CN102313708A - Method for quantitatively detecting capsaicine - Google Patents
Method for quantitatively detecting capsaicine Download PDFInfo
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- CN102313708A CN102313708A CN2010102213019A CN201010221301A CN102313708A CN 102313708 A CN102313708 A CN 102313708A CN 2010102213019 A CN2010102213019 A CN 2010102213019A CN 201010221301 A CN201010221301 A CN 201010221301A CN 102313708 A CN102313708 A CN 102313708A
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Abstract
The invention provides a method for quantitatively detecting capsaicine. The method comprises the following steps of: acquiring optical data of the capsaicine in a sample by using a near-infrared spectrometer; correlating the optical data with content data of the capsaicine in the sample detected by a chemical analysis method; establishing a calibration model by a partial least squares method; and substituting the optical data of the capsaicine in a sample to be detected into the model to acquire the content of the capsaicine in the sample to be detected. A capsaicine quantitative analysis model established by the method has high accuracy, and can accurately and reliably predict the content of the capsaicine in an actual sample. The content of the capsaicine measured by a near-infrared spectrometer method and a result measured by the chemical analysis method do not have obvious differences; and by the method, the content of the capsaicine can be detected nondestructively.
Description
Technical field
The present invention relates to the analytical chemistry field, specifically, relate to the method for utilizing capsaicin content in the near-infrared spectral analysis technology test sample.
Background technology
Near infrared spectrum has been with fastest developing speed, the most noticeable spectral analysis technique since the nineties in 20th century.Near infrared light is the electromagnetic wave between visible light and mid-infrared light, and wavelength coverage is 700～2500nm, and it mainly is to contain hydrogen group (O-H, C-H, N-H, S-H, frequency multiplication that P-H) waits and sum of fundamental frequencies absorption that the near infrared spectrum of general organism in this district absorbs.Because nearly all more organic primary structures can find signal with forming in their near infrared spectrum, and spectrogram is stable, and it is easy to obtain spectrum, so near infrared spectrum (NIRS) is described as the giant of analysis in the analytical chemistry field.
Capsaicin content is bigger to whole pungent quality of capsicum and mouthfeel influence, is one of important indicator of estimating capsicum quality quality.With traditional chemical analysis method, measure capsaicin content like high performance liquid chromatography and ultraviolet spectrophotometry, belong to destructive analysis, and used experimental drug costs an arm and a leg experimental implementation complicacy, time and effort consuming etc.Near-infrared spectral analysis technology has analysis speed and soon, does not destroy sample, simple to operate, good stability, efficient advantages of higher, on the attributional analysis of fruits and vegetables series products, has obtained increasingly extensive application.
Summary of the invention
The purpose of this invention is to provide a kind of method of utilizing capsaicin content in the near-infrared spectral analysis technology test sample.
In order to realize the object of the invention; The quantitative detecting method of a kind of capsicim of the present invention; It comprises the optical data of utilizing capsicim in the near infrared spectrometer collected specimens, and carries out relatedly in the sample that records through chemical analysis method between the capsaicin content data, adopts PLS to set up calibration model; With this model of optical data substitution of testing sample capsicim, obtain the capsaicin content of testing sample.
Aforesaid detection method, wherein said chemical analysis method are high performance liquid chromatography or ultraviolet spectrophotometry etc.
The present invention utilizes the method for capsaicin content in the near-infrared spectral analysis technology test sample; Its advantage is: (1) no pre-service, nothing destructiveness, pollution-free: near infrared light has very strong penetration capacity; Can penetrate glass and plastics package directly detects sample, sample need not pre-service, also without any need for chemical reagent; Can realize the non-destructive of capsaicin content is detected; Compare with conventional method of analysis, neither can pollute, can save great amount of manpower and material resources again environment; (2) finding speed is fast: the minute of nir instrument is short, and a few minutes even a few second promptly can be accomplished mensuration; (3) the capsicim Quantitative Analysis Model precision that the present invention built is good; Can be accurately and predict the capsaicin content of actual sample reliably, adopting between the result of capsaicin content that near-infrared spectrum method of the present invention measures and chemical analysis method mensuration does not have significant difference.
