Feasibility of combining spectra with texture data of multispectral imaging to predict heme and non-heme iron contents in pork sausages

Potential use of multispectral imaging technology to identify moisture content and water‐holding capacity in cooked pork sausages

“…Pork (hind leg) from a Carrefour supermarket was pretreated as described by Ma et al . To set up a reliable dataset of MC and WHC variations, the prepared pork was first mixed with six salt concentrations (0%, 0.5%, 1%, 1.5%, 2.0% and 2.5% NaCl), then stirred fully to form six mixtures and stored at 4 °C for about 12 h. Next, these mixtures were made into CPS (five CPS for each mixture) according to a previously reported method .…”

“…Moreover, compared with the hyperspectral imaging system, MSI can be more easily designed into various constructions based on different requirements, such as the Ulbricht sphere, which can reduce or eliminate light scattering caused by non‐flat surfaces of samples. Images collected by the MSI system have been seen as a ‘ hypercube ’ or ‘ datacube ’, which could be applied to analysis of minor and/or subtle variations in physicochemical properties of tested objects; accordingly, the MSI technique should also be able to identify the MC and WHC in cooked pork sausages (CPS). As a fast and non‐invasive analysis technology, MSI has recently been adopted to evaluate quality and safety of meat and meat products including spoilage detection, adulteration identification, nutritional assessment, microbiological analysis, measurement of color and tenderness, etc.…”

Using a Combination of Spectral and Textural Data to Measure Water-Holding Capacity in Fresh Chicken Breast Fillets

“…Four textural features, namely contrast, correlation, energy, and homogeneity, were extracted from ROIs using the gray level co-occurrence matrix (GLCM) analysis method. The GLCM provides a number of second-order statistics that can be used to describe the gray level relationships within a neighborhood around a certain pixel [35][36][37]. Generally, contrast measures local variations in the image, and higher values indicate larger partial variations [38].…”

Multispectral imaging for predicting the water status in mushroom during hot‐air dehydration

“…The multispectral imaging (MSI)/hyperspectral imaging technology has gained widespread acceptance for agro‐food, remote sensing, and biomedical applications. Recently, it has been used in different studies in quality evaluation of different agro‐food products, for example, for predicting heme and nonheme iron contents in pork sausage (Ma et al., 2016), transgenic assessment of crop seeds (Liu et al., 2014a, 2016a), classification of storage period in tea (Xiong et al., 2015), quality evaluation of rocket (Løkke, Seefeldt, Skov, & Edelenbos, 2013), quality monitoring of in‐shell infestation in almonds and sunflower seeds (Ma et al., 2015; Yu et al., 2019), characterizing sensory properties and physicochemical parameters in strawberry and tomato fruits (Li et al., 2014; Liu, Liu, Chen, Yang, & Zheng, 2015; Liu et al, 2014b), pork microbial safety identification (Dissing et al., 2013; Ma et al., 2014), and adulteration prediction in meat, tomato paste, and infant formula powder (Liu et al., 2017; Liu, Liu, Yang, Chen, & Zheng, 2017; Ropodi, Pavlidis, Mohareb, Panagou, & Nychas, 2015a). Furthermore, spectral imaging holds high potential for quality assessment relevant to moisture contents, for example, for inspection of frozen minced beef followed by thawing process (Ropodi, Panagou, & Nychas, 2018), identification of moisture contents in carrots (Liu et al., 2016b), and dehydrated prawns (Wu et al., 2012).…”