A mint essential oil compounding method based on taste fingerprint

Abstract

The application discloses a mint essential oil compounding method based on taste fingerprint. The method comprises the following steps: detecting pre-processed mint essential oil samples by using an electronic tongue, collecting umami, richness, saltiness, sourness, bitterness and astringency and other taste information; obtaining the closest target raw material combination by a numerical optimization method, that is, obtaining the proportion coefficient value of the sample; and obtaining the compounded mint essential oil according to the proportion coefficient. The application also provides a quality verification method for the compounded mint oil, which comprises the following steps: after obtaining the compounded mint oil, detecting the mint oil by using the electronic tongue, obtaining a model for quantitatively describing the content of essential oil characteristic compound components by using a multiple linear regression method, and verifying the quality of the compounded mint oil. The compounding method disclosed by the application can realize the compounding of high-quality mint essential oil, and has the advantages of simple and quick method, low cost, short detection time and low cost compared with traditional methods.

Description

Technical Field

[0001] This invention belongs to the field of essential oil blending, specifically relating to a method for blending peppermint essential oils based on taste fingerprinting. Background Technology

[0002] Peppermint essential oils are widely used in the food and cosmetics industries, with huge demand. Essential oils from different origins, harvesting cycles, and extraction processes exhibit significant differences in chemical composition, leading to fluctuations in quality and affecting their application in final products. Therefore, to obtain essential oils of stable quality, further refining is often necessary. This requires analysis and comparison of the essential oil fingerprint spectrum using testing equipment such as GC-MS, re-blending, and finally, sensory evaluation by perfumers to ensure quality. GC-MS can identify adulterated essential oils (Zhou Fangfang, Quality Identification and Analysis of Natural Peppermint Oil, Ginger Oil, and Olive Oil, Master's Thesis, Anhui Agricultural University), and can also identify the quality of essential oils at different growth stages (Hao Ruifen, Content and Quality Evaluation of Spearmint Essential Oil at Different Growth Stages, China Food Additives, 2017(09)). However, the versatility of GC-MS technology is limited by different testing platforms (Shimadzu, Agilent, or other manufacturers' equipment), and qualitative and quantitative studies of samples are time-consuming and costly.

[0003] Human sensory evaluation is subjective and highly dependent on perfumers. Electronic tongue technology, as a novel analytical and detection method, has been widely applied in industries such as food. The electronic tongue contacts the sample through a sensor array, collecting characteristic electrical signals. Using data analysis and processing techniques, it obtains the sensory characteristics of the sample, offering advantages such as simple operation, rapid analysis, and high efficiency. Existing patented technologies utilize electronic tongues to identify food quality, primarily based on taste characteristic data to establish identification models, such as identifying the quality of crayfish (CN202010694862.4), ham (CN202010529264.1), and citrus fruits (CN201510227723.X). It can even predict key quality parameters of products, such as the total phenols, amino acids, catechins, and caffeine content in tea (CN201910286810.0). The applicant has also developed a method using electronic tongue technology to identify the processing method of tea seed oil and predict acid value and peroxide value (CN202010618720.X).

[0004] In existing essential oil blending techniques, testing methods are time-consuming and costly, and sensory evaluation of products relies heavily on perfumers. Although research and application of electronic tongue technology have made some progress, it remains a gap in guiding the blending of peppermint essential oils. The industry still urgently needs convenient and efficient blending methods to replace traditional models. Summary of the Invention

[0005] To address the shortcomings and deficiencies of existing technologies, this invention provides a convenient and efficient method for blending essential oils.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] A method for blending peppermint essential oils based on taste fingerprinting includes the following steps:

[0008] 1) Electronic tongue detection of peppermint essential oil samples: The taste sensor array is brought into contact with the pre-treated peppermint essential oil samples to collect taste information, including umami, richness, saltiness, sourness, bitterness, and astringency; the samples include target Y and a series of raw materials A, B, C, D...X, etc.

[0009] 2) Optimize the essential oil ratio based on taste characteristics, combining raw materials in the optimal proportions:

[0010]

[0011] Y1 to Y6 represent the six taste characteristics of the target sample Y. Similarly, A1 to A6 represent the six taste characteristics of the raw material A, and so on. 1 to 6 represent umami, richness, saltiness, sourness, bitterness, and astringency, respectively. a, b, c, d...x represent the proportioning coefficients.

