CN111982975A - Method for noninvasive evaluation of donkey whey protein anti-aging performance by using odor fingerprint spectrum - Google Patents

Abstract

The invention belongs to the technical field of rapid evaluation of functional food efficacy, and discloses a method for non-invasively evaluating the anti-aging performance of donkey whey protein by using an odor fingerprint. The method comprises the following steps: (1) respectively taking donkey whey protein to intervene mouse feces in different time periods in containers, sealing and standing to obtain headspace gas of volatile odor substances; (2) connecting the electronic nose sensor array with the headspace body contact, generate sensor response signals, and obtain the odor fingerprints of mouse feces of donkey whey protein intervention at different times; (3) extract characteristic data from the odor fingerprints, and treat donkey whey protein intervention at different times and the feces of mice in the control group. Qualitative classification was performed, and multiple linear regression analysis was used to establish the correlation between odor fingerprints and mouse age, and a model for predicting mouse age was established. This method uses odor fingerprints to quickly discriminate different stages of donkey whey protein intervention, and can non-invasively evaluate the antioxidant properties of donkey whey protein.

Description

A method for non-invasive evaluation of the anti-aging properties of donkey whey protein using odor fingerprints method

technical field

The invention relates to the technical field of fast evaluation of functional food efficacy, relates to a method for evaluating food functionality based on fecal odor, and in particular relates to a method for non-invasively evaluating the anti-aging performance of donkey whey protein using odor fingerprints.

Background technique

Donkey milk is rich in protein, unsaturated fatty acids, high in linoleic acid, low in cholesterol, and contains more vitamin C and trace elements. Donkey milk belongs to whey protein milk. Many studies have shown that various biologically active components of whey protein have antioxidant activities, such as the reaction product of β-lactoglobulin and some carbohydrates, which has strong scavenging freedom. base, antioxidant activity. In addition, lactalbumin and lactoferrin in whey protein are rich in cystine residues, which can enter the cell membrane through the digestive tract and be reduced to cysteine, the raw material of GSH, to maintain the level of GSH in cells and tissues, thereby enhancing the body's resistance to Oxidative capacity, whey protein is a soluble protein, which is more easily digested and absorbed by the human body. At present, the research on the functional properties of whey protein is mainly carried out through the establishment of animal model experiments and human clinical trials. However, this research method relies heavily on experimental animals, resulting in an increase in the use of experimental animals year by year. Physiological, biochemical and morphological indicators require the execution of experimental animals, which runs counter to animal protectionism. In addition, the analysis process is cumbersome and consumes a lot of human, material and financial resources. Therefore, it is of great scientific significance to implement rapid and non-invasive evaluation of experimental animals in the study of functional properties of whey protein.

Electronic noses use the response of gas sensor arrays to volatile odor substances to identify simple and complex odor information, and have been widely used in food and agricultural product quality testing. Feces are one of the main pathways for the output of the body's overall metabolic end products, and the changes of its metabolites can not only reflect the overall metabolic characteristics of the body, but also the external manifestations of dietary differences and nutritional regulation. However, the current research based on the detection of volatile components in metabolites by electronic nose mainly includes in vivo efficacy evaluation of food functional components.

SUMMARY OF THE INVENTION

The object of the present invention is to provide a method for non-invasively evaluating the anti-aging properties of donkey whey protein using odor fingerprints in order to overcome the deficiencies of the above-mentioned background technology. This method uses the odor fingerprint to quickly discriminate different stages of donkey whey protein intervention, and can realize the rapid determination and prediction of the age of donkey whey protein intervention mice.

In order to achieve the object of the present invention, the present invention utilizes the method for non-invasive evaluation of donkey whey protein anti-aging performance by odor fingerprint, comprising the following steps:

(1) respectively taking donkey whey protein to intervene mouse feces in different time periods in containers, sealing and standing to obtain headspace gas of volatile odor substances;

(2) contacting the electronic nose sensor array with headspace gas to generate sensor response signals to obtain the odor fingerprints of mouse feces of donkey whey protein intervention at different times;

(3) Extract characteristic data from odor fingerprints, use pattern recognition method to qualitatively classify donkey whey protein intervention at different times and the feces of mice in the control group, and use multiple linear regression analysis to establish the relationship between odor fingerprints and mouse age and establish a model for predicting the age of mice.

Further, in some embodiments of the present invention, 100-400 mg/(Kg·d) donkey whey protein is taken in the step (1).

Further, in some embodiments of the present invention, the amount of mouse feces in the step (1) is 1-3 grains.

Further, in some embodiments of the present invention, the time of sealing and standing in the step (1) is 5-10 min.

Further, in some embodiments of the present invention, the volume of the headspace gas in the step (1) is 150-500 mL.

