Application of near infrared reflectance (<scp>NIR</scp>) spectroscopy to identify potential <scp>PSE</scp> meat

Xiao, Li; Feng, Fang; Gao, Runze; Wang, Lu; Qian, Yu; Li, Chunbao; Zhou, Guanghong

doi:10.1002/jsfa.7493

Cited by 23 publications

(12 citation statements)

References 37 publications

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“…It has been found that PSE pork meat has a higher absorption at 1940 nm than normal pork. The population of free water showed a remarkable negative correlation, while the population of immobilised water showed a remarkable positive correlation, with the absorption of the 1940‐ and 1460‐nm peaks (Li et al ., ).…”

Section: Resultsmentioning

confidence: 97%

Chicken breast quality – normal, pale, soft and exudative (PSE) and woody – influences the functional properties of meat batters

Chen¹,

Wang²,

Qi³

et al. 2017

Int J of Food Sci Tech

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Summary Three classes (normal, PSE‐like and woody, thirty for per class) of fresh chicken breast meats were selected from a Chinese processing plant. Then, three classes of chicken breast meats were used to prepare meat batters and meatballs. This study investigated the effects of three classes of chicken breast meat on the physicochemical properties, water distribution, protein secondary structures and microstructures of meatballs. PSE‐like and woody meatballs both had lower water holding capacity and textural properties than normal meatballs. Furthermore, the free water mobility and proportion in PSE‐like and woody meatballs were increased (P < 0.05). According to near‐infrared spectroscopy results, the intensity of the 1940‐nm bands of PSE‐like and woody classes both increased. Both PSE‐like and woody meatballs formed more aggregated gel matrices than normal class. PSE‐like and woody classes had higher α‐helix and lower β‐structure contents (P < 0.05). Overall, compared with normal meatballs, PSE‐like and woody meatballs showed inferior functional properties.

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Section: Resultsmentioning

confidence: 97%

Chicken breast quality – normal, pale, soft and exudative (PSE) and woody – influences the functional properties of meat batters

Chen¹,

Wang²,

Qi³

et al. 2017

Int J of Food Sci Tech

Self Cite

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“…The authors reported that the best equation provided an agreement of 72 and 77% for the classification of carcasses in the superior and inferior categories, respectively, which is lower than the correct classification obtained in the present study. Better results were found by Li et al (2016) and Neyrinck et al (2015) with the NIR technology to differentiate potential PSE and normal meat, reaching a correct classification of 93.3%, as well as by Barbin et al (2012), who used NIR hyperspectral imaging to obtain 96% correct classification of pork in the classes: PSE; reddish-pink, firm, and non-exudative; and DFD meat. Chmiel et al (2011) showed that the computer vision system is efficient to detect potential PSE meat in the longissimus lumborum but not in the semimembranosus muscle (Chmiel et al, 2016); however, according to the authors, since these results were obtained by scanning samples at the laboratory level, it is necessary to develop a system with a nondestructive or time-consuming method that can be implemented online for commercial use.…”

Section: Resultsmentioning

confidence: 99%

“…However, in general, these attempts have only been relatively successful and, in some cases, have used unfeasible slaughter line measurements, some of which were taken 24 hours after slaughter, making it impossible to classify the carcasses in a timely manner and to earmark them for specific products. More recently, other technologies -including low frequency dielectric spectra (CastroGiráldez et al, 2010), near-infrared reflectance spectroscopy (NIR) (Liao et al, 2010;Kapper et al, 2012;Neyrinck et al, 2015;Li et al, 2016;), NIR hyperspectral imaging (Barbin et al, 2012), and computer vision system (Chmiel et al, 2011(Chmiel et al, , 2016 -were investigated as tools for the prediction and classification of carcasses according to pork quality, with some promising results. However, most of these studies involved sample collection from carcasses and analyses at the laboratory level, not on the slaughter line at the industrial level.…”

Section: Introductionmentioning

confidence: 99%

Swine carcasses classified by degree of exudation and marbling content

Bertol

Coldebella

Filho

et al. 2017

Pesq. agropec. bras.