[0012] The raw material combination that is closest to the target Y is obtained by numerical optimization (i.e., solving a multivariate linear equation). This means obtaining the proportion coefficients a, b, c, d, ... x of samples A, B, C, D...X. Based on these proportion coefficients, the blended peppermint essential oil can be obtained.

[0013] 3) Quality verification of compound peppermint oil:

[0014] ① Establishment of a quantitative model for compound content and taste information: For a series of peppermint essential oil samples (more than 30 samples), the content of representative compounds (α-pinene, eucalyptol, menthone, menthol, and menthyl acetate) was quantitatively obtained by GC-MS. At the same time, the values ​​of various taste information of electronic tongue were detected. A model describing the content of representative compounds with taste information was obtained by fitting multiple linear regression.

[0015] ② Using electronic tongue detection, a model was obtained to quantitatively describe the content of characteristic compounds in essential oils (area normalization method) through multiple linear regression fitting. The contents of representative compounds α-pinene, eucalyptol, menthone, menthol, and menthyl acetate in the front, middle, and back segments of peppermint essential oils were obtained, thereby confirming the quality of the compound peppermint oil.

[0016] Table 1. Quantitative Model of Representative Compound Content and Taste Sensory Perception

[0017]

[0018] Umami, Bitterness, Sourness, and Astringency are the response values ​​of umami, bitterness, sourness, and astringency in the taste signal, respectively. Other taste information with less correlation was not used for modeling.

[0019] In the above-mentioned method for evaluating the quality of peppermint essential oils based on taste fingerprinting,

[0020] Step 1) The pretreated peppermint essential oil sample is pretreated as follows: peppermint essential oil and water are thoroughly mixed and cooled to room temperature. Then, a reference solution is added, and after centrifugation, the supernatant is collected. The resulting oil sample is then refrigerated for later use. The mixing of peppermint essential oil and water is preferably carried out at 40–50°C. The mass ratio of peppermint essential oil, water, and reference solution is 1–2:8–10:5–10. The reference solution is a mixture of KCl and tartaric acid in a mass ratio of 45–55:1, preferably 50:1. The centrifugation speed is 3000–10000 rpm, and the centrifugation time is 5–15 min.

[0021] The peppermint essential oils mentioned in step 1) can specifically be peppermint oil, peppermint oil, lemon mint oil, spearmint oil, etc.

[0022] The specific steps of step 1) are as follows: the activated taste sensor array is brought into contact with the sample to be tested, the potential signal between different taste substances in the sample and the sensor membrane is extracted, and then converted by Weber-Fechner law to obtain taste information.

[0023] The electronic tongue system to which the taste sensor array in step 1) belongs is the SA402B taste analysis system from INSENT Corporation of Japan. It contains 5 sensors, namely umami (AAE), saltiness (CTO), sourness (CAO), bitterness (C0O), and astringency (AE1). It can obtain umami and richness, saltiness, sourness, bitterness and aftertaste (Aftertaste-B), and astringency and aftertaste (Aftertaste-A) respectively.

[0024] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0025] This invention provides a method for blending peppermint essential oils based on taste fingerprinting. This method uses electronic tongue detection to achieve high-quality blending of peppermint essential oils. The method is simple, fast, and low-cost. Compared with traditional methods, it has advantages such as short detection time and low cost. Specifically, this method establishes a multiple linear regression model using multiple taste information to obtain the content value. After the model is established, only one electronic tongue detection is needed. It does not require the establishment of standard curves for each compound like GC-MS to obtain the content of the compound. The detection time is short and the cost is low (no need to buy standard products). Attached Figure Description

[0026] Figure 1 This outlines the methods, steps, and thought process for blending peppermint essential oils based on taste fingerprinting.

[0027] Figure 2 These are GC-MS spectra of eight peppermint oils. In the figure, number 1 represents high-quality peppermint oil Y, and numbers 2 to 8 represent commercially available peppermint oil raw materials. Detailed Implementation

[0028] The present invention will be further described in detail below with reference to embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto. All raw materials involved in the present invention can be purchased directly from the market. For process parameters not specifically specified, conventional techniques can be referred to.

[0029] Example 1

[0030] In this embodiment, one high-quality peppermint oil Y was used as the target, and seven commercially available peppermint oils were used as raw materials. The GC-MS spectra of the above eight peppermint oils are shown in [reference needed]. Figure 2 .

