CN114136917B - Method for rapidly detecting nutrient content of dishes by spectrum and predicting mass ratio of main materials - Google Patents
Method for rapidly detecting nutrient content of dishes by spectrum and predicting mass ratio of main materials Download PDFInfo
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- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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Abstract
The invention provides a method for rapidly detecting the nutrient content of dishes by using a spectrum and predicting the mass ratio of main materials. The invention establishes a dish nutrient content prediction model based on a near infrared spectrum technology and a method for predicting the dish main material mass ratio by using the nutrient content. The method has the advantages that nutrients in the dishes are accurately detected, data support can be provided for reasonable diet, meanwhile, according to the established nutrient content prediction model, the mass ratio of different main materials under the condition that the total quality of the dishes is fixed can be deduced, and guarantee is provided for different requirements of special people on nutrients.
Description
Technical Field
The invention relates to the field of spectrum detection, in particular to a method for rapidly detecting the nutrient content of dishes by using a spectrum and predicting the mass ratio of main materials.
Background
The detection of the content of the nutritional ingredients in the food has important significance for guiding consumers to reasonably eat and guaranteeing the health of human bodies. Chinese dishes are main food which is indispensable for daily diet in China, and have the problems of complex main material components, various cooking means, different product forms, complex operation, low efficiency and the like in national standard detection. The nutritional ingredient table is adopted for calculation, the result is rough and inaccurate, the difference between dishes with different mass ratios is ignored, and the nutrient content in the dishes cannot be accurately determined. Taking protein as an example, according to the method of GB-5009.5-2016 'determination of protein in food', detection is mainly carried out by Kjeldahl nitrogen determination method, spectrophotometry and combustion method, and the method has complex sample processing operation and consumes a large amount of chemical reagents.
Near infrared (NEAR INFRARED, NIR) spectrum analysis technology has the advantages of non-destructive, pollution-free, high analysis speed and the like, and has been widely used for detecting the content of nutrients in single food. Zhang Dongyan and the like detect the protein content of hazelnuts and corylus avellana by utilizing a method of combining near infrared spectrum with an extreme learning machine, wherein R P of the hazelnuts and the corylus avellana are 0.8806 and 0.5993 respectively, and RMSEP is 0.8823 and 0.5984 respectively. Lu Hui and the like, the near infrared spectrum technology is utilized to carry out rapid nondestructive testing on the protein content in the rice, and the R P of the prediction model is 0.8624. Fan Nai and the like realize safe and rapid nondestructive detection of the protein content of the beach mutton by adopting a Box-Behnken method and combining a visible/near infrared hyperspectral (400-1000 nm). Prieto et al used near infrared to predict protein content in ground beef, with good results. Near infrared spectrum detection technology has a certain result on detection and research of nutrient content in single food, but a multi-complex system of centered dishes is still reported. Therefore, the method for simply, reliably and quickly determining the nutrient content in the dishes is established, not only can provide data support for guiding the balanced intake of human nutrition, but also can provide theoretical support for the development of quick detection technology in the Chinese cooking industry.
At present, the near infrared spectrum technology is still difficult to rapidly detect the nutrient content in a complex sample, so that the rapid detection of the nutrient content in a complex system sample is crucial to the prediction of the mass ratio of the main materials, and theoretical support can be provided for precise nutrition and precise personalized nutrition design.
Disclosure of Invention
The invention aims to provide a method for rapidly detecting the nutrient content of dishes by using a spectrum and predicting the mass ratio of main materials.
In order to achieve the aim of the invention, in a first aspect, the invention provides a method for rapidly detecting the nutrient content of dishes by using a spectrum, wherein the raw materials of the dishes comprise main materials and seasonings; the nutrients include dish carbohydrates, fats, proteins, vitamins, water, dietary fibers, and trace elements.
