JP2005233824A - Food quality inspection method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a food quality inspection method omitting labor and having high reliability. <P>SOLUTION: The food quality inspection method is constituted so as to determine food on the basis of a visible and/or near infrared spectrum obtained by irradiating the food with visible light and/or near infrared rays and includes a process for obtaining the visible and/or near infrared spectrum obtained by irradiating the food with visible light and/or near infrared rays, a process for analyzing the spectrum by a chemometrics and a process for judging the quality of the food on the basis of the obtained analyzed result. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a method for quality inspection in foods.

  In recent years, organic substance quality evaluation methods using near-infrared spectroscopy have attracted attention as non-destructive measurement methods, and many studies have been made in the past 20 years and are being put into practical use. For example, in the food industry, rice taste meters that measure the content of amylose and the like (see, for example, Patent Document 1), fruit sugar content meters that measure carbohydrates (for example, see Patent Documents 2 and 3), and the like have been put into practical use. .

Also, a method for evaluating the pH (hydrogen ion content) of a cocoa-containing food (for example, see Patent Document 4), a method for measuring a difference in ingredients before and after the processing of the food, and evaluating a processing state (for example, Patent Document 5) , 6).
JP 63-11841 A JP-A-6-186159 JP-A-1-301147 JP 2001-17084 A JP-A-7-12722 JP-A-7-107925

  The only methods for evaluating the heating state of foods are the conventional methods of monitoring the heating temperature and indirectly evaluating the moisture content by using near-infrared spectroscopy, and sampling as quality control in the manufacturing process. We had to destroy the food itself and check the internal heating condition.

  Furthermore, when confirming by sampling, the required number calculated statistically is inspected. However, if the number of production lots increases, the number of inspections becomes enormous, which is very troublesome. Even if it takes time and effort, all products are not inspected, so some products may deviate from quality standards.

  Therefore, an object of the present invention is to provide a food quality inspection method that saves labor and has high reliability.

That is, the present invention
(1) A food quality inspection method for determining based on a visible and / or near-infrared spectrum obtained by irradiating food with visible light and / or near-infrared rays,
(2) The inspection method according to (1), wherein the heating state of the food is determined,
(3) The inspection method according to (1), wherein the sterilization state of the food is determined,
(4) A step of obtaining a visible and / or near infrared spectrum by irradiating food with visible light and / or near infrared, a step of analyzing the spectrum by chemometrics, and a quality of the food based on the obtained analysis result The inspection method according to any one of (1) to (3), including the step of determining, and (5) the wavelength of the visible and / or near infrared spectrum is 400 to 2500 nm (1) to (4) It relates to any one of the inspection methods.

  The present invention provides a food quality inspection method that saves labor, is highly reliable, can be non-destructively inspected, and can be inspected quickly.

  The present invention has a great feature in conducting a quality inspection of food based on a visible and / or near infrared spectrum obtained by irradiating food with visible light and / or near infrared light.

  By having such a feature, the quality of the food can be confirmed in a short time, and thorough quality control in the food production process can be achieved.

  The food used in the present invention is not particularly limited as long as it is a food that requires a heating step during its production process. For example, dairy products such as eggs, raw milk, and fresh cream, coffee, tea, cocoa, fruit juice, etc. Beverages, alcohols, and retort foods. In particular, eggs and the like are preferable from the viewpoint of difficulty in thermal management.

  Examples of quality inspection items that can be inspected according to the present invention include heating state inspection and sterilization state inspection.

  In the case of irradiating only visible light, the wavelength may be appropriately selected depending on the sample to be measured, but is preferably 360 to 830 nm, more preferably 400 to 700 nm. May be appropriately selected depending on the sample to be measured, but is preferably 700 to 2500 nm, more preferably 830 to 1350 nm. Moreover, what is necessary is just to select suitably the wavelength in the case of irradiating visible light and near infrared rays with the sample to measure, Preferably it is 400-2500 nm, More preferably, it is 400-1350 nm.

  The device used in the present invention is not particularly limited as long as it is a device that can measure visible light and / or near-infrared light. Examples thereof include filter-type spectroscopic devices, dispersion-type spectroscopic devices, and Fourier transform spectroscopic devices. .

  In the quality inspection method of the present invention, in order to obtain a more accurate inspection result, a visible light and / or near infrared spectrum is obtained by irradiating food with visible light and / or near infrared light, and the spectrum is analyzed by chemometrics. It is preferable that the process to determine the quality of food based on the process to perform and the obtained analysis result is included.

  As specific analysis methods, quantitative analysis includes multiple regression analysis, principal component regression analysis, PLS regression analysis, and qualitative analysis includes principal component analysis, cluster analysis, discriminant analysis, SIMCA, and the like.

  For example, a calibration curve for performing a quantitative analysis is prepared by preparing samples processed (for example, heating) under predetermined conditions (for example, heating time, heating temperature, etc.). The near infrared spectrum is measured and the component value can be created as the processing time (for example, the heating time) by the analysis method. In order to confirm the reliability of the obtained calibration curve, it is preferable to calculate a correlation coefficient. The correlation coefficient is preferably as high as possible from the viewpoint of ensuring the inspection accuracy, but may be 0.85 or more in order to guarantee the reliability of the calibration curve.

  By comparing the calibration curve prepared in advance as described above with the value obtained by analysis, it is possible to determine whether the quality of each sample conforms to the quality standard, and further, food processing It is also possible to check the quality change of the food inside.

