A detection method for identifying fruit juice samples from different sources using NMR.
By processing the 1H-NMR spectrum of fruit juice samples using NMR technology to remove interference from water and sugar peaks, accurate detection of component differences in fruit juice samples is achieved. This solves the problem of insufficient accuracy in existing technologies and improves the reliability and flexibility of detection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INNOVATION ACAD FOR PRECISION MEASUREMENT SCI & TECH CAS
- Filing Date
- 2024-09-24
- Publication Date
- 2026-06-30
AI Technical Summary
Existing detection methods cannot accurately identify the true differences in composition between fruit juice samples from different sources, and are subject to interference from water peaks and sugar content, resulting in insufficient accuracy of the detection and analysis data.
1H-NMR spectra of fruit juice samples were obtained using NMR technology. By identifying and removing residual water and sugar peaks, data normalization and calibration were performed. Combined with multivariate analysis, the true differences in the composition of the fruit juice samples were determined.
It improves the accuracy and reliability of detection and analysis data, can more accurately reflect the true differences in composition between different fruit juice samples, improves fruit juice quality control and adulteration detection, and adapts to the characteristics and analytical requirements of different fruit juice samples.
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Figure CN119438284B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to an improvement in juice detection technology, belonging to the field of juice detection, and particularly to a detection method that uses NMR to identify juice samples from different sources. Background Technology
[0002] Current methods for quality testing and analysis of fruit juice include physicochemical analysis and instrumental analysis. Physicochemical analysis includes methods for identifying sugars, organic acids, pectin, phenols, inorganic elements, amino acids, and ketones. These methods only focus on the content and changes of one or a few components, ignoring the presence of numerous other adulterants, and thus cannot meet the quality control requirements of fruit juice products. Instrumental analysis includes techniques such as chromatography, stable isotope mass spectrometry, and mass spectrometry. However, fruit juice has a complex composition and comes from a wide range of sources, and current technologies have limited ability to detect cross-contamination between samples from different sources, as well as unknown illegal additives. Furthermore, these methods typically only focus on the content and changes of one or a few components, ignoring the presence of numerous other adulterants, thus failing to meet the quality control requirements of fruit juice products. On the other hand, due to the complexity and wide range of sources of fruit juice, cross-contamination between samples from different sources and the addition of unknown illegal additives also affect the test results. Among these, the interference from water peaks and sugar content has the most significant impact on the data, resulting in insufficient accuracy of the analytical data.
[0003] Chinese patent application CN202111395181.9, filed on November 23, 2021, discloses a method for detecting adulteration of sugar content in raw fruit juice rich in anthocyanins, belonging to the field of food testing technology. The method employs ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) to detect the presence and content of anthocyanins bound to furfural compounds, a sugar degradation product, in the fruit juice sample. A concentration of 2 mg / mL of the bound product in the fruit juice sample is used as a standard to determine whether sugar adulteration exists. However, this technique does not solve the problem of insufficient accuracy in existing detection methods.
[0004] The information disclosed in this background section is intended only to enhance the understanding of the overall background of this patent application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention
[0005] The purpose of this invention is to overcome the problem of insufficient accuracy in existing detection methods and to provide a more accurate detection method using NMR to identify fruit juice samples from different sources.
[0006] To achieve the above objectives, the technical solution of the present invention is: a detection method for identifying fruit juice samples from different sources using NMR, wherein the detection method for identifying fruit juice samples from different sources using NMR includes the following steps:
[0007] Step 1: Obtain the 1H-NMR spectrum of the juice sample to be analyzed;
[0008] Step 2: Perform hydrogen spectrum integration on the 1H-NMR spectrum of the obtained juice sample to identify and remove residual water peaks in the 1H-NMR spectrum, and obtain the water-removed 1H-NMR spectrum.
[0009] Step 3: Identify each sugar component in the water-depleted peak 1H-NMR spectrum and determine the chemical shift range of each sugar component. Record each shift range to obtain the sugar-containing 1H-NMR spectrum.
[0010] Step 4: First, remove the sugar peak range from the 1H-NMR spectrum containing sugar, then perform data normalization and calibration. After calibration, perform multivariate analysis to obtain the true component differences between the juice samples, thereby determining the authenticity of the juice.