The method comprises the following steps:
A. processing and manufacturing N dish samples with different mass ratios by using different main materials, and then selecting optimal cooking time and cooking power according to sensory evaluation to manufacture the dish samples;
the mass ratio of the main materials to the seasonings for making dishes is 20-60:1; preparing dish samples from various food materials in the main materials according to different mass ratios, and mixing all the samples to form a total sample set;
B. mashing cooked dishes with N mass ratios by using a chef machine, and obtaining T samples to be tested from each mashed dish;
C. Collecting spectral data of T×N samples;
D. Respectively measuring the nutrient content of each sample in the total sample set by adopting a physicochemical method;
E. dividing the collected spectrum data into a training set and a testing set, taking the training set spectrum data as an independent variable and the nutrient content as a dependent variable, simultaneously combining spectrum data preprocessing and spectrum characteristic wave band extraction, establishing a mathematical model, and verifying the modeled model by adopting the testing set;
F. evaluating the modeling type in the step E, judging the effectiveness of the model, and obtaining an effective mathematical model;
G. Under the same experimental conditions, collecting spectral data of a dish sample to be tested, and predicting the nutrient content of the dish sample to be tested by utilizing the effective mathematical model obtained in the step F;
The preparation process of the dish samples in the step A and the step G is the same.
The preparation process of the dish samples in the steps A and G is the same.
In the method, the contents of various nutrients including carbohydrate, fat, protein, vitamin, water, dietary fiber and trace elements in the step D are detected according to the national standard method.
Preferably, the spectral data preprocessing in step E may employ generalized partial least squares weighting (Generalized Least Squares Weighting, GLWS GLSW), S-G convolution smoothing, vector normalization (Standard Normal Variate transform, SNV), or multivariate scatter correction (Multiplicative Scatter Correction, MSC), etc.
Preferably, the characteristic band extraction in step E may employ forward interval partial least Squares (Forword INTERVAL PARTIAL LEAST Squares, FIPLS), backward interval partial least Squares (Backword INTERVAL PARTIAL LEAST Squares, BIiPLS), continuous projection algorithm (Successive projections algorithm, SPA), backward interval partial least Squares in combination with continuous projection algorithm (BIPLS-SPA), genetic algorithm ((Genetic algorithm, GA) or competitive adaptive weighting algorithm (Competitive ADAPTIVE REWEIGHTING algorithm, CARS), etc.
Preferably, the mathematical model in step E may be a partial least squares model (PARTICAL LEAST-Square Method, PLS), a principal component regression model (PRINCIPAL COMPONENT REGRESSION, PCR), a support vector machine model (Support Vector Machin, SVM), or the like.
Further, in step F, validity of The model is determined using The prediction set decision coefficient (R 2 Pred), the prediction root mean square Error (Root Mean Squared Error of Prediction, RMSEP), the Error range rate (The Error RANGE RATE, RER), and The relative analysis Error (Residual Predictive Deviation, RPD) as evaluation indexes.
In one specific embodiment of the invention, a method for rapidly detecting nutrient content of a dish by using a spectrum is provided, wherein the dish is a tomato fried egg, and raw materials of the dish comprise main materials and seasonings, wherein the main materials comprise tomatoes and egg liquid; the flavoring comprises vegetable oil, salt and white sugar; the nutrients comprise proteins, fats and dietary fibers; the method comprises the following steps:
① Processing and manufacturing tomato fried egg dish samples by the raw materials, and then selecting optimal cooking time and cooking power according to sensory evaluation to manufacture dish samples;
Wherein the cooking time is selected between 2-4.5 min;
the cooking power may be selected between 800-1200W;
The mass ratio of the main material to the seasoning for preparing the tomato fried eggs is 500:20 (25:1), and the mass ratio of the tomato to the egg liquid in the main material is 50:450, 100:400, 150:350, 200:300 and 250:250 respectively; preparing dish samples from tomato and egg liquid according to different mass ratios, and mixing the samples according to the mass ratio to form a total sample set;
② Collecting spectrum data (near infrared spectrum data) of a sample in the range of 900-1700 nm;
③ Measuring the total protein content of each sample in the total sample set by adopting a physicochemical method;
④ Dividing the collected spectrum data into a training set and a testing set, taking the training set spectrum data as an independent variable and the protein content as a dependent variable, simultaneously combining spectrum data preprocessing and spectrum characteristic wave band extraction, establishing a mathematical model, and verifying the modeled model by adopting the testing set;
⑤ Evaluating the model modeled in the step ④, judging the effectiveness of the model, and obtaining an effective mathematical model;
⑥ Under the same experimental conditions, collecting spectral data of the tomato fried egg dish sample to be detected, and calculating the protein content of the tomato fried egg dish sample to be detected by utilizing the effective mathematical model obtained in the step ⑤.
