Evaluation of the Quality of Different Brands of Beef Upper Brain

based on Correlation and Principal Component Analysis

Wanyue Hua

, Xiaoning Jiang

, Liting Yan

, Xuefei Hou

and Huijun Liu

Food Science and Engineering College, Beijing University of Agriculture, Beijing, 102206, China

RDFZ Xishan School, Beijing, 100193, China

Beijing Yanxi Yueshengzhai Moslem Food Co.,Ltd., Beijing, 101400, China

Keywords: Beef Upper Brain, Correlation Analysis, Principal Component Analysis.

Abstract: In order to establish the evaluation standard of beef upper brain quality, correlation and principal component

analysis were used to comprehensively evaluate the quality of different brands of beef upper brain.

Conventional methods were used to determine the color difference (Lab) value, colonies number, pH value,

volatile base nitrogen (TVB-N), sensory quality and texture quality indicators of three commercially available

beef upper brain. The results showed that the correlation analysis showed that the colonies number and

chewiness were two key factors that affected the storage quality of cattle upper brain. Principal component

analysis extracted three principal component factors, the first principal component variance contribution was

40.978%, the second principal component variance contribution was 32.993%, the third principal component

variance contribution was 11.893%, and the cumulative variance contribution was 85.863%. The original

complex comprehensive evaluation model of cattle upper brain quality can be replaced by these three principal

components. Using this model to rank the comprehensive meat quality score is: B>A>C. It was expected to

provide technical reference and theoretical basis for follow-up researchers to evaluate the quality of beef upper

brain.

1 INTRODUTION

Beef had many advantages such as high protein, low

fat, rich in minerals, vitamins and a variety of amino

acids. It was an indispensable meat food for ordinary

people on the dinner table (Bai 2020). According to

different processing methods of beef, it can be

divided into three categories (hot fresh beef, cold

fresh beef, frozen beef)0. Cold fresh beef referred to

a low-temperature fresh meat product that rapidly

cools and deacidifies the carcass after slaughtered in

strict compliance with veterinary inspection and

quarantine regulations, and kept it in the range of

0~4℃ during the later processing, transportation and

sales0. From slaughter, processing to marketing, cold

fresh beef had undergone a process of stiffness, de-

rigorization and maturation. During storage, the

protein was normally degraded, Under the strict

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https://orcid.org/0000-0002-5258-7777

control of the food quality and safety management

system, the meat quality, color and elasticity of cold

fresh meat had been improved, and it had become

tender and juicy and has a good taste. At the same

time, the decrease of pH caused lactic acid to inhibit

the reproduction of microorganisms, made cold

Colonies numbers meat safer when eating, and also

prolonged the freshness period0. Cold fresh beef was

first popular in developed countries in Europe and

America, accounting for about 90% of the meat

market circulation. At present, developed countries

such as Europe, America and Japan had more

advanced technology for cold fresh beef processing

technology and circulation technology, and the

quality control and tracking system were also

relatively complete to ensure that the cold beef that

people buy for consumption was safe and reliable0.

At present, there were many indicators for

evaluating beef quality, but there were few studies

https://orcid.org/0000-0001-7760-8282

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Hua, W., Jiang, X., Yan, L., Hou, X. and Liu, H.

Evaluation of the Quality of Different Brands of Beef Upper Brain based on Correlation and Principal Component Analysis.

DOI: 10.5220/0011164000003444

In Proceedings of the 2nd Conference on Artiﬁcial Intelligence and Healthcare (CAIH 2021), pages 52-57

ISBN: 978-989-758-594-4

and evaluations on the quality of beef upper brain, the

weights of various traits were difficult to

scientifically determine, and a comprehensive

evaluation method for beef upper brain quality had

not been established. The principal component

analysis (PCA) was to recombine many indicators

with certain correlations (such as P indicators) into a

new set of independent comprehensive indicators to

replace the original indicators0, this evaluation

method had been widely used in the field of food

quality evaluation. The more the number of indicators

measured by this method and the higher the

correlation between the indicators, the less the

number of corresponding principal components.