Description of drawings
The capsicum sample original spectrum that Fig. 1 utilizes near infrared spectrometer to gather for the present invention;
Fig. 2 is the near infrared second derivative spectra figure of sample of the present invention;
Fig. 3 is the predicted value and the measured value correlogram of PLS calibration model of the present invention.
Embodiment
Following examples are used to explain the present invention, but are not used for limiting scope of the present invention.
Embodiment
1 materials and methods
1.1 material
121 ripe capsicum samples from the Chinese Academy of Agricultural Sciences.Wherein, 93 samples are calibration set, and 28 samples are the checking collection.Capsaicin content is at 0.406mg/g～6.71mg/g.
1.2 instrument and equipment
NIRLab N-200 type near infrared attributional analysis appearance, Switzerland Buchi company (NIRCalV4.21 software, 12cm sample cup).
1.3 method
1.3.1 sample pre-treatments
The chilli sample is connected the seed belt leather pulverize together, cross 40 mesh sieves, the capsicum powder is paved with sample cup, thickness is not less than 1cm.
1.3.2 near infrared spectra collection
At room temperature, measure the near-infrared diffuse reflection spectrum of capsicum powdered sample.During mensuration, resolution is 1cm
-1, scanning times is 3, figure spectral limit 1100～2500nm.Behind instrument preheating 20～30min, the capsicum powdered sample is placed sample rim of a cup top.
1.3.3HPLC analytical approach
Adopt the capsaicin content of the above-mentioned capsicum sample of high effective liquid chromatography for measuring.
1.4 the foundation of near infrared correction and evaluation
The modelling process will be carried out related exactly through pretreated near infrared light spectrum signature and capsaicin content data, set up correlationship.Adopt offset minimum binary (PLS) method to set up calibration model.Confirm its best major component number (N) according to inner validation-cross.
Come forecast test collection sample with this model, come the accuracy and the reliability of testing model with this.Estimate the quality of calibration model with related coefficient (R), calibration set standard deviation (SEC) and checking collection standard deviation (SEP).Coefficient R is bigger, EC is more little for the calibration set standard deviation S, and the spectral information that is extracted is good more with the correlativity of analyzing component, and the model that obtains is excellent more.
2 results and analysis
2.1 original spectrum is to the influence of PLS model
The spectrum of choosing each capsicum sample is as original spectrum.The original spectrum of 121 samples is as shown in Figure 1.
As can beappreciated from fig. 1, original diffuse reflection spectrum change is little, and spectra overlapping is not serious, and the diffuse reflection spectrum of most of capsicum samples if directly be used for quantitative test, can not covered the SPECTRAL DIVERSITY that is caused by sample composition not than evident difference.Original spectrum can be eliminated the error that measuring condition causes effectively, improves the degree of accuracy of Quantitative Analysis Model, also can reflect the actual conditions of capsicum sample itself more all sidedly.
At 4000～10000cm
-1In the spectral range, set up capsicim PLS calibration model with the original spectrum of 93 correcting samples respectively.The result is as shown in table 1 in calibration model internal chiasma checking.
Table 1 original spectrum is to the influence of PLS calibration model
Annotate: R: related coefficient; N: best major component number; SEC: calibration set standard deviation; SLOPE: slope; BIAS: deviation.
Can know from table 1; In the major component number is 15 o'clock; The PLS calibration model of capsicim that original spectrum is set up, the related coefficient of its predicted value and measured value are 0.9791, and standard deviation is 0.3602; Show spectral information that is extracted and the good relationship of analyzing component, the model that obtains is better.
2.2 (second derivative, SD) disposal route is to the influence of capsicim calibration model for second derivative
Select the second derivative disposal route that the capsicim spectroscopic data is carried out pre-service, gained spectrum is as shown in Figure 2.At 4000～10000cm
-1In the spectral range, analyze the influence of preprocess method to building LEAST SQUARES MODELS FITTING, the result is as shown in table 2.
Table 2 pre-service is to the influence of PLS calibration model
Annotate: R: related coefficient; N: best major component number; SEP: checking collection standard deviation; SLOPE: slope; BIAS: deviation.