Further, step ④ includes: spectral data preprocessing, characteristic band extraction and mathematical model establishment. The extracted characteristic waves are 900, 1083, 1131, 1177, 1226, 1250, 1268, 1283, 1299, 1316, 1345, 1364, 1373, 1385, 1394, 1408, 1435nm.
Or directly extracting characteristic wave bands without preprocessing the optical data, and then establishing a mathematical model.
Preferably, the sample mass ratio of the training set to the testing set in step ④ is 5:1.
In a second aspect, the present invention provides a method for predicting the mass ratio of a dish main ingredient based on the above method.
The sample composition is in accordance with the formula (1) -formula (3), wherein the main material proportion is in accordance with the formula (1); the proportion of the seasonings accords with the formula (2), the total nutrient content of the seasonings is a fixed value, and the nutrient content in each seasoning is far smaller than the nutrient content in the main material; the total weight of the dish is as shown in formula (3):
H＝S×M(I1)+O×M(I2)+……+U×M(In) (2)
J＝M+H (3)
wherein G 1、G2、……、Gq represents the category of the 1 st, 2 nd, 3 rd, … th and q th main materials; a. b, c, … and w respectively represent the mass ratio of the 1 st, 2 nd, 3 rd, … th and q th main materials to the whole main materials; m represents the total mass of the main material; h represents the total mass of the flavoring; i 1、I2、…、In respectively represents vegetable oil, salt, …, white sugar and other seasonings; s, O, … and U respectively represent the ratio of the mass of n seasonings to the mass M of the main material under the condition of fixing the mass M of the main material, wherein the mass of n seasonings is respectively represented by S multiplied by M (I 1)、O×M(I2)、…、U×M(In); j represents the total mass of the sample;
When M is a fixed value, the value of G 1、G2、…、Gq is not limited, i.e., a+b+c … +w=1.
When q main materials exist, the contents of at least q-1 nutrients K, L, R, … and N are required to be measured, and the mass ratio of the main materials in the dish is calculated according to a matrix (4):
Wherein K 1、L1、R1、…、N1 respectively represents the contents of K, L, R, … and N nutrients in the main material 1; k 2、L2、R2、…、N2 respectively represents K, L, R, … and N nutrient content in the main material 2; k q-1、Lq-1、Rq-1、…、Nq-1 respectively represents the contents of K, L, R, … and N nutrients in the main material q-1; k q、Lq、Rq、…、Nq respectively represents the contents of K, L, R, … and N nutrients in the main material q; k Total (S) 、L Total (S) 、R Total (S) 、…、N Total (S) represents the content of K, L, R, …, N nutrients in the dishes, respectively.
Therefore, when the content of the nutrients in the dish is obtained by utilizing the spectrum data, the mass ratio of the main materials in the dish can be predicted by the content of the nutrients. On the premise of knowing the total mass of food intake, the mass ratio of the main ingredients of dishes can be predicted to guide industrial production when the total intake of different nutrients is exactly required for special people.
By means of the technical scheme, the invention has at least the following advantages and beneficial effects:
the invention establishes a rapid prediction model for the nutrient content in dishes based on a near infrared spectrum technology and a method for predicting the mass ratio of main materials of dishes according to the nutrient content under a certain weight. The change of nutrient content in dishes can be accurately detected, data support can be provided for reasonable diet, and guarantee is provided for different requirements of special people on nutrients. Meanwhile, different nutrient contents of dishes are detected according to the established nutrient content prediction model, and the mass ratio of different main materials is predicted under the condition of fixed total quality of the dishes, so that guidance is provided for industrial production.
Drawings
FIG. 1 is a flowchart for predicting the nutrient content of dishes based on the spectrum technology.
FIG. 2 is a flow chart of the method for predicting the mass ratio of the main materials by using the nutrient content.
Detailed Description
The following examples are illustrative of the invention and are not intended to limit the scope of the invention. Unless otherwise indicated, the technical means used in the examples are conventional means well known to those skilled in the art, and all the main materials used are commercially available.
The near infrared spectrometer used in the following examples was purchased from the spectrum acquisition system of ocean optics, U.S.A. The main hardware components comprise a 900-1700nm FLAME-S-VIS-NIR-ES spectrometer, an HL-2000-FHSA light source, a diffuse reflection standard plate, a reflection probe bracket and a 400 μm laboratory-grade reflection probe. The supporting software system used was OceanView1.6.7. The spectral resolution was 3.1nm.
