Rapid and Nondestructive Classification of Cantonese Sausage Degree Using Hyperspectral Images

Abstract

Hyperspectral images are widely used in the food industry as a fast and non-destructive analytical technique. Cantonese sausage has a long history and is a very old food production and meat preservation technology. According to the physical and chemical characteristics of the sausage, the Chinese business industry standard SB/t10003-92 divides the sausage into three grades, which are called excellent grade, level 1, and level 2. In this paper, k-means is adopted first to separate two parts of the meat adaptively to improve the discriminant rate. The hyperspectral information of the near-infrared band is extracted by successive projections algorithm (SPA). The multiple linear regression (MLR) and partial least squares regression (PLSR) algorithms are used to classify the sausage grade. The experimental results show that the lean meat and fat of the sausage have different characteristics in the near-infrared band, and the modeling results have higher accuracy and anti-interference after separating lean meat and fat meat. The best model of sausage classification is using SPA-MLR method to model the fat region of Cantonese sausage; the prediction accuracy of which is 100%. It was found that the modeling results of fat were better than lean meat in both PLSR and SPA-MLR, which indicated that there were obvious differences in fat composition among different grades of sausage, and the fat of sausage was more suitable for classification.

A New Rule-Based Classification Method Using Shape-Based Properties of Spectral Curves

“…In particular, the range below 500 nm exhibits strong noise effects and was not considered in the rule formation. Taraxacum (1), Inula conyza (2), Campanula rotundifolia (3), moss (4), Geranium robertianum (5), Asplenium trichomanes (6), green sedum (7), red sedum (8), moss (9), and lichen (10).…”

“…As examples, [2][3][4] use HSI to maintain and increase crop yields in precision agriculture. e evaluation of food quality and safety through the use of HSI is part of the research of [5][6][7]. Other applications of HSI can be found in medicine to diagnose diseases or to monitor wound healing [8][9][10], in the art market to verify the authenticity of artworks [11][12][13], and in forensics to analyze crime scenes [14][15][16].…”

“…Figure 1: Images of the captured scenes. (a) Seven classes of different plastics (plastic samples were kindly provided by PlasticsEurope Deutschland e.V (https://www.plasticseurope.org/de)) consisting of phenolic resin (PF), polyamide (PA), polyvinylchloride (PVC), polyethylene (PE), polymethylmethacrylate (PMMA), polyester resins (UP), and polystyrene (PS); (b) ten classes of different vegetation types consisting of Taraxacum (1), Inula conyza (2), Campanula rotundifolia (3), moss (4), Geranium robertianum(5), Asplenium trichomanes (6), green sedum(7), moss and red sedum (8), moss (9), moss and lichen(10).…”

Multivariate Statistical Analysis for the Classification of Sausages Based on Physicochemical Attributes, Using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS)

“…However, the determination of physicochemical properties in foods is time-consuming and complicated [16][17][18][19][20]. In the case of the utilization of hyperspectral imaging, additional algorithms should be used to reduce the infuence of the distribution of meat and fat in the sausage [26]. Tus, to simplify the classifcation, spectral data from FT-IR analysis and multielemental composition from the ICP-MS technique were employed to conduct the multivariate statistical analysis.…”

Evaluation of pH in Sausages Stuffed in a Modified Casing with Orange Extracts by Hyperspectral Imaging Coupled with Response Surface Methodology

“…Compared to a conventional digital pH meter that can only identify the measuring spot, distribution maps generated by HSI can elaborate on how many different pH values exist in every spot in different test samples. The Cantonese sausage grade was classified by using multiple linear regression (MLR) and partial least squares regression (PLSR), and the best classification was achieved by using a successive projections algorithm coupled with MLR, with a predictive accuracy of 100% [ 22 ]. Shi et al (2019) employed HSI to classify colonies from food fragments (sausage, bacon, and millet fragments) in an agar plate [ 23 ].…”