Comprehensive evaluation using principal

component analysis had the advantages of

comprehensiveness, comparability, rationality,

feasibility, etc0. Therefore, this article selected three

commercially available beef upper brains to

determine the sensory quality, color difference (Lab)

value, colonies number, pH value, total-volatile basic

nitrogen (TVB-N) and texture quality indicators,

established a principal component analysis evaluation

model for beef upper brain meat quality, extracted

key influencing factors, and through sensory

evaluation to verify the model, in order to provide

technical reference for the evaluation methods and

standards of beef upper brain quality.

2 MATERIALS AND METHODS

2.1 Materials and Instruments

Three kinds of cold fresh beef upper brain samples

were purchased from Hema Xiansheng Supermarket

in Changping District, Beijing, stored in ice packs,

transported back to the laboratory within 30 minutes,

and kept in a refrigerator at 4 ℃ for later use; Plate

Counting Agar Beijing Luqiao Technology Co., Ltd.;

Hydrochloric Acid Beijing Chemical Plant;

Magnesium Oxide, Methyl Red, and

Bromomethylphenol Green Sinopharm Group

Chemical Reagent Co., Ltd.

YXQ-75G Vertical Pressure Steam Sterilizer

Shanghai Boxun Industrial Co., Ltd.; Texture

Analyzer US BROOKFIELD; Testo 205 pH

measuring instrument Testo International Trade

Shanghai Co., Ltd.; CheckMate3 Headspace

Analyzer AMETEK Trading Shanghai Co., Ltd.;

CM-700d Spectrophotometer Shanghai Gaozhi

Precision Instrument Co., Ltd.

2.2 Experimental Method

2.2.1 Sensory Quality Measurement

With reference to the method of Yang Wenting0and

others, this experiment selected four indicators of

color, smell, viscosity, and broth after boiling as the

sensory evaluation indicators of cold fresh beef upper

brain. The total evaluation was divided into the

average value of the sum of the four indicators. The

sensory quality evaluation was performed using a 5-

point system, and the evaluation criteria were shown

in Table 1. Evaluation by 10 professionals.

Table 1: Sensory Evaluation Table.

Evaluation

index

Sensory level

5 points (very good) 4 points (good) 3 points (average) 2 points (bad) 1 point (very bad)

Color

Very bright red and

shiny

Bright red, shiny

Color dark red,

matt

Grayish or pale

color, dull

Dark brown color,

unacceptable

Smell

Has the peculiar smell of

fresh lamb without any

peculiar smell

With the smell of

lamb, no peculiar

smell

Slightly ammonia

smell

Smell of ammonia

Smell of corruption,

unacceptable

viscosity

Moist surface, not sticky

to the touch

The surface is

slightly dry, not

sticky to the touch

Dry surface, moist

cut surface

Dry surface,

slightly sticky

hands

Extremely dry surface,

sticky

Broth after

boiling

The broth is transparent

and clear, and the fat is

agglomerated on the

surface with a fragrance

The broth is more

fragrant and the fat

accumulates on the

surface

The broth has no

fragrance

The broth is

muddy and smelly

Broth is discolored

and has a strong

peculiar smell

Evaluation of the Quality of Different Brands of Beef Upper Brain based on Correlation and Principal Component Analysis

2.2.2 Determination of Flesh Color

Expose the sample to 4 ℃ air to develop color for 30

minutes before measurement0, used a calibrated

portable colorimeter to measure the L, a, and b values

of the sample every day, each box of beef upper brain

was randomly measured at 6 sites, and each site was

measured 3 times in parallel, and the average value

was taken.

2.2.3 The Determination of the Colonies

Number Was Determined

According to the method of GB 4789.2 - 2016

"Determination of the colonies number"0.