Further, in some embodiments of the present invention, in the step (2), when the electronic nose sensor array is in contact with the headspace gas, the flow rate of the carrier gas is 200-400 mL/min.

Further, in some embodiments of the present invention, the pattern recognition method in the step (3) is canonical discriminant analysis and multiple linear regression analysis.

Compared with the prior art, the method provided by the present invention can non-invasively evaluate the antioxidant properties of donkey whey protein, fills the research gap of odor fingerprint analysis in food functional evaluation, broadens the method for evaluating the effect of animal experiments, and avoids the The experimental animals were sacrificed. Moreover, the method of the present invention does not need pretreatment steps, is simple in operation, has high detection efficiency and is sensitive, can realize rapid determination and prediction of the age of mice with donkey whey protein intervention, and is suitable as a real-time and rapid method for food functional evaluation.

Description of drawings

Figure 1 is a radar map of mouse feces smell at different times of donkey whey protein intervention;

Fig. 2 is the canonical discriminant analysis of the smell of mouse feces after 7 weeks between the donkey whey protein groups of different concentrations of the present invention and the control group, wherein, the low concentration of gavage administration is 100 mg/(kg·d) each, and the middle concentration is 200mg/(kg·d) for each, high concentration is 400mg/(kg·d) for each;

Fig. 3 is the canonical discriminant analysis two-dimensional score chart of the donkey whey protein of the present invention intervening the smell of mouse feces at different times.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail below with reference to the accompanying drawings and embodiments. Additional aspects and advantages of the present invention will be set forth, in part, from the following description, and in part will be apparent from the following description, or may be learned by practice of the invention. It should be understood that the following description is only used to explain the present invention, but not to limit the present invention.

As used herein, the terms "comprising," "including," "having," "containing," or any other variation thereof, are intended to cover non-exclusive inclusion. For example, a composition, step, method, article or device comprising the listed elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such composition, step, method, article or device elements.

When an amount, concentration, or other value or parameter is expressed as a range, preferred range, or a range bounded by a series of upper preferred values and lower preferred values, this should be understood as specifically disclosing any upper range limit or preferred value and any lower range limit or all ranges formed by any pairing of preferred values, whether or not the ranges are individually disclosed. For example, when a range of "1 to 5" is disclosed, the described range should be construed to include the ranges "1 to 4," "1 to 3," "1 to 2," "1 to 2, and 4 to 5." , "1 to 3 and 5", etc. When numerical ranges are described herein, unless stated otherwise, the ranges are intended to include the endpoints and all integers and fractions within the range.

Furthermore, descriptions of the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" and the like described below mean specific features, structures, and descriptions in connection with the embodiment or example. , material or feature is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the technical features involved in the various embodiments of the present invention can be combined with each other as long as they do not conflict with each other.

Example 1

A method for non-invasively evaluating the anti-aging properties of donkey whey protein using odor fingerprints, comprising the following steps:

(1) Take 100-400 mg/(Kg·d) donkey whey protein to intervene 1-3 mice feces in different time periods respectively, put them in a 150-500 mL beaker, seal and let them stand for 5-10 minutes to obtain the top of volatile odor substances. air;

(2) Contact the electronic nose sensor array with the sample headspace gas under the condition that the carrier gas flow rate is 200-400 mL/min, to generate sensor response signals, and obtain the odor fingerprints of mouse feces of donkey whey protein intervention at different times;

(3) Extract characteristic data from odor fingerprints, use pattern recognition method to qualitatively classify donkey whey protein intervention at different times and the feces of mice in the control group, and use multiple linear regression analysis to establish the relationship between odor fingerprints and mouse age and establish a model for predicting the age of mice.

Example 2

The treatment method of donkey whey protein interfering with mouse feces and the data processing and modeling method of odor fingerprint. An electronic nose based on a metal sensor odor sensor array is used, and the sensor array consists of 10 sensors. The names and performances of each sensor are shown in Table 1.

Table 1 Odor information and its corresponding sensors and sensitive substances

The function of these sensors is to convert donkey whey protein, which interferes with the action of different odorants in mouse feces, on its surface into a measurable electrical signal.

Mice were intervened with 100-400 mg/(Kg·d) donkey whey protein, feces were collected in different time periods (0, 1, 3, 5, and 7 weeks) of intervention, and one fecal sample of donkey whey protein interfering mice was taken. In a 150mL beaker, seal and let stand for 10min. In the modeling set and the validation set, 40 parallel samples were prepared for each time period of donkey whey protein interference mouse fecal samples. The electronic nose detection time was set to 60s, the sampling interval was 80s, and the steady-state response value of the sensor at the 59th s was selected for analysis.