Can know from table 2, be 15 o'clock in the major component number, the PLS calibration model of capsicim that original spectrum is set up; The related coefficient of its predicted value and measured value is 0.9244, and SEP is 0.512, and SEP/SEC is 1.42; Less than 1.6; Show that model does not have over-fitting, selecting preprocessing procedures is necessary to Optimization Model, has only the purpose of selecting suitable preprocessing procedures just can reach Optimization Model.
2.3 the foundation of calibration model
As calibration set,, select the pretreated spectrum of second dervative with 93 samples at 4000～10000cm by the analysis of front
-1Set up the calibration model of capsaicin content in the spectral region with the PLS method, collect as checking with 28 samples, in the model that the spectrum importing is built, the result as shown in Figure 3.
2.4 the check of calibration model
28 samples using near-infrared models are measured, predicted its content, and with the stability of HPLC measured value as the measured value verification model.The result is as shown in table 3.
Predicting the outcome of table 3 capsicum sample Quantitative Analysis Model
Have or not significant difference between two kinds of analytical approachs of method check of t check in pairs.For given level of significance 0.01, t
0.005(19)=2.861, the t value of calculating gained is 0.1986, less than t
0.005Therefore (19), can think that the result does not have significant difference between the capsaicin content that the capsaicin content that adopts the prediction of near-infrared analysis method and HPLC analytical approach record.
The related coefficient of checking collection sample predicted value and measured value is 0.9244, and SEP/SEC is 1.42, proves that the near infrared PLS calibration model of building has stability preferably, can satisfy the detection requirement of capsaicin content.
3 conclusions
3.1 set up the PLS Quantitative Analysis Model of capsaicin content with the original spectrum of capsicim multiposition point spectrum.
3.2 adopt the second dervative preprocess method, at 4000～10000cm
-1In the spectral region, the capsicim Quantitative Analysis Model precision of being built is good, and the coefficient correlation of its predicted value and measured value is 0.9244, and SEP/SEC is 1.42, and model is more excellent.Near-infrared spectral analysis technology can be realized the non-destructive of capsaicin content is detected.
3.3 the paired t assay to the checking collection shows do not have significant difference between the result that capsaicin content that near-infrared spectrum method is measured and chemical analysis method are measured.
Though, the present invention has been done detailed description in the preceding text with general explanation and specific embodiments, on basis of the present invention, can to some modifications of do or improvement, this will be apparent to those skilled in the art.Therefore, these modifications or the improvement on the basis of not departing from spirit of the present invention, made all belong to the scope that requirement of the present invention is protected.
List of references:
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Claims (2)
1. the quantitative detecting method of a capsicim; It is characterized in that; It comprises the optical data of utilizing capsicim in the near infrared spectrometer collected specimens, and carries out relatedly in the sample that records through chemical analysis method between the capsaicin content data, adopts PLS to set up calibration model; With this model of optical data substitution of testing sample capsicim, obtain the capsaicin content of testing sample.
2. detection method as claimed in claim 1 is characterized in that, wherein said chemical analysis method is high performance liquid chromatography or ultraviolet spectrophotometry.
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| CN104132720A (en) * | 2014-07-25 | 2014-11-05 | 重庆医科大学 | Method for quickly detecting tablet weight of medicine tablets through near infrared spectroscopy |
| CN111077103A (en) * | 2019-11-30 | 2020-04-28 | 贵州中烟工业有限责任公司 | Method for measuring content of glyceryl triacetate |
| CN111398212A (en) * | 2020-04-08 | 2020-07-10 | 四川虹微技术有限公司 | Method for establishing pepper detection model based on portable near-infrared spectrometer |
| CN112697888A (en) * | 2019-10-22 | 2021-04-23 | 重庆德庄农产品开发有限公司 | Method for measuring capsaicin content |
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Cited By (7)
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| CN111077103A (en) * | 2019-11-30 | 2020-04-28 | 贵州中烟工业有限责任公司 | Method for measuring content of glyceryl triacetate |
| CN111398212A (en) * | 2020-04-08 | 2020-07-10 | 四川虹微技术有限公司 | Method for establishing pepper detection model based on portable near-infrared spectrometer |
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Application publication date: 20120111 |