2.2.4 Determination of pH Value

Used a hand-held pH meter to directly insert the

bovine upper brain sample to determine the pH value

of the product. Each box of samples was randomly

measured at 3 points and the average value was taken.

2.2.5 The Determination of TVB-N Was

Determined

According to GB/T 2009.228 - 2016, the first method

of semi-trace nitrogen determination0.

2.2.6 Determination of Texture Index

The hardness, elasticity and chewiness of fresh beef

upper brain were measured using the texture analyzer

TPA mode. The measurement mode was0: the probe

model was T/46, the moving distance was 5 mm, the

holding time was 2 s, the trigger load was 2 g, the test

speed was 2 mm/s, the recovery time was 5 s, and the

cycle was 2 times. Randomly selected the area and

take the average of 3 measurements.

2.3 Data Processing

Used SPSS 19.0 software (SPSS company) to

perform correlation and principal component analysis

on the experimental data, and the data were all

expressed as the mean±standard deviation of the 3

parallel results.

3 RESULTS AND ANALYSIS

3.1 Correlation Analysis of the Effects

of Different Brands of Bovine

Supramencephalon on Sensory

Quality

Table 2: Pearson correlation analysis of the influence of different brands of cattle on the quality of the upper brain.

L a b pH

TVBN

value

Colonies

number

Hardness Elasticity Chewiness Senses

L 1

a -0.02 1

b 0.278 0.197 1

pH -0.359 -0.5 -0.154 1

TVBN

value

-0.468 -0.182 -0.309 0.583 1

Colonies

numbers

-.687* -0.236 -0.077 0.552 0.826** 1

Hardness -0.187 0.52 0.018 -0.749* -0.096 0.073 1

Elasticity -0.636 0.174 0.015 0.143 0.616 0.823** 0.531 1

Chewiness -0.419 0.445 -0.23 -0.417 0.248 0.383 0.894** 0.766* 1

Senses 0.254 -0.189 -0.204 -0.148 -0.586 -0.69 -0.204 -0.571 -0.336 1

Note: *. Significantly correlated at the 0.05 level (two-sided). **. Significantly correlated at the .01 level (bilateral).

Used SPSS software to analyze the correlation

between the indicators of different brands of cattle on

the brain, The colonies number was significantly

negatively correlated with sensory scores and L

values, and the correlation coefficients were -0.690

and -687 (p < 0.05), which were significantly positive

with the TVB-N value and elasticity, the correlation

coefficients were 0.826, 0.823 (p < 0.01), chewiness

and hardness had a very significant positive

correlation, with a correlation coefficient of 0.894 (p

< 0.01), and a significant positive correlation with

elasticity, with a correlation coefficient of 0.766 (p <

0.05), and a significant positive correlation between

hardness and pH, and its correlation coefficient was -

0.749 (p<0.05) It showed that colonies number and

chewiness were two key factors that affected the

storage quality of cattle upper brain. It can be seen

that the correlation analysis between the indicators

CAIH 2021 - Conference on Artiﬁcial Intelligence and Healthcare

showed that the information reflected by the

measurement indicators overlaps. Therefore, it was

necessary to perform principal component analysis

on each quality indicator, which helped to improve

the efficiency and accuracy of the comprehensive

evaluation.

3.2 Principal Component Analysis of

Quality Traits of Beef Upper Brain

of Different Brands

The sensory, physical and chemical microbial

indicators of three different brands of beef upper

brain samples were measured, and SPSS software

was used for principal component analysis. The

number of principal components was determined

according to the principle that the cumulative

variance contribution rate reaches more than 85% and

the eigenvalue was greater than 1

. From Figure 1,

Figure 2, and Table 3, we can seen that the first

principal component was 3.688, the variance

contribution was 40.978%; the second principal

component was 2.969, the variance contribution was

32.993%; the third principal component was 1.07,

and the variance contribution was 11.893%. The

cumulative contribution rate of the third principal

component had exceeded 85%. Therefore, it was

feasible to use the first three principal components to

evaluate the quality of beef upper brain of different

brands. This showed that the first three principal

components were sufficient to describe each index to

represent the quality of beef brain meat, and the

variance contribution rate of the principal

components was used as the weighting coefficient to

obtain the comprehensive evaluation function

. The

comprehensive evaluation function of beef upper

brain quality was obtained: K = 40.978 K

+ 32.993

+ 11.893 K

, (K was the number of evaluation

functions, K

was the main component 1, K

was the

main component 2, and K

was the main component

3).