  The method of the present invention can be used, for example, for confirming a heating state or a sterilization state in a liquid egg manufacturing process.

  For example, in the case of a liquid egg, the liquid egg is sampled during the heating or sterilization process or after the heating or sterilization process, and batchwise or continuously with respect to the liquid egg being fed by the method of the present invention. You can check the heating or sterilization state of the egg, you can check that the heating or sterilization process is performed without any abnormalities, and check if the desired liquid egg is manufactured after the heating or sterilization process is completed You can also. Furthermore, since the heating or sterilization state of all the liquid eggs can be confirmed in a short time, it is possible to stop the product that deviates from the quality standard before flowing into the market.

Example 1 Preparation of calibration curve (1) Preparation of sample for spectral measurement Whole egg liquid, egg yolk liquid and egg white liquid were filled in a polybac cell (light path length: 2 mm), respectively, and the total egg liquid was 56 ° C., yolk. The solution was heated at 60 ° C. and the egg white solution was heated in a water bath at 61 ° C. for 1.5, 2.5, 3.0, 3.5, 4.0, 4.5 and 5.5 minutes. A sample for spectrum measurement was used together with the heated product.

(2) Measurement of spectrum After each sample was left in a 20 ° C water bath for 10 minutes, the spectrum was measured by a transmission method at a wavelength of 400 to 2500 nm (2 nm interval) using a dispersion-type spectroscopic instrument (Model 6500 manufactured by NIRsystems). . The number of wavelength scans was 32, and air was used as a reference. The reference was measured for each sample.

(3) Spectrum analysis PLS regression to Factor 16 by full cross validation using the smoothed original spectrum and second derivative spectrum of each sample of unheated and heated products obtained in (2) as the heating time. Analysis was carried out.

(4) Preparation of calibration curve As shown in Table 1, the whole egg solution is analyzed using a smoothed original spectrum obtained at a wavelength of 700 to 1100 nm, and as a result, a calibration curve with a high correlation coefficient is created. It was possible.

  Even when the egg yolk liquid was analyzed using either the smoothed original spectrum or the second derivative spectrum obtained at a wavelength of 1100 to 1350 nm, it was possible to create a calibration curve with a high correlation coefficient.

  As a result of analyzing the egg white liquid using the second derivative spectrum obtained at a wavelength of 400 to 700 nm, it was possible to create a calibration curve with a high correlation coefficient.

Example 2 Relationship between Measured Value and Expected Value 20 ml of whole egg liquid was filled in a 30 ml test tube, and 1.5, 2.5, 3.0, 3.5, 4.0, After heating for 4.5 and 5.5 minutes and cooling with water, a sample for spectrum measurement was prepared together with an unheated product (n = 8).

  Among the samples prepared at each heating time, the first, third, fifth and seventh samples and the unheated sample were left in a 20 ° C. water bath for 10 minutes, and then 700 using a dispersive spectroscopic instrument (Model 6500 manufactured by NIRsystems). The spectrum was measured by the transmission method at a wavelength of ˜1100 nm (2 nm interval). The number of wavelength scans was 32, and air was used as a reference. The reference was measured for each sample.

  Using the smoothed original spectrum and second derivative spectrum of each sample obtained above, PLS regression analysis was performed up to Factor 16 by full cross validation using the component values as heating time, and a calibration curve was created.

  Next, among the samples prepared at each heating time, the second, fourth, sixth and eighth samples and the unheated sample were left in a 20 ° C. water bath for 10 minutes, and then a dispersive spectrometer (Model 6500 manufactured by NIRsystems) was attached. The spectrum was measured by the transmission method at a wavelength of 700 to 1100 nm (2 nm interval). The number of wavelength scans was 32, and air was used as a reference. The reference was measured for each sample.

  Using the smoothed original spectrum and second-order derivative spectrum of each sample obtained above, PLS regression analysis was performed up to Factor 16 by full cross validation using the component values as heating time, and applied to the calibration curve created above.

  From Table 2, the expected value and the actual measurement value obtained from the prepared calibration curve are close to each other. As a result of the t-test, there is no significant difference between the two at a risk rate of 5%.

Example 3 Relationship between measured values and predicted values
A calibration curve was created in the same manner as in Example 2 except that multiple regression analysis was performed up to the fourth wavelength using the smoothed original spectrum instead of PLS regression analysis, and a significant difference was confirmed.

  From Table 3, the expected value and the actual measurement value obtained from the prepared calibration curve showed close values. As a result of the t-test, the risk rate was 5% and there was no significant difference between the two.

  Therefore, it is understood that the quality inspection of food can be performed by analyzing the visible and / or near infrared spectrum obtained by irradiating visible light and / or near infrared light.

  The present invention can be used for a test for confirming a heating state in food production.

Claims (5)

  A food quality inspection method for determining a food based on a visible and / or near infrared spectrum obtained by irradiating the food with visible light and / or near infrared light.   The inspection method according to claim 1, wherein the heating state of the food is determined.   The inspection method according to claim 1, wherein the sterilization state of the food is determined.   A step of obtaining a visible and / or near-infrared spectrum by irradiating the food with visible light and / or near infrared, a step of analyzing the spectrum by chemometrics, and a step of determining the quality of the food based on the obtained analysis result The test | inspection method in any one of Claims 1-3 containing.   The inspection method according to claim 1, wherein the wavelength of the visible and / or near-infrared spectrum is 400 to 2500 nm.