[0011] The acquisition of the 1H-NMR spectrum of the juice sample to be analyzed refers to:
[0012] The acquired samples were placed on a Bruker Avance III 600MHz spectrometer. For each sample, the 90° pulse width was set to approximately 10-12 μs, the spectral width was set to 20 ppm, the number of sampling points was 64K, and the FID was accumulated 64 times. All one-dimensional 1H-NMR spectra were processed by multiplying the FID by an exponential window function with a broadening factor of 1.0 Hz before Fourier transform. The number of data transformation points was 128K, thus obtaining the 1H-NMR spectrum of the juice sample.
[0013] The proton resonance frequency was 600.13 MHz, and the experimental temperature was set at 298.0 K.
[0014] Before acquiring the 1H-NMR spectra of the juice samples, phase and baseline corrections were manually performed on each spectrum.
[0015] The identification and removal of residual water peaks in the 1H-NMR spectrum refers to removing the signal in the δ5.0-4.68 range, which is the water peak removal operation.
[0016] The specific method for determining the chemical shift range of each sugar component is as follows: TSP is added to the sample as an internal standard, and the chemical shift of this peak is set to 0 ppm. In the calibrated spectrum, the signals of sugar components such as glucose, sucrose, and fructose are between δ5.46 and 3.20.
[0017] The chemical shift range of each carbohydrate component is δ5.46-3.20.
[0018] The removal of the sugar peak range in the 1H-NMR spectrum refers to removing the signal from the chemical shift range of sugar components, δ5.46-3.20.
[0019] The data normalization and calibration refers to: performing segmented integration on the δ9.0–0.5 range in the 1H-NMR spectrum of each sample, and performing normalization processing at the same time, that is, dividing the area of each segment by the sum of all the segments, with each segment being 0.004 ppm.
[0020] The data were normalized and then subjected to automatic standardization using least partial squares discriminant analysis to further analyze the intergroup distinctions of metabolites.
[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0022] 1. This invention provides a detection method for identifying fruit juice samples from different sources using NMR. The method identifies and removes residual water peaks in the 1H-NMR spectrum and sugar peak regions in the 1H-NMR spectrum of sugar-containing samples. Data normalization and calibration are then performed, followed by multivariate analysis to obtain the true component differences between fruit juice samples, thereby determining the authenticity of the fruit juice. In application, this method eliminates the influence of residual signals and sugar components in the 1H-NMR spectrum of fruit juice samples through spectral processing, thus removing these interfering factors. This allows PLA-DA analysis to more accurately reflect the true component differences between different fruit juice samples, improving the accuracy and reliability of the detection and analysis data. This enhances fruit juice quality control and adulteration detection, providing a reliable analytical method for fruit juice quality evaluation and variety identification. Therefore, this invention offers higher accuracy and reliability of the detection and analysis data and is simple to operate.
[0023] 2. In this invention, a detection method for identifying fruit juice samples from different sources using NMR is employed. High-resolution NMR technology is used to analyze and detect the chemical components of the fruit juice, providing information on compositional changes in fruit juices from different sources, varieties, and brands. This allows for analysis of the patterns of component changes in fruit juices from different sources, enabling real-time and dynamic analysis and detection. Therefore, this invention offers convenient detection and allows for real-time monitoring and querying.
[0024] 3. This invention provides a detection method for identifying fruit juice samples from different sources using NMR. This method allows for the analysis and comparison of various fruit juice samples in the laboratory. The specific data processing method can be adjusted and optimized according to actual needs to adapt to the characteristics and analytical requirements of different fruit juice samples. Simultaneously, it eliminates interference from water peaks and sugar components, thus more accurately reflecting the intrinsic differences between fruit juice samples. Therefore, this invention can be adapted to the detection of various fruits, making the analytical method more flexible. Attached Figure Description
[0025] Figure 1 This is the NMR spectrum of the present invention. Detailed Implementation
[0026] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0027] See Figure 1 A detection method for identifying fruit juice samples from different sources using NMR, the method comprising the following steps:
[0028] Step 1: Obtain the 1H-NMR spectrum of the juice sample to be analyzed;
[0029] Step 2: Perform hydrogen spectrum integration on the 1H-NMR spectrum of the obtained juice sample to identify and remove residual water peaks in the 1H-NMR spectrum, and obtain the water-removed 1H-NMR spectrum.