[0031] 1) Electronic tongue detection of peppermint oil samples

[0032] ① Sample pretreatment

[0033] In a 200mL beaker, mix 12g of peppermint oil with 85g of distilled water, keep warm at 40℃, homogenize for 3min, and after cooling to room temperature, add 50g of standard solution (a mixed solution of KCl and tartaric acid (mass ratio 50:1)), centrifuge at 3000rpm for 10min, collect the supernatant, and refrigerate for later use.

[0034] ② Electronic tongue detection

[0035] At room temperature, the activated taste sensor array was brought into contact with the sample to be tested. The potential signals between different taste substances in the sample and the sensor membrane were extracted, and then converted using the Weber-Fechner law to obtain the taste information file. The taste information values ​​are shown in Table 2.

[0036] Table 2. Taste information of different peppermint oils based on electronic tongue.

[0037]

[0038] 2) The raw material ratio coefficients were obtained through numerical optimization, as shown in Table 3.

[0039] Table 3 Raw Material Proportion Coefficients

[0040]

[0041] 3) Quality verification of compound peppermint oil

[0042] The electronic tongue characteristics of the compounded peppermint oil sample 9 are shown in Table 4.

[0043] Table 4. Electronic tongue taste characteristics of compound peppermint oil

[0044]

[0045] The content of characteristic compounds in sample 9 obtained after compounding is shown in Table 5, and the content of characteristic compounds in target sample 1 is also shown in Table 5 (including the detection results of multiple linear regression method and GC-MS method). The comparison results show that the content of key components in the compounded sample and the target sample are similar.

[0046] Table 5. Content of characteristic compounds in compound peppermint oil

[0047]

[0048] This invention enables the rapid and efficient blending of high-quality peppermint essential oils through electronic tongue detection. The method is simple, quick, and low-cost.

[0049] Example 2

[0050] In this embodiment, one type of high-quality peppermint oil Y is used as the target, and ten types of commercially available peppermint oils are used as raw materials.

[0051] ① Peppermint oil sample pretreatment

[0052] In a 200mL beaker, mix 10g of peppermint oil with 100g of distilled water, keep warm at 50℃, homogenize for 3min, and after cooling to room temperature, add 100g of standard solution (a mixed solution of KCl and tartaric acid (mass ratio 50:1)), centrifuge at 3000rpm for 10min, take the supernatant, and refrigerate for later use.

[0053] ② Electronic tongue detection

[0054] At room temperature, the activated taste sensor array was brought into contact with the sample to be tested. The potential signals between different taste substances in the sample and the sensor membrane were extracted, and then converted using the Weber-Fechner law to obtain the taste information file. The taste information values ​​are shown in the table below:

[0055] Table 6. Taste information of different peppermint oils based on electronic tongue.

[0056]

[0057]

[0058] 2) The raw material ratio coefficients were obtained through numerical optimization, as shown in Table 7, and sample 12 was prepared.

[0059] Table 7 Raw Material Proportion Coefficients

[0060]

[0061] 3) Quality verification of compound peppermint oil

[0062] The electronic tongue characteristics of the compounded peppermint oil sample 12 are shown in Table 8.

[0063] Table 8. Taste characteristics of compound peppermint oil on electronic tongue.

[0064]

[0065] The content of characteristic compounds in sample 12 obtained after compounding is shown in Table 9, and the content of characteristic compounds in target sample 1 is also shown in Table 9 (including the detection results of multiple linear regression method and GC-MS method). The comparison results show that the content of key components in the compounded sample and the target sample is similar.

[0066] Table 9. Content of characteristic compounds in compound peppermint oil

[0067]

[0068] Example 3

[0069] In this embodiment, one type of high-quality peppermint oil Y is used as the target, and five types of commercially available peppermint oils are used as raw materials.

[0070] ① Peppermint oil sample pretreatment

[0071] In a 200mL beaker, mix 10g of peppermint oil with 100g of distilled water, keep warm at 45℃, homogenize for 3min, and after cooling to room temperature, add 80g of standard solution (a mixed solution of KCl and tartaric acid (mass ratio 50:1)), centrifuge at 3000rpm for 10min, take the supernatant, and refrigerate for later use.

[0072] ② Electronic tongue detection

[0073] At room temperature, the activated taste sensor array was brought into contact with the sample to be tested. The potential signals between different taste substances in the sample and the sensor membrane were extracted, and then converted using the Weber-Fechner law to obtain the taste information file. The taste information values ​​are shown in the table below:

[0074] Table 10 Taste information of different peppermint oils based on electronic tongue

[0075]

[0076] 2) The raw material ratio coefficients were obtained by numerical optimization, as shown in Table 11, and sample 7 was prepared.