Table 3: Correlation matrix eigenvalues and cumulative contribution rate.

Ingredients

Initial eigenvalue Extract the sum of squares and load

Eigenvalues Variance%

Cumulative

contribution Rate

Total Variance%

Cumulative

contribution Rate

1 3.688 40.978 40.978 3.688 40.978 40.978

2 2.969 32.993 73.971 2.969 32.993 73.971

3 1.07 11.893 85.863 1.07 11.893 85.863

Note: Extraction method-principal component analysis

Figure 1: Component Plot.

Evaluation of the Quality of Different Brands of Beef Upper Brain based on Correlation and Principal Component Analysis

Figure 2: Scree Plot.

Table 4: Component loading matrix after principal

component analysis rotation.

Serial

number

variable

Ingredient

1 2 3

X1 L -0.737 0.126 0.287

X2 a 0.093 0.729 0.195

X3 b -0.174 0.178 0.888

X4 pH 0.279 -0.912 0.045

TVBN

value

0.781 -0.442 -0.007

Colonies

number

0.917 -0.342 0.125

X7 Hardness 0.394 0.885 -0.119

X8 Elasticity 0.935 0.175 0.074

X9 Chewiness 0.686 0.659 -0.253

X10 Senses -0.694 -0.007 -0.544

Note: Extraction method-principal component analysis

method. a. Three components have been extracted.

It can be seen from Table 4 that three principal

components were obtained through principal

component analysis, and the expressions of each

principal component were:

= - 0.737 X

+ 0.093 X

- 0.174 X

+ 0.297 X

+ 0.781 X

+ 0.917 X

+ 0.394 X

+ 0.935 X

0.686X

- 0.694 X

；

= 0.126 X

+ 0.729 X

+ 0.178 X

- 0.912 X

0.442 X

+ 0.342 X

+ 0.885 X

+ 0.175 X

+ 0.659

- 0.007 X

；

Z3 = 0.287 X

+ 0.195 X

+ 0.888 X

+ 0.045 X

- 0.007 X

+ 0.125 X

- 0.119 X

+ 0.074 X

- 0.253

- 0.544 X

；

The magnitude of the principal component factor

load represented the contribution rate of the original

variable in the comprehensive variable that was

formed after dimensionality reduction. Therefore, the

factor loading diagram can be used to determine the

main original variables closely related to the principal

components0. According to the absolute value of

each index load, it can be seen that Z

mainly

represents elasticity, colonies number, TVB-N value,

L value, Z

mainly represents pH hardness and a

value, and Z

mainly represents b value.

Table 5: Comprehensive scores and rankings of physical

and chemical indicators of beef upper brain of different

brands.