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-The objective of this work was to develop and evaluate prediction equations, based on measurements obtained on the slaughter line, for carcass classification according to the degree of exudation and the content of marbling. A total of 747 pig carcasses originated from 27 producers were randomly selected on the slaughter line at two slaughterhouses, constituting representative samples from each one. Hot carcass weight, loin depth, back fat thickness, and pH and temperature 45 min after slaughter were used to generate the equations. The carcasses were classified as normal or exudative for drip loss of loin and ham, and as acceptable or undesirable for marbling content. For drip loss of loin and ham, respectively, the overall correct classification of the carcasses was 87.3 and 76.8%, using the discriminant functions, for both categories. For marbling, the overall correct classification was 67.2%, and only 69% of the carcasses were correctly classified as acceptable (marbling >1). The equations developed based on the measurements obtained on the slaughter line allow classifying swine carcasses with high accuracy, according to the degree of exudation, but not with the content of marbling.Index terms: drip loss, ham, loin, pigs, prediction equations. Carcaças suínas classificadas por grau de exsudação e conteúdo de marmoreioResumo -O objetivo deste trabalho foi desenvolver e avaliar equações de predição, baseadas em medidas obtidas na linha de abate, para classificação de carcaças suínas de acordo com o grau de exsudação e o conteúdo de marmoreio. Um total de 747 carcaças suínas, provenientes de 27 produtores, foi selecionado ao acaso na linha de abate, em dois abatedouros, tendo constituído amostras representativas de cada um. Para gerar as equações, foram utilizados peso da carcaça quente, profundidade do lombo, espessura de toucinho, e pH e temperatura medidos 45 min após o abate. As carcaças foram classificadas em normais ou exsudativas, quanto à perda por gotejamento do lombo e do pernil, e em aceitáveis ou indesejáveis, quanto ao marmoreio. Para a perda por gotejamento do lombo e do pernil, respectivamente, a classificação correta global das carcaças foi de 87,3 e 76,8%, a partir das funções discriminantes, para ambas as categorias. Para o marmoreio, a classificação correta global foi de 67,2%, e apenas 69% das carcaças foram classificadas corretamente como aceitáveis (marmoreio >1). As equações desenvolvidas com base nas medidas obtidas na linha de abate permitem classificar com elevada acurácia as carcaças suínas, de acordo com o grau de exsudação, mas não com o conteúdo de marmoreio.Termos para indexação: perda por gotejamento, pernil, lombo, porcos, equações de predição.

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“…The search for new, non-invasive, quick, and low-cost methods for the analysis of the chemical composition of meat and other raw materials is the subject of many studies [10]. For example, there were some attempts to use near infra-red spectrometry (NIRS) [9,11], dual energy x-ray (DXR) [4,12,13], ultrasound [14,15], computed tomography (CT) [16], and 3D scanning [17,18] for this purpose. Among the methods for density determination, the hydrostatic method (using Archimedes' principle) is the simplest to use, and also the cheapest.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Chemical Composition of Pork Trimmings by Use of Density Measurement—Hydrostatic Method

et al. 2020

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This study aims to determine the possibility of using density measurements by using the hydrostatic method for the estimation of the chemical composition of pork. The research material included 75 pork samples obtained during industrial butchering and cutting. The density measurements were performed using the hydrostatic method based on Archimedes’ principle. The meat samples were minced, and the content of the basic chemical components in them was determined. The usefulness of density measurement using the hydrostatic method in chemical composition estimation was determined by analyzing the correlation for the entire population, and after grouping the samples with a low (<15%), medium (15–25%), and high (>25%) fat content. High (in absolute value) coefficients of correlation between the meat density and the content of water (0.96), protein (0.94), and fat (−0.96) were found based on the results obtained. In order to achieve higher accuracy of the estimation, the applied regression equations should be adjusted to the presumed fat content in the meat. The standard error of prediction (SEP) values ranged from 0.67% to 2.82%, which indicates that the calculated estimation accuracy may be sufficient for proper planning of the production. Higher SEP values were found in fat content estimation and the lowest ones were found in protein content estimation.

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Application of near infrared reflectance (NIR) spectroscopy to identify potential PSE meat

Abstract: NIR spectra coupled with the Factorisation Method could be a promising technology to identify potential PSE meat. The difference in the intensity of H2 O absorbance peaks between PSE and normal meat might be the basis of this identification method. © 2015 Society of Chemical Industry.

Cited by 23 publications

References 37 publications

Chicken breast quality – normal, pale, soft and exudative (PSE) and woody – influences the functional properties of meat batters

Chicken breast quality – normal, pale, soft and exudative (PSE) and woody – influences the functional properties of meat batters

Swine carcasses classified by degree of exudation and marbling content

Estimation of Chemical Composition of Pork Trimmings by Use of Density Measurement—Hydrostatic Method

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