[0030] Step 3: Identify each sugar component in the water-depleted peak 1H-NMR spectrum and determine the chemical shift range of each sugar component. Record each shift range to obtain the sugar-containing 1H-NMR spectrum.
[0031] Step 4: First, remove the sugar peak range from the 1H-NMR spectrum containing sugar, then perform data normalization and calibration. After calibration, perform multivariate analysis to obtain the true component differences between the juice samples, thereby determining the authenticity of the juice.
[0032] The acquisition of the 1H-NMR spectrum of the juice sample to be analyzed refers to:
[0033] The acquired samples were placed on a Bruker Avance III 600MHz spectrometer. For each sample, the 90° pulse width was set to approximately 10-12 μs, the spectral width was set to 20 ppm, the number of sampling points was 64K, and the FID was accumulated 64 times. All one-dimensional 1H-NMR spectra were processed by multiplying the FID by an exponential window function with a broadening factor of 1.0 Hz before Fourier transform. The number of data transformation points was 128K, thus obtaining the 1H-NMR spectrum of the juice sample.
[0034] The proton resonance frequency was 600.13 MHz, and the experimental temperature was set at 298.0 K.
[0035] Before acquiring the 1H-NMR spectra of the juice samples, phase and baseline corrections were manually performed on each spectrum.
[0036] The identification and removal of residual water peaks in the 1H-NMR spectrum refers to removing the signal in the δ5.0-4.68 range, which is the water peak removal operation.
[0037] The specific method for determining the chemical shift range of each sugar component is as follows: TSP is added to the sample as an internal standard, and the chemical shift of this peak is set to 0 ppm. In the calibrated spectrum, the signals of sugar components such as glucose, sucrose, and fructose are between δ5.46 and 3.20.
[0038] The chemical shift range of each carbohydrate component is δ5.46-3.20.
[0039] The removal of the sugar peak range in the 1H-NMR spectrum refers to removing the signal from the chemical shift range of sugar components, δ5.46-3.20.
[0040] The data normalization and calibration refers to: performing segmented integration on the δ9.0–0.5 range in the 1H-NMR spectrum of each sample, and performing normalization processing at the same time, that is, dividing the area of each segment by the sum of all the segments, with each segment being 0.004 ppm.
[0041] The data were normalized and then subjected to automatic standardization using least partial squares discriminant analysis to further analyze the intergroup distinctions of metabolites.
[0042] The principle of this invention is explained as follows:
[0043] High-resolution NMR technology can be used to analyze and detect the chemical components of fruit juice, which can provide information on the compositional changes of fruit juice from different sources, varieties and brands. This allows for the analysis of the patterns of component changes in fruit juice from different sources, enabling real-time and dynamic analysis and detection.
[0044] Example 1:
[0045] A method for identifying fruit juice samples from different sources using NMR, the method comprising the following steps:
[0046] Step 1: Obtain the 1H-NMR spectrum of the juice sample to be analyzed;
[0047] Step 2: Perform hydrogen spectrum integration on the 1H-NMR spectrum of the obtained juice sample to identify and remove residual water peaks in the 1H-NMR spectrum, and obtain the water-removed 1H-NMR spectrum.
[0048] Step 3: Identify each sugar component in the water-depleted peak 1H-NMR spectrum and determine the chemical shift range of each sugar component. Record each shift range to obtain the sugar-containing 1H-NMR spectrum.
[0049] Step 4: First, remove the sugar peak range from the 1H-NMR spectrum containing sugar, then perform data normalization and calibration. After calibration, perform multivariate analysis to obtain the true component differences between the juice samples, thereby determining the authenticity of the juice.