Example 1 method for spectral rapid detection of protein content in tomato fried eggs
The method comprises the steps of preparing two main materials with different mass ratios into dishes, collecting spectral data of a sample by using a spectrometer, measuring the protein content by a physicochemical method, and finally establishing a protein content prediction model based on a spectral technology by combining the two main materials, wherein the specific flow is shown in a figure 1.
Tomato fried egg dish samples were prepared, then the cooking time and the cooking time were determined between 2-4.5min and 800-1200W, respectively, according to the sensory scoring criteria (table 1), and finally the optimal time for cooking and the cooking time were 2.5min and 1200W, respectively. The mass ratio of the main material to the seasoning for preparing the tomato fried eggs is 500:20 (25:1), and the mass ratio of the tomato to the egg liquid in the main material is 50:450, 100:400, 150:350, 200:300 and 250:250 respectively; preparing tomato and egg liquid into dish samples according to different mass ratios, and mixing the samples according to the mass ratio to form a total sample set.
TABLE 1 sensory scoring criteria
The total mass of each sample was 520 g, the seasoning was 20 g, and the main material was 500 g (after the sample was crushed, the sample was smoothly placed in a petri dish having a diameter of 35mm, and the sample height was leveled with the petri dish height and spread. Collecting spectral data of dishes by near infrared spectrum technology (900-1700 nm), and measuring protein content in each sample by GB 5009.5-2016 method, wherein the total collected spectral data and protein content in two batches of dishes with 120 (group A) and 180 (group B) samples.
In the modeling analysis, PLS, PCR and SVM prediction models established over the full wavelength range were compared, PLS being the best model for group a, SVM being the best model for group B and group C, group C model predictions being higher than those with group a model predictions but lower than those with group B model predictions, R 2 (Pred) for the A, B, C three groups being 0.7802, 0.9335 and 0.7949, respectively, and rmsep being 0.8536, 0.4916 and 0.8792g/100g, respectively. After GLSW pretreatment, the SVM prediction model has the best effect, the C group model prediction result is higher than the A group model prediction result, but lower than the B group model prediction result, R 2 (Pred) is 0.9132, 0.9834 and 0.9595 respectively, and RMSEP is 0.5553, 0.2449 and 0.3860g/100g respectively, so that the model precision is effectively improved, and the influence of moisture or temperature on a spectrum can be eliminated to a certain extent.
And simultaneously comparing FIPLS, BIPLS, SPA with BIPLS-SPA for characteristic wavelength screening, and then establishing PLS, PCR and SVM models. After extracting characteristic bands (900, 1083, 1131, 1177, 1226, 1250, 1268, 1283, 1299, 1316, 1345, 1364, 1373, 1385, 1394, 1408, 1435 nm) compared with the optimal result of full-band modeling, the method mainly comprises proteins mainly comprising groups such as-CH n, -NH, -OH and the like, and 1083nm is second-order frequency multiplication of N-H group extension. The relatively weak peak at 1226nm corresponds to a second order multiple of the C-H group stretching vibration. 1364nm and 1640nm are first order frequency multiplication absorption bands of CH 3 groups, and first order frequency multiplication of O-H stretching vibration is near 1435 nm. Group A, group B and group C, R 2 (Pred) was 0.9142, 0.9710 and 0.9551, respectively, and RMSEP was 0.5424, 0.3235 and 0.4047g/100g, respectively. After the characteristic wave band is extracted, R 2 (Pred) is basically consistent with the full wave band modeling result, and the RMSEP is slightly improved. After the characteristic wave band is extracted, irrelevant information can be effectively removed, modeling variables are reduced, and model robustness is improved.
Example 2 method for spectral rapid detection of fat content in Zanthoxylum piperitum omel
Firstly, preparing two kinds of peppers and eggs with different mass ratios into dishes, then collecting sample spectrum data by utilizing a spectrometer, simultaneously measuring the fat content of the samples, and finally establishing a fat content prediction model based on a spectrum technology by combining the two kinds of peppers and eggs.
