB severity. The results demonstrated a detection accuracy of up to 100% in detecting severe WB samples. The machine learning models achieved a classification accuracy of 93.3% in distinguishing normal from WB fillets. Overall, the successful application of large-scale OCT imaging demonstrated its effectiveness as a non-invasive method for evaluating WB in chicken meat. Furthermore, the study suggests that OCT imaging holds the potential for evaluating other agricultural and food products.