[0050] Example 2:
[0051] Example 2 is basically the same as Example 1, except that:
[0052] The acquisition of the 1H-NMR spectrum of the juice sample to be analyzed refers to:
[0053] A detection method using NMR to identify fruit juice samples from different sources involves placing the acquired samples on a Bruker Avance III 600MHz spectrometer. For each sample, the 90° pulse width is set to approximately 10 μs, and the spectral width is set to 20 ppm. The 20 ppm width is chosen because the signal distribution of the fruit juice sample solution is within the range of 0–10 ppm; the area outside this range represents the baseline signal. Selecting a 20 ppm sampling range facilitates baseline determination and data baseline correction. The number of sampling points is 64K, and the FID is accumulated 64 times to ensure high resolution in the processed spectra. All one-dimensional 1H-NMR spectra are processed by multiplying the FID by an exponential window function with a broadening factor of 1.0 Hz before Fourier transform. The number of data transformation points is 128K. In NMR experiments, the number of transformation points is typically twice the number of sampling points during proton NMR processing. During NMR data acquisition, the relaxation times of different substances vary. Complete decay may not occur within a single sampling time, resulting in a truncation effect (sawtooth-like lines appearing on both sides of the bottom of a single peak) during spectral transformation. Doubling the number of processing points can effectively reduce this effect, making the spectral peaks smoother and more symmetrical, thus improving the spectral quality and obtaining the 1H-NMR spectrum of the juice sample. The proton resonance frequency is 600.13MHz, and the experimental temperature is set to 298.0K. Before acquiring the 1H-NMR spectrum of the juice sample, phase and baseline corrections are manually performed on each spectrum. Due to differences in the physicochemical properties of each sample, the spectrum generated after Fourier transform of the acquired raw data will have phase differences, resulting in an uneven baseline. Therefore, baseline adjustment is required. When adjusting the baseline, first set 0ppm as the 0th order phase, and then adjust the 1st order phase to make the baseline of the entire NMR spectrum flat.
[0054] Example 3:
[0055] Example 3 is basically the same as Example 1, except that:
[0056] A detection method for identifying fruit juice samples from different sources using NMR involves placing the acquired samples on a Bruker Avance III 600MHz spectrometer. For each sample, the 90° pulse width is set to approximately 12 μs, the spectral width to 20 ppm, the number of sampling points to 64 K, and the FID (fiber oscillation index) accumulated 64 times. All one-dimensional 1H-NMR spectra are processed by multiplying the FID by an exponential window function with a broadening factor of 1.0 Hz before Fourier transform, resulting in a data transformation point count of 128 K, thus obtaining the 1H-NMR spectrum of the fruit juice sample. The proton resonance frequency is 600.13 MHz, and the experimental temperature is set to 298.0 K. Phase and baseline corrections are manually performed on each spectrum before acquisition.
[0057] Example 4:
[0058] Example 4 is basically the same as Example 1, except that:
[0059] A detection method for identifying fruit juice samples from different sources using NMR involves identifying and removing residual water peaks in the 1H-NMR spectrum. This means removing the signal in the δ 5.0-4.68 range, which is the water peak removal operation. Water peaks are not analyzed as variables in NMR data processing of fruit juice, so the water signal must be removed; otherwise, the data analysis will not reflect the true differences between different fruit juice samples. Determining the chemical shift range of each sugar component specifically involves adding TSP as an internal standard to the sample and setting the chemical shift of this peak to 0 ppm. The calibrated spectrum... In the 1H-NMR spectrum, the signals of sugar components such as glucose, sucrose, and fructose are between δ5.46 and 3.20; the chemical shift range of each sugar component is δ5.46-3.20; removing the sugar peak range in the sugar-containing 1H-NMR spectrum means removing the signal of the sugar component in the chemical shift range of δ5.46-3.20. Since the proportion of sugar in fruit juice is quite high regardless of its source, the sugar signal peak accounts for a large proportion, which affects the signal peaks of other components in the fruit juice. Removing the chemical shift of sugar makes it easy to distinguish fruit juices from different sources.