The sample of the pepper fried egg dish was prepared, then the cooking time and the cooking time were determined between 2-4.5min and 800-1200W, respectively, according to the sensory scoring criteria (table 2), and finally the optimal time for cooking and the cooking time were 3min and 1200W, respectively. The mass ratio of the main material to the seasoning for preparing the pepper fried eggs is 500:20 (25:1), and the mass ratio of tomatoes to egg liquid in the main material is 50:450, 100:400, 150:350, 200:300 and 250:250 respectively; the dish samples prepared by the peppers and the egg liquid according to different mass ratios are mixed to form a total sample set.
TABLE 2 sensory scoring criteria
The total mass of each sample was 520 g, the seasoning was 20 g, and the main material was 500 g (after the sample was crushed, the sample was smoothly placed in a petri dish having a diameter of 35mm, and the sample height was leveled with the petri dish height and spread. Collecting spectral data of dishes by near infrared spectrum technology (900-1700 nm), and measuring fat content in each sample by GB 5009.6-2016 method to obtain spectral data and fat content in 140 dishes.
In the modeling analysis, PLS, PCR and SVM predictive models established over the full wavelength range were compared, SVM being the best model, R 2 (Pred) being 0.7859 each, and RMSEP being 0.5369g/100g each. After GLSW pretreatment, the SVM prediction model has the best effect, R 2 (Pred) is respectively 0.8932, RMSEP is respectively 0.3898g/100g, R 2 (Pred) is slightly reduced along with the reduction of the RMSEP, and GLSW pretreatment effectively improves the model precision and can effectively eliminate the influence of moisture or temperature on a spectrum.
Meanwhile, after the PLS, PCR and SVM models established after carrying out characteristic wavelength comparison on FIPLS, BIPLS, SPA and BIPLS-SPA are compared, characteristic wave bands (931, 977, 982, 1058, 1067, 1203, 1206, 1226, 1263, 1440 and 1459 nm) are extracted compared with the optimal result of full-wave modeling, wherein 1343nm reflects the secondary frequency combination of a C-H group and the secondary frequency multiplication absorption band of the stretching vibration of an O-H group, 1489nm reflects the secondary frequency multiplication of the stretching vibration of the O-H group, 1583nm reflects the primary frequency multiplication absorption band of the stretching vibration of the O-H group, R 2 (Pred) is respectively 0.9951, and RMSEP is respectively 0.3987g/100g. R 2 (Pred) after feature band extraction is substantially identical to RMSEP. After the characteristic wave band is extracted, irrelevant information can be effectively removed, modeling variables are reduced, and model robustness is improved. Example 3 method for rapidly detecting protein and fat content in fried eggs of pumpkin and Auricularia by spectrum
Three main materials (pumpkin, agaric and egg) with different mass ratios are made into dishes, then a spectrometer is used for collecting spectral data of a sample, the content of dietary fiber and protein of the samples is measured, and finally the dietary fiber and protein content prediction model based on a spectral technology is established by combining the three main materials.
The preparation method comprises the steps of preparing a pumpkin black fungus fried egg dish sample, determining cooking time and cooking time according to sensory scoring standards (table 3) between 2-4.5min and 800-1200W respectively, and finally, determining optimal time and cooking time for cooking to be 4min and 1200W respectively. The mass ratio of the main materials to the seasonings for preparing the pumpkin and agaric fried eggs is 500:20 (25:1), and the mass ratio of the pumpkin to the agaric to the egg liquid in the main materials is 50:50:400, 50:100:350, 100:50:350, 100:100:300 and 150:150:200 respectively; the method comprises the steps of mixing dish samples prepared from zucchini, agaric and egg liquid according to different mass ratios, wherein the samples with the mass ratios are mixed to form a total sample set.
TABLE 3 sensory scoring criteria
The total mass of each sample was 520 g, the seasoning was 20 g, and the main material was 500 g (after the sample was crushed, the sample was smoothly placed in a petri dish having a diameter of 35mm, and the sample height was leveled with the petri dish height and spread. Collecting spectral data of dishes by near infrared spectrum technology (900-1700 nm), measuring protein content in each sample by GB 5009.5-2016 method, measuring dietary fiber content in each sample by GB 5009.88-2014 method, and collecting spectral data and protein and dietary fiber content in 120 dishes.