[0077] Table 11 Raw Material Proportion Coefficients

[0078]

[0079] 3) Quality verification of compound peppermint oil

[0080] The electronic tongue characteristics of the compounded peppermint oil sample 7 are shown in Table 12.

[0081] Table 12. Electronic tongue taste characteristics of compound peppermint oil

[0082]

[0083] The content of characteristic compounds in sample 7 obtained after compounding is shown in Table 13, and the content of characteristic compounds in target sample 1 is also shown in Table 13 (including the detection results of multiple linear regression method and GC-MS method). The comparison results show that the content of key components in the compounded sample and the target sample is similar.

[0084] Table 13 Content of Characteristic Compounds in Compound Peppermint Oil

[0085]

[0086] Example 4

[0087] In this embodiment, one high-quality peppermint oil Y is used as the target, and seven commercially available peppermint oils are used as raw materials.

[0088] ①Pretreatment of peppermint oil samples

[0089] In a 200mL beaker, mix 10g peppermint oil with 100g distilled water, keep warm at 45℃, homogenize for 3min, and after cooling to room temperature, add 100g of standard solution (a mixture of KCl and tartaric acid (mass ratio 50:1)), centrifuge at 3000rpm for 10min, collect the supernatant, and refrigerate for later use.

[0090] ② Electronic tongue detection

[0091] At room temperature, the activated taste sensor array was brought into contact with the sample to be tested. The potential signals between different taste substances in the sample and the sensor membrane were extracted, and then converted using the Weber-Fechner law to obtain the taste information file. The taste information values ​​are shown in the table below:

[0092] Table 14 Taste information of different peppermint oils based on electronic tongue

[0093]

[0094] 2) The raw material ratio coefficients were obtained through numerical optimization, as shown in Table 15, and sample 9 was prepared.

[0095] Table 15 Raw Material Proportion Coefficients

[0096]

[0097] 3) Quality verification of compound peppermint oil

[0098] The electronic tongue characteristics of the compounded peppermint oil sample 9 are shown in Table 16.

[0099] Table 16. Electronic tongue taste characteristics of compound peppermint oil

[0100]

[0101] The content of characteristic compounds in sample 9 obtained after compounding is shown in Table 17, and the content of characteristic compounds in target sample 1 is also shown in Table 17 (including the detection results of multiple linear regression method and GC-MS method). The comparison results show that the content of key components in the compounded sample and the target sample is similar.

[0102] Table 17 Content of Characteristic Compounds in Compound Peppermint Oil

[0103]

[0104] Example 5

[0105] The target is one type of high-quality spearmint oil Y, and the raw materials are seven types of commercially available spearmint oil.

[0106] ① Spearmint oil sample pretreatment

[0107] In a 200mL beaker, mix 10g spearmint oil with 100g distilled water, keep warm at 40℃, homogenize for 3min, and after cooling to room temperature, add 100g of standard solution (a mixture of KCl and tartaric acid (mass ratio 50:1)), centrifuge at 3000rpm for 10min, take the supernatant, and refrigerate for later use.

[0108] ② Electronic tongue detection

[0109] At room temperature, the activated taste sensor array was brought into contact with the sample to be tested. The potential signals between different taste substances in the sample and the sensor membrane were extracted, and then converted using the Weber-Fechner law to obtain the taste information file. The taste information values ​​are shown in the table below:

[0110] Table 18 Taste information of different spearmint oils based on electronic tongue

[0111]

[0112] 2) The raw material ratio coefficients were obtained through numerical optimization, as shown in Table 19, and sample 9 was prepared.

[0113] Table 19 Raw Material Proportion Coefficients

[0114]

[0115] 3) Quality verification of blended spearmint oil

[0116] The electronic tongue characteristics of the blended spearmint oil sample 9 are shown in Table 20.

[0117] Table 20. Electronic tongue taste characteristics of blended spearmint oil

[0118]

[0119] The content of characteristic compounds in sample 9 obtained after compounding is shown in Table 21, and the content of characteristic compounds in target sample 1 is also shown in Table 21 (including the detection results of multiple linear regression method and GC-MS method). The comparison results show that the content of key components in the compounded sample and the target sample is similar.