As shown in Fig. 1, donkey whey protein interferes with mouse feces in different time periods, and the odor fingerprint information of sensors S1, S2, S3, S4, S5, S8, S9 and S10 has little difference; Fingerprint information is quite different.

Accompanying drawing 2 is the canonical discriminant analysis of mouse feces odor after 7 weeks of intervention of donkey whey protein and control group. Using the electronic nose odor of feces, canonical discriminant analysis can basically identify the odor of feces of different intervention mice, which provides a basis for the functional evaluation of food in vivo based on odor information.

Figure 3 is a canonical discriminant analysis two-dimensional score chart of donkey whey protein intervening mouse feces smell at different times. The contribution rates of the first two principal components are 73.39% and 17.41%, respectively, and their total contribution rate reaches 90.80%. It can be seen from Figure 3 that the fecal samples of mice with donkey whey protein intervention for 0, 1, 3, 5, and 7 weeks are regularly distributed, that is, the longer the intervention time, the smaller the first principal component score. Then discriminant analysis could well distinguish donkey whey protein intervention time.

Example 3

On the basis of canonical discriminant analysis, multiple linear regression analysis was further used to establish the correlation between odor information and mouse age. The odor information of mouse feces at five intervention times (weeks 0, 1, 3, 5, and 7) was used as the modeling set, and 12.5% of the data was used as the prediction set. Using the electronic nose odor information as the parameter of multiple linear regression analysis, a regression model was established to predict the age of mice.

Multiple linear regression analysis was used to obtain the prediction model of mouse week age:

Age of mice = -35.986S ₁ +1.234S ₂ -21.064S ₃ -1.664S ₄ +0.131S ₅ -1.879S ₆ +1.384S ₇ +4.309S ₈ -7.928S ₉ -7.657S ₁₀ +71.717

In the above formula, S1 to S10 are the odors such as aromatic components, alkanes and organic sulfides in the odor fingerprint information.

The coefficient of determination of the prediction model R ² =0.8901, indicating that the prediction model established by the multiple linear regression analysis is effective.

Table 2 shows the prediction results of the prediction model established by the multiple linear regression analysis for the modeling set samples and the prediction set samples. The error range of the prediction results is allowed to fluctuate between ±1 (large differences in animal experiments), and the prediction accuracy is 100%. . It can be seen from the model prediction results that the relationship between the odor fingerprint information and the age of the mice can be established, indicating that the present invention is feasible for the prediction of the age of the mice with donkey whey protein intervention.

Table 2 The prediction results of the multiple linear regression analysis model for the modeling set samples and the prediction set samples

Those skilled in the art can easily understand that the above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention, etc., All should be included within the protection scope of the present invention.

Claims (7)

1. a method for non-invasive evaluation of donkey whey protein anti-aging performance utilizing smell fingerprints, is characterized in that, described method comprises the following steps: (1) respectively taking donkey whey protein to intervene mouse feces in different time periods in containers, sealing and standing to obtain headspace gas of volatile odor substances; (2) Contact the electronic nose sensor array with headspace gas to generate sensor response signals, and obtain the odor fingerprints of mouse feces of donkey whey protein intervention at different times; (3) Extract characteristic data from odor fingerprints, use pattern recognition method to qualitatively classify donkey whey protein intervention at different times and the feces of mice in the control group, and use multiple linear regression analysis to establish the relationship between odor fingerprints and mouse age and establish a model for predicting the age of mice. 2. The method for non-invasively evaluating the anti-aging performance of donkey whey protein by using odor fingerprint according to claim 1, it is characterized in that, in described step (1), take 100～400mg/(Kg·d) donkey whey protein . 3 . The method for non-invasively evaluating the anti-aging properties of donkey whey protein by using odor fingerprint according to claim 1 , wherein in the step (1), the amount of mouse feces is 1-3 grains. 4 . 4 . The method for non-invasively evaluating the anti-aging performance of donkey whey protein by using odor fingerprint according to claim 1 , wherein, in the step (1), the time for sealing and standing still is 5-10 min. 5 . 5 . The method for non-invasively evaluating the anti-aging properties of donkey whey protein by using odor fingerprint according to claim 1 , wherein the volume of the headspace gas in the step (1) is 150-500 mL. 6 . 6. the method for non-invasive evaluation of donkey whey protein anti-aging performance using odor fingerprint according to claim 1, is characterized in that, when electronic nose sensor array contacts with headspace gas in described step (2), the flow rate of carrier gas is 200～400mL/min. 7. The method for non-invasively evaluating the anti-aging performance of donkey whey protein using odor fingerprint according to claim 1, wherein the pattern recognition method in the step (3) is canonical discriminant analysis and multiple linear regression analysis.

Images