CN110779897A

CN110779897A - Method for determining inorganic selenium in nutritional rice flour

Info

Publication number: CN110779897A
Application number: CN201911088391.6A
Authority: CN
Inventors: 莫开菊; 程超; 陈业; 陈美林; 杜芬妮; 周志; 李伟; 汪兴平
Original assignee: Hubei University for Nationalities
Current assignee: Hubei University for Nationalities
Priority date: 2019-11-08
Filing date: 2019-11-08
Publication date: 2020-02-11
Anticipated expiration: 2039-11-08
Also published as: CN110779897B

Abstract

The invention relates to a method for measuring inorganic selenium in nutritional rice flour, which is characterized in that a nutritional rice flour sample containing inorganic selenium is prepared; performing mid-infrared spectrum scanning on the nutritional rice flour sample to obtain original data of an attenuated total reflection mid-infrared spectrum; preprocessing the original data of the attenuated total reflection mid-infrared spectrum to obtain the spectral data of the nutritional rice flour sample; modeling the inorganic selenium content in the nutritional rice flour sample and the spectral data of the nutritional rice flour sample by adopting a partial least square method, and establishing a model of the inorganic selenium content in the nutritional rice flour and the spectral data of the nutritional rice flour sample. The method adopts the attenuated total reflection infrared spectroscopy to measure the inorganic selenium in the food, does not have complex chemical reagent pretreatment process, ensures that the measurement of the inorganic selenium is not influenced by the elution degree and the micromolecular organic selenium, and achieves the aim of accurately measuring the inorganic selenium, thereby improving the accuracy of measuring the organic selenium and solving the measurement of the selenium content and the selenium type state in the food.

Description

Method for determining inorganic selenium in nutritional rice flour

Technical Field

The invention belongs to the technical field of detection, and particularly relates to a method for determining inorganic selenium in nutritional rice flour.

Background

The proper amount of selenium in the food is a nutrient necessary for human bodies, but too much or too little selenium is harmful, and the proper amount range is narrow and is only 50-400 ug. The food contains both organic selenium and inorganic selenium, and the organic selenium has higher safety than the inorganic selenium. Therefore, the accurate determination of the contents of organic selenium and inorganic selenium in the food can accurately monitor and produce the high-quality selenium-enriched food, which is responsible for the health of people.

The current national standard method for measuring selenium adopts GB5009.93, the basic method is to measure total selenium, and the occurrence form of selenium in food cannot be distinguished. The general method for measuring the organic selenium at present is to measure the total selenium, adopt the inorganic selenium in the elution sample, collect the washing liquid, measure the content of the inorganic selenium, and then calculate the content of the organic selenium by the subtraction method. One of the disadvantages of the method is that inorganic strong acid is used for digestion and chemical reagent is used for reduction, so that the environmental pollution is serious and the physical health of operators is seriously influenced. Secondly, the operation is complicated, the factors influencing the measurement result are more, the probability of generating accidental errors is high, and the fluctuation of data is large; especially, the method for measuring the inorganic selenium adopts ultra pure water and ultrasonic wave to elute the inorganic selenium and measure the content of the inorganic selenium in the washing liquid, so that the measuring accuracy of the inorganic selenium is influenced by multiple factors such as particle size, washing times, washing time, small molecular organic selenium which is washed out together, and the like, the data fluctuation of the inorganic selenium is large, and the data fluctuation of the organic selenium is directly caused. Thirdly, the national standard only requires the determination of total selenium, which causes that the safety and functional value of the organic selenium-rich food produced by food conversion cannot be correctly reflected because a plurality of selenium-rich foods directly add inorganic selenium to reach the selenium-rich standard.

Disclosure of Invention

Aiming at the problems, the attenuated total reflection infrared spectroscopy is adopted to measure the inorganic selenium in the food, the complex chemical reagent pretreatment process is not needed, the measurement of the inorganic selenium is not influenced by the elution degree and the micromolecular organic selenium, and the aim of accurately measuring the inorganic selenium is fulfilled, so that the accuracy of measuring the organic selenium is improved, the measurement of the selenium content and the selenium type state in the food is solved, and the foundation is laid for the monitoring and production of the selenium-enriched food.

In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:

a method for measuring inorganic selenium in nutritional rice flour comprises the following steps:

s1: preparing a nutritional rice flour sample containing inorganic selenium;

s2: collecting infrared spectrum data: carrying out mid-infrared spectrum scanning on the nutritional rice flour sample in the step 1) to obtain original data of an attenuated total reflection mid-infrared spectrum;

s3: preprocessing the original data of the attenuated total reflection mid-infrared spectrum to obtain the spectral data of the nutritional rice flour sample;

s4: model establishment and screening: modeling the inorganic selenium content in the nutritional rice flour sample in the step 1) and the spectral data of the nutritional rice flour sample in the step 3) by adopting a partial least square method, establishing a model of the inorganic selenium content in the nutritional rice flour and the spectral data of the nutritional rice flour sample, and primarily screening out a better model establishing method;

s5: model establishment and prediction verification: modeling by using a better model establishing method, and judging the prediction effect of the model by using the relative deviation of prediction set data and a true value;

s6: and (3) determining the inorganic selenium content of an unknown sample: performing infrared spectrum scanning on the nutritional rice flour sample with unknown inorganic selenium content to obtain infrared spectrum data of the unknown sample, pretreating, and then bringing the pretreated data into the model of the step S5 to obtain the inorganic selenium content in the unknown nutritional rice flour sample;

and (4) completing the determination of inorganic selenium in the nutritional rice flour.

Preferably, in step S1: the method for preparing the nutrient rice flour sample containing inorganic selenium comprises the following steps: adding sodium selenite to prepare 101 nutritional rice flour samples with different concentrations, wherein the concentration range of the samples is 0-200 ug/100 g.