In modeling analysis, contrast FIPLS, BIPLS, SPA and BIPLS-SPA were subjected to characteristic wavelength screening and compared using PLS, PCR and SVM modeling and methods. Comparing PLS, PCR and SVM predictive models established over the full wavelength range, SVM is the best model, R 2 (Pred) is 0.6743,0.5784, RMSEP is 0.4618g/100g,0.5612g/100g, respectively. After GLSW pretreatment, the SVM prediction model has the best effect, R 2 (Pred) is respectively 0.8341 and 0.7986, RMSEP is respectively 0.3423g/100g and 0.3523g/100g, R 2 (Pred) is increased along with the reduction of the RMSEP, and the GLSW pretreatment effectively improves the model precision and can effectively eliminate the influence of moisture or temperature on a spectrum.
For the prediction of protein content, after extracting characteristic bands (900, 931, 974, 986, 1058, 1067, 1203, 1206, 1229, 1266, 1443, 1459 nm) compared with the optimal result of full band modeling, R 2 (Pred) is 0.9521, and RMSEP is 0.4012g/100g, respectively; for the prediction of dietary fiber content, after extracting characteristic bands (1024, 1038, 1139, 1192, 1212, 1291, 1343, 1414, 1542, 1574, 1583, 1646) compared to the optimal result of full-band modeling, wherein 1343nm reflects the second combined frequency of the C-H group and the second frequency doubling absorption band of the stretching vibration of the O-H group, 1489nm reflects the second frequency doubling of the stretching vibration of the O-H group, and 1583nm reflects the first frequency doubling absorption band of the stretching vibration of the O-H group. R 2 (Pred) of 0.9634 and RMSEP of 0.3746g/100g respectively after characteristic wave band extraction, R 2 (Pred) and RMSEP are slightly improved. Irrelevant information can be effectively removed after the characteristic wave band is extracted, modeling variables are reduced, and model robustness is improved.
Example 4 rapid prediction of the mass ratio of two Main Material dishes based on nutrient content
And (3) preparing the main materials with two different mass ratios into dishes, collecting sample spectrum data by utilizing a spectrometer, and measuring the protein content according to the established model.
When the mass ratio of the main materials in the dishes is rapidly determined by utilizing the spectrum, firstly, the total mass of the sample is determined to be 520 g, the seasoning is determined to be 20 g, and the main materials are determined to be 500 g, so that dishes with different mass ratios of the main materials are prepared. The sample composition accords with formulas (3), (5) and (6), wherein the main material proportion accords with formula (5), the seasoning proportion accords with formula (6), and the total weight of the dish accords with formula (3):
J＝M+H (3)
H＝S×M(I1)+O×M(I2)+P×M(I3) (6)
Wherein G 1 and G 2 represent the class of the main materials; m represents the total mass (unit, g) of the main material; a and b represent the mass ratio of the main materials 1 and 2; h represents the total mass (units, g) of the flavoring; i 1、I2、I3 respectively represents vegetable oil, salt and white sugar, and the protein content of the vegetable oil, the salt and the white sugar is 0g/100g; s is 10%, O and P are 5%, and the mass ratio of the S to the P is the mass (M) ratio of the main materials of different seasonings; s, O, P represents the ratio of the required 3 seasoning masses to the main material mass M under the condition of fixing the main material mass M, and the 3 seasoning masses are respectively expressed by S×M (I 1)、O×M(I2)、P×M(I3).
When M is a fixed value, the values of G 1 and G 2 are not limited, i.e., a+b=1.
Wherein K 1、K2、K Total (S) represents the content (unit, g/100 g) of the nutrient K in the main ingredients 1, 2 and dishes respectively. Detecting the nutrient content in the dish by adopting a near infrared spectrum technology (900-1700 nm), obtaining the nutrient content K Total (S) (unit, g/100 g) by using the built model, and obtaining the nutrient content K 1、K2 (unit, g/100 g) by referring to a main material nutrient composition table. And calculating the mass ratio of the main materials, namely the numerical values of a and b, by using a popularization formula obtained in the instruction book, and calculating the mass ratio of the main materials in the dish according to the matrix (7).
According to the nutrient composition table of the main materials, K 1、K2 in the main materials 1 and 2 is 13.5g/100g and 1.15g/100g respectively, and according to the nutrient content prediction model, the nutrient K Total (S) in the dish is 8.56g/100g. From the matrix (7) the formula (8) is obtained:
From the calculation, a: b=0.59:0.41, i.e., main material G 1,G2 was about 300G and 200G, respectively.