Brand K Sort

A -0.133 2

B 1.06 1

C -0.933 3

The comprehensive score and ranking of different

brands of beef upper brains were calculated. The

results were shown in Table 5. The higher the score,

the better the quality of beef upper brain. The order

of quality of different brands was as follows: B> A>

CAIH 2021 - Conference on Artiﬁcial Intelligence and Healthcare

4 CONCLUSIONS

In this study, 10 quality indicators of 3 brands on the

market were measured. Correlation analysis showed

that the colonies number was significantly negatively

correlated with sensory score and L value, and was

extremely significantly positively correlated with

TVB-N value and elasticity; Chewiness had a very

significant positive correlation with hardness, a

significant positive correlation with elasticity, and a

significant positive correlation between hardness and

pH. Further, through principal component analysis,

the dimensionality reduction analysis of 10 indicators

can be used to extract 3 principal components. The

first principal component selected elasticity, colonies

number, TVB-N value and L value; the second

principal component selected pH, hardness and a

value, and the third principal component selected b

value. The cumulative variance contribution rate

reached 85.863%, which can represent large some

indicators, and built an evaluation model: K = 40.978

+ 32.993 K

+ 11.893 K

. Using this model to rank

the comprehensive quality of the selected three

different brands of beef upper brains, the results

showed that the best quality variety was brand B.

Based on correlation analysis and principal

component analysis, the comprehensive evaluation of

different brands of beef upper brain quality can

provide theoretical guidance for the evaluation of

beef upper brain quality.

REFERENCES

Bai, J, Wang, H. (2020) Study on the preparation of onion

extract/PVA film and its effect on the quality of chilled

meat. Packaging Engineering, 41(05): 103-108.

Guo, L. Cai, LS. Lin, Z. et al. (2010) Construction of white

tea aroma quality evaluation model based on principal

component analysis. Journal of Tropical Crops, 31(9):

1606-1610.

Liang, L. Du, ARN. Ma, Tao. et al. (2018) Optimization of

pretreatment technology of low-salt cowpea kimchi and

analysis of storage characteristics. Food science,

39(06): 246-251.

Liu, JM. Lin, LJ. Liu, YJ. et al. (2021) Comprehensive

Evaluation of Dry Quality of Different Varieties of

Longan Based on Principal Component Analysis.

Preservation and processing, 21(05) : 127-133.

Liu, Q. (2019) Study on the fresh-keeping effect of cumin,

prickly ashand cinnamon essential oil soncold-fresh

lamb. Gansu Agricultural University.

Mao, Yt. (2017) Research on Governance of Pig Supply

Chain from the Perspective of Quality. Southwest Jiao

tong University.

National Health and Family Planning Commission of the

People's Republic of China. GB4789.2-2010, (2017)

Determination of the total number of bacterial colonies

in food microbiology examination. Beijing: China

Standards Press.

National Health and Family Planning Commission of the

People's Republic of China. GB5009.228-2016, (2017)

Determination of Volatile Basic Nitrogen in Food.

Beijing: China Standard Press.

Wang, H. Chen, L. Chen, K. et al. (2007) Multi-index

comprehensive evaluation method and choice of weight

coefficient. Journal of Guangdong College of

Pharmacy, 23(5): 583-589.

Wang, JJ. Qiao, Q. Ma, LE. et al. (2004) The metabolic

syndrome defined by factor analysis and incident type

2 diabetes in a Chinese population with high

postprandial glucose. Diabe-tes Care, 27:2429-2437.

Wang, P. He, Bb. Li, Zc. et al. (2019) The effect of

biological preservatives on the preservation of chilled

meat. Chinese Journal of Food Science and

Technology, 19(11) : 199-207.

Wu, D. Li, X. (2019) Microbial Contamination and

Preventive Measures in the Process of Chilled Fresh

Meat Preservation. Food Industry Science and

Technology, 40(21): 320-325.

Xu, H. Hao, LZ. Liu, SJ. et al. (2020) Comparison and

analysis of nutritional quality of eliminated female yak

meat in different regions. Food Industry Technology,

41(09): 232-237.

Yang, WT. Li, JL. Kong, F. et al. (2017) Construction of

Frozen Tan Mutton Quality Evaluation Model Based on

Principal Component Analysis. Food Industry

Technology, 38(09): 300-303+313.

Zhao, J. Zhang, T. Liu, Yf. et al. (2018) The effect of long

freezing time on beef quality. Food and Fermentation

Industry, 44(11): 200-209; 215.

Evaluation of the Quality of Different Brands of Beef Upper Brain based on Correlation and Principal Component Analysis