Preferably, the infrared spectrum scanning conditions in step S2 adopt: fourier transform mid-infrared spectrometer, attenuated Total reflection Attachment (ATR) ID7 Transmission, Spectrum scanning Range 4000cm ^-1-400cm ^-1The number of scanning times: 32 times; scanning interval: 2cm ^-1。

Preferably, the preprocessing method in step S3 is to use 9-point smoothing plus the first derivative as the preprocessing method for infrared spectrum data.

Preferably, the step S3 is performed at 1500cm and 1000- ^-1As the infrared spectrum analysis band of the sample.

Preferably, in the step S5 model building process, trisection modeling is adopted, that is, all the nutritional rice flour with sodium selenite added for sample preparation are 3 aliquots, wherein 2/3 is used for modeling, the rest 1/3 is used for verification, and all samples obtain 3 prediction models and 3 prediction data sets.

Preferably, the step S4: by R _C ²、RMSEC、Rv ²Primarily screening out a better model establishing method by the RMSECV parameters;

the step S5: with R _C ²、RMSEC、Rv ²RMSECV, Rp2, RMSEP, RPD as characteristic parameters of the suitability of the model, and the suitability thereof was judged.

The invention has the following beneficial effects:

the attenuated total reflection mid-infrared spectrum is combined with a partial least square method to establish an effective quantitative analysis model of inorganic selenium in rice. As a rapid and accurate quantitative detection method, 1000-1500cm is selected ^-1As a characteristic wave band, 9 points are smoothed and modeled after first derivative processing, the coefficient of determination of linear fitting between a predicted value and a true value can reach more than 0.9, the relative deviation between the predicted value and the true value is up to more than 65% within 20%, the relative deviation between a sample with the content of more than 30 mu g/100g is up to 90% within 20%, and the prediction is more accurate when the content is high. The prediction effect meets the quantitative analysis requirement.

Drawings

FIG. 1 is an infrared spectrum of sodium selenite;

fig. 2 compares the predicted values with the true values of the correlation.

Detailed Description

The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited to the scope of the examples. These examples are intended to illustrate the invention only and are not intended to limit the scope of the invention.

Examples

1 band selection

TABLE 1 original data different wave zone modeling characteristic data

As can be seen from Table 1, the sample size is 1000- ^-1The modeling effect is good, and the preprocessing mode is discussed next.

2 model building and verification

TABLE 2 modeling of multiple preprocessed data

Table 2 data illustrates the data at 1000- ^-1The wave zone 9 point smoothing plus one-lead and SNV + one-lead preprocessing modeling has the best effect, and other characteristic parameters are poor.

Determination of selection 1500- ^-1The wavenumber is used as a modeling waveband, and 9-point smoothing + first derivative is used for modeling as a spectrum preprocessing mode.

TABLE 31000-1500 cm ^-19-point smoothing and first derivative preprocessing modeling characteristic parameters and prediction effect

After removing abnormal values, performing primary modeling on all original data of different wave regions, establishing a suitable wave region, and then screening a data preprocessing mode to obtain an optimal mode, namely-9-point smoothing and first derivative processing. Modeling characteristic parameter R _C ²、RMSEC、Rv ²RMSECV. The internal robustness and fitting effect of the model were evaluated using correlation coefficient Rc and corrected mean square Error of Calibration (RMSEC). The prediction result of the established model on the sample of the verification set takes a correlation coefficient Rv and a prediction mean square error RMSECV as evaluation indexes. The larger the correlation coefficient is, the smaller the mean square error is, and the better the corresponding model fitting effect is. Therefore, all data are modeled and verified by a 3-division method by adopting the optimal wave band and optimal data preprocessing together, optimal modeling characteristic parameters and verification effect data are determined, and accuracy statistics is carried out on the verification data. As can be seen from Table 3 and FIG. 2, the value of the peak is 1500cm at 1000- ^-19-point smoothing and first derivative preprocessing modeling have good prediction effect on data.

Claims

1. The method for determining inorganic selenium in the nutritional rice flour is characterized by comprising the following steps:

s1: preparing a nutritional rice flour sample containing inorganic selenium;

s5: model establishment and prediction verification: modeling by using a better model establishing method, and judging the prediction accuracy of the model by using the relative deviation of the prediction data and the truth value of the prediction set;

2. The method of claim 1, wherein: in the step S1: the method for preparing the nutrient rice flour sample containing inorganic selenium comprises the following steps: adding sodium selenite to prepare 101 nutritional rice flour samples with different concentrations, wherein the concentration range of the samples is 0-200 ug/100 g.

3. The method of claim 1, wherein: the infrared spectrum scanning conditions in the step S2 adopt: the Fourier transform intermediate infrared spectrometer has an attenuated total reflection accessory ID7 Transmission and a spectrum scanning range of 4000cm ^-1-400cm ^-1The number of scanning times: 32 times; scanning interval: 2cm ^-1。

4. The method of claim 1, wherein: the preprocessing method in the step S3 is to adopt 9-point smoothing plus a first derivative as an infrared spectrum data preprocessing method.

5. The method of claim 1, wherein: the step S3 is performed at 1000-1500cm in the analysis of the spectral data ^-1As the infrared spectrum analysis band of the sample.

6. The method of claim 1, wherein: in the step S5 model establishing process, a trisection method is adopted for modeling, namely 3 aliquots are carried out on all the nutritional rice flour added with the sodium selenite for sample preparation, wherein 2/3 is used for modeling, the rest 1/3 is used for verification, and all samples obtain 3 prediction models and 3 prediction data sets.

7. The method of claim 1, wherein: the step S4: by R _C ²、RMSEC、Rv ²Primarily screening out a better model establishing method by the RMSECV parameters;