[0120] Table 21 Content of Characteristic Compounds in Blended Spearmint Oil

[0121]

[0122] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for blending peppermint essential oils based on taste fingerprinting, characterized in that... Includes the following steps: 1) Electronic tongue detection of peppermint essential oil samples: The taste sensor array is brought into contact with the pre-treated peppermint essential oil samples to collect taste information, including umami, richness, saltiness, sourness, bitterness and astringency; the samples include target Y and a series of raw materials A, B, C, D...X; 2) Based on taste characteristics, optimize the essential oil ratio to obtain the optimal combination of raw materials: Y1 to Y6 represent the six taste information of the target sample Y. Similarly, A1 to A6 represent the six taste information of the raw material A, and so on. 1 to 6 represent umami, richness, saltiness, sourness, bitterness and astringency, respectively. a, b, c, d...x represent the proportion coefficients. The raw material combination that is closest to the target Y is obtained by numerical optimization, that is, the proportion coefficients a, b, c, d...x of samples A, B, C, D...X are obtained; according to the proportion coefficients, the compounded peppermint essential oil can be obtained.

2. The method for blending peppermint essential oils based on taste fingerprinting according to claim 1, characterized in that, The specific steps of step 1) are as follows: the activated taste sensor array is brought into contact with the sample to be tested, the potential signal between different taste substances in the sample and the sensor membrane is extracted, and then converted by Weber-Fechner law to obtain taste information.

3. The method for blending peppermint essential oils based on taste fingerprinting according to claim 1, characterized in that, Step 1) The pretreated peppermint essential oil sample is pretreated as follows: peppermint essential oil and water are thoroughly mixed and cooled to room temperature. Then, a reference solution is added, and after centrifugation, the supernatant is collected. The resulting oil sample is then refrigerated for later use. The mass ratio of peppermint essential oil, water and reference solution is 1-2:8-10:5-10. The reference solution is a mixed solution of KCl and tartaric acid with a mass ratio of 45-55:

1.

4. The method for blending peppermint essential oils based on taste fingerprinting according to claim 3, characterized in that, The centrifugation speed is 3000-10000 rpm, and the centrifugation time is 5-15 min.

5. The method for blending peppermint essential oils based on taste fingerprinting according to claim 3, characterized in that, The peppermint essential oil and water are mixed at 40–50°C.

6. The method for blending peppermint essential oils based on taste fingerprinting according to claim 3, characterized in that, The reference solution is a mixed solution of KCl and tartaric acid in a mass ratio of 50:

1.

7. The method for blending peppermint essential oils based on taste fingerprinting according to claim 1, characterized in that, Step 1) The peppermint essential oils mentioned include one or more of peppermint oil, peppermint oil, lemon mint oil, and spearmint oil.

8. The method for blending peppermint essential oils based on taste fingerprinting according to claim 1, characterized in that, Step 1) The electronic tongue system to which the taste sensor array belongs is the SA402B taste analysis system of INSENT Corporation of Japan. It contains 5 sensors, namely umami, saltiness, sourness, bitterness and astringency, and can obtain umami and richness, saltiness, sourness, bitterness and aftertaste, and astringency and aftertaste respectively.

9. The method for blending peppermint essential oils based on taste fingerprinting according to claim 1, characterized in that, After obtaining the compound peppermint oil in step 2), the method also includes quality verification of the obtained compound peppermint oil, as follows: ① Establishment of a quantitative model for compound content and taste information: For at least 30 peppermint essential oil samples, the contents of representative compounds α-pinene, eucalyptol, menthone, menthol, and menthyl acetate were quantitatively obtained by GC-MS. At the same time, the values ​​of various taste information of electronic tongue were detected. A model describing the contents of representative compounds with taste information was obtained by fitting multiple linear regression. ② Using electronic tongue detection, a model was obtained to quantitatively describe the content of characteristic compounds in essential oils through multiple linear regression fitting. The content of representative compounds in peppermint essential oils, such as α-pinene, eucalyptol, menthone, menthol, and menthyl acetate, was obtained, thereby confirming the quality of the compound peppermint oil. Table 1. Quantitative Model of Representative Compound Content and Taste Sensory Perception In Table 1, Umami, Bitterness, Sourness, and Astringency are the response values ​​of umami, bitterness, sourness, and astringency in the taste signal, respectively. Other taste information with less correlation was not used for modeling.

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