Therefore, when the content of nutrients in the dish is obtained by utilizing the spectrum data, the mass ratio of the main materials in the dish can be predicted by the content of the nutrients, and on the premise of knowing the total mass of food intake, the mass ratio of the main materials of the dish can be predicted when the total intake of different nutrients is exactly required by special people, so that guidance is provided for industrial production.
Example 5 rapid prediction of the mass ratio of three Main Material dishes based on nutrient content
And (3) preparing three main materials with different mass ratios into dishes, collecting sample spectrum data by using a spectrometer, and measuring the nutrient content according to the built model. When the mass ratio of the main materials in the dishes is rapidly determined by utilizing the spectrum, firstly, the total mass of the sample is determined to be 520 g, the seasoning is determined to be 20 g, and the main materials are determined to be 500 g, so that dishes with different mass ratios of the main materials are prepared. The formula (3), (6) and (9) are satisfied:
J＝M+H (3)
H＝S×N(I1)+U×M(I2)+U×M(I3) (6)
Wherein G 1、G2、G3 respectively represents the types of main materials; m represents the total mass (unit, g) of the main material; a. b and c represent mass ratio of main materials 1,2 and 3; h represents the total mass (units, g) of the flavoring; i 1、I2、I3 respectively represents vegetable oil, salt and white sugar, and the protein and vitamin contents are 0g/100g; s is 10%, O and P are 5%, and the mass ratio of the S to the P is the mass (M) ratio of the main materials of different seasonings; s, O, P represents the ratio of the required 3 seasoning masses to the main material mass M under the condition of fixing the main material mass M, and the 3 seasoning masses are respectively expressed by S×M (I 1)、O×M(I2)、P×M(I3). When M is a fixed value, the value of G 1、G2、G3 is not limited, i.e., a+b+c=1.
Wherein K 1、K2、K3、K Total (S) represents the content (unit, g/100 g) of the nutrient K in the main ingredients 1, 2,3 and the dish, respectively, and L 1、L2、L3、L Total (S) represents the content (unit, g/100 g) of the nutrient L in the main ingredients 1, 2,3 and the dish, respectively. Detecting the nutrient content in the dish by adopting a near infrared spectrum technology (900-1700 nm), obtaining the nutrient content K Total (S) 、L Total (S) by using the built model, and obtaining K 1、K2、K3、L1、L2、L3 by referring to a main material nutrient composition table. And calculating the mass ratio of the main materials, namely the numerical values of a, b and c, by using a popularization formula obtained in the instruction, and calculating the mass ratio of the main materials in the dish according to a formula (10).
According to the nutrient composition table of the main material, K 1、K2、K3 is 5.52g/100g, 2.37g/100g, 8.97g/100g and L 1、L2、L3 is 2.16g/100g, 5.88g/100g and 7.71g/100g respectively, and according to the nutrient content prediction model, the nutrient K Total (S) in the dish is 6.17g/100g and L Total (S) is 5.67g/100g. Formula (11) is obtainable according to formula (10):
From the calculation a: b: c=0.32:0.19:0.49, i.e. main material G 1,G2,G3 was approximately 150G,100G,250G, respectively.
Therefore, when the content of nutrients in the dish is obtained by utilizing the spectrum data, the mass ratio of the main materials in the dish can be predicted by the content of the nutrients, and on the premise of knowing the total mass of food intake, the mass ratio of the main materials of the dish can be predicted when the total intake of different nutrients is exactly required by special people, so that the industrial production is guided.
While the invention has been described in detail in the foregoing general description and with reference to specific embodiments thereof, it will be apparent to one skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the invention and are intended to be within the scope of the invention as claimed.