[0060] The data normalization and calibration mentioned above refer to the following: the δ9.0–0.5 range in the 1H-NMR spectrum of each sample is segmented and integrated, and normalization is performed simultaneously. That is, the area of each segment is divided by the sum of all integrated areas, and each segment is 0.004 ppm. Each integrated interval is a variable factor reflecting the chemical components and their content in the juice. In the spectrum acquired on a 600MHz spectrometer, 0.004 ppm is converted to 2.4 Hz. This width is exactly equivalent to the width of the spectral peak. Using this width as the segmentation standard is conducive to fully extracting spectral information. Too large a segment will result in too low data resolution and will not be able to reflect subtle differences, while too small a segment will increase the difficulty of data analysis. Further analysis of the intergroup differentiation of metabolites can be performed to find the differential characteristics of juices from different sources from small molecule chemical substances other than sugar components.
[0061] Multivariate analysis was performed on the normalized NMR dataset using SIMCA-P+ software (version 11.0, Umetrics). In principal component analysis (PCA), mean-centered (Ctr) was used to standardize the data, and the results were represented by Scores plots and coefficient plots labeled with correlation coefficients. To determine the predictive quality of the model, i.e., the model's effectiveness, a permutation test was used to examine the model's reliability, with n = 200 random permutations.
[0062] The above description is only a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. Any equivalent modifications or changes made by those skilled in the art based on the content disclosed in the present invention should be included within the scope of protection set forth in the claims.
Claims
1. A method for detecting different origin juice samples using NMR, characterized in that: The detection method for identifying fruit juice samples from different sources using NMR includes the following steps: Step 1: Obtain the 1H-NMR spectrum of the juice sample to be analyzed; Before acquiring the 1H-NMR spectrum of the juice sample, the phase and baseline of each spectrum were manually corrected. When adjusting the baseline, 0 ppm was first set as the 0th order phase, and then the 1st order phase was adjusted to make the baseline of the entire NMR spectrum flat. Step 2: Perform hydrogen spectrum integration on the 1H-NMR spectrum of the obtained juice sample to identify and remove residual water peaks in the 1H-NMR spectrum, and obtain the water-removed 1H-NMR spectrum. The identification and removal of residual water peaks in the 1H-NMR spectrum refers to removing the signal in the δ5.0-4.68 range, which is the water peak removal operation. Step 3: Identify each sugar component in the water-depleted peak 1H-NMR spectrum and determine the chemical shift range of each sugar component. Record each shift range to obtain the sugar-containing 1H-NMR spectrum. The specific method for determining the chemical shift range of each sugar component is as follows: TSP is added to the sample as an internal standard, and the chemical shift of this peak is set to 0 ppm. In the calibrated spectrum, the signals of glucose, sucrose, and fructose sugar components are between δ5.46 and 3.
20. The chemical shift range of each carbohydrate component is δ5.46-3.20; Step 4: First, remove the sugar peak range in the 1H-NMR spectrum containing sugar, then perform data normalization and calibration, and after calibration, perform multivariate analysis to obtain the true component differences between the juice samples, thereby determining the authenticity of the juice. The removal of the sugar peak range in the 1H-NMR spectrum of sugar-containing components refers to removing the signal from the chemical shift range of δ5.46-3.20 for sugar components. The data normalization and calibration mentioned above refer to: performing segmented integration on the d9.0–0.5 range in the 1H-NMR spectrum of each sample, and performing normalization processing at the same time, that is, dividing the area of each segment by the sum of all the segments, with each segment being 0.004ppm. The data were normalized and then subjected to automatic standardization using least partial squares discriminant analysis to further analyze the intergroup distinctions of metabolites.
2. The method of claim 1, wherein the method is characterized by: The acquisition of the 1H-NMR spectrum of the juice sample to be analyzed refers to: The acquired samples were placed on a Bruker Avance III 600MHz spectrometer. For each sample, the 90º pulse width was set to 10–12 ms, the spectral width was set to 20 ppm, the number of sampling points was 64 K, and the FID was accumulated 64 times. All one-dimensional 1H-NMR spectra were processed by multiplying the FID by an exponential window function with a broadening factor of 1.0 Hz before Fourier transform. The number of data transformation points was 128 K, thus obtaining the 1H-NMR spectrum of the juice sample.
3. The method of claim 2, wherein the method is characterized by: The proton resonance frequency was 600.13 MHz, and the experimental temperature was set at 298.0 K.
4. The method of claim 3, wherein the method is characterized by: Before acquiring the 1H-NMR spectra of the juice samples, phase and baseline corrections were manually performed on each spectrum.