Claims (7)
1. The method for predicting the mass ratio of the main ingredients of the dish based on the method for rapidly detecting the nutrient content of the dish by using the spectrum is characterized in that the composition of a sample is in accordance with the formulas (1) - (3), wherein the proportion of the main ingredients is in accordance with the formula (1); the composition of the seasoning accords with the formula (2), the total nutrient content of the seasoning is a fixed value, and the nutrient content in each seasoning is far less than the nutrient content in the main material; the total weight of the dish is as shown in formula (3):
H＝S×M(I1)+O×M(I2)+……+U×M(In) (2)
J＝M+H (3)
Wherein G 1、G2、...、Gq represents the class of 1, 2, 3, & q major ingredients; a. b, c, w represent the mass ratio of the 1 st, 2 nd, 3 rd, q th main materials to the overall main material, respectively; m represents the total mass of the main material; h represents the total mass of the flavoring; i 1、I2、...、In represents n flavors such as vegetable oil, salt, & gt, white sugar, etc., respectively; s, O, U respectively represent the ratio between the mass of the n flavors required and the mass of the main material M under the condition of fixing the mass of the main material M, and the mass of the n flavors is respectively expressed by S multiplied by M (I 1)、O×M(I2)、...、U×M(In); j represents the total mass of the sample;
When M is a fixed value, the value of G 1、G2、...、Gq is not limited, i.e., a+b+c. +w=1;
when q main materials exist, the content of at least q-1 nutrients K, L, R, the total amount of the main materials and N is required to be measured, and the mass ratio of the main materials in the dish is calculated according to a matrix (4):
wherein K 1、L1、R1、...、N1 represents the content of K, L, R,..and N nutrients in main material 1, respectively; k 2、L2、R2、...、N2 represents the content of K, L, R,..and N nutrients in main material 2, respectively; k q-1、Lq-1、Rq-1、...、Nq-1 represents the content of K, L, R, the..and the N nutrient in the main material q-1 respectively; k q、Lq、Rq、...、Nq represents the content of K, L, R,..and N nutrients in main material q, respectively; k Total (S) 、L Total (S) 、R Total (S) 、...、N Total (S) represents the content of K, L, R, respectively, N nutrients in the dish;
The method for rapidly detecting the nutrient content of the dish by using the spectrum comprises the following steps of raw materials of the dish, namely a main material and a seasoning; the nutrients comprise dish carbohydrates, fat, proteins, vitamins, water, dietary fibers and trace elements;
the method comprises the following steps:
A. processing and manufacturing N dish samples with different mass ratios by using different main materials, and then selecting optimal cooking time and cooking power according to sensory evaluation to manufacture the dish samples;
the mass ratio of the main materials to the seasonings for making dishes is 20-60:1; preparing dish samples from various food materials in the main materials according to different mass ratios, and mixing all the samples to form a total sample set;
B. mashing cooked dishes with N mass ratios by using a chef machine, and obtaining T samples to be tested from each mashed dish;
C. Collecting spectral data of T×N samples;
D. Respectively measuring the nutrient content of each sample in the total sample set by adopting a physicochemical method;
E. dividing the collected spectrum data into a training set and a testing set, taking the training set spectrum data as an independent variable and the nutrient content as a dependent variable, simultaneously combining spectrum data preprocessing and spectrum characteristic wave band extraction, establishing a mathematical model, and verifying the modeled model by adopting the testing set;
F. evaluating the modeling type in the step E, judging the effectiveness of the model, and obtaining an effective mathematical model;
G. Under the same experimental conditions, collecting spectral data of a dish sample to be tested, and predicting the nutrient content of the dish sample to be tested by utilizing the effective mathematical model obtained in the step F;
The preparation process of the dish samples in the step A and the step G is the same.
2. The method of claim 1, wherein the level of each nutrient in step D is measured according to national standard methods.
3. The method of claim 1, wherein the preprocessing of the spectral data in step E employs generalized partial least squares weighting, S-G convolution smoothing, vector normalization, or multivariate scatter correction.
4. The method of claim 1, wherein the characteristic band extraction in step D employs forward interval partial least squares, backward interval partial least squares, a continuous projection algorithm, a combination of backward interval partial least squares and continuous projection algorithm, a genetic algorithm, or a competitive adaptive re-weighting algorithm.
5. The method of claim 1, wherein the mathematical model is built in step D using a partial least squares model, a principal component regression model, or a support vector machine model.
6. The method according to claim 1, wherein in step E, validity of the model is determined using the prediction set decision coefficient, the prediction set root mean square error, the error range rate, and the relative analysis error as evaluation indexes.
7. Method according to any of claims 1-6, characterized in that the content of nutrients in the dish is predicted from the modeling in the method, while the mass ratio of the main ingredients in the dish is predicted in combination with